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sql-reference-109	sql-reference.pdf	109	on the left side of the table represents the ﬁrst operand in the calculation, and the top row represents the second operand. Operand 1 Operand 2 Return type SMALLINT or SHORT SMALLINT or SHORT SMALLINT or SHORT INTEGER BIGINT DECIMAL FLOAT8 FLOAT8 INTEGER SMALLINT or SHORT INTEGER SMALLINT or SHORT BIGINT SMALLINT or SHORT DECIMAL FLOAT4 FLOAT8 INTEGER SMALLINT or SHORT SMALLINT or SHORT INTEGER INTEGER INTEGER INTEGER INTEGER BIGINT or LONG BIGINT or LONG DECIMAL FLOAT4 FLOAT8 DECIMAL FLOAT8 FLOAT8 BIGINT or LONG BIGINT or LONG BIGINT or LONG BIGINT or LONG DECIMAL BIGINT or LONG BIGINT or LONG DECIMAL DECIMAL DECIMAL FLOAT4 Numeric types FLOAT4 FLOAT8 DECIMAL FLOAT4 FLOAT8 FLOAT8 DECIMAL FLOAT8 FLOAT8 DECIMAL FLOAT8 FLOAT8 FLOAT8 400 AWS Clean Rooms Operand 1 FLOAT8 Operand 2 FLOAT8 Return type FLOAT8 SQL Reference Precision and scale of computed DECIMAL results The following table summarizes the rules for computing resulting precision and scale when mathematical operations return DECIMAL results. In this table, p1 and s1 represent the precision and scale of the ﬁrst operand in a calculation. p2 and s2 represent the precision and scale of the second operand. (Regardless of these calculations, the maximum result precision is 38, and the maximum result scale is 38.) Operation Result precision and scale + or - Scale = max(s1,s2) Precision = max(p1-s1,p2-s2)+1+scale * / Scale = s1+s2 Precision = p1+p2+1 Scale = max(4,s1+p2-s2+1) Precision = p1-s1+ s2+scale For example, the PRICEPAID and COMMISSION columns in the SALES table are both DECIMAL(8,2) columns. If you divide PRICEPAID by COMMISSION (or vice versa), the formula is applied as follows: Precision = 8-2 + 2 + max(4,2+8-2+1) = 6 + 2 + 9 = 17 Scale = max(4,2+8-2+1) = 9 Result = DECIMAL(17,9) Numeric types 401 AWS Clean Rooms SQL Reference The following calculation is the general rule for computing the resulting precision and scale for operations performed on DECIMAL values with set operators such as UNION, INTERSECT, and EXCEPT or functions such as COALESCE and DECODE: Scale = max(s1,s2) Precision = min(max(p1-s1,p2-s2)+scale,19) For example, a DEC1 table with one DECIMAL(7,2) column is joined with a DEC2 table with one DECIMAL(15,3) column to create a DEC3 table. The schema of DEC3 shows that the column becomes a NUMERIC(15,3) column. select * from dec1 union select * from dec2; In the above example, the formula is applied as follows: Precision = min(max(7-2,15-3) + max(2,3), 19) = 12 + 3 = 15 Scale = max(2,3) = 3 Result = DECIMAL(15,3) Notes on division operations For division operations, divide-by-zero conditions return errors. The scale limit of 100 is applied after the precision and scale are calculated. If the calculated result scale is greater than 100, division results are scaled as follows: • Precision = precision - (scale - max_scale) • Scale = max_scale If the calculated precision is greater than the maximum precision (38), the precision is reduced to 38, and the scale becomes the result of: max(38 + scale - precision), min(4, 100)) Overﬂow conditions Overﬂow is checked for all numeric computations. DECIMAL data with a precision of 19 or less is stored as 64-bit integers. DECIMAL data with a precision that is greater than 19 is stored as 128- Numeric types 402 AWS Clean Rooms SQL Reference bit integers. The maximum precision for all DECIMAL values is 38, and the maximum scale is 37. Overﬂow errors occur when a value exceeds these limits, which apply to both intermediate and ﬁnal result sets: • Explicit casting results in runtime overﬂow errors when speciﬁc data values don't ﬁt the requested precision or scale speciﬁed by the cast function. For example, you can't cast all values from the PRICEPAID column in the SALES table (a DECIMAL(8,2) column) and return a DECIMAL(7,3) result: select pricepaid::decimal(7,3) from sales; ERROR: Numeric data overflow (result precision) This error occurs because some of the larger values in the PRICEPAID column can't be cast. • Multiplication operations produce results in which the result scale is the sum of the scale of each operand. If both operands have a scale of 4, for example, the result scale is 8, leaving only 10 digits for the left side of the decimal point. Therefore, it is relatively easy to run into overﬂow conditions when multiplying two large numbers that both have signiﬁcant scale. Numeric calculations with INTEGER and DECIMAL types When one of the operands in a calculation has an INTEGER data type and the other operand is DECIMAL, the INTEGER operand is implicitly cast as a DECIMAL. • SMALLINT or SHORT is cast as DECIMAL(5,0) • INTEGER is cast as DECIMAL(10,0) • BIGINT or LONG is cast as DECIMAL(19,0) For example, if you multiply SALES.COMMISSION, a DECIMAL(8,2) column, and SALES.QTYSOLD, a SMALLINT column, this calculation is cast as: DECIMAL(8,2) * DECIMAL(5,0) Character types Character data types include
sql-reference-110	sql-reference.pdf	110	to run into overﬂow conditions when multiplying two large numbers that both have signiﬁcant scale. Numeric calculations with INTEGER and DECIMAL types When one of the operands in a calculation has an INTEGER data type and the other operand is DECIMAL, the INTEGER operand is implicitly cast as a DECIMAL. • SMALLINT or SHORT is cast as DECIMAL(5,0) • INTEGER is cast as DECIMAL(10,0) • BIGINT or LONG is cast as DECIMAL(19,0) For example, if you multiply SALES.COMMISSION, a DECIMAL(8,2) column, and SALES.QTYSOLD, a SMALLINT column, this calculation is cast as: DECIMAL(8,2) * DECIMAL(5,0) Character types Character data types include CHAR (character) and VARCHAR (character varying). Topics Character types 403 AWS Clean Rooms • CHAR or CHARACTER • VARCHAR or CHARACTER VARYING • Signiﬁcance of trailing blanks CHAR or CHARACTER SQL Reference Use a CHAR or CHARACTER column to store ﬁxed-length strings. These strings are padded with blanks, so a CHAR(10) column always occupies 10 bytes of storage. char(10) A CHAR column without a length speciﬁcation results in a CHAR(1) column. CHAR data types are deﬁned in terms of bytes, not characters. A CHAR column can only contain single-byte characters, so a CHAR(10) column can contain a string with a maximum length of 10 bytes. Name CHAR or CHARACTER Storage Range (width of column) Length of string, including trailing blanks (if any) 4096 bytes VARCHAR or CHARACTER VARYING Use a VARCHAR or CHARACTER VARYING column to store variable-length strings with a ﬁxed limit. These strings are not padded with blanks, so a VARCHAR(120) column consists of a maximum of 120 single-byte characters, 60 two-byte characters, 40 three-byte characters, or 30 four-byte characters. varchar(120) VARCHAR data types are deﬁned in terms of bytes, not characters. A VARCHAR can contain multibyte characters, up to a maximum of four bytes per character. For example, a VARCHAR(12) column can contain 12 single-byte characters, 6 two-byte characters, 4 three-byte characters, or 3 four-byte characters. Character types 404 AWS Clean Rooms Name VARCHAR or CHARACTER VARYING Storage Range (width of column) SQL Reference 65535 bytes (64K -1) 4 bytes + total bytes for character s, where each character can be 1 to 4 bytes. Signiﬁcance of trailing blanks Both CHAR and VARCHAR data types store strings up to n bytes in length. An attempt to store a longer string into a column of these types results in an error. However, if the extra characters are all spaces (blanks), the string is truncated to the maximum length. If the string is shorter than the maximum length, CHAR values are padded with blanks, but VARCHAR values store the string without blanks. Trailing blanks in CHAR values are always semantically insigniﬁcant. They are disregarded when you compare two CHAR values, not included in LENGTH calculations, and removed when you convert a CHAR value to another string type. Trailing spaces in VARCHAR and CHAR values are treated as semantically insigniﬁcant when values are compared. Length calculations return the length of VARCHAR character strings with trailing spaces included in the length. Trailing blanks are not counted in the length for ﬁxed-length character strings. Datetime types Datetime data types include DATE, TIME, TIMETZ, TIMESTAMP, and TIMESTAMPTZ. Topics • Storage and ranges • DATE • TIME • TIMETZ • TIMESTAMP Datetime types 405 AWS Clean Rooms • TIMESTAMPTZ • Examples with datetime types • Date, time, and timestamp literals • Interval literals • Interval data types and literals Storage and ranges SQL Reference Name Storage Range Resolution DATE TIME 4 bytes 4713 BC to 294276 AD 1 day 8 bytes 00:00:00 to 24:00:00 1 microsecond TIMETZ 8 bytes 00:00:00+1459 to 00:00:00+1459 1 microsecond TIMESTAMP 8 bytes 4713 BC to 294276 AD 1 microsecond 8 bytes 4713 BC to 294276 AD 1 microsecond TIMESTAMP TZ DATE Use the DATE data type to store simple calendar dates without timestamps. TIME Use the TIME data type to store the time of day. TIME columns store values with up to a maximum of six digits of precision for fractional seconds. By default, TIME values are Coordinated Universal Time (UTC) in both user tables and AWS Clean Rooms system tables. TIMETZ Use the TIMETZ data type to store the time of day with a time zone. TIMETZ columns store values with up to a maximum of six digits of precision for fractional seconds. Datetime types 406 AWS Clean Rooms SQL Reference By default, TIMETZ values are UTC in both user tables and AWS Clean Rooms system tables. TIMESTAMP Use the TIMESTAMP data type to store complete timestamp values that include the date and the time of day. TIMESTAMP columns store values with up to a maximum of six digits of precision for fractional seconds. If you insert a date into a TIMESTAMP column, or a date with a partial timestamp value, the value is implicitly converted into a
sql-reference-111	sql-reference.pdf	111	store values with up to a maximum of six digits of precision for fractional seconds. Datetime types 406 AWS Clean Rooms SQL Reference By default, TIMETZ values are UTC in both user tables and AWS Clean Rooms system tables. TIMESTAMP Use the TIMESTAMP data type to store complete timestamp values that include the date and the time of day. TIMESTAMP columns store values with up to a maximum of six digits of precision for fractional seconds. If you insert a date into a TIMESTAMP column, or a date with a partial timestamp value, the value is implicitly converted into a full timestamp value. This full timestamp value has default values (00) for missing hours, minutes, and seconds. Time zone values in input strings are ignored. By default, TIMESTAMP values are UTC in both user tables and AWS Clean Rooms system tables. TIMESTAMPTZ Use the TIMESTAMPTZ data type to input complete timestamp values that include the date, the time of day, and a time zone. When an input value includes a time zone, AWS Clean Rooms uses the time zone to convert the value to UTC and stores the UTC value. To view a list of supported time zone names, run the following command. select my_timezone_names(); To view a list of supported time zone abbreviations, run the following command. select my_timezone_abbrevs(); You can also ﬁnd current information about time zones in the IANA Time Zone Database. The following table has examples of time zone formats. Format Example dd mon hh:mi:ss yyyy tz 17 Dec 07:37:16 1997 PST mm/dd/yyyy hh:mi:ss.ss tz 12/17/1997 07:37:16.00 PST mm/dd/yyyy hh:mi:ss.ss tz 12/17/1997 07:37:16.00 US/Paciﬁc Datetime types 407 AWS Clean Rooms Format Example SQL Reference yyyy-mm-dd hh:mi:ss+/-tz 1997-12-17 07:37:16-08 dd.mm.yyyy hh:mi:ss tz 17.12.1997 07:37:16.00 PST TIMESTAMPTZ columns store values with up to a maximum of six digits of precision for fractional seconds. If you insert a date into a TIMESTAMPTZ column, or a date with a partial timestamp, the value is implicitly converted into a full timestamp value. This full timestamp value has default values (00) for missing hours, minutes, and seconds. TIMESTAMPTZ values are UTC in user tables. Examples with datetime types The following examples show you how to work with datetime types that are supported by AWS Clean Rooms. Date examples The following examples insert dates that have diﬀerent formats and display the output. select * from datetable order by 1; start_date \| end_date ----------------------- 2008-06-01 \| 2008-12-31 2008-06-01 \| 2008-12-31 If you insert a timestamp value into a DATE column, the time portion is ignored and only the date is loaded. Time examples The following examples insert TIME and TIMETZ values that have diﬀerent formats and display the output. select * from timetable order by 1; start_time \| end_time Datetime types 408 AWS Clean Rooms ------------------------ 19:11:19 \| 20:41:19+00 19:11:19 \| 20:41:19+00 Time stamp examples SQL Reference If you insert a date into a TIMESTAMP or TIMESTAMPTZ column, the time defaults to midnight. For example, if you insert the literal 20081231, the stored value is 2008-12-31 00:00:00. The following examples insert timestamps that have diﬀerent formats and display the output. timeofday --------------------- 2008-06-01 09:59:59 2008-12-31 18:20:00 (2 rows) Date, time, and timestamp literals Following are rules for working with date, time, and timestamp literals that are supported by AWS Clean Rooms. Dates The following table shows input dates that are valid examples of literal date values that you can load into AWS Clean Rooms tables. The default MDY DateStyle mode is assumed to be in eﬀect. This mode means that the month value precedes the day value in strings such as 1999-01-08 and 01/02/00. Note A date or timestamp literal must be enclosed in quotation marks when you load it into a table. Input date January 8, 1999 1999-01-08 Datetime types Full date January 8, 1999 January 8, 1999 409 AWS Clean Rooms Input date 1/8/1999 01/02/00 2000-Jan-31 Jan-31-2000 31-Jan-2000 20080215 080215 2008.366 Times SQL Reference Full date January 8, 1999 January 2, 2000 January 31, 2000 January 31, 2000 January 31, 2000 February 15, 2008 February 15, 2008 December 31, 2008 (the three-digit part of date must be between 001 and 366) The following table shows input times that are valid examples of literal time values that you can load into AWS Clean Rooms tables. Input times 04:05:06.789 04:05:06 04:05 040506 04:05 AM 04:05 PM 16:05 Datetime types Description (of time part) 4:05 AM and 6.789 seconds 4:05 AM and 6 seconds 4:05 AM exactly 4:05 AM and 6 seconds 4:05 AM exactly; AM is optional 4:05 PM exactly; the hour value must be less than 12 4:05 PM exactly 410 AWS Clean Rooms Timestamps SQL Reference The following table shows input timestamps that are valid examples of literal time values that you can load into AWS Clean Rooms tables. All of the
sql-reference-112	sql-reference.pdf	112	literal time values that you can load into AWS Clean Rooms tables. Input times 04:05:06.789 04:05:06 04:05 040506 04:05 AM 04:05 PM 16:05 Datetime types Description (of time part) 4:05 AM and 6.789 seconds 4:05 AM and 6 seconds 4:05 AM exactly 4:05 AM and 6 seconds 4:05 AM exactly; AM is optional 4:05 PM exactly; the hour value must be less than 12 4:05 PM exactly 410 AWS Clean Rooms Timestamps SQL Reference The following table shows input timestamps that are valid examples of literal time values that you can load into AWS Clean Rooms tables. All of the valid date literals can be combined with the following time literals. Input timestamps (concatenated dates and times) Description (of time part) 20080215 04:05:06.789 4:05 AM and 6.789 seconds 20080215 04:05:06 4:05 AM and 6 seconds 20080215 04:05 20080215 040506 4:05 AM exactly 4:05 AM and 6 seconds 20080215 04:05 AM 4:05 AM exactly; AM is optional 20080215 04:05 PM 20080215 16:05 20080215 Special datetime values 4:05 PM exactly; the hour value must be less than 12 4:05 PM exactly Midnight (by default) The following table shows special values that can be used as datetime literals and as arguments to date functions. They require single quotation marks and are converted to regular timestamp values during query processing. Special value now Description Evaluates to the start time of the current transaction and returns a timestamp with microsecond precision. Datetime types 411 AWS Clean Rooms Special value today tomorrow yesterday SQL Reference Description Evaluates to the appropriate date and returns a timestamp with zeroes for the time parts. Evaluates to the appropriate date and returns a timestamp with zeroes for the time parts. Evaluates to the appropriate date and returns a timestamp with zeroes for the time parts. The following examples show how now and today work with the DATEADD function. select dateadd(day,1,'today'); date_add --------------------- 2009-11-17 00:00:00 (1 row) select dateadd(day,1,'now'); date_add ---------------------------- 2009-11-17 10:45:32.021394 (1 row) Interval literals Following are rules for working with interval literals that are supported by AWS Clean Rooms. Use an interval literal to identify speciﬁc periods of time, such as 12 hours or 6 weeks. You can use these interval literals in conditions and calculations that involve datetime expressions. Note You can't use the INTERVAL data type for columns in AWS Clean Rooms tables. Datetime types 412 AWS Clean Rooms SQL Reference An interval is expressed as a combination of the INTERVAL keyword with a numeric quantity and a supported date part, for example INTERVAL '7 days' or INTERVAL '59 minutes'. You can connect several quantities and units to form a more precise interval, for example: INTERVAL '7 days, 3 hours, 59 minutes'. Abbreviations and plurals of each unit are also supported; for example: 5 s, 5 second, and 5 seconds are equivalent intervals. If you don't specify a date part, the interval value represents seconds. You can specify the quantity value as a fraction (for example: 0.5 days). Examples The following examples show a series of calculations with diﬀerent interval values. The following example adds 1 second to the speciﬁed date. select caldate + interval '1 second' as dateplus from date where caldate='12-31-2008'; dateplus --------------------- 2008-12-31 00:00:01 (1 row) The following example adds 1 minute to the speciﬁed date. select caldate + interval '1 minute' as dateplus from date where caldate='12-31-2008'; dateplus --------------------- 2008-12-31 00:01:00 (1 row) The following example adds 3 hours and 35 minutes to the speciﬁed date. select caldate + interval '3 hours, 35 minutes' as dateplus from date where caldate='12-31-2008'; dateplus --------------------- 2008-12-31 03:35:00 (1 row) The following example adds 52 weeks to the speciﬁed date. Datetime types 413 AWS Clean Rooms SQL Reference select caldate + interval '52 weeks' as dateplus from date where caldate='12-31-2008'; dateplus --------------------- 2009-12-30 00:00:00 (1 row) The following example adds 1 week, 1 hour, 1 minute, and 1 second to the speciﬁed date. select caldate + interval '1w, 1h, 1m, 1s' as dateplus from date where caldate='12-31-2008'; dateplus --------------------- 2009-01-07 01:01:01 (1 row) The following example adds 12 hours (half a day) to the speciﬁed date. select caldate + interval '0.5 days' as dateplus from date where caldate='12-31-2008'; dateplus --------------------- 2008-12-31 12:00:00 (1 row) The following example subtracts 4 months from February 15, 2023 and the result is October 15, 2022. select date '2023-02-15' - interval '4 months'; ?column? --------------------- 2022-10-15 00:00:00 The following example subtracts 4 months from March 31, 2023 and the result is November 30, 2022. The calculation considers the number of days in a month. select date '2023-03-31' - interval '4 months'; ?column? Datetime types 414 AWS Clean Rooms --------------------- 2022-11-30 00:00:00 Interval data types and literals SQL Reference You can use an interval data type to store durations of time in units such as, seconds, minutes, hours, days, months, and years.
sql-reference-113	sql-reference.pdf	113	example subtracts 4 months from February 15, 2023 and the result is October 15, 2022. select date '2023-02-15' - interval '4 months'; ?column? --------------------- 2022-10-15 00:00:00 The following example subtracts 4 months from March 31, 2023 and the result is November 30, 2022. The calculation considers the number of days in a month. select date '2023-03-31' - interval '4 months'; ?column? Datetime types 414 AWS Clean Rooms --------------------- 2022-11-30 00:00:00 Interval data types and literals SQL Reference You can use an interval data type to store durations of time in units such as, seconds, minutes, hours, days, months, and years. Interval data types and literals can be used in datetime calculations, such as, adding intervals to dates and timestamps, summing intervals, and subtracting an interval from a date or timestamp. Interval literals can be used as input values to interval data type columns in a table. Syntax of interval data type To specify an interval data type to store a duration of time in years and months: INTERVAL year_to_month_qualifier To specify an interval data type to store a duration in days, hours, minutes, and seconds: INTERVAL day_to_second_qualifier [ (fractional_precision) ] Syntax of interval literal To specify an interval literal to deﬁne a duration of time in years and months: INTERVAL quoted-string year_to_month_qualifier To specify an interval literal to deﬁne a duration in days, hours, minutes, and seconds: INTERVAL quoted-string day_to_second_qualifier [ (fractional_precision) ] Arguments quoted-string Speciﬁes a positive or negative numeric value specifying a quantity and the datetime unit as an input string. If the quoted-string contains only a numeric, then AWS Clean Rooms determines the units from the year_to_month_qualiﬁer or day_to_second_qualiﬁer. For example, '23' MONTH represents 1 year 11 months, '-2' DAY represents -2 days 0 hours 0 minutes Datetime types 415 AWS Clean Rooms SQL Reference 0.0 seconds, '1-2' MONTH represents 1 year 2 months, and '13 day 1 hour 1 minute 1.123 seconds' SECOND represents 13 days 1 hour 1 minute 1.123 seconds. For more information about output formats of an interval, see Interval styles. year_to_month_qualiﬁer Speciﬁes the range of the interval. If you use a qualiﬁer and create an interval with time units smaller than the qualiﬁer, AWS Clean Rooms truncates and discards the smaller parts of the interval. Valid values for year_to_month_qualiﬁer are: • YEAR • MONTH • YEAR TO MONTH day_to_second_qualiﬁer Speciﬁes the range of the interval. If you use a qualiﬁer and create an interval with time units smaller than the qualiﬁer, AWS Clean Rooms truncates and discards the smaller parts of the interval. Valid values for day_to_second_qualiﬁer are: • DAY • HOUR • MINUTE • SECOND • DAY TO HOUR • DAY TO MINUTE • DAY TO SECOND • HOUR TO MINUTE • HOUR TO SECOND • MINUTE TO SECOND The output of the INTERVAL literal is truncated to the smallest INTERVAL component speciﬁed. For example, when using a MINUTE qualiﬁer, AWS Clean Rooms discards the time units smaller than MINUTE. select INTERVAL '1 day 1 hour 1 minute 1.123 seconds' MINUTE The resulting value is truncated to '1 day 01:01:00'. Datetime types 416 AWS Clean Rooms fractional_precision SQL Reference Optional parameter that speciﬁes the number of fractional digits allowed in the interval. The fractional_precision argument should only be speciﬁed if your interval contains SECOND. For example, SECOND(3) creates an interval that allows only three fractional digits, such as 1.234 seconds. The maximum number of fractional digits is six. The session conﬁguration interval_forbid_composite_literals determines whether an error is returned when an interval is speciﬁed with both YEAR TO MONTH and DAY TO SECOND parts. Interval arithmetic You can use interval values with other datetime values to perform arithmetic operations. The following tables describe the available operations and what data type results from each operation. Note Operations that can produce both date and timestamp results do so based on the smallest unit of time involved in the equation. For example, when you add an interval to a date the result is a date if it is a YEAR TO MONTH interval, and a timestamp if it is a DAY TO SECOND interval. Operations where the ﬁrst operand is an interval produce the following results for the given second operand: Operator - + * / Date N/A Date N/A N/A Timestamp Interval Numeric N/A Interval Date/Timestamp Interval N/A N/A N/A N/A N/A N/A Interval Interval Datetime types 417 AWS Clean Rooms SQL Reference Operations where the ﬁrst operand is a date produce the following results for the given second operand: Operator Date Timestamp Interval Numeric - + Numeric Interval Date/Timestamp Date N/A N/A N/A N/A Operations where the ﬁrst operand is a timestamp produce the following results for the given second operand: Operator Date Timestamp Interval Numeric Numeric Interval Timestamp Timestamp N/A N/A N/A N/A - + Interval styles • postgres – follows PostgreSQL
sql-reference-114	sql-reference.pdf	114	/ Date N/A Date N/A N/A Timestamp Interval Numeric N/A Interval Date/Timestamp Interval N/A N/A N/A N/A N/A N/A Interval Interval Datetime types 417 AWS Clean Rooms SQL Reference Operations where the ﬁrst operand is a date produce the following results for the given second operand: Operator Date Timestamp Interval Numeric - + Numeric Interval Date/Timestamp Date N/A N/A N/A N/A Operations where the ﬁrst operand is a timestamp produce the following results for the given second operand: Operator Date Timestamp Interval Numeric Numeric Interval Timestamp Timestamp N/A N/A N/A N/A - + Interval styles • postgres – follows PostgreSQL style. This is the default. • postgres_verbose – follows PostgreSQL verbose style. • sql_standard – follows the SQL standard interval literals style. The following command sets the interval style to sql_standard. SET IntervalStyle to 'sql_standard'; postgres output format The following is the output format for postgres interval style. Each numeric value can be negative. '<numeric> <unit> [, <numeric> <unit> ...]' select INTERVAL '1-2' YEAR TO MONTH::text Datetime types 418 SQL Reference AWS Clean Rooms varchar --------------- 1 year 2 mons select INTERVAL '1 2:3:4.5678' DAY TO SECOND::text varchar ------------------ 1 day 02:03:04.5678 postgres_verbose output format postgres_verbose syntax is similar to postgres, but postgres_verbose outputs also contain the unit of time. '[@] <numeric> <unit> [, <numeric> <unit> ...] [direction]' select INTERVAL '1-2' YEAR TO MONTH::text varchar ----------------- @ 1 year 2 mons select INTERVAL '1 2:3:4.5678' DAY TO SECOND::text varchar --------------------------- @ 1 day 2 hours 3 mins 4.56 secs sql_standard output format Interval year to month values are formatted as the following. Specifying a negative sign before the interval indicates the interval is a negative value and applies to the entire interval. '[-]yy-mm' Interval day to second values are formatted as the following. '[-]dd hh:mm:ss.ffffff' Datetime types 419 AWS Clean Rooms SQL Reference SELECT INTERVAL '1-2' YEAR TO MONTH::text varchar ------- 1-2 select INTERVAL '1 2:3:4.5678' DAY TO SECOND::text varchar --------------- 1 2:03:04.5678 Examples of interval data type The following examples demonstrate how to use INTERVAL data types with tables. create table sample_intervals (y2m interval month, h2m interval hour to minute); insert into sample_intervals values (interval '20' month, interval '2 days 1:1:1.123456' day to second); select y2m::text, h2m::text from sample_intervals; y2m \| h2m ---------------+----------------- 1 year 8 mons \| 2 days 01:01:00 update sample_intervals set y2m = interval '2' year where y2m = interval '1-8' year to month; select * from sample_intervals; y2m \| h2m ---------+----------------- 2 years \| 2 days 01:01:00 delete from sample_intervals where h2m = interval '2 1:1:0' day to second; select * from sample_intervals; y2m \| h2m -----+----- Datetime types 420 AWS Clean Rooms Examples of interval literals SQL Reference The following examples are run with interval style set to postgres. The following example demonstrates how to create an INTERVAL literal of 1 year. select INTERVAL '1' YEAR intervaly2m --------------- 1 years 0 mons If you specify a quoted-string that exceeds the qualiﬁer, the remaining units of time are truncated from the interval. In the following example, an interval of 13 months becomes 1 year and 1 month, but the remaining 1 month is left out because of the YEAR qualiﬁer. select INTERVAL '13 months' YEAR intervaly2m --------------- 1 years 0 mons If you use a qualiﬁer lower than your interval string, leftover units are included. select INTERVAL '13 months' MONTH intervaly2m --------------- 1 years 1 mons Specifying a precision in your interval truncates the number of fractional digits to the speciﬁed precision. select INTERVAL '1.234567' SECOND (3) intervald2s -------------------------------- 0 days 0 hours 0 mins 1.235 secs If you don't specify a precision, AWS Clean Rooms uses the maximum precision of 6. Datetime types 421 AWS Clean Rooms SQL Reference select INTERVAL '1.23456789' SECOND intervald2s ----------------------------------- 0 days 0 hours 0 mins 1.234567 secs The following example demonstrates how to create a ranged interval. select INTERVAL '2:2' MINUTE TO SECOND intervald2s ------------------------------ 0 days 0 hours 2 mins 2.0 secs Qualiﬁers dictate the units that you're specifying. For example, even though the following example uses the same quoted-string of '2:2' as the previous example, AWS Clean Rooms recognizes that it uses diﬀerent units of time because of the qualiﬁer. select INTERVAL '2:2' HOUR TO MINUTE intervald2s ------------------------------ 0 days 2 hours 2 mins 0.0 secs Abbreviations and plurals of each unit are also supported. For example, 5s, 5 second, and 5 seconds are equivalent intervals. Supported units are years, months, hours, minutes, and seconds. select INTERVAL '5s' SECOND intervald2s ------------------------------ 0 days 0 hours 0 mins 5.0 secs select INTERVAL '5 HOURS' HOUR intervald2s ------------------------------ 0 days 5 hours 0 mins 0.0 secs select INTERVAL '5 h' HOUR Datetime types 422 AWS Clean Rooms SQL Reference intervald2s ------------------------------ 0 days 5 hours 0 mins 0.0 secs Examples of interval literals without qualiﬁer syntax Note The
sql-reference-115	sql-reference.pdf	115	intervald2s ------------------------------ 0 days 2 hours 2 mins 0.0 secs Abbreviations and plurals of each unit are also supported. For example, 5s, 5 second, and 5 seconds are equivalent intervals. Supported units are years, months, hours, minutes, and seconds. select INTERVAL '5s' SECOND intervald2s ------------------------------ 0 days 0 hours 0 mins 5.0 secs select INTERVAL '5 HOURS' HOUR intervald2s ------------------------------ 0 days 5 hours 0 mins 0.0 secs select INTERVAL '5 h' HOUR Datetime types 422 AWS Clean Rooms SQL Reference intervald2s ------------------------------ 0 days 5 hours 0 mins 0.0 secs Examples of interval literals without qualiﬁer syntax Note The following examples demonstrate using an interval literal without a YEAR TO MONTH or DAY TO SECOND qualiﬁer. For information about using the recommended interval literal with a qualiﬁer, see Interval data types and literals. Use an interval literal to identify speciﬁc periods of time, such as 12 hours or 6 months. You can use these interval literals in conditions and calculations that involve datetime expressions. An interval literal is expressed as a combination of the INTERVAL keyword with a numeric quantity and a supported date part, for example INTERVAL '7 days' or INTERVAL '59 minutes'. You can connect several quantities and units to form a more precise interval, for example: INTERVAL '7 days, 3 hours, 59 minutes'. Abbreviations and plurals of each unit are also supported; for example: 5 s, 5 second, and 5 seconds are equivalent intervals. If you don't specify a date part, the interval value represents seconds. You can specify the quantity value as a fraction (for example: 0.5 days). The following examples show a series of calculations with diﬀerent interval values. The following adds 1 second to the speciﬁed date. select caldate + interval '1 second' as dateplus from date where caldate='12-31-2008'; dateplus --------------------- 2008-12-31 00:00:01 (1 row) The following adds 1 minute to the speciﬁed date. select caldate + interval '1 minute' as dateplus from date where caldate='12-31-2008'; Datetime types 423 AWS Clean Rooms dateplus --------------------- 2008-12-31 00:01:00 (1 row) SQL Reference The following adds 3 hours and 35 minutes to the speciﬁed date. select caldate + interval '3 hours, 35 minutes' as dateplus from date where caldate='12-31-2008'; dateplus --------------------- 2008-12-31 03:35:00 (1 row) The following adds 52 weeks to the speciﬁed date. select caldate + interval '52 weeks' as dateplus from date where caldate='12-31-2008'; dateplus --------------------- 2009-12-30 00:00:00 (1 row) The following adds 1 week, 1 hour, 1 minute, and 1 second to the speciﬁed date. select caldate + interval '1w, 1h, 1m, 1s' as dateplus from date where caldate='12-31-2008'; dateplus --------------------- 2009-01-07 01:01:01 (1 row) The following adds 12 hours (half a day) to the speciﬁed date. select caldate + interval '0.5 days' as dateplus from date where caldate='12-31-2008'; dateplus --------------------- 2008-12-31 12:00:00 (1 row) The following subtracts 4 months from February 15, 2023 and the result is October 15, 2022. Datetime types 424 AWS Clean Rooms SQL Reference select date '2023-02-15' - interval '4 months'; ?column? --------------------- 2022-10-15 00:00:00 The following subtracts 4 months from March 31, 2023 and the result is November 30, 2022. The calculation considers the number of days in a month. select date '2023-03-31' - interval '4 months'; ?column? --------------------- 2022-11-30 00:00:00 Boolean type Use the BOOLEAN data type to store true and false values in a single-byte column. The following table describes the three possible states for a Boolean value and the literal values that result in that state. Regardless of the input string, a Boolean column stores and outputs "t" for true and "f" for false. State True False Valid literal values Storage TRUE 't' 1 byte 'true' 'y' 'yes' '1' FALSE 'f' 1 byte 'false' 'n' 'no' '0' Unknown NULL 1 byte You can use an IS comparison to check a Boolean value only as a predicate in the WHERE clause. You can't use the IS comparison with a Boolean value in the SELECT list. Boolean type 425 AWS Clean Rooms Examples SQL Reference You can use a BOOLEAN column to store an "Active/Inactive" state for each customer in a CUSTOMER table. select * from customer; custid \| active_flag -------+-------------- 100 \| t In this example, the following query selects users from the USERS table who like sports but do not like theatre: select firstname, lastname, likesports, liketheatre from users where likesports is true and liketheatre is false order by userid limit 10; firstname \| lastname \| likesports \| liketheatre ----------+------------+------------+------------- Alejandro \| Rosalez \| t \| f Akua \| Mansa \| t \| f Arnav \| Desai \| t \| f Carlos \| Salazar \| t \| f Diego \| Ramirez \| t \| f Efua \| Owusu \| t \| f John \| Stiles \| t \| f Jorge \| Souza \| t \| f Kwaku \| Mensah \| t \| f Kwesi
sql-reference-116	sql-reference.pdf	116	who like sports but do not like theatre: select firstname, lastname, likesports, liketheatre from users where likesports is true and liketheatre is false order by userid limit 10; firstname \| lastname \| likesports \| liketheatre ----------+------------+------------+------------- Alejandro \| Rosalez \| t \| f Akua \| Mansa \| t \| f Arnav \| Desai \| t \| f Carlos \| Salazar \| t \| f Diego \| Ramirez \| t \| f Efua \| Owusu \| t \| f John \| Stiles \| t \| f Jorge \| Souza \| t \| f Kwaku \| Mensah \| t \| f Kwesi \| Manu \| t \| f (10 rows) The following example selects users from the USERS table for whom is it unknown whether they like rock music. select firstname, lastname, likerock from users where likerock is unknown order by userid limit 10; firstname \| lastname \| likerock Boolean type 426 AWS Clean Rooms SQL Reference ----------+----------+---------- Alejandro \| Rosalez \| Carlos \| Salazar \| Diego \| Ramirez \| John \| Stiles \| Kwaku \| Mensah \| Martha \| Rivera \| Mateo \| Jackson \| Paulo \| Santos \| Richard \| Roe \| Saanvi \| Sarkar \| (10 rows) The following example returns an error because it uses an IS comparison in the SELECT list. select firstname, lastname, likerock is true as "check" from users order by userid limit 10; [Amazon](500310) Invalid operation: Not implemented The following example succeeds because it uses an equal comparison ( = ) in the SELECT list instead of the IS comparison. select firstname, lastname, likerock = true as "check" from users order by userid limit 10; firstname \| lastname \| check ----------+-----------+------ Alejandro \| Rosalez \| Carlos \| Salazar \| Diego \| Ramirez \| true John \| Stiles \| Kwaku \| Mensah \| true Martha \| Rivera \| true Mateo \| Jackson \| Paulo \| Santos \| false Richard \| Roe \| Saanvi \| Sarkar \| Boolean type 427 AWS Clean Rooms SUPER type SQL Reference Use the SUPER data type to store semistructured data or documents as values. Semistructured data doesn't conform to the rigid and tabular structure of the relational data model used in SQL databases. The SUPER data type contains tags that reference distinct entities within the data. SUPER data types can contain complex values such as arrays, nested structures, and other complex structures that are associated with serialization formats, such as JSON. The SUPER data type is a set of schemaless array and structure values that encompass all other scalar types of AWS Clean Rooms. The SUPER data type supports up to 1 MB of data for an individual SUPER ﬁeld or object. The SUPER data type has the following properties: • An AWS Clean Rooms scalar value: • A null • A boolean • A number, such as smallint, integer, bigint, decimal, or ﬂoating point (such as ﬂoat4 or ﬂoat8) • A string value, such as varchar or char • A complex value: • An array of values, including scalar or complex • A structure, also known as tuple or object, that is a map of attribute names and values (scalar or complex) Any of the two types of complex values contain their own scalars or complex values without having any restrictions for regularity. The SUPER data type supports the persistence of semistructured data in a schemaless form. Although hierarchical data model can change, the old versions of data can coexist in the same SUPER column. Nested type AWS Clean Rooms supports queries involving data with nested data types, speciﬁcally the AWS Glue struct, array, and map column types. Only the custom analysis rule supports nested data types. SUPER type 428 AWS Clean Rooms SQL Reference Notably, nested data types don't conform to the rigid, tabular structure of the relational data model of SQL databases. Nested data types contains tags that reference distinct entities within the data. They can contain complex values such as arrays, nested structures, and other complex structures that are associated with serialization formats, such as JSON. Nested data types support up to 1 MB of data for an individual nested data type ﬁeld or object. Topics • ARRAY type • MAP type • STRUCT type • Examples of nested data types ARRAY type Use the ARRAY type to represent values comprising a sequence of elements with the type of elementType. array(elementType, containsNull) Use containsNull to indicate if elements in an ARRAY type can have null values. MAP type Use the MAP type to represent values comprising a set of key-value pairs. map(keyType, valueType, valueContainsNull) keyType: the data type of keys valueType: the data type of values Keys aren't allowed to have null values. Use valueContainsNull to indicate if values of a MAP type value can have null values. Nested type 429 AWS Clean Rooms STRUCT type
sql-reference-117	sql-reference.pdf	117	Examples of nested data types ARRAY type Use the ARRAY type to represent values comprising a sequence of elements with the type of elementType. array(elementType, containsNull) Use containsNull to indicate if elements in an ARRAY type can have null values. MAP type Use the MAP type to represent values comprising a set of key-value pairs. map(keyType, valueType, valueContainsNull) keyType: the data type of keys valueType: the data type of values Keys aren't allowed to have null values. Use valueContainsNull to indicate if values of a MAP type value can have null values. Nested type 429 AWS Clean Rooms STRUCT type SQL Reference Use the STRUCT type to represent values with the structure described by a sequence of StructFields (ﬁelds). struct(name, dataType, nullable) StructField(name, dataType, nullable): Represents a ﬁeld in a StructType. dataType: the data type a ﬁeld name: the name of a ﬁeld Use nullable to indicate if values of these ﬁelds can have null values. Examples of nested data types For the struct<given:varchar, family:varchar> type, there are two attribute names: given, and family, each corresponding to a varchar value. For the array<varchar> type, the array is speciﬁed as a list of varchar. The array<struct<shipdate:timestamp, price:double>> type refers to a list of elements with struct<shipdate:timestamp, price:double> type. The map data type behaves like an array of structs, where the attribute name for each element in the array is denoted by key and it maps to a value. Example For example, the map<varchar(20), varchar(20)> type is treated as array<struct<key:varchar(20), value:varchar(20)>>, where key and value refer to the attributes of the map in the underlying data. For information about how AWS Clean Rooms enables navigation into arrays and structures, see Navigation. For information about how AWS Clean Rooms enables iteration over arrays by navigating the array using the FROM clause of a query, see Unnesting queries. Nested type 430 AWS Clean Rooms VARBYTE type SQL Reference Use a VARBYTE, VARBINARY, or BINARY VARYING column to store variable-length binary value with a ﬁxed limit. varbyte [ (n) ] The maximum number of bytes (n) can range from 1 – 1,024,000. The default is 64,000. Some examples where you might want to use a VARBYTE data type are as follows: • Joining tables on VARBYTE columns. • Creating materialized views that contain VARBYTE columns. Incremental refresh of materialized views that contain VARBYTE columns is supported. However, aggregate functions other than COUNT, MIN, and MAX and GROUP BY on VARBYTE columns don't support incremental refresh. To ensure that all bytes are printable characters, AWS Clean Rooms uses the hex format to print VARBYTE values. For example, the following SQL converts the hexadecimal string 6162 into a binary value. Even though the returned value is a binary value, the results are printed as hexadecimal 6162. select from_hex('6162'); from_hex ---------- 6162 AWS Clean Rooms supports casting between VARBYTE and the following data types: • CHAR • VARCHAR • SMALLINT or SHORT • INTEGER • BIGINT or LONG The following SQL statement casts a VARCHAR string to a VARBYTE. Even though the returned value is a binary value, the results are printed as hexadecimal 616263. VARBYTE type 431 AWS Clean Rooms SQL Reference select 'abc'::varbyte; varbyte --------- 616263 The following SQL statement casts a CHAR value in a column to a VARBYTE. This example creates a table with a CHAR(10) column (c), inserts character values that are shorter than the length of 10. The resulting cast pads the result with a space characters (hex'20') to the deﬁned column size. Even though the returned value is a binary value, the results are printed as hexadecimal. create table t (c char(10)); insert into t values ('aa'), ('abc'); select c::varbyte from t; c ---------------------- 61612020202020202020 61626320202020202020 The following SQL statement casts a SMALLINT string to a VARBYTE. Even though the returned value is a binary value, the results are printed as hexadecimal 0005, which is two bytes or four hexadecimal characters. select 5::smallint::varbyte; varbyte --------- 0005 The following SQL statement casts an INTEGER to a VARBYTE. Even though the returned value is a binary value, the results are printed as hexadecimal 00000005, which is four bytes or eight hexadecimal characters. select 5::int::varbyte; varbyte ---------- 00000005 VARBYTE type 432 AWS Clean Rooms SQL Reference The following SQL statement casts a BIGINT to a VARBYTE. Even though the returned value is a binary value, the results are printed as hexadecimal 0000000000000005, which is eight bytes or 16 hexadecimal characters. select 5::bigint::varbyte; varbyte ------------------ 0000000000000005 Limitations when using the VARBYTE data type with AWS Clean Rooms The following are limitations when using the VARBYTE data type with AWS Clean Rooms: • AWS Clean Rooms supports the VARBYTE data type only for Parquet and ORC ﬁles. • AWS Clean Rooms query editor don't yet fully support VARBYTE data type. Therefore, use a diﬀerent SQL client
sql-reference-118	sql-reference.pdf	118	SQL Reference The following SQL statement casts a BIGINT to a VARBYTE. Even though the returned value is a binary value, the results are printed as hexadecimal 0000000000000005, which is eight bytes or 16 hexadecimal characters. select 5::bigint::varbyte; varbyte ------------------ 0000000000000005 Limitations when using the VARBYTE data type with AWS Clean Rooms The following are limitations when using the VARBYTE data type with AWS Clean Rooms: • AWS Clean Rooms supports the VARBYTE data type only for Parquet and ORC ﬁles. • AWS Clean Rooms query editor don't yet fully support VARBYTE data type. Therefore, use a diﬀerent SQL client when working with VARBYTE expressions. As a workaround to use the query editor, if the length of your data is below 64 KB and the content is valid UTF-8, you can cast the VARBYTE values to VARCHAR, for example: select to_varbyte('6162', 'hex')::varchar; • You can't use VARBYTE data types with Python or Lambda user-deﬁned functions (UDFs). • You can't create a HLLSKETCH column from a VARBYTE column or use APPROXIMATE COUNT DISTINCT on a VARBYTE column. Type compatibility and conversion The following topics describe how type conversion rules and data type compatibility work in AWS Clean Rooms SQL. Topics • Compatibility • General compatibility and conversion rules • Implicit conversion types Type compatibility and conversion 433 AWS Clean Rooms Compatibility SQL Reference Data type matching and matching of literal values and constants to data types occurs during various database operations, including the following: • Data manipulation language (DML) operations on tables • UNION, INTERSECT, and EXCEPT queries • CASE expressions • Evaluation of predicates, such as LIKE and IN • Evaluation of SQL functions that do comparisons or extractions of data • Comparisons with mathematical operators The results of these operations depend on type conversion rules and data type compatibility. Compatibility implies that a one-to-one matching of a certain value and a certain data type is not always required. Because some data types are compatible, an implicit conversion, or coercion, is possible. For more information, see Implicit conversion types. When data types are incompatible, you can sometimes convert a value from one data type to another by using an explicit conversion function. General compatibility and conversion rules Note the following compatibility and conversion rules: • In general, data types that fall into the same type category (such as diﬀerent numeric data types) are compatible and can be implicitly converted. For example, with implicit conversion you can insert a decimal value into an integer column. The decimal is rounded to produce a whole number. Or you can extract a numeric value, such as 2008, from a date and insert that value into an integer column. • Numeric data types enforce overﬂow conditions that occur when you attempt to insert out- of-range values. For example, a decimal value with a precision of 5 does not ﬁt into a decimal column that was deﬁned with a precision of 4. An integer or the whole part of a decimal is never truncated. However, the fractional part of a decimal can be rounded up or down, as appropriate. However, results of explicit casts of values selected from tables are not rounded. • Diﬀerent types of character strings are compatible. VARCHAR column strings containing single- byte data and CHAR column strings are comparable and implicitly convertible. VARCHAR strings Type compatibility and conversion 434 AWS Clean Rooms SQL Reference that contain multibyte data are not comparable. Also, you can convert a character string to a date, time, timestamp, or numeric value if the string is an appropriate literal value. Any leading or trailing spaces are ignored. Conversely, you can convert a date, time, timestamp, or numeric value to a ﬁxed-length or variable-length character string. Note A character string that you want to cast to a numeric type must contain a character representation of a number. For example, you can cast the strings '1.0' or '5.9' to decimal values, but you can't cast the string 'ABC' to any numeric type. • If you compare DECIMAL values with character strings, AWS Clean Rooms attempts to convert the character string to a DECIMAL value. When comparing all other numeric values with character strings, the numeric values are converted to character strings. To enforce the opposite conversion (for example, converting character strings to integers, or converting DECIMAL values to character strings), use an explicit function, such as CAST function. • To convert 64-bit DECIMAL or NUMERIC values to a higher precision, you must use an explicit conversion function such as the CAST or CONVERT functions. • When converting DATE or TIMESTAMP to TIMESTAMPTZ, or converting TIME to TIMETZ, the time zone is set to the current session time zone. The session time zone is UTC by default. • Similarly, TIMESTAMPTZ is converted to DATE, TIME, or TIMESTAMP based on the current
sql-reference-119	sql-reference.pdf	119	strings. To enforce the opposite conversion (for example, converting character strings to integers, or converting DECIMAL values to character strings), use an explicit function, such as CAST function. • To convert 64-bit DECIMAL or NUMERIC values to a higher precision, you must use an explicit conversion function such as the CAST or CONVERT functions. • When converting DATE or TIMESTAMP to TIMESTAMPTZ, or converting TIME to TIMETZ, the time zone is set to the current session time zone. The session time zone is UTC by default. • Similarly, TIMESTAMPTZ is converted to DATE, TIME, or TIMESTAMP based on the current session time zone. The session time zone is UTC by default. After the conversion, time zone information is dropped. • Character strings that represent a timestamp with time zone speciﬁed are converted to TIMESTAMPTZ using the current session time zone, which is UTC by default. Likewise, character strings that represent a time with time zone speciﬁed are converted to TIMETZ using the current session time zone, which is UTC by default. Implicit conversion types There are two types of implicit conversions: • Implicit conversions in assignments, such as setting values in INSERT or UPDATE commands • Implicit conversions in expressions, such as performing comparisons in the WHERE clause Type compatibility and conversion 435 AWS Clean Rooms SQL Reference The following table lists the data types that can be converted implicitly in assignments or expressions. You can also use an explicit conversion function to perform these conversions. From type BIGINT CHAR DATE To type BOOLEAN CHAR DECIMAL (NUMERIC) DOUBLE PRECISION (FLOAT8) INTEGER REAL (FLOAT4) SMALLINT or SHORT VARCHAR VARCHAR CHAR VARCHAR TIMESTAMP TIMESTAMPTZ DECIMAL (NUMERIC) BIGINT or LONG CHAR DOUBLE PRECISION (FLOAT8) INTEGER INT) REAL (FLOAT4) SMALLINT or SHORT Type compatibility and conversion 436 AWS Clean Rooms From type SQL Reference To type VARCHAR DOUBLE PRECISION (FLOAT8) BIGINT or LONG CHAR DECIMAL (NUMERIC) INTEGER (INT) REAL (FLOAT4) SMALLINT or SHORT VARCHAR BIGINT or LONG BOOLEAN CHAR DECIMAL (NUMERIC) DOUBLE PRECISION (FLOAT8) REAL (FLOAT4) SMALLINT or SHORT VARCHAR BIGINT or LONG CHAR DECIMAL (NUMERIC) INTEGER (INT) INTEGER (INT) REAL (FLOAT4) Type compatibility and conversion 437 AWS Clean Rooms From type SMALLINT TIMESTAMP TIMESTAMPTZ SQL Reference To type SMALLINT or SHORT VARCHAR BIGINT or LONG BOOLEAN CHAR DECIMAL (NUMERIC) DOUBLE PRECISION (FLOAT8) INTEGER (INT) REAL (FLOAT4) VARCHAR CHAR DATE VARCHAR TIMESTAMPTZ TIME CHAR DATE VARCHAR TIMESTAMP TIMETZ Type compatibility and conversion 438 AWS Clean Rooms From type TIME TIMETZ Note SQL Reference To type VARCHAR TIMETZ VARCHAR TIME Implicit conversions between TIMESTAMPTZ, TIMESTAMP, DATE, TIME, TIMETZ, or character strings use the current session time zone. The VARBYTE data type can't be implicitly converted to any other data type. For more information, see CAST function. AWS Clean Rooms SQL commands The following SQL commands are supported in AWS Clean Rooms: Topics • SELECT SELECT The SELECT command returns rows from tables and user-deﬁned functions. The following SELECT SQL commands, clauses, and set operators are supported in AWS Clean Rooms SQL: Topics • SELECT list • WITH clause • FROM clause • WHERE clause SQL commands 439 SQL Reference AWS Clean Rooms • GROUP BY clause • HAVING clause • Set operators • ORDER BY clause • Subquery examples • Correlated subqueries SELECT list The SELECT list names the columns, functions, and expressions that you want the query to return. The list represents the output of the query. Syntax SELECT [ TOP number ] [ DISTINCT ] \| expression [ AS column_alias ] [, ...] Parameters TOP number TOP takes a positive integer as its argument, which deﬁnes the number of rows that are returned to the client. The behavior with the TOP clause is the same as the behavior with the LIMIT clause. The number of rows that is returned is ﬁxed, but the set of rows is not ﬁxed. To return a consistent set of rows, use TOP or LIMIT in conjunction with an ORDER BY clause. DISTINCT Option that eliminates duplicate rows from the result set, based on matching values in one or more columns. expression An expression formed from one or more columns that exist in the tables referenced by the query. An expression can contain SQL functions. For example: coalesce(dimension, 'stringifnull') AS column_alias SELECT 440 AWS Clean Rooms AS column_alias SQL Reference A temporary name for the column that is used in the ﬁnal result set. The AS keyword is optional. For example: coalesce(dimension, 'stringifnull') AS dimensioncomplete If you don't specify an alias for an expression that isn't a simple column name, the result set applies a default name to that column. Note The alias is recognized right after it is deﬁned in the target list. You cannot use an alias in other expressions deﬁned after it in the same target list. Usage notes TOP is a SQL extension. TOP
sql-reference-120	sql-reference.pdf	120	'stringifnull') AS column_alias SELECT 440 AWS Clean Rooms AS column_alias SQL Reference A temporary name for the column that is used in the ﬁnal result set. The AS keyword is optional. For example: coalesce(dimension, 'stringifnull') AS dimensioncomplete If you don't specify an alias for an expression that isn't a simple column name, the result set applies a default name to that column. Note The alias is recognized right after it is deﬁned in the target list. You cannot use an alias in other expressions deﬁned after it in the same target list. Usage notes TOP is a SQL extension. TOP provides an alternative to the LIMIT behavior. You can't use TOP and LIMIT in the same query. WITH clause A WITH clause is an optional clause that precedes the SELECT list in a query. The WITH clause deﬁnes one or more common_table_expressions. Each common table expression (CTE) deﬁnes a temporary table, which is similar to a view deﬁnition. You can reference these temporary tables in the FROM clause. They're used only while the query they belong to runs. Each CTE in the WITH clause speciﬁes a table name, an optional list of column names, and a query expression that evaluates to a table (a SELECT statement). WITH clause subqueries are an eﬃcient way of deﬁning tables that can be used throughout the execution of a single query. In all cases, the same results can be achieved by using subqueries in the main body of the SELECT statement, but WITH clause subqueries may be simpler to write and read. Where possible, WITH clause subqueries that are referenced multiple times are optimized as common subexpressions; that is, it may be possible to evaluate a WITH subquery once and reuse its results. (Note that common subexpressions aren't limited to those deﬁned in the WITH clause.) Syntax [ WITH common_table_expression [, common_table_expression , ...] ] SELECT 441 AWS Clean Rooms SQL Reference where common_table_expression can be non-recursive. Following is the non-recursive form: CTE_table_name AS ( query ) Parameters common_table_expression Deﬁnes a temporary table that you can reference in the FROM clause and is used only during the execution of the query to which it belongs. CTE_table_name A unique name for a temporary table that deﬁnes the results of a WITH clause subquery. You can't use duplicate names within a single WITH clause. Each subquery must be given a table name that can be referenced in the FROM clause. query Any SELECT query that AWS Clean Rooms supports. See SELECT. Usage notes You can use a WITH clause in the following SQL statement: • SELECT, WITH, UNION, UNION ALL, INTERSECT, or EXCEPT. If the FROM clause of a query that contains a WITH clause doesn't reference any of the tables deﬁned by the WITH clause, the WITH clause is ignored and the query runs as normal. A table deﬁned by a WITH clause subquery can be referenced only in the scope of the SELECT query that the WITH clause begins. For example, you can reference such a table in the FROM clause of a subquery in the SELECT list, WHERE clause, or HAVING clause. You can't use a WITH clause in a subquery and reference its table in the FROM clause of the main query or another subquery. This query pattern results in an error message of the form relation table_name doesn't exist for the WITH clause table. You can't specify another WITH clause inside a WITH clause subquery. You can't make forward references to tables deﬁned by WITH clause subqueries. For example, the following query returns an error because of the forward reference to table W2 in the deﬁnition of table W1: SELECT 442 AWS Clean Rooms SQL Reference with w1 as (select * from w2), w2 as (select * from w1) select * from sales; ERROR: relation "w2" does not exist Examples The following example shows the simplest possible case of a query that contains a WITH clause. The WITH query named VENUECOPY selects all of the rows from the VENUE table. The main query in turn selects all of the rows from VENUECOPY. The VENUECOPY table exists only for the duration of this query. with venuecopy as (select * from venue) select * from venuecopy order by 1 limit 10; venueid \| venuename \| venuecity \| venuestate \| venueseats ---------+----------------------------+-----------------+------------+------------ 1 \| Toyota Park \| Bridgeview \| IL \| 0 2 \| Columbus Crew Stadium \| Columbus \| OH \| 0 3 \| RFK Stadium \| Washington \| DC \| 0 4 \| CommunityAmerica Ballpark \| Kansas City \| KS \| 0 5 \| Gillette Stadium \| Foxborough \| MA \| 68756 6 \| New York Giants Stadium \| East Rutherford \| NJ \| 80242 7 \| BMO Field \| Toronto \| ON \| 0 8 \| The Home Depot Center \|
sql-reference-121	sql-reference.pdf	121	* from venuecopy order by 1 limit 10; venueid \| venuename \| venuecity \| venuestate \| venueseats ---------+----------------------------+-----------------+------------+------------ 1 \| Toyota Park \| Bridgeview \| IL \| 0 2 \| Columbus Crew Stadium \| Columbus \| OH \| 0 3 \| RFK Stadium \| Washington \| DC \| 0 4 \| CommunityAmerica Ballpark \| Kansas City \| KS \| 0 5 \| Gillette Stadium \| Foxborough \| MA \| 68756 6 \| New York Giants Stadium \| East Rutherford \| NJ \| 80242 7 \| BMO Field \| Toronto \| ON \| 0 8 \| The Home Depot Center \| Carson \| CA \| 0 9 \| Dick's Sporting Goods Park \| Commerce City \| CO \| 0 v 10 \| Pizza Hut Park \| Frisco \| TX \| 0 (10 rows) The following example shows a WITH clause that produces two tables, named VENUE_SALES and TOP_VENUES. The second WITH query table selects from the ﬁrst. In turn, the WHERE clause of the main query block contains a subquery that constrains the TOP_VENUES table. with venue_sales as (select venuename, venuecity, sum(pricepaid) as venuename_sales from sales, venue, event where venue.venueid=event.venueid and event.eventid=sales.eventid group by venuename, venuecity), top_venues as (select venuename from venue_sales SELECT 443 AWS Clean Rooms SQL Reference where venuename_sales > 800000) select venuename, venuecity, venuestate, sum(qtysold) as venue_qty, sum(pricepaid) as venue_sales from sales, venue, event where venue.venueid=event.venueid and event.eventid=sales.eventid and venuename in(select venuename from top_venues) group by venuename, venuecity, venuestate order by venuename; venuename \| venuecity \| venuestate \| venue_qty \| venue_sales ------------------------+---------------+------------+-----------+------------- August Wilson Theatre \| New York City \| NY \| 3187 \| 1032156.00 Biltmore Theatre \| New York City \| NY \| 2629 \| 828981.00 Charles Playhouse \| Boston \| MA \| 2502 \| 857031.00 Ethel Barrymore Theatre \| New York City \| NY \| 2828 \| 891172.00 Eugene O'Neill Theatre \| New York City \| NY \| 2488 \| 828950.00 Greek Theatre \| Los Angeles \| CA \| 2445 \| 838918.00 Helen Hayes Theatre \| New York City \| NY \| 2948 \| 978765.00 Hilton Theatre \| New York City \| NY \| 2999 \| 885686.00 Imperial Theatre \| New York City \| NY \| 2702 \| 877993.00 Lunt-Fontanne Theatre \| New York City \| NY \| 3326 \| 1115182.00 Majestic Theatre \| New York City \| NY \| 2549 \| 894275.00 Nederlander Theatre \| New York City \| NY \| 2934 \| 936312.00 Pasadena Playhouse \| Pasadena \| CA \| 2739 \| 820435.00 Winter Garden Theatre \| New York City \| NY \| 2838 \| 939257.00 (14 rows) The following two examples demonstrate the rules for the scope of table references based on WITH clause subqueries. The ﬁrst query runs, but the second fails with an expected error. The ﬁrst query has WITH clause subquery inside the SELECT list of the main query. The table deﬁned by the WITH clause (HOLIDAYS) is referenced in the FROM clause of the subquery in the SELECT list: select caldate, sum(pricepaid) as daysales, (with holidays as (select * from date where holiday ='t') select sum(pricepaid) from sales join holidays on sales.dateid=holidays.dateid where caldate='2008-12-25') as dec25sales from sales join date on sales.dateid=date.dateid where caldate in('2008-12-25','2008-12-31') group by caldate SELECT 444 AWS Clean Rooms order by caldate; caldate \| daysales \| dec25sales -----------+----------+------------ 2008-12-25 \| 70402.00 \| 70402.00 2008-12-31 \| 12678.00 \| 70402.00 (2 rows) SQL Reference The second query fails because it attempts to reference the HOLIDAYS table in the main query as well as in the SELECT list subquery. The main query references are out of scope. select caldate, sum(pricepaid) as daysales, (with holidays as (select * from date where holiday ='t') select sum(pricepaid) from sales join holidays on sales.dateid=holidays.dateid where caldate='2008-12-25') as dec25sales from sales join holidays on sales.dateid=holidays.dateid where caldate in('2008-12-25','2008-12-31') group by caldate order by caldate; ERROR: relation "holidays" does not exist FROM clause The FROM clause in a query lists the table references (tables, views, and subqueries) that data is selected from. If multiple table references are listed, the tables must be joined, using appropriate syntax in either the FROM clause or the WHERE clause. If no join criteria are speciﬁed, the system processes the query as a cross-join (Cartesian product). Topics • Syntax • Parameters • Usage notes Syntax FROM table_reference [, ...] where table_reference is one of the following: SELECT 445 AWS Clean Rooms SQL Reference with_subquery_table_name \| table_name \| ( subquery ) [ [ AS ] alias ] table_reference [ NATURAL ] join_type table_reference [ USING ( join_column [, ...] ) ] table_reference [ INNER ] join_type table_reference ON expr Parameters with_subquery_table_name A table deﬁned by a subquery in the WITH clause. table_name Name of a table or view. alias Temporary alternative name for a table or view. An alias must be supplied for a table derived from a subquery. In
sql-reference-122	sql-reference.pdf	122	• Parameters • Usage notes Syntax FROM table_reference [, ...] where table_reference is one of the following: SELECT 445 AWS Clean Rooms SQL Reference with_subquery_table_name \| table_name \| ( subquery ) [ [ AS ] alias ] table_reference [ NATURAL ] join_type table_reference [ USING ( join_column [, ...] ) ] table_reference [ INNER ] join_type table_reference ON expr Parameters with_subquery_table_name A table deﬁned by a subquery in the WITH clause. table_name Name of a table or view. alias Temporary alternative name for a table or view. An alias must be supplied for a table derived from a subquery. In other table references, aliases are optional. The AS keyword is always optional. Table aliases provide a convenient shortcut for identifying tables in other parts of a query, such as the WHERE clause. For example: select * from sales s, listing l where s.listid=l.listid If you deﬁne a table alias is deﬁned, then the alias must be used to reference that table in the query. For example, if the query is SELECT "tbl"."col" FROM "tbl" AS "t", the query would fail because the table name is essentially overridden now. A valid query in this case would be SELECT "t"."col" FROM "tbl" AS "t". column_alias Temporary alternative name for a column in a table or view. subquery A query expression that evaluates to a table. The table exists only for the duration of the query and is typically given a name or alias. However, an alias isn't required. You can also deﬁne column names for tables that derive from subqueries. Naming column aliases is important when you want to join the results of subqueries to other tables and when you want to select or constrain those columns elsewhere in the query. SELECT 446 AWS Clean Rooms SQL Reference A subquery may contain an ORDER BY clause, but this clause may have no eﬀect if a LIMIT or OFFSET clause isn't also speciﬁed. NATURAL Deﬁnes a join that automatically uses all pairs of identically named columns in the two tables as the joining columns. No explicit join condition is required. For example, if the CATEGORY and EVENT tables both have columns named CATID, a natural join of those tables is a join over their CATID columns. Note If a NATURAL join is speciﬁed but no identically named pairs of columns exist in the tables to be joined, the query defaults to a cross-join. join_type Specify one of the following types of join: • [INNER] JOIN • LEFT [OUTER] JOIN • RIGHT [OUTER] JOIN • FULL [OUTER] JOIN • CROSS JOIN Cross-joins are unqualiﬁed joins; they return the Cartesian product of the two tables. Inner and outer joins are qualiﬁed joins. They are qualiﬁed either implicitly (in natural joins); with the ON or USING syntax in the FROM clause; or with a WHERE clause condition. An inner join returns matching rows only, based on the join condition or list of joining columns. An outer join returns all of the rows that the equivalent inner join would return plus non- matching rows from the "left" table, "right" table, or both tables. The left table is the ﬁrst- listed table, and the right table is the second-listed table. The non-matching rows contain NULL values to ﬁll the gaps in the output columns. ON join_condition Type of join speciﬁcation where the joining columns are stated as a condition that follows the ON keyword. For example: SELECT 447 AWS Clean Rooms SQL Reference sales join listing on sales.listid=listing.listid and sales.eventid=listing.eventid USING ( join_column [, ...] ) Type of join speciﬁcation where the joining columns are listed in parentheses. If multiple joining columns are speciﬁed, they are delimited by commas. The USING keyword must precede the list. For example: sales join listing using (listid,eventid) Usage notes Joining columns must have comparable data types. A NATURAL or USING join retains only one of each pair of joining columns in the intermediate result set. A join with the ON syntax retains both joining columns in its intermediate result set. See also WITH clause. WHERE clause The WHERE clause contains conditions that either join tables or apply predicates to columns in tables. Tables can be inner-joined by using appropriate syntax in either the WHERE clause or the FROM clause. Outer join criteria must be speciﬁed in the FROM clause. Syntax [ WHERE condition ] condition Any search condition with a Boolean result, such as a join condition or a predicate on a table column. The following examples are valid join conditions: sales.listid=listing.listid SELECT 448 AWS Clean Rooms sales.listid<>listing.listid SQL Reference The following examples are valid conditions on columns in tables: catgroup like 'S%' venueseats between 20000 and 50000 eventname in('Jersey Boys','Spamalot') year=2008 length(catdesc)>25 date_part(month, caldate)=6 Conditions can be simple or complex; for complex conditions, you can use parentheses to isolate logical units.
sql-reference-123	sql-reference.pdf	123	the WHERE clause or the FROM clause. Outer join criteria must be speciﬁed in the FROM clause. Syntax [ WHERE condition ] condition Any search condition with a Boolean result, such as a join condition or a predicate on a table column. The following examples are valid join conditions: sales.listid=listing.listid SELECT 448 AWS Clean Rooms sales.listid<>listing.listid SQL Reference The following examples are valid conditions on columns in tables: catgroup like 'S%' venueseats between 20000 and 50000 eventname in('Jersey Boys','Spamalot') year=2008 length(catdesc)>25 date_part(month, caldate)=6 Conditions can be simple or complex; for complex conditions, you can use parentheses to isolate logical units. In the following example, the join condition is enclosed by parentheses. where (category.catid=event.catid) and category.catid in(6,7,8) Usage notes You can use aliases in the WHERE clause to reference select list expressions. You can't restrict the results of aggregate functions in the WHERE clause; use the HAVING clause for this purpose. Columns that are restricted in the WHERE clause must derive from table references in the FROM clause. Example The following query uses a combination of diﬀerent WHERE clause restrictions, including a join condition for the SALES and EVENT tables, a predicate on the EVENTNAME column, and two predicates on the STARTTIME column. select eventname, starttime, pricepaid/qtysold as costperticket, qtysold from sales, event where sales.eventid = event.eventid and eventname='Hannah Montana' and date_part(quarter, starttime) in(1,2) and date_part(year, starttime) = 2008 order by 3 desc, 4, 2, 1 limit 10; SELECT 449 AWS Clean Rooms SQL Reference eventname \| starttime \| costperticket \| qtysold ----------------+---------------------+-------------------+--------- Hannah Montana \| 2008-06-07 14:00:00 \| 1706.00000000 \| 2 Hannah Montana \| 2008-05-01 19:00:00 \| 1658.00000000 \| 2 Hannah Montana \| 2008-06-07 14:00:00 \| 1479.00000000 \| 1 Hannah Montana \| 2008-06-07 14:00:00 \| 1479.00000000 \| 3 Hannah Montana \| 2008-06-07 14:00:00 \| 1163.00000000 \| 1 Hannah Montana \| 2008-06-07 14:00:00 \| 1163.00000000 \| 2 Hannah Montana \| 2008-06-07 14:00:00 \| 1163.00000000 \| 4 Hannah Montana \| 2008-05-01 19:00:00 \| 497.00000000 \| 1 Hannah Montana \| 2008-05-01 19:00:00 \| 497.00000000 \| 2 Hannah Montana \| 2008-05-01 19:00:00 \| 497.00000000 \| 4 (10 rows) GROUP BY clause The GROUP BY clause identiﬁes the grouping columns for the query. Grouping columns must be declared when the query computes aggregates with standard functions such as SUM, AVG, and COUNT. If an aggregate function is present in the SELECT expression, any column in the SELECT expression that is not in an aggregate function must be in the GROUP BY clause. For more information, see AWS Clean Rooms SQL functions. Syntax GROUP BY group_by_clause [, ...] group_by_clause := { expr \| ROLLUP ( expr [, ...] ) \| } Parameters expr The list of columns or expressions must match the list of non-aggregate expressions in the select list of the query. For example, consider the following simple query. select listid, eventid, sum(pricepaid) as revenue, count(qtysold) as numtix SELECT 450 SQL Reference AWS Clean Rooms from sales group by listid, eventid order by 3, 4, 2, 1 limit 5; listid \| eventid \| revenue \| numtix -------+---------+---------+-------- 89397 \| 47 \| 20.00 \| 1 106590 \| 76 \| 20.00 \| 1 124683 \| 393 \| 20.00 \| 1 103037 \| 403 \| 20.00 \| 1 147685 \| 429 \| 20.00 \| 1 (5 rows) In this query, the select list consists of two aggregate expressions. The ﬁrst uses the SUM function and the second uses the COUNT function. The remaining two columns, LISTID and EVENTID, must be declared as grouping columns. Expressions in the GROUP BY clause can also reference the select list by using ordinal numbers. For example, the previous example could be abbreviated as follows. select listid, eventid, sum(pricepaid) as revenue, count(qtysold) as numtix from sales group by 1,2 order by 3, 4, 2, 1 limit 5; listid \| eventid \| revenue \| numtix -------+---------+---------+-------- 89397 \| 47 \| 20.00 \| 1 106590 \| 76 \| 20.00 \| 1 124683 \| 393 \| 20.00 \| 1 103037 \| 403 \| 20.00 \| 1 147685 \| 429 \| 20.00 \| 1 (5 rows) ROLLUP You can use the aggregation extension ROLLUP to perform the work of multiple GROUP BY operations in a single statement. For more information on aggregation extensions and related functions, see Aggregation extensions. SELECT 451 AWS Clean Rooms Aggregation extensions SQL Reference AWS Clean Rooms supports aggregation extensions to do the work of multiple GROUP BY operations in a single statement. GROUPING SETS Computes one or more grouping sets in a single statement. A grouping set is the set of a single GROUP BY clause, a set of 0 or more columns by which you can group a query's result set. GROUP BY GROUPING SETS is equivalent to running a UNION ALL query on one result set grouped by diﬀerent columns. For example, GROUP BY GROUPING SETS((a), (b)) is equivalent to
sql-reference-124	sql-reference.pdf	124	Aggregation extensions. SELECT 451 AWS Clean Rooms Aggregation extensions SQL Reference AWS Clean Rooms supports aggregation extensions to do the work of multiple GROUP BY operations in a single statement. GROUPING SETS Computes one or more grouping sets in a single statement. A grouping set is the set of a single GROUP BY clause, a set of 0 or more columns by which you can group a query's result set. GROUP BY GROUPING SETS is equivalent to running a UNION ALL query on one result set grouped by diﬀerent columns. For example, GROUP BY GROUPING SETS((a), (b)) is equivalent to GROUP BY a UNION ALL GROUP BY b. The following example returns the cost of the order table's products grouped according to both the products' categories and the kind of products sold. SELECT category, product, sum(cost) as total FROM orders GROUP BY GROUPING SETS(category, product); category \| product \| total ----------------------+----------------------+------- computers \| \| 2100 cellphones \| \| 1610 \| laptop \| 2050 \| smartphone \| 1610 \| mouse \| 50 (5 rows) ROLLUP Assumes a hierarchy where preceding columns are considered the parents of subsequent columns. ROLLUP groups data by the provided columns, returning extra subtotal rows representing the totals throughout all levels of grouping columns, in addition to the grouped rows. For example, you can use GROUP BY ROLLUP((a), (b)) to return a result set grouped ﬁrst by a, then by b while assuming that b is a subsection of a. ROLLUP also returns a row with the whole result set without grouping columns. GROUP BY ROLLUP((a), (b)) is equivalent to GROUP BY GROUPING SETS((a,b), (a), ()). SELECT 452 AWS Clean Rooms SQL Reference The following example returns the cost of the order table's products grouped ﬁrst by category and then product, with product as a subdivision of category. SELECT category, product, sum(cost) as total FROM orders GROUP BY ROLLUP(category, product) ORDER BY 1,2; category \| product \| total ----------------------+----------------------+------- cellphones \| smartphone \| 1610 cellphones \| \| 1610 computers \| laptop \| 2050 computers \| mouse \| 50 computers \| \| 2100 \| \| 3710 (6 rows) CUBE Groups data by the provided columns, returning extra subtotal rows representing the totals throughout all levels of grouping columns, in addition to the grouped rows. CUBE returns the same rows as ROLLUP, while adding additional subtotal rows for every combination of grouping column not covered by ROLLUP. For example, you can use GROUP BY CUBE ((a), (b)) to return a result set grouped ﬁrst by a, then by b while assuming that b is a subsection of a, then by b alone. CUBE also returns a row with the whole result set without grouping columns. GROUP BY CUBE((a), (b)) is equivalent to GROUP BY GROUPING SETS((a, b), (a), (b), ()). The following example returns the cost of the order table's products grouped ﬁrst by category and then product, with product as a subdivision of category. Unlike the preceding example for ROLLUP, the statement returns results for every combination of grouping column. SELECT category, product, sum(cost) as total FROM orders GROUP BY CUBE(category, product) ORDER BY 1,2; category \| product \| total ----------------------+----------------------+------- cellphones \| smartphone \| 1610 cellphones \| \| 1610 computers \| laptop \| 2050 computers \| mouse \| 50 SELECT 453 AWS Clean Rooms SQL Reference computers \| \| 2100 \| laptop \| 2050 \| mouse \| 50 \| smartphone \| 1610 \| \| 3710 (9 rows) HAVING clause The HAVING clause applies a condition to the intermediate grouped result set that a query returns. Syntax [ HAVING condition ] For example, you can restrict the results of a SUM function: having sum(pricepaid) >10000 The HAVING condition is applied after all WHERE clause conditions are applied and GROUP BY operations are completed. The condition itself takes the same form as any WHERE clause condition. Usage notes • Any column that is referenced in a HAVING clause condition must be either a grouping column or a column that refers to the result of an aggregate function. • In a HAVING clause, you can't specify: • An ordinal number that refers to a select list item. Only the GROUP BY and ORDER BY clauses accept ordinal numbers. Examples The following query calculates total ticket sales for all events by name, then eliminates events where the total sales were less than $800,000. The HAVING condition is applied to the results of the aggregate function in the select list: sum(pricepaid). select eventname, sum(pricepaid) SELECT 454 AWS Clean Rooms SQL Reference from sales join event on sales.eventid = event.eventid group by 1 having sum(pricepaid) > 800000 order by 2 desc, 1; eventname \| sum ------------------+----------- Mamma Mia! \| 1135454.00 Spring Awakening \| 972855.00 The Country Girl \| 910563.00 Macbeth \| 862580.00 Jersey Boys \| 811877.00 Legally Blonde \| 804583.00 (6 rows) The
sql-reference-125	sql-reference.pdf	125	following query calculates total ticket sales for all events by name, then eliminates events where the total sales were less than $800,000. The HAVING condition is applied to the results of the aggregate function in the select list: sum(pricepaid). select eventname, sum(pricepaid) SELECT 454 AWS Clean Rooms SQL Reference from sales join event on sales.eventid = event.eventid group by 1 having sum(pricepaid) > 800000 order by 2 desc, 1; eventname \| sum ------------------+----------- Mamma Mia! \| 1135454.00 Spring Awakening \| 972855.00 The Country Girl \| 910563.00 Macbeth \| 862580.00 Jersey Boys \| 811877.00 Legally Blonde \| 804583.00 (6 rows) The following query calculates a similar result set. In this case, however, the HAVING condition is applied to an aggregate that isn't speciﬁed in the select list: sum(qtysold). Events that did not sell more than 2,000 tickets are eliminated from the ﬁnal result. select eventname, sum(pricepaid) from sales join event on sales.eventid = event.eventid group by 1 having sum(qtysold) >2000 order by 2 desc, 1; eventname \| sum ------------------+----------- Mamma Mia! \| 1135454.00 Spring Awakening \| 972855.00 The Country Girl \| 910563.00 Macbeth \| 862580.00 Jersey Boys \| 811877.00 Legally Blonde \| 804583.00 Chicago \| 790993.00 Spamalot \| 714307.00 (8 rows) Set operators The set operators are used to compare and merge the results of two separate query expressions. AWS Clean Rooms SQL supports the following set operators as listed in the following table. SELECT 455 AWS Clean Rooms INTERSECT EXCEPT UNION UNION ALL SQL Reference For example, if you want to know which users of a website are both buyers and sellers but their user names are stored in separate columns or tables, you can ﬁnd the intersection of these two types of users. If you want to know which website users are buyers but not sellers, you can use the EXCEPT operator to ﬁnd the diﬀerence between the two lists of users. If you want to build a list of all users, regardless of role, you can use the UNION operator. Note The ORDER BY, LIMIT, SELECT TOP, and OFFSET clauses can't be used in the query expressions merged by the UNION, UNION ALL, INTERSECT, and EXCEPT set operators. Topics • Syntax • Parameters • Order of evaluation for set operators • Usage notes • Example UNION queries • Example UNION ALL query • Example INTERSECT queries • Example EXCEPT query Syntax subquery1 { { UNION [ ALL \| DISTINCT ] \| SELECT 456 AWS Clean Rooms SQL Reference INTERSECT [ ALL \| DISTINCT ] \| EXCEPT [ ALL \| DISTINCT ] } subquery2 } [...] } Parameters subquery1, subquery2 A query expression that corresponds, in the form of its select list, to a second query expression that follows the UNION, UNION ALL, INTERSECT, or EXCEPT operator. The two expressions must contain the same number of output columns with compatible data types; otherwise, the two result sets can't be compared and merged. Set operations don't allow implicit conversion between diﬀerent categories of data types. For more information, see Type compatibility and conversion. You can build queries that contain an unlimited number of query expressions and link them with UNION, INTERSECT, and EXCEPT operators in any combination. For example, the following query structure is valid, assuming that the tables T1, T2, and T3 contain compatible sets of columns: select * from t1 union select * from t2 except select * from t3 UNION [ALL \| DISTINCT] Set operation that returns rows from two query expressions, regardless of whether the rows derive from one or both expressions. INTERSECT Set operation that returns rows that derive from two query expressions. Rows that aren't returned by both expressions are discarded. EXCEPT Set operation that returns rows that derive from one of two query expressions. To qualify for the result, rows must exist in the ﬁrst result table but not the second. MINUS and EXCEPT are exact synonyms. SELECT 457 AWS Clean Rooms SQL Reference Order of evaluation for set operators The UNION and EXCEPT set operators are left-associative. If parentheses aren't speciﬁed to inﬂuence the order of precedence, a combination of these set operators is evaluated from left to right. For example, in the following query, the UNION of T1 and T2 is evaluated ﬁrst, then the EXCEPT operation is performed on the UNION result: select * from t1 union select * from t2 except select * from t3 The INTERSECT operator takes precedence over the UNION and EXCEPT operators when a combination of operators is used in the same query. For example, the following query evaluates the intersection of T2 and T3, then union the result with T1: select * from t1 union select * from t2 intersect select * from t3 By adding parentheses, you can enforce a diﬀerent order of evaluation. In the following case, the result of the union
sql-reference-126	sql-reference.pdf	126	evaluated ﬁrst, then the EXCEPT operation is performed on the UNION result: select * from t1 union select * from t2 except select * from t3 The INTERSECT operator takes precedence over the UNION and EXCEPT operators when a combination of operators is used in the same query. For example, the following query evaluates the intersection of T2 and T3, then union the result with T1: select * from t1 union select * from t2 intersect select * from t3 By adding parentheses, you can enforce a diﬀerent order of evaluation. In the following case, the result of the union of T1 and T2 is intersected with T3, and the query is likely to produce a diﬀerent result. (select * from t1 union select * from t2) intersect (select * from t3) Usage notes • The column names returned in the result of a set operation query are the column names (or aliases) from the tables in the ﬁrst query expression. Because these column names are potentially misleading, in that the values in the column derive from tables on either side of the set operator, you might want to provide meaningful aliases for the result set. SELECT 458 AWS Clean Rooms SQL Reference • When set operator queries return decimal results, the corresponding result columns are promoted to return the same precision and scale. For example, in the following query, where T1.REVENUE is a DECIMAL(10,2) column and T2.REVENUE is a DECIMAL(8,4) column, the decimal result is promoted to DECIMAL(12,4): select t1.revenue union select t2.revenue; The scale is 4 because that is the maximum scale of the two columns. The precision is 12 because T1.REVENUE requires 8 digits to the left of the decimal point (12 - 4 = 8). This type promotion ensures that all values from both sides of the UNION ﬁt in the result. For 64-bit values, the maximum result precision is 19 and the maximum result scale is 18. For 128-bit values, the maximum result precision is 38 and the maximum result scale is 37. If the resulting data type exceeds AWS Clean Rooms precision and scale limits, the query returns an error. • For set operations, two rows are treated as identical if, for each corresponding pair of columns, the two data values are either equal or both NULL. For example, if tables T1 and T2 both contain one column and one row, and that row is NULL in both tables, an INTERSECT operation over those tables returns that row. Example UNION queries In the following UNION query, rows in the SALES table are merged with rows in the LISTING table. Three compatible columns are selected from each table; in this case, the corresponding columns have the same names and data types. select listid, sellerid, eventid from listing union select listid, sellerid, eventid from sales listid \| sellerid \| eventid --------+----------+--------- 1 \| 36861 \| 7872 2 \| 16002 \| 4806 3 \| 21461 \| 4256 4 \| 8117 \| 4337 5 \| 1616 \| 8647 SELECT 459 AWS Clean Rooms SQL Reference The following example shows how you can add a literal value to the output of a UNION query so you can see which query expression produced each row in the result set. The query identiﬁes rows from the ﬁrst query expression as "B" (for buyers) and rows from the second query expression as "S" (for sellers). The query identiﬁes buyers and sellers for ticket transactions that cost $10,000 or more. The only diﬀerence between the two query expressions on either side of the UNION operator is the joining column for the SALES table. select listid, lastname, firstname, username, pricepaid as price, 'S' as buyorsell from sales, users where sales.sellerid=users.userid and pricepaid >=10000 union select listid, lastname, firstname, username, pricepaid, 'B' as buyorsell from sales, users where sales.buyerid=users.userid and pricepaid >=10000 listid \| lastname \| firstname \| username \| price \| buyorsell --------+----------+-----------+----------+-----------+----------- 209658 \| Lamb \| Colette \| VOR15LYI \| 10000.00 \| B 209658 \| West \| Kato \| ELU81XAA \| 10000.00 \| S 212395 \| Greer \| Harlan \| GXO71KOC \| 12624.00 \| S 212395 \| Perry \| Cora \| YWR73YNZ \| 12624.00 \| B 215156 \| Banks \| Patrick \| ZNQ69CLT \| 10000.00 \| S 215156 \| Hayden \| Malachi \| BBG56AKU \| 10000.00 \| B The following example uses a UNION ALL operator because duplicate rows, if found, need to be retained in the result. For a speciﬁc series of event IDs, the query returns 0 or more rows for each sale associated with each event, and 0 or 1 row for each listing of that event. Event IDs are unique to each row in the LISTING and EVENT tables, but there might be multiple sales for the same combination of event and listing IDs in the SALES table. The
sql-reference-127	sql-reference.pdf	127	\| 10000.00 \| S 215156 \| Hayden \| Malachi \| BBG56AKU \| 10000.00 \| B The following example uses a UNION ALL operator because duplicate rows, if found, need to be retained in the result. For a speciﬁc series of event IDs, the query returns 0 or more rows for each sale associated with each event, and 0 or 1 row for each listing of that event. Event IDs are unique to each row in the LISTING and EVENT tables, but there might be multiple sales for the same combination of event and listing IDs in the SALES table. The third column in the result set identiﬁes the source of the row. If it comes from the SALES table, it is marked "Yes" in the SALESROW column. (SALESROW is an alias for SALES.LISTID.) If the row comes from the LISTING table, it is marked "No" in the SALESROW column. SELECT 460 AWS Clean Rooms SQL Reference In this case, the result set consists of three sales rows for listing 500, event 7787. In other words, three diﬀerent transactions took place for this listing and event combination. The other two listings, 501 and 502, did not produce any sales, so the only row that the query produces for these list IDs comes from the LISTING table (SALESROW = 'No'). select eventid, listid, 'Yes' as salesrow from sales where listid in(500,501,502) union all select eventid, listid, 'No' from listing where listid in(500,501,502) eventid \| listid \| salesrow ---------+--------+---------- 7787 \| 500 \| No 7787 \| 500 \| Yes 7787 \| 500 \| Yes 7787 \| 500 \| Yes 6473 \| 501 \| No 5108 \| 502 \| No If you run the same query without the ALL keyword, the result retains only one of the sales transactions. select eventid, listid, 'Yes' as salesrow from sales where listid in(500,501,502) union select eventid, listid, 'No' from listing where listid in(500,501,502) eventid \| listid \| salesrow ---------+--------+---------- 7787 \| 500 \| No 7787 \| 500 \| Yes 6473 \| 501 \| No 5108 \| 502 \| No SELECT 461 AWS Clean Rooms Example UNION ALL query SQL Reference The following example uses a UNION ALL operator because duplicate rows, if found, need to be retained in the result. For a speciﬁc series of event IDs, the query returns 0 or more rows for each sale associated with each event, and 0 or 1 row for each listing of that event. Event IDs are unique to each row in the LISTING and EVENT tables, but there might be multiple sales for the same combination of event and listing IDs in the SALES table. The third column in the result set identiﬁes the source of the row. If it comes from the SALES table, it is marked "Yes" in the SALESROW column. (SALESROW is an alias for SALES.LISTID.) If the row comes from the LISTING table, it is marked "No" in the SALESROW column. In this case, the result set consists of three sales rows for listing 500, event 7787. In other words, three diﬀerent transactions took place for this listing and event combination. The other two listings, 501 and 502, did not produce any sales, so the only row that the query produces for these list IDs comes from the LISTING table (SALESROW = 'No'). select eventid, listid, 'Yes' as salesrow from sales where listid in(500,501,502) union all select eventid, listid, 'No' from listing where listid in(500,501,502) eventid \| listid \| salesrow ---------+--------+---------- 7787 \| 500 \| No 7787 \| 500 \| Yes 7787 \| 500 \| Yes 7787 \| 500 \| Yes 6473 \| 501 \| No 5108 \| 502 \| No If you run the same query without the ALL keyword, the result retains only one of the sales transactions. select eventid, listid, 'Yes' as salesrow from sales where listid in(500,501,502) union SELECT 462 AWS Clean Rooms SQL Reference select eventid, listid, 'No' from listing where listid in(500,501,502) eventid \| listid \| salesrow ---------+--------+---------- 7787 \| 500 \| No 7787 \| 500 \| Yes 6473 \| 501 \| No 5108 \| 502 \| No Example INTERSECT queries Compare the following example with the ﬁrst UNION example. The only diﬀerence between the two examples is the set operator that is used, but the results are very diﬀerent. Only one of the rows is the same: 235494 \| 23875 \| 8771 This is the only row in the limited result of 5 rows that was found in both tables. select listid, sellerid, eventid from listing intersect select listid, sellerid, eventid from sales listid \| sellerid \| eventid --------+----------+--------- 235494 \| 23875 \| 8771 235482 \| 1067 \| 2667 235479 \| 1589 \| 7303 235476 \| 15550 \| 793 235475 \| 22306 \| 7848 The following query ﬁnds events (for which tickets were sold) that occurred at
sql-reference-128	sql-reference.pdf	128	the set operator that is used, but the results are very diﬀerent. Only one of the rows is the same: 235494 \| 23875 \| 8771 This is the only row in the limited result of 5 rows that was found in both tables. select listid, sellerid, eventid from listing intersect select listid, sellerid, eventid from sales listid \| sellerid \| eventid --------+----------+--------- 235494 \| 23875 \| 8771 235482 \| 1067 \| 2667 235479 \| 1589 \| 7303 235476 \| 15550 \| 793 235475 \| 22306 \| 7848 The following query ﬁnds events (for which tickets were sold) that occurred at venues in both New York City and Los Angeles in March. The diﬀerence between the two query expressions is the constraint on the VENUECITY column. select distinct eventname from event, sales, venue where event.eventid=sales.eventid and event.venueid=venue.venueid and date_part(month,starttime)=3 and venuecity='Los Angeles' intersect select distinct eventname from event, sales, venue SELECT 463 AWS Clean Rooms SQL Reference where event.eventid=sales.eventid and event.venueid=venue.venueid and date_part(month,starttime)=3 and venuecity='New York City'; eventname ---------------------------- A Streetcar Named Desire Dirty Dancing Electra Running with Annalise Hairspray Mary Poppins November Oliver! Return To Forever Rhinoceros South Pacific The 39 Steps The Bacchae The Caucasian Chalk Circle The Country Girl Wicked Woyzeck Example EXCEPT query The CATEGORY table in the database contains the following 11 rows: catid \| catgroup \| catname \| catdesc -------+----------+-----------+-------------------------------------------- 1 \| Sports \| MLB \| Major League Baseball 2 \| Sports \| NHL \| National Hockey League 3 \| Sports \| NFL \| National Football League 4 \| Sports \| NBA \| National Basketball Association 5 \| Sports \| MLS \| Major League Soccer 6 \| Shows \| Musicals \| Musical theatre 7 \| Shows \| Plays \| All non-musical theatre 8 \| Shows \| Opera \| All opera and light opera 9 \| Concerts \| Pop \| All rock and pop music concerts 10 \| Concerts \| Jazz \| All jazz singers and bands 11 \| Concerts \| Classical \| All symphony, concerto, and choir concerts (11 rows) Assume that a CATEGORY_STAGE table (a staging table) contains one additional row: SELECT 464 AWS Clean Rooms SQL Reference catid \| catgroup \| catname \| catdesc -------+----------+-----------+-------------------------------------------- 1 \| Sports \| MLB \| Major League Baseball 2 \| Sports \| NHL \| National Hockey League 3 \| Sports \| NFL \| National Football League 4 \| Sports \| NBA \| National Basketball Association 5 \| Sports \| MLS \| Major League Soccer 6 \| Shows \| Musicals \| Musical theatre 7 \| Shows \| Plays \| All non-musical theatre 8 \| Shows \| Opera \| All opera and light opera 9 \| Concerts \| Pop \| All rock and pop music concerts 10 \| Concerts \| Jazz \| All jazz singers and bands 11 \| Concerts \| Classical \| All symphony, concerto, and choir concerts 12 \| Concerts \| Comedy \| All stand up comedy performances (12 rows) Return the diﬀerence between the two tables. In other words, return rows that are in the CATEGORY_STAGE table but not in the CATEGORY table: select * from category_stage except select * from category; catid \| catgroup \| catname \| catdesc -------+----------+---------+---------------------------------- 12 \| Concerts \| Comedy \| All stand up comedy performances (1 row) The following equivalent query uses the synonym MINUS. select * from category_stage minus select * from category; catid \| catgroup \| catname \| catdesc -------+----------+---------+---------------------------------- 12 \| Concerts \| Comedy \| All stand up comedy performances (1 row) If you reverse the order of the SELECT expressions, the query returns no rows. SELECT 465 AWS Clean Rooms ORDER BY clause The ORDER BY clause sorts the result set of a query. SQL Reference Note The outermost ORDER BY expression must only have columns that are in the select list. Topics • Syntax • Parameters • Usage notes • Examples with ORDER BY Syntax [ ORDER BY expression [ ASC \| DESC ] ] [ NULLS FIRST \| NULLS LAST ] [ LIMIT { count \| ALL } ] [ OFFSET start ] Parameters expression Expression that deﬁnes the sort order of the query result. It consists of one or more columns in the select list. Results are returned based on binary UTF-8 ordering. You can also specify the following: • Ordinal numbers that represent the position of select list entries (or the position of columns in the table if no select list exists) • Aliases that deﬁne select list entries When the ORDER BY clause contains multiple expressions, the result set is sorted according to the ﬁrst expression, then the second expression is applied to rows that have matching values from the ﬁrst expression, and so on. SELECT 466 AWS Clean Rooms ASC \| DESC SQL Reference Option that deﬁnes the sort order for the expression, as follows: • ASC: ascending (for example,
sql-reference-129	sql-reference.pdf	129	You can also specify the following: • Ordinal numbers that represent the position of select list entries (or the position of columns in the table if no select list exists) • Aliases that deﬁne select list entries When the ORDER BY clause contains multiple expressions, the result set is sorted according to the ﬁrst expression, then the second expression is applied to rows that have matching values from the ﬁrst expression, and so on. SELECT 466 AWS Clean Rooms ASC \| DESC SQL Reference Option that deﬁnes the sort order for the expression, as follows: • ASC: ascending (for example, low to high for numeric values and 'A' to 'Z' for character strings). If no option is speciﬁed, data is sorted in ascending order by default. • DESC: descending (high to low for numeric values; 'Z' to 'A' for strings). NULLS FIRST \| NULLS LAST Option that speciﬁes whether NULL values should be ordered ﬁrst, before non-null values, or last, after non-null values. By default, NULL values are sorted and ranked last in ASC ordering, and sorted and ranked ﬁrst in DESC ordering. LIMIT number \| ALL Option that controls the number of sorted rows that the query returns. The LIMIT number must be a positive integer; the maximum value is 2147483647. LIMIT 0 returns no rows. You can use this syntax for testing purposes: to check that a query runs (without displaying any rows) or to return a column list from a table. An ORDER BY clause is redundant if you are using LIMIT 0 to return a column list. The default is LIMIT ALL. OFFSET start Option that speciﬁes to skip the number of rows before start before beginning to return rows. The OFFSET number must be a positive integer; the maximum value is 2147483647. When used with the LIMIT option, OFFSET rows are skipped before starting to count the LIMIT rows that are returned. If the LIMIT option isn't used, the number of rows in the result set is reduced by the number of rows that are skipped. The rows skipped by an OFFSET clause still have to be scanned, so it might be ineﬃcient to use a large OFFSET value. Usage notes Note the following expected behavior with ORDER BY clauses: • NULL values are considered "higher" than all other values. With the default ascending sort order, NULL values sort at the end. To change this behavior, use the NULLS FIRST option. • When a query doesn't contain an ORDER BY clause, the system returns result sets with no predictable ordering of the rows. The same query run twice might return the result set in a diﬀerent order. SELECT 467 AWS Clean Rooms SQL Reference • The LIMIT and OFFSET options can be used without an ORDER BY clause; however, to return a consistent set of rows, use these options in conjunction with ORDER BY. • In any parallel system like AWS Clean Rooms, when ORDER BY doesn't produce a unique ordering, the order of the rows is nondeterministic. That is, if the ORDER BY expression produces duplicate values, the return order of those rows might vary from other systems or from one run of AWS Clean Rooms to the next. • AWS Clean Rooms doesn't support string literals in ORDER BY clauses. Examples with ORDER BY Return all 11 rows from the CATEGORY table, ordered by the second column, CATGROUP. For results that have the same CATGROUP value, order the CATDESC column values by the length of the character string. Then order by columns CATID and CATNAME. select * from category order by 2, 1, 3; catid \| catgroup \| catname \| catdesc -------+----------+-----------+---------------------------------------- 10 \| Concerts \| Jazz \| All jazz singers and bands 9 \| Concerts \| Pop \| All rock and pop music concerts 11 \| Concerts \| Classical \| All symphony, concerto, and choir conce 6 \| Shows \| Musicals \| Musical theatre 7 \| Shows \| Plays \| All non-musical theatre 8 \| Shows \| Opera \| All opera and light opera 5 \| Sports \| MLS \| Major League Soccer 1 \| Sports \| MLB \| Major League Baseball 2 \| Sports \| NHL \| National Hockey League 3 \| Sports \| NFL \| National Football League 4 \| Sports \| NBA \| National Basketball Association (11 rows) Return selected columns from the SALES table, ordered by the highest QTYSOLD values. Limit the result to the top 10 rows: select salesid, qtysold, pricepaid, commission, saletime from sales order by qtysold, pricepaid, commission, salesid, saletime desc salesid \| qtysold \| pricepaid \| commission \| saletime ---------+---------+-----------+------------+--------------------- 15401 \| 8 \| 272.00 \| 40.80 \| 2008-03-18 06:54:56 SELECT 468 AWS Clean Rooms SQL Reference 61683 \| 8 \| 296.00 \| 44.40 \| 2008-11-26 04:00:23 90528 \| 8 \| 328.00 \| 49.20
sql-reference-130	sql-reference.pdf	130	\| Sports \| NFL \| National Football League 4 \| Sports \| NBA \| National Basketball Association (11 rows) Return selected columns from the SALES table, ordered by the highest QTYSOLD values. Limit the result to the top 10 rows: select salesid, qtysold, pricepaid, commission, saletime from sales order by qtysold, pricepaid, commission, salesid, saletime desc salesid \| qtysold \| pricepaid \| commission \| saletime ---------+---------+-----------+------------+--------------------- 15401 \| 8 \| 272.00 \| 40.80 \| 2008-03-18 06:54:56 SELECT 468 AWS Clean Rooms SQL Reference 61683 \| 8 \| 296.00 \| 44.40 \| 2008-11-26 04:00:23 90528 \| 8 \| 328.00 \| 49.20 \| 2008-06-11 02:38:09 74549 \| 8 \| 336.00 \| 50.40 \| 2008-01-19 12:01:21 130232 \| 8 \| 352.00 \| 52.80 \| 2008-05-02 05:52:31 55243 \| 8 \| 384.00 \| 57.60 \| 2008-07-12 02:19:53 16004 \| 8 \| 440.00 \| 66.00 \| 2008-11-04 07:22:31 489 \| 8 \| 496.00 \| 74.40 \| 2008-08-03 05:48:55 4197 \| 8 \| 512.00 \| 76.80 \| 2008-03-23 11:35:33 16929 \| 8 \| 568.00 \| 85.20 \| 2008-12-19 02:59:33 Return a column list and no rows by using LIMIT 0 syntax: select * from venue limit 0; venueid \| venuename \| venuecity \| venuestate \| venueseats ---------+-----------+-----------+------------+------------ (0 rows) Subquery examples The following examples show diﬀerent ways in which subqueries ﬁt into SELECT queries. SELECT list subquery The following example contains a subquery in the SELECT list. This subquery is scalar: it returns only one column and one value, which is repeated in the result for each row that is returned from the outer query. The query compares the Q1SALES value that the subquery computes with sales values for two other quarters (2 and 3) in 2008, as deﬁned by the outer query. select qtr, sum(pricepaid) as qtrsales, (select sum(pricepaid) from sales join date on sales.dateid=date.dateid where qtr='1' and year=2008) as q1sales from sales join date on sales.dateid=date.dateid where qtr in('2','3') and year=2008 group by qtr order by qtr; qtr \| qtrsales \| q1sales -------+-------------+------------- 2 \| 30560050.00 \| 24742065.00 3 \| 31170237.00 \| 24742065.00 SELECT 469 AWS Clean Rooms (2 rows) WHERE clause subquery SQL Reference The following example contains a table subquery in the WHERE clause. This subquery produces multiple rows. In this case, the rows contain only one column, but table subqueries can contain multiple columns and rows, just like any other table. The query ﬁnds the top 10 sellers in terms of maximum tickets sold. The top 10 list is restricted by the subquery, which removes users who live in cities where there are ticket venues. This query can be written in diﬀerent ways; for example, the subquery could be rewritten as a join within the main query. select firstname, lastname, city, max(qtysold) as maxsold from users join sales on users.userid=sales.sellerid where users.city not in(select venuecity from venue) group by firstname, lastname, city order by maxsold desc, city desc limit 10; firstname \| lastname \| city \| maxsold -----------+-----------+----------------+--------- Noah \| Guerrero \| Worcester \| 8 Isadora \| Moss \| Winooski \| 8 Kieran \| Harrison \| Westminster \| 8 Heidi \| Davis \| Warwick \| 8 Sara \| Anthony \| Waco \| 8 Bree \| Buck \| Valdez \| 8 Evangeline \| Sampson \| Trenton \| 8 Kendall \| Keith \| Stillwater \| 8 Bertha \| Bishop \| Stevens Point \| 8 Patricia \| Anderson \| South Portland \| 8 (10 rows) WITH clause subqueries See WITH clause. Correlated subqueries The following example contains a correlated subquery in the WHERE clause; this kind of subquery contains one or more correlations between its columns and the columns produced by the outer SELECT 470 AWS Clean Rooms SQL Reference query. In this case, the correlation is where s.listid=l.listid. For each row that the outer query produces, the subquery is run to qualify or disqualify the row. select salesid, listid, sum(pricepaid) from sales s where qtysold= (select max(numtickets) from listing l where s.listid=l.listid) group by 1,2 order by 1,2 limit 5; salesid \| listid \| sum --------+--------+---------- 27 \| 28 \| 111.00 81 \| 103 \| 181.00 142 \| 149 \| 240.00 146 \| 152 \| 231.00 194 \| 210 \| 144.00 (5 rows) Correlated subquery patterns that are not supported The query planner uses a query rewrite method called subquery decorrelation to optimize several patterns of correlated subqueries for execution in an MPP environment. A few types of correlated subqueries follow patterns that AWS Clean Rooms can't decorrelate and doesn't support. Queries that contain the following correlation references return errors: • Correlation references that skip a query block, also known as "skip-level correlation references." For example, in the following query, the block containing the correlation reference and the skipped block are connected by a NOT EXISTS predicate: select event.eventname from event where not exists (select * from listing where not exists (select *
sql-reference-131	sql-reference.pdf	131	uses a query rewrite method called subquery decorrelation to optimize several patterns of correlated subqueries for execution in an MPP environment. A few types of correlated subqueries follow patterns that AWS Clean Rooms can't decorrelate and doesn't support. Queries that contain the following correlation references return errors: • Correlation references that skip a query block, also known as "skip-level correlation references." For example, in the following query, the block containing the correlation reference and the skipped block are connected by a NOT EXISTS predicate: select event.eventname from event where not exists (select * from listing where not exists (select * from sales where event.eventid=sales.eventid)); The skipped block in this case is the subquery against the LISTING table. The correlation reference correlates the EVENT and SALES tables. • Correlation references from a subquery that is part of an ON clause in an outer query: SELECT 471 AWS Clean Rooms SQL Reference select * from category left join event on category.catid=event.catid and eventid = (select max(eventid) from sales where sales.eventid=event.eventid); The ON clause contains a correlation reference from SALES in the subquery to EVENT in the outer query. • Null-sensitive correlation references to an AWS Clean Rooms system table. For example: select attrelid from my_locks sl, my_attribute where sl.table_id=my_attribute.attrelid and 1 not in (select 1 from my_opclass where sl.lock_owner = opcowner); • Correlation references from within a subquery that contains a window function. select listid, qtysold from sales s where qtysold not in (select sum(numtickets) over() from listing l where s.listid=l.listid); • References in a GROUP BY column to the results of a correlated subquery. For example: select listing.listid, (select count (sales.listid) from sales where sales.listid=listing.listid) as list from listing group by list, listing.listid; • Correlation references from a subquery with an aggregate function and a GROUP BY clause, connected to the outer query by an IN predicate. (This restriction doesn't apply to MIN and MAX aggregate functions.) For example: select * from listing where listid in (select sum(qtysold) from sales where numtickets>4 group by salesid); SELECT 472 AWS Clean Rooms SQL Reference AWS Clean Rooms SQL functions AWS Clean Rooms supports the following SQL functions: Topics • Aggregate functions • Array functions • Conditional expressions • Data type formatting functions • Date and time functions • Hash functions • JSON functions • Math functions • String functions • SUPER type information functions • VARBYTE functions • Window functions Aggregate functions Aggregate functions in AWS Clean Rooms SQL are used to perform calculations or operations on a group of rows and return a single value. They are essential for data analysis and summarization tasks. AWS Clean Rooms SQL supports the following aggregate functions: Topics • ANY_VALUE function • APPROXIMATE PERCENTILE_DISC function • AVG function • BOOL_AND function • BOOL_OR function • COUNT and COUNT DISTINCT functions • COUNT function SQL functions 473 SQL Reference AWS Clean Rooms • LISTAGG function • MAX function • MEDIAN function • MIN function • PERCENTILE_CONT function • STDDEV_SAMP and STDDEV_POP functions • SUM and SUM DISTINCT functions • VAR_SAMP and VAR_POP functions ANY_VALUE function The ANY_VALUE function returns any value from the input expression values nondeterministically. This function can return NULL if the input expression doesn't result in any rows being returned. Syntax ANY_VALUE ( [ DISTINCT \| ALL ] expression ) Arguments DISTINCT \| ALL Specify either DISTINCT or ALL to return any value from the input expression values. The DISTINCT argument has no eﬀect and is ignored. expression The target column or expression on which the function operates. The expression is one of the following data types: • SMALLINT • INTEGER • BIGINT • DECIMAL • REAL • DOUBLE PRECISON • BOOLEAN Aggregate functions 474 SQL Reference AWS Clean Rooms • CHAR • VARCHAR • DATE • TIMESTAMP • TIMESTAMPTZ • TIME • TIMETZ • VARBYTE • SUPER Returns Returns the same data type as expression. Usage notes If a statement that speciﬁes the ANY_VALUE function for a column also includes a second column reference, the second column must appear in a GROUP BY clause or be included in an aggregate function. Examples The following example returns an instance of any dateid where the eventname is Eagles. select any_value(dateid) as dateid, eventname from event where eventname ='Eagles' group by eventname; Following are the results. dateid \| eventname -------+--------------- 1878 \| Eagles The following example returns an instance of any dateid where the eventname is Eagles or Cold War Kids. select any_value(dateid) as dateid, eventname from event where eventname in('Eagles', 'Cold War Kids') group by eventname; Aggregate functions 475 AWS Clean Rooms Following are the results. dateid \| eventname -------+--------------- 1922 \| Cold War Kids 1878 \| Eagles SQL Reference APPROXIMATE PERCENTILE_DISC function APPROXIMATE PERCENTILE_DISC is an inverse distribution function that assumes a discrete distribution model. It takes a percentile value and a sort speciﬁcation
sql-reference-132	sql-reference.pdf	132	where eventname ='Eagles' group by eventname; Following are the results. dateid \| eventname -------+--------------- 1878 \| Eagles The following example returns an instance of any dateid where the eventname is Eagles or Cold War Kids. select any_value(dateid) as dateid, eventname from event where eventname in('Eagles', 'Cold War Kids') group by eventname; Aggregate functions 475 AWS Clean Rooms Following are the results. dateid \| eventname -------+--------------- 1922 \| Cold War Kids 1878 \| Eagles SQL Reference APPROXIMATE PERCENTILE_DISC function APPROXIMATE PERCENTILE_DISC is an inverse distribution function that assumes a discrete distribution model. It takes a percentile value and a sort speciﬁcation and returns an element from the given set. Approximation enables the function to run much faster, with a low relative error of around 0.5 percent. For a given percentile value, APPROXIMATE PERCENTILE_DISC uses a quantile summary algorithm to approximate the discrete percentile of the expression in the ORDER BY clause. APPROXIMATE PERCENTILE_DISC returns the value with the smallest cumulative distribution value (with respect to the same sort speciﬁcation) that is greater than or equal to percentile. APPROXIMATE PERCENTILE_DISC is a compute-node only function. The function returns an error if the query doesn't reference a user-deﬁned table or AWS Clean Rooms system table. Syntax APPROXIMATE PERCENTILE_DISC ( percentile ) WITHIN GROUP (ORDER BY expr) Arguments percentile Numeric constant between 0 and 1. Nulls are ignored in the calculation. WITHIN GROUP ( ORDER BY expr) Clause that speciﬁes numeric or date/time values to sort and compute the percentile over. Returns The same data type as the ORDER BY expression in the WITHIN GROUP clause. Aggregate functions 476 AWS Clean Rooms Usage notes SQL Reference If the APPROXIMATE PERCENTILE_DISC statement includes a GROUP BY clause, the result set is limited. The limit varies based on node type and the number of nodes. If the limit is exceeded, the function fails and returns the following error. GROUP BY limit for approximate percentile_disc exceeded. If you need to evaluate more groups than the limit permits, consider using PERCENTILE_CONT function. Examples The following example returns the number of sales, total sales, and ﬁftieth percentile value for the top 10 dates. select top 10 date.caldate, count(totalprice), sum(totalprice), approximate percentile_disc(0.5) within group (order by totalprice) from listing join date on listing.dateid = date.dateid group by date.caldate order by 3 desc; caldate \| count \| sum \| percentile_disc -----------+-------+------------+---------------- 2008-01-07 \| 658 \| 2081400.00 \| 2020.00 2008-01-02 \| 614 \| 2064840.00 \| 2178.00 2008-07-22 \| 593 \| 1994256.00 \| 2214.00 2008-01-26 \| 595 \| 1993188.00 \| 2272.00 2008-02-24 \| 655 \| 1975345.00 \| 2070.00 2008-02-04 \| 616 \| 1972491.00 \| 1995.00 2008-02-14 \| 628 \| 1971759.00 \| 2184.00 2008-09-01 \| 600 \| 1944976.00 \| 2100.00 2008-07-29 \| 597 \| 1944488.00 \| 2106.00 2008-07-23 \| 592 \| 1943265.00 \| 1974.00 AVG function The AVG function returns the average (arithmetic mean) of the input expression values. The AVG function works with numeric values and ignores NULL values. Aggregate functions 477 AWS Clean Rooms Syntax AVG (column) Arguments column SQL Reference The target column that the function operates on. The column is one of the following data types: • SMALLINT • INTEGER • BIGINT • DECIMAL • DOUBLE Data types The argument types supported by the AVG function are SMALLINT, INTEGER, BIGINT, DECIMAL, and DOUBLE. The return types supported by the AVG function are: • BIGINT for any integer type argument • DOUBLE for a ﬂoating point argument • Returns the same data type as expression for any other argument type The default precision for an AVG function result with a DECIMAL argument is 38. The scale of the result is the same as the scale of the argument. For example, an AVG of a DEC(5,2) column returns a DEC(38,2) data type. Example Find the average quantity sold per transaction from the SALES table. select avg(qtysold)from sales; Aggregate functions 478 AWS Clean Rooms BOOL_AND function SQL Reference The BOOL_AND function operates on a single Boolean or integer column or expression. This function applies similar logic to the BIT_AND and BIT_OR functions. For this function, the return type is a Boolean value (true or false). If all values in a set are true, the BOOL_AND function returns true (t). If any value is false, the function returns false (f). Syntax BOOL_AND ( [DISTINCT \| ALL] expression ) Arguments expression The target column or expression that the function operates on. This expression must have a BOOLEAN or integer data type. The return type of the function is BOOLEAN. DISTINCT \| ALL With the argument DISTINCT, the function eliminates all duplicate values for the speciﬁed expression before calculating the result. With the argument ALL, the function retains all duplicate values. ALL is the default. Examples You can use the Boolean functions against either Boolean expressions or integer expressions. For example, the following query return results
sql-reference-133	sql-reference.pdf	133	false, the function returns false (f). Syntax BOOL_AND ( [DISTINCT \| ALL] expression ) Arguments expression The target column or expression that the function operates on. This expression must have a BOOLEAN or integer data type. The return type of the function is BOOLEAN. DISTINCT \| ALL With the argument DISTINCT, the function eliminates all duplicate values for the speciﬁed expression before calculating the result. With the argument ALL, the function retains all duplicate values. ALL is the default. Examples You can use the Boolean functions against either Boolean expressions or integer expressions. For example, the following query return results from the standard USERS table in the TICKIT database, which has several Boolean columns. The BOOL_AND function returns false for all ﬁve rows. Not all users in each of those states likes sports. select state, bool_and(likesports) from users group by state order by state limit 5; state \| bool_and ------+--------- AB \| f Aggregate functions 479 AWS Clean Rooms AK \| f AL \| f AZ \| f BC \| f (5 rows) BOOL_OR function SQL Reference The BOOL_OR function operates on a single Boolean or integer column or expression. This function applies similar logic to the BIT_AND and BIT_OR functions. For this function, the return type is a Boolean value (true, false, or NULL). If a value in a set is true, the BOOL_OR function returns true (t). If a value in a set is false, the function returns false (f). NULL can be returned if the value is unknown. Syntax BOOL_OR ( [DISTINCT \| ALL] expression ) Arguments expression The target column or expression that the function operates on. This expression must have a BOOLEAN or integer data type. The return type of the function is BOOLEAN. DISTINCT \| ALL With the argument DISTINCT, the function eliminates all duplicate values for the speciﬁed expression before calculating the result. With the argument ALL, the function retains all duplicate values. ALL is the default. Examples You can use the Boolean functions with either Boolean expressions or integer expressions. For example, the following query return results from the standard USERS table in the TICKIT database, which has several Boolean columns. The BOOL_OR function returns true for all ﬁve rows. At least one user in each of those states likes sports. select state, bool_or(likesports) from users Aggregate functions 480 AWS Clean Rooms SQL Reference group by state order by state limit 5; state \| bool_or ------+-------- AB \| t AK \| t AL \| t AZ \| t BC \| t (5 rows) The following example returns NULL. SELECT BOOL_OR(NULL = '123') bool_or ------ NULL COUNT and COUNT DISTINCT functions The COUNT function counts the rows deﬁned by the expression. The COUNT DISTINCT function computes the number of distinct non-NULL values in a column or expression. It eliminates all duplicate values from the speciﬁed expression before doing the count. Syntax COUNT (column) COUNT (DISTINCT column) Arguments column The target column that the function operates on. Data types The COUNT function and the COUNT DISTINCT function supports all argument data types. The COUNT DISTINCT function returns BIGINT. Aggregate functions 481 SQL Reference AWS Clean Rooms Examples Count all of the users from the state of Florida. select count (identifier) from users where state='FL'; Count all of the unique venue IDs from the EVENT table. select count (distinct (venueid)) as venues from event; COUNT function The COUNT function counts the rows deﬁned by the expression. The COUNT function has the following variations. • COUNT ( * ) counts all the rows in the target table whether they include nulls or not. • COUNT ( expression ) computes the number of rows with non-NULL values in a speciﬁc column or expression. • COUNT ( DISTINCT expression ) computes the number of distinct non-NULL values in a column or expression. • APPROXIMATE COUNT DISTINCT approximates the number of distinct non-NULL values in a column or expression. Syntax COUNT( * \| expression ) COUNT ( [ DISTINCT \| ALL ] expression ) APPROXIMATE COUNT ( DISTINCT expression ) Arguments expression The target column or expression that the function operates on. The COUNT function supports all argument data types. Aggregate functions 482 AWS Clean Rooms DISTINCT \| ALL SQL Reference With the argument DISTINCT, the function eliminates all duplicate values from the speciﬁed expression before doing the count. With the argument ALL, the function retains all duplicate values from the expression for counting. ALL is the default. APPROXIMATE When used with APPROXIMATE, a COUNT DISTINCT function uses a HyperLogLog algorithm to approximate the number of distinct non-NULL values in a column or expression. Queries that use the APPROXIMATE keyword run much faster, with a low relative error of around 2%. Approximation is warranted for queries that return a large number of distinct values, in the millions or more
sql-reference-134	sql-reference.pdf	134	SQL Reference With the argument DISTINCT, the function eliminates all duplicate values from the speciﬁed expression before doing the count. With the argument ALL, the function retains all duplicate values from the expression for counting. ALL is the default. APPROXIMATE When used with APPROXIMATE, a COUNT DISTINCT function uses a HyperLogLog algorithm to approximate the number of distinct non-NULL values in a column or expression. Queries that use the APPROXIMATE keyword run much faster, with a low relative error of around 2%. Approximation is warranted for queries that return a large number of distinct values, in the millions or more per query, or per group, if there is a group by clause. For smaller sets of distinct values, in the thousands, approximation might be slower than a precise count. APPROXIMATE can only be used with COUNT DISTINCT. Return type The COUNT function returns BIGINT. Examples Count all of the users from the state of Florida: select count() from users where state='FL'; count ------- 510 Count all of the event names from the EVENT table: select count(eventname) from event; count ------- 8798 Count all of the event names from the EVENT table: select count(all eventname) from event; Aggregate functions 483 SQL Reference AWS Clean Rooms count ------- 8798 Count all of the unique venue IDs from the EVENT table: select count(distinct venueid) as venues from event; venues -------- 204 Count the number of times each seller listed batches of more than four tickets for sale. Group the results by seller ID: select count(), sellerid from listing where numtickets > 4 group by sellerid order by 1 desc, 2; count \| sellerid ------+---------- 12 \| 6386 11 \| 17304 11 \| 20123 11 \| 25428 ... The following examples compare the return values and execution times for COUNT and APPROXIMATE COUNT. select count(distinct pricepaid) from sales; count ------- 4528 Time: 48.048 ms Aggregate functions 484 AWS Clean Rooms SQL Reference select approximate count(distinct pricepaid) from sales; count ------- 4553 Time: 21.728 ms LISTAGG function For each group in a query, the LISTAGG aggregate function orders the rows for that group according to the ORDER BY expression, then concatenates the values into a single string. LISTAGG is a compute-node only function. The function returns an error if the query doesn't reference a user-deﬁned table or AWS Clean Rooms system table. Syntax LISTAGG( [DISTINCT] aggregate_expression [, 'delimiter' ] ) [ WITHIN GROUP (ORDER BY order_list) ] Arguments DISTINCT (Optional) A clause that eliminates duplicate values from the speciﬁed expression before concatenating. Trailing spaces are ignored, so the strings 'a' and 'a ' are treated as duplicates. LISTAGG uses the ﬁrst value encountered. For more information, see Signiﬁcance of trailing blanks. aggregate_expression Any valid expression (such as a column name) that provides the values to aggregate. NULL values and empty strings are ignored. delimiter (Optional) The string constant to separate the concatenated values. The default is NULL. AWS Clean Rooms supports any amount of leading or trailing whitespace around an optional comma or colon as well as an empty string or any number of spaces. Examples of valid values are: Aggregate functions 485 AWS Clean Rooms SQL Reference ", " ": " " " WITHIN GROUP (ORDER BY order_list) (Optional) A clause that speciﬁes the sort order of the aggregated values. Returns VARCHAR(MAX). If the result set is larger than the maximum VARCHAR size (64K – 1, or 65535), then LISTAGG returns the following error: Invalid operation: Result size exceeds LISTAGG limit Usage notes If a statement includes multiple LISTAGG functions that use WITHIN GROUP clauses, each WITHIN GROUP clause must use the same ORDER BY values. For example, the following statement will return an error. select listagg(sellerid) within group (order by dateid) as sellers, listagg(dateid) within group (order by sellerid) as dates from winsales; The following statements will run successfully. select listagg(sellerid) within group (order by dateid) as sellers, listagg(dateid) within group (order by dateid) as dates from winsales; select listagg(sellerid) within group (order by dateid) as sellers, listagg(dateid) as dates from winsales; Aggregate functions 486 AWS Clean Rooms Examples SQL Reference The following example aggregates seller IDs, ordered by seller ID. select listagg(sellerid, ', ') within group (order by sellerid) from sales where eventid = 4337; listagg ---------------------------------------------------------------------------------------------------------------------------------------- 380, 380, 1178, 1178, 1178, 2731, 8117, 12905, 32043, 32043, 32043, 32432, 32432, 38669, 38750, 41498, 45676, 46324, 47188, 47188, 48294 The following example uses DISTINCT to return a list of unique seller IDs. select listagg(distinct sellerid, ', ') within group (order by sellerid) from sales where eventid = 4337; listagg ------------------------------------------------------------------------------------------- 380, 1178, 2731, 8117, 12905, 32043, 32432, 38669, 38750, 41498, 45676, 46324, 47188, 48294 The following example aggregates seller IDs in date order. select listagg(sellerid) within group (order by dateid) from winsales; listagg ------------- 31141242333 The following example returns a pipe-separated list of
sql-reference-135	sql-reference.pdf	135	eventid = 4337; listagg ---------------------------------------------------------------------------------------------------------------------------------------- 380, 380, 1178, 1178, 1178, 2731, 8117, 12905, 32043, 32043, 32043, 32432, 32432, 38669, 38750, 41498, 45676, 46324, 47188, 47188, 48294 The following example uses DISTINCT to return a list of unique seller IDs. select listagg(distinct sellerid, ', ') within group (order by sellerid) from sales where eventid = 4337; listagg ------------------------------------------------------------------------------------------- 380, 1178, 2731, 8117, 12905, 32043, 32432, 38669, 38750, 41498, 45676, 46324, 47188, 48294 The following example aggregates seller IDs in date order. select listagg(sellerid) within group (order by dateid) from winsales; listagg ------------- 31141242333 The following example returns a pipe-separated list of sales dates for buyer B. select listagg(dateid,'\|') within group (order by sellerid desc,salesid asc) from winsales where buyerid = 'b'; listagg --------------------------------------- Aggregate functions 487 AWS Clean Rooms SQL Reference 2003-08-02\|2004-04-18\|2004-04-18\|2004-02-12 The following example returns a comma-separated list of sales IDs for each buyer ID. select buyerid, listagg(salesid,',') within group (order by salesid) as sales_id from winsales group by buyerid order by buyerid; buyerid \| sales_id -----------+------------------------ a \|10005,40001,40005 b \|20001,30001,30004,30003 c \|10001,20002,30007,10006 MAX function The MAX function returns the maximum value in a set of rows. DISTINCT or ALL might be used but do not aﬀect the result. Syntax MAX ( [ DISTINCT \| ALL ] expression ) Arguments expression The target column or expression that the function operates on. The expression is one of the following data types: • SMALLINT • INTEGER • BIGINT • DECIMAL • REAL • DOUBLE PRECISON • CHAR Aggregate functions 488 SQL Reference AWS Clean Rooms • VARCHAR • DATE • TIMESTAMP • TIMESTAMPTZ • TIME • TIMETZ • VARBYTE • SUPER DISTINCT \| ALL With the argument DISTINCT, the function eliminates all duplicate values from the speciﬁed expression before calculating the maximum. With the argument ALL, the function retains all duplicate values from the expression for calculating the maximum. ALL is the default. Data types Returns the same data type as expression. Examples Find the highest price paid from all sales: select max(pricepaid) from sales; max ---------- 12624.00 (1 row) Find the highest price paid per ticket from all sales: select max(pricepaid/qtysold) as max_ticket_price from sales; max_ticket_price ----------------- 2500.00000000 (1 row) Aggregate functions 489 AWS Clean Rooms MEDIAN function SQL Reference Calculates the median value for the range of values. NULL values in the range are ignored. MEDIAN is an inverse distribution function that assumes a continuous distribution model. MEDIAN is a special case of PERCENTILE_CONT(.5). MEDIAN is a compute-node only function. The function returns an error if the query doesn't reference a user-deﬁned table or AWS Clean Rooms system table. Syntax MEDIAN ( median_expression ) Arguments median_expression The target column or expression that the function operates on. Usage notes If the median_expression argument is a DECIMAL data type deﬁned with the maximum precision of 38 digits, it is possible that MEDIAN will return either an inaccurate result or an error. If the return value of the MEDIAN function exceeds 38 digits, the result is truncated to ﬁt, which causes a loss of precision. If, during interpolation, an intermediate result exceeds the maximum precision, a numeric overﬂow occurs and the function returns an error. To avoid these conditions, we recommend either using a data type with lower precision or casting the median_expression argument to a lower precision. If a statement includes multiple calls to sort-based aggregate functions (LISTAGG, PERCENTILE_CONT, or MEDIAN), they must all use the same ORDER BY values. Note that MEDIAN applies an implicit order by on the expression value. For example, the following statement returns an error. select top 10 salesid, sum(pricepaid), percentile_cont(0.6) within group (order by salesid), median (pricepaid) from sales group by salesid, pricepaid; Aggregate functions 490 AWS Clean Rooms SQL Reference An error occurred when executing the SQL command: select top 10 salesid, sum(pricepaid), percentile_cont(0.6) within group (order by salesid), median (pricepaid) from sales group by salesid, pricepai... ERROR: within group ORDER BY clauses for aggregate functions must be the same The following statement runs successfully. select top 10 salesid, sum(pricepaid), percentile_cont(0.6) within group (order by salesid), median (salesid) from sales group by salesid, pricepaid; Examples The following example shows that MEDIAN produces the same results as PERCENTILE_CONT(0.5). select top 10 distinct sellerid, qtysold, percentile_cont(0.5) within group (order by qtysold), median (qtysold) from sales group by sellerid, qtysold; sellerid \| qtysold \| percentile_cont \| median ---------+---------+-----------------+------- 1 \| 1 \| 1.0 \| 1.0 2 \| 3 \| 3.0 \| 3.0 5 \| 2 \| 2.0 \| 2.0 9 \| 4 \| 4.0 \| 4.0 12 \| 1 \| 1.0 \| 1.0 16 \| 1 \| 1.0 \| 1.0 19 \| 2 \| 2.0 \| 2.0 19 \| 3 \| 3.0 \| 3.0 22 \| 2 \| 2.0 \| 2.0 25 \| 2 \| 2.0 \| 2.0 MIN function The MIN function returns the minimum value in
sql-reference-136	sql-reference.pdf	136	group (order by qtysold), median (qtysold) from sales group by sellerid, qtysold; sellerid \| qtysold \| percentile_cont \| median ---------+---------+-----------------+------- 1 \| 1 \| 1.0 \| 1.0 2 \| 3 \| 3.0 \| 3.0 5 \| 2 \| 2.0 \| 2.0 9 \| 4 \| 4.0 \| 4.0 12 \| 1 \| 1.0 \| 1.0 16 \| 1 \| 1.0 \| 1.0 19 \| 2 \| 2.0 \| 2.0 19 \| 3 \| 3.0 \| 3.0 22 \| 2 \| 2.0 \| 2.0 25 \| 2 \| 2.0 \| 2.0 MIN function The MIN function returns the minimum value in a set of rows. DISTINCT or ALL might be used but do not aﬀect the result. Aggregate functions 491 AWS Clean Rooms Syntax MIN ( [ DISTINCT \| ALL ] expression ) Arguments expression SQL Reference The target column or expression that the function operates on. The expression is one of the following data types: • SMALLINT • INTEGER • BIGINT • DECIMAL • REAL • DOUBLE PRECISON • CHAR • VARCHAR • DATE • TIMESTAMP • TIMESTAMPTZ • TIME • TIMETZ • VARBYTE • SUPER DISTINCT \| ALL With the argument DISTINCT, the function eliminates all duplicate values from the speciﬁed expression before calculating the minimum. With the argument ALL, the function retains all duplicate values from the expression for calculating the minimum. ALL is the default. Data types Returns the same data type as expression. Aggregate functions 492 SQL Reference AWS Clean Rooms Examples Find the lowest price paid from all sales: select min(pricepaid) from sales; min ------- 20.00 (1 row) Find the lowest price paid per ticket from all sales: select min(pricepaid/qtysold)as min_ticket_price from sales; min_ticket_price ------------------ 20.00000000 (1 row) PERCENTILE_CONT function PERCENTILE_CONT is an inverse distribution function that assumes a continuous distribution model. It takes a percentile value and a sort speciﬁcation, and returns an interpolated value that would fall into the given percentile value with respect to the sort speciﬁcation. PERCENTILE_CONT computes a linear interpolation between values after ordering them. Using the percentile value (P) and the number of not null rows (N) in the aggregation group, the function computes the row number after ordering the rows according to the sort speciﬁcation. This row number (RN) is computed according to the formula RN = (1+ (P(N-1)). The ﬁnal result of the aggregate function is computed by linear interpolation between the values from rows at row numbers CRN = CEILING(RN) and FRN = FLOOR(RN). The ﬁnal result will be as follows. If (CRN = FRN = RN) then the result is (value of expression from row at RN) Otherwise the result is as follows: (CRN - RN) (value of expression for row at FRN) + (RN - FRN) * (value of expression for row at CRN). Aggregate functions 493 AWS Clean Rooms SQL Reference PERCENTILE_CONT is a compute-node only function. The function returns an error if the query doesn't reference a user-deﬁned table or AWS Clean Rooms system table. Syntax PERCENTILE_CONT ( percentile ) WITHIN GROUP (ORDER BY expr) Arguments percentile Numeric constant between 0 and 1. Nulls are ignored in the calculation. WITHIN GROUP ( ORDER BY expr) Speciﬁes numeric or date/time values to sort and compute the percentile over. Returns The return type is determined by the data type of the ORDER BY expression in the WITHIN GROUP clause. The following table shows the return type for each ORDER BY expression data type. Input type SMALLINT, INTEGER, BIGINT, NUMERIC, DECIMAL FLOAT, DOUBLE DATE TIMESTAMP TIMESTAMPTZ Usage notes Return type DECIMAL DOUBLE DATE TIMESTAMP TIMESTAMPTZ If the ORDER BY expression is a DECIMAL data type deﬁned with the maximum precision of 38 digits, it is possible that PERCENTILE_CONT will return either an inaccurate result or an error. If Aggregate functions 494 AWS Clean Rooms SQL Reference the return value of the PERCENTILE_CONT function exceeds 38 digits, the result is truncated to ﬁt, which causes a loss of precision. If, during interpolation, an intermediate result exceeds the maximum precision, a numeric overﬂow occurs and the function returns an error. To avoid these conditions, we recommend either using a data type with lower precision or casting the ORDER BY expression to a lower precision. If a statement includes multiple calls to sort-based aggregate functions (LISTAGG, PERCENTILE_CONT, or MEDIAN), they must all use the same ORDER BY values. Note that MEDIAN applies an implicit order by on the expression value. For example, the following statement returns an error. select top 10 salesid, sum(pricepaid), percentile_cont(0.6) within group (order by salesid), median (pricepaid) from sales group by salesid, pricepaid; An error occurred when executing the SQL command: select top 10 salesid, sum(pricepaid), percentile_cont(0.6) within group (order by salesid), median (pricepaid) from sales group by salesid, pricepai... ERROR: within group ORDER BY clauses for aggregate functions must be the same
sql-reference-137	sql-reference.pdf	137	statement includes multiple calls to sort-based aggregate functions (LISTAGG, PERCENTILE_CONT, or MEDIAN), they must all use the same ORDER BY values. Note that MEDIAN applies an implicit order by on the expression value. For example, the following statement returns an error. select top 10 salesid, sum(pricepaid), percentile_cont(0.6) within group (order by salesid), median (pricepaid) from sales group by salesid, pricepaid; An error occurred when executing the SQL command: select top 10 salesid, sum(pricepaid), percentile_cont(0.6) within group (order by salesid), median (pricepaid) from sales group by salesid, pricepai... ERROR: within group ORDER BY clauses for aggregate functions must be the same The following statement runs successfully. select top 10 salesid, sum(pricepaid), percentile_cont(0.6) within group (order by salesid), median (salesid) from sales group by salesid, pricepaid; Examples The following example shows that MEDIAN produces the same results as PERCENTILE_CONT(0.5). select top 10 distinct sellerid, qtysold, percentile_cont(0.5) within group (order by qtysold), median (qtysold) from sales group by sellerid, qtysold; Aggregate functions 495 AWS Clean Rooms SQL Reference sellerid \| qtysold \| percentile_cont \| median ---------+---------+-----------------+------- 1 \| 1 \| 1.0 \| 1.0 2 \| 3 \| 3.0 \| 3.0 5 \| 2 \| 2.0 \| 2.0 9 \| 4 \| 4.0 \| 4.0 12 \| 1 \| 1.0 \| 1.0 16 \| 1 \| 1.0 \| 1.0 19 \| 2 \| 2.0 \| 2.0 19 \| 3 \| 3.0 \| 3.0 22 \| 2 \| 2.0 \| 2.0 25 \| 2 \| 2.0 \| 2.0 STDDEV_SAMP and STDDEV_POP functions The STDDEV_SAMP and STDDEV_POP functions return the sample and population standard deviation of a set of numeric values (integer, decimal, or ﬂoating-point). The result of the STDDEV_SAMP function is equivalent to the square root of the sample variance of the same set of values. STDDEV_SAMP and STDDEV are synonyms for the same function. Syntax STDDEV_SAMP \| STDDEV ( [ DISTINCT \| ALL ] expression) STDDEV_POP ( [ DISTINCT \| ALL ] expression) The expression must have an integer, decimal, or ﬂoating point data type. Regardless of the data type of the expression, the return type of this function is a double precision number. Note Standard deviation is calculated using ﬂoating point arithmetic, which might result in slight imprecision. Usage notes When the sample standard deviation (STDDEV or STDDEV_SAMP) is calculated for an expression that consists of a single value, the result of the function is NULL not 0. Aggregate functions 496 AWS Clean Rooms Examples SQL Reference The following query returns the average of the values in the VENUESEATS column of the VENUE table, followed by the sample standard deviation and population standard deviation of the same set of values. VENUESEATS is an INTEGER column. The scale of the result is reduced to 2 digits. select avg(venueseats), cast(stddev_samp(venueseats) as dec(14,2)) stddevsamp, cast(stddev_pop(venueseats) as dec(14,2)) stddevpop from venue; avg \| stddevsamp \| stddevpop -------+------------+----------- 17503 \| 27847.76 \| 27773.20 (1 row) The following query returns the sample standard deviation for the COMMISSION column in the SALES table. COMMISSION is a DECIMAL column. The scale of the result is reduced to 10 digits. select cast(stddev(commission) as dec(18,10)) from sales; stddev ---------------- 130.3912659086 (1 row) The following query casts the sample standard deviation for the COMMISSION column as an integer. select cast(stddev(commission) as integer) from sales; stddev -------- 130 (1 row) The following query returns both the sample standard deviation and the square root of the sample variance for the COMMISSION column. The results of these calculations are the same. Aggregate functions 497 AWS Clean Rooms SQL Reference select cast(stddev_samp(commission) as dec(18,10)) stddevsamp, cast(sqrt(var_samp(commission)) as dec(18,10)) sqrtvarsamp from sales; stddevsamp \| sqrtvarsamp ----------------+---------------- 130.3912659086 \| 130.3912659086 (1 row) SUM and SUM DISTINCT functions The SUM function returns the sum of the input column or expression values. The SUM function works with numeric values and ignores NULL values. The SUM DISTINCT function eliminates all duplicate values from the speciﬁed expression before calculating the sum. Syntax SUM (column) SUM (DISTINCT column ) Arguments column The target column that the function operates on. The column is one of the following data types: • SMALLINT • INTEGER • BIGINT • DECIMAL • DOUBLE Data types The argument types supported by the SUM function are SMALLINT, INTEGER, BIGINT, DECIMAL, and DOUBLE. Aggregate functions 498 AWS Clean Rooms SQL Reference The SUM function supports the following return types: • BIGINT for BIGINT, SMALLINT, and INTEGER arguments • DOUBLE for ﬂoating point arguments • Returns the same data type as expression for any other argument type The default precision for a SUM function result with a DECIMAL argument is 38. The scale of the result is the same as the scale of the argument. For example, a SUM of a DEC(5,2) column returns a DEC(38,2) data type. Examples Find the sum of all commissions paid from the SALES table. select sum(commission) from sales
sql-reference-138	sql-reference.pdf	138	Aggregate functions 498 AWS Clean Rooms SQL Reference The SUM function supports the following return types: • BIGINT for BIGINT, SMALLINT, and INTEGER arguments • DOUBLE for ﬂoating point arguments • Returns the same data type as expression for any other argument type The default precision for a SUM function result with a DECIMAL argument is 38. The scale of the result is the same as the scale of the argument. For example, a SUM of a DEC(5,2) column returns a DEC(38,2) data type. Examples Find the sum of all commissions paid from the SALES table. select sum(commission) from sales Find the sum of all distinct commissions paid from the SALES table. select sum (distinct (commission)) from sales VAR_SAMP and VAR_POP functions The VAR_SAMP and VAR_POP functions return the sample and population variance of a set of numeric values (integer, decimal, or ﬂoating-point). The result of the VAR_SAMP function is equivalent to the squared sample standard deviation of the same set of values. VAR_SAMP and VARIANCE are synonyms for the same function. Syntax VAR_SAMP \| VARIANCE ( [ DISTINCT \| ALL ] expression) VAR_POP ( [ DISTINCT \| ALL ] expression) The expression must have an integer, decimal, or ﬂoating-point data type. Regardless of the data type of the expression, the return type of this function is a double precision number. Aggregate functions 499 AWS Clean Rooms Note SQL Reference The results of these functions might vary across data warehouse clusters, depending on the conﬁguration of the cluster in each case. Usage notes When the sample variance (VARIANCE or VAR_SAMP) is calculated for an expression that consists of a single value, the result of the function is NULL not 0. Examples The following query returns the rounded sample and population variance of the NUMTICKETS column in the LISTING table. select avg(numtickets), round(var_samp(numtickets)) varsamp, round(var_pop(numtickets)) varpop from listing; avg \| varsamp \| varpop -----+---------+-------- 10 \| 54 \| 54 (1 row) The following query runs the same calculations but casts the results to decimal values. select avg(numtickets), cast(var_samp(numtickets) as dec(10,4)) varsamp, cast(var_pop(numtickets) as dec(10,4)) varpop from listing; avg \| varsamp \| varpop -----+---------+--------- 10 \| 53.6291 \| 53.6288 (1 row) Array functions This section describes the array functions for SQL supported in AWS Clean Rooms. Array functions 500 SQL Reference AWS Clean Rooms Topics • ARRAY function • ARRAY_CONCAT function • ARRAY_FLATTEN function • GET_ARRAY_LENGTH function • SPLIT_TO_ARRAY function • SUBARRAY function ARRAY function Creates an array of the SUPER data type. Syntax ARRAY( [ expr1 ] [ , expr2 [ , ... ] ] ) Argument expr1, expr2 Expressions of any data type except date and time types. The arguments don't need to be of the same data type. Return type The array function returns the SUPER data type. Example The following example shows an array of numeric values and an array of diﬀerent data types. --an array of numeric values select array(1,50,null,100); array ------------------ [1,50,null,100] (1 row) --an array of different data types Array functions 501 AWS Clean Rooms SQL Reference select array(1,'abc',true,3.14); array ----------------------- [1,"abc",true,3.14] (1 row) ARRAY_CONCAT function The array_concat function concatenates two arrays to create an array that contains all the elements in the ﬁrst array followed by all the elements in the second array. The two arguments must be valid arrays. Syntax array_concat( super_expr1, super_expr2 ) Arguments super_expr1 The value that speciﬁes the ﬁrst of the two arrays to concatenate. super_expr2 The value that speciﬁes the second of the two arrays to concatenate. Return type The array_concat function returns a SUPER data value. Example The following example shows concatenation of two arrays of the same type and concatenation of two arrays of diﬀerent types. -- concatenating two arrays SELECT ARRAY_CONCAT(ARRAY(10001,10002),ARRAY(10003,10004)); array_concat ------------------------------------ [10001,10002,10003,10004] (1 row) -- concatenating two arrays of different types Array functions 502 AWS Clean Rooms SQL Reference SELECT ARRAY_CONCAT(ARRAY(10001,10002),ARRAY('ab','cd')); array_concat ------------------------------ [10001,10002,"ab","cd"] (1 row) ARRAY_FLATTEN function The ARRAY_FLATTEN function merges multiple arrays into a single array of SUPER type. Syntax array_flatten( super_expr1,super_expr2,.. ) Arguments super_expr1, super_expr2 A valid SUPER expression of array form. Return type The ARRAY_FLATTEN function returns a SUPER data value. Example The following example shows an ARRAY_FLATTEN function. SELECT ARRAY_FLATTEN(ARRAY(ARRAY(1,2,3,4),ARRAY(5,6,7,8),ARRAY(9,10))); array_flatten ------------------------ [1,2,3,4,5,6,7,8,9,10] (1 row) GET_ARRAY_LENGTH function Returns the length of the speciﬁed array. The GET_ARRAY_LENGTH function returns the length of a SUPER array given an object or array path. Syntax get_array_length( super_expr ) Array functions 503 SQL Reference AWS Clean Rooms Arguments super_expr A valid SUPER expression of array form. Return type The get_array_length function returns a BIGINT. Example The following example shows a get_array_length function. SELECT GET_ARRAY_LENGTH(ARRAY(1,2,3,4,5,6,7,8,9,10)); get_array_length ---------------------- 10 (1 row) SPLIT_TO_ARRAY function Uses a delimiter as an optional parameter. If no delimiter is present, then the default is a comma. Syntax split_to_array( string,delimiter ) Arguments string The input string to be split. delimiter An optional
sql-reference-139	sql-reference.pdf	139	length of the speciﬁed array. The GET_ARRAY_LENGTH function returns the length of a SUPER array given an object or array path. Syntax get_array_length( super_expr ) Array functions 503 SQL Reference AWS Clean Rooms Arguments super_expr A valid SUPER expression of array form. Return type The get_array_length function returns a BIGINT. Example The following example shows a get_array_length function. SELECT GET_ARRAY_LENGTH(ARRAY(1,2,3,4,5,6,7,8,9,10)); get_array_length ---------------------- 10 (1 row) SPLIT_TO_ARRAY function Uses a delimiter as an optional parameter. If no delimiter is present, then the default is a comma. Syntax split_to_array( string,delimiter ) Arguments string The input string to be split. delimiter An optional value on which the input string will be split. The default is a comma. Return type The split_to_array function returns a SUPER data value. Array functions 504 SQL Reference AWS Clean Rooms Example The following example show a split_to_array function. SELECT SPLIT_TO_ARRAY('12\|345\|6789', '\|'); split_to_array ------------------------- ["12","345","6789"] (1 row) SUBARRAY function Manipulates arrays to return a subset of the input arrays. Syntax SUBARRAY( super_expr, start_position, length ) Arguments super_expr A valid SUPER expression in array form. start_position The position within the array to begin the extraction, starting at index position 0. A negative position counts backward from the end of the array. length The number of elements to extract (the length of the substring). Return type The subarray function returns a SUPER data value. Example The following is an example of a subarray function. SELECT SUBARRAY(ARRAY('a', 'b', 'c', 'd', 'e', 'f'), 2, 3); subarray --------------- Array functions 505 AWS Clean Rooms ["c","d","e"] (1 row) Conditional expressions SQL Reference In SQL, conditional expressions are used to make decisions based on certain conditions. They allow you to control the ﬂow of your SQL statements and return diﬀerent values or perform diﬀerent actions based on the evaluation of one or more conditions. AWS Clean Rooms supports the following conditional expressions: Topics • CASE conditional expression • COALESCE expression • GREATEST and LEAST expression • NVL and COALESCE functions • NVL2 function • NULLIF function CASE conditional expression The CASE expression is a conditional expression, similar to if/then/else statements found in other languages. CASE is used to specify a result when there are multiple conditions. Use CASE where a SQL expression is valid, such as in a SELECT command. There are two types of CASE expressions: simple and searched. • In simple CASE expressions, an expression is compared with a value. When a match is found, the speciﬁed action in the THEN clause is applied. If no match is found, the action in the ELSE clause is applied. • In searched CASE expressions, each CASE is evaluated based on a Boolean expression, and the CASE statement returns the ﬁrst matching CASE. If no match is found among the WHEN clauses, the action in the ELSE clause is returned. Syntax Simple CASE statement used to match conditions: Conditional expressions 506 AWS Clean Rooms SQL Reference CASE expression WHEN value THEN result [WHEN...] [ELSE result] END Searched CASE statement used to evaluate each condition: CASE WHEN condition THEN result [WHEN ...] [ELSE result] END Arguments expression A column name or any valid expression. value Value that the expression is compared with, such as a numeric constant or a character string. result The target value or expression that is returned when an expression or Boolean condition is evaluated. The data types of all the result expressions must be convertible to a single output type. condition A Boolean expression that evaluates to true or false. If condition is true, the value of the CASE expression is the result that follows the condition, and the remainder of the CASE expression is not processed. If condition is false, any subsequent WHEN clauses are evaluated. If no WHEN condition results are true, the value of the CASE expression is the result of the ELSE clause. If the ELSE clause is omitted and no condition is true, the result is null. Examples Use a simple CASE expression to replace New York City with Big Apple in a query against the VENUE table. Replace all other city names with other. Conditional expressions 507 SQL Reference AWS Clean Rooms select venuecity, case venuecity when 'New York City' then 'Big Apple' else 'other' end from venue order by venueid desc; venuecity \| case -----------------+----------- Los Angeles \| other New York City \| Big Apple San Francisco \| other Baltimore \| other ... Use a searched CASE expression to assign group numbers based on the PRICEPAID value for individual ticket sales: select pricepaid, case when pricepaid <10000 then 'group 1' when pricepaid >10000 then 'group 2' else 'group 3' end from sales order by 1 desc; pricepaid \| case ----------+--------- 12624 \| group 2 10000 \| group 3 10000 \| group 3 9996 \| group 1 9988 \| group 1 ... COALESCE expression A COALESCE expression returns
sql-reference-140	sql-reference.pdf	140	order by venueid desc; venuecity \| case -----------------+----------- Los Angeles \| other New York City \| Big Apple San Francisco \| other Baltimore \| other ... Use a searched CASE expression to assign group numbers based on the PRICEPAID value for individual ticket sales: select pricepaid, case when pricepaid <10000 then 'group 1' when pricepaid >10000 then 'group 2' else 'group 3' end from sales order by 1 desc; pricepaid \| case ----------+--------- 12624 \| group 2 10000 \| group 3 10000 \| group 3 9996 \| group 1 9988 \| group 1 ... COALESCE expression A COALESCE expression returns the value of the ﬁrst expression in the list that is not null. If all expressions are null, the result is null. When a non-null value is found, the remaining expressions in the list are not evaluated. Conditional expressions 508 AWS Clean Rooms SQL Reference This type of expression is useful when you want to return a backup value for something when the preferred value is missing or null. For example, a query might return one of three phone numbers (cell, home, or work, in that order), whichever is found ﬁrst in the table (not null). Syntax COALESCE (expression, expression, ... ) Examples Apply COALESCE expression to two columns. select coalesce(start_date, end_date) from datetable order by 1; The default column name for an NVL expression is COALESCE. The following query returns the same results. select coalesce(start_date, end_date) from datetable order by 1; GREATEST and LEAST expression Returns the largest or smallest value from a list of any number of expressions. Syntax GREATEST (value [, ...]) LEAST (value [, ...]) Parameters expression_list A comma-separated list of expressions, such as column names. The expressions must all be convertible to a common data type. NULL values in the list are ignored. If all of the expressions evaluate to NULL, the result is NULL. Conditional expressions 509 AWS Clean Rooms Returns SQL Reference Returns the greatest (for GREATEST) or least (for LEAST) value from the provided list of expressions. Example The following example returns the highest value alphabetically for firstname or lastname. select firstname, lastname, greatest(firstname,lastname) from users where userid < 10 order by 3; firstname \| lastname \| greatest -----------+-----------+----------- Alejandro \| Rosalez \| Ratliff Carlos \| Salazar \| Carlos Jane \| Doe \| Doe John \| Doe \| Doe John \| Stiles \| John Shirley \| Rodriguez \| Rodriguez Terry \| Whitlock \| Terry Richard \| Roe \| Richard Xiulan \| Wang \| Wang (9 rows) NVL and COALESCE functions Returns the value of the ﬁrst expression that isn't null in a series of expressions. When a non-null value is found, the remaining expressions in the list aren't evaluated. NVL is identical to COALESCE. They are synonyms. This topic explains the syntax and contains examples for both. Syntax NVL( expression, expression, ... ) The syntax for COALESCE is the same: COALESCE( expression, expression, ... ) Conditional expressions 510 AWS Clean Rooms SQL Reference If all expressions are null, the result is null. These functions are useful when you want to return a secondary value when a primary value is missing or null. For example, a query might return the ﬁrst of three available phone numbers: cell, home, or work. The order of the expressions in the function determines the order of evaluation. Arguments expression An expression, such as a column name, to be evaluated for null status. Return type AWS Clean Rooms determines the data type of the returned value based on the input expressions. If the data types of the input expressions don't have a common type, then an error is returned. Examples If the list contains integer expressions, the function returns an integer. SELECT COALESCE(NULL, 12, NULL); coalesce -------------- 12 This example, which is the same as the previous example, except that it uses NVL, returns the same result. SELECT NVL(NULL, 12, NULL); coalesce -------------- 12 The following example returns a string type. SELECT COALESCE(NULL, 'AWS Clean Rooms', NULL); Conditional expressions 511 AWS Clean Rooms coalesce -------------- AWS Clean Rooms SQL Reference The following example results in an error because the data types vary in the expression list. In this case, there is both a string type and a number type in the list. SELECT COALESCE(NULL, 'AWS Clean Rooms', 12); ERROR: invalid input syntax for integer: "AWS Clean Rooms" NVL2 function Returns one of two values based on whether a speciﬁed expression evaluates to NULL or NOT NULL. Syntax NVL2 ( expression, not_null_return_value, null_return_value ) Arguments expression An expression, such as a column name, to be evaluated for null status. not_null_return_value The value returned if expression evaluates to NOT NULL. The not_null_return_value value must either have the same data type as expression or be implicitly convertible to that data type. null_return_value The value returned if expression evaluates to NULL. The
sql-reference-141	sql-reference.pdf	141	type in the list. SELECT COALESCE(NULL, 'AWS Clean Rooms', 12); ERROR: invalid input syntax for integer: "AWS Clean Rooms" NVL2 function Returns one of two values based on whether a speciﬁed expression evaluates to NULL or NOT NULL. Syntax NVL2 ( expression, not_null_return_value, null_return_value ) Arguments expression An expression, such as a column name, to be evaluated for null status. not_null_return_value The value returned if expression evaluates to NOT NULL. The not_null_return_value value must either have the same data type as expression or be implicitly convertible to that data type. null_return_value The value returned if expression evaluates to NULL. The null_return_value value must either have the same data type as expression or be implicitly convertible to that data type. Return type The NVL2 return type is determined as follows: • If either not_null_return_value or null_return_value is null, the data type of the not-null expression is returned. Conditional expressions 512 AWS Clean Rooms SQL Reference If both not_null_return_value and null_return_value are not null: • If not_null_return_value and null_return_value have the same data type, that data type is returned. • If not_null_return_value and null_return_value have diﬀerent numeric data types, the smallest compatible numeric data type is returned. • If not_null_return_value and null_return_value have diﬀerent datetime data types, a timestamp data type is returned. • If not_null_return_value and null_return_value have diﬀerent character data types, the data type of not_null_return_value is returned. • If not_null_return_value and null_return_value have mixed numeric and non-numeric data types, the data type of not_null_return_value is returned. Important In the last two cases where the data type of not_null_return_value is returned, null_return_value is implicitly cast to that data type. If the data types are incompatible, the function fails. Usage notes For NVL2, the return will have the value of either the not_null_return_value or null_return_value parameter, whichever is selected by the function, but will have the data type of not_null_return_value. For example, assuming column1 is NULL, the following queries will return the same value. However, the DECODE return value data type will be INTEGER and the NVL2 return value data type will be VARCHAR. select decode(column1, null, 1234, '2345'); select nvl2(column1, '2345', 1234); Example The following example modiﬁes some sample data, then evaluates two ﬁelds to provide appropriate contact information for users: Conditional expressions 513 AWS Clean Rooms SQL Reference update users set email = null where firstname = 'Aphrodite' and lastname = 'Acevedo'; select (firstname + ' ' + lastname) as name, nvl2(email, email, phone) AS contact_info from users where state = 'WA' and lastname like 'A%' order by lastname, firstname; name contact_info --------------------+------------------------------------------- Aphrodite Acevedo (555) 555-0100 Caldwell Acevedo Nunc.sollicitudin@example.ca Quinn Adams vel@example.com Kamal Aguilar quis@example.com Samson Alexander hendrerit.neque@example.com Hall Alford ac.mattis@example.com Lane Allen et.netus@example.com Xander Allison ac.facilisis.facilisis@example.com Amaya Alvarado dui.nec.tempus@example.com Vera Alvarez at.arcu.Vestibulum@example.com Yetta Anthony enim.sit@example.com Violet Arnold ad.litora@example.comm August Ashley consectetuer.euismod@example.com Karyn Austin ipsum.primis.in@example.com Lucas Ayers at@example.com NULLIF function Compares two arguments and returns null if the arguments are equal. If they aren't equal, the ﬁrst argument is returned. Syntax The NULLIF expression compares two arguments and returns null if the arguments are equal. If they aren't equal, the ﬁrst argument is returned. This expression is the inverse of the NVL or COALESCE expression. NULLIF ( expression1, expression2 ) Conditional expressions 514 AWS Clean Rooms Arguments expression1, expression2 SQL Reference The target columns or expressions that are compared. The return type is the same as the type of the ﬁrst expression. The default column name of the NULLIF result is the column name of the ﬁrst expression. Examples In the following example, the query returns the string first because the arguments are not equal. SELECT NULLIF('first', 'second'); case ------- first In the following example, the query returns NULL because the string literal arguments are equal. SELECT NULLIF('first', 'first'); case ------- NULL In the following example, the query returns 1 because the integer arguments are not equal. SELECT NULLIF(1, 2); case ------- 1 In the following example, the query returns NULL because the integer arguments are equal. SELECT NULLIF(1, 1); case ------- Conditional expressions 515 AWS Clean Rooms NULL SQL Reference In the following example, the query returns null when the LISTID and SALESID values match: select nullif(listid,salesid), salesid from sales where salesid<10 order by 1, 2 desc; listid \| salesid --------+--------- 4 \| 2 5 \| 4 5 \| 3 6 \| 5 10 \| 9 10 \| 8 10 \| 7 10 \| 6 \| 1 (9 rows) Data type formatting functions Using a data type formatting function, you can convert values from one data type to another. For each of these functions, the ﬁrst argument is always the value to be formatted and the second argument contains the template for the new format. AWS Clean Rooms supports several data type formatting functions. Topics • CAST function • CONVERT function • TO_CHAR • TO_DATE function •
sql-reference-142	sql-reference.pdf	142	listid \| salesid --------+--------- 4 \| 2 5 \| 4 5 \| 3 6 \| 5 10 \| 9 10 \| 8 10 \| 7 10 \| 6 \| 1 (9 rows) Data type formatting functions Using a data type formatting function, you can convert values from one data type to another. For each of these functions, the ﬁrst argument is always the value to be formatted and the second argument contains the template for the new format. AWS Clean Rooms supports several data type formatting functions. Topics • CAST function • CONVERT function • TO_CHAR • TO_DATE function • TO_NUMBER • Datetime format strings • Numeric format strings • Teradata-style formatting characters for numeric data Data type formatting functions 516 AWS Clean Rooms CAST function SQL Reference The CAST function converts one data type to another compatible data type. For instance, you can convert a string to a date, or a numeric type to a string. CAST performs a runtime conversion, which means that the conversion doesn't change a value's data type in a source table. It's changed only in the context of the query. The CAST function is very similar to the section called “CONVERT”, in that they both convert one data type to another, but they are called diﬀerently. Certain data types require an explicit conversion to other data types using the CAST or CONVERT function. Other data types can be converted implicitly, as part of another command, without using CAST or CONVERT. See Type compatibility and conversion. Syntax Use either of these two equivalent syntax forms to cast expressions from one data type to another. CAST ( expression AS type ) expression :: type Arguments expression An expression that evaluates to one or more values, such as a column name or a literal. Converting null values returns nulls. The expression can't contain blank or empty strings. type One of the supported Data types, except for VARBYTE, BINARY, and BINARY VARYING data types. Return type CAST returns the data type speciﬁed by the type argument. Note AWS Clean Rooms returns an error if you try to perform a problematic conversion, such as a DECIMAL conversion that loses precision, like the following: Data type formatting functions 517 AWS Clean Rooms SQL Reference select 123.456::decimal(2,1); or an INTEGER conversion that causes an overﬂow: select 12345678::smallint; Examples The following two queries are equivalent. They both cast a decimal value to an integer: select cast(pricepaid as integer) from sales where salesid=100; pricepaid ----------- 162 (1 row) select pricepaid::integer from sales where salesid=100; pricepaid ----------- 162 (1 row) The following produces a similar result. It doesn't require sample data to run: select cast(162.00 as integer) as pricepaid; pricepaid ----------- 162 (1 row) In this example, the values in a timestamp column are cast as dates, which results in removing the time from each result: Data type formatting functions 518 AWS Clean Rooms SQL Reference select cast(saletime as date), salesid from sales order by salesid limit 10; saletime \| salesid -----------+--------- 2008-02-18 \| 1 2008-06-06 \| 2 2008-06-06 \| 3 2008-06-09 \| 4 2008-08-31 \| 5 2008-07-16 \| 6 2008-06-26 \| 7 2008-07-10 \| 8 2008-07-22 \| 9 2008-08-06 \| 10 (10 rows) If you didn't use CAST as illustrated in the previous sample, the results would include the time: 2008-02-18 02:36:48. The following query casts variable character data to a date. It doesn't require sample data to run. select cast('2008-02-18 02:36:48' as date) as mysaletime; mysaletime -------------------- 2008-02-18 (1 row) In this example, the values in a date column are cast as timestamps: select cast(caldate as timestamp), dateid from date order by dateid limit 10; caldate \| dateid --------------------+-------- 2008-01-01 00:00:00 \| 1827 2008-01-02 00:00:00 \| 1828 2008-01-03 00:00:00 \| 1829 2008-01-04 00:00:00 \| 1830 2008-01-05 00:00:00 \| 1831 Data type formatting functions 519 AWS Clean Rooms SQL Reference 2008-01-06 00:00:00 \| 1832 2008-01-07 00:00:00 \| 1833 2008-01-08 00:00:00 \| 1834 2008-01-09 00:00:00 \| 1835 2008-01-10 00:00:00 \| 1836 (10 rows) In a case like the previous sample, you can gain additional control over output formatting by using TO_CHAR. In this example, an integer is cast as a character string: select cast(2008 as char(4)); bpchar -------- 2008 In this example, a DECIMAL(6,3) value is cast as a DECIMAL(4,1) value: select cast(109.652 as decimal(4,1)); numeric --------- 109.7 This example shows a more complex expression. The PRICEPAID column (a DECIMAL(8,2) column) in the SALES table is converted to a DECIMAL(38,2) column and the values are multiplied by 100000000000000000000: select salesid, pricepaid::decimal(38,2)*100000000000000000000 as value from sales where salesid<10 order by salesid; salesid \| value ---------+---------------------------- 1 \| 72800000000000000000000.00 2 \| 7600000000000000000000.00 3 \| 35000000000000000000000.00 4 \| 17500000000000000000000.00 5 \| 15400000000000000000000.00 Data type formatting functions 520 AWS Clean Rooms SQL Reference 6 \| 39400000000000000000000.00 7 \| 78800000000000000000000.00 8 \| 19700000000000000000000.00 9 \| 59100000000000000000000.00 (9 rows) CONVERT
sql-reference-143	sql-reference.pdf	143	value is cast as a DECIMAL(4,1) value: select cast(109.652 as decimal(4,1)); numeric --------- 109.7 This example shows a more complex expression. The PRICEPAID column (a DECIMAL(8,2) column) in the SALES table is converted to a DECIMAL(38,2) column and the values are multiplied by 100000000000000000000: select salesid, pricepaid::decimal(38,2)*100000000000000000000 as value from sales where salesid<10 order by salesid; salesid \| value ---------+---------------------------- 1 \| 72800000000000000000000.00 2 \| 7600000000000000000000.00 3 \| 35000000000000000000000.00 4 \| 17500000000000000000000.00 5 \| 15400000000000000000000.00 Data type formatting functions 520 AWS Clean Rooms SQL Reference 6 \| 39400000000000000000000.00 7 \| 78800000000000000000000.00 8 \| 19700000000000000000000.00 9 \| 59100000000000000000000.00 (9 rows) CONVERT function Like the CAST function, the CONVERT function converts one data type to another compatible data type. For instance, you can convert a string to a date, or a numeric type to a string. CONVERT performs a runtime conversion, which means that the conversion doesn't change a value's data type in a source table. It's changed only in the context of the query. Certain data types require an explicit conversion to other data types using the CONVERT function. Other data types can be converted implicitly, as part of another command, without using CAST or CONVERT. See Type compatibility and conversion. Syntax CONVERT ( type, expression ) Arguments type One of the supported Data types, except for VARBYTE, BINARY, and BINARY VARYING data types. expression An expression that evaluates to one or more values, such as a column name or a literal. Converting null values returns nulls. The expression can't contain blank or empty strings. Return type CONVERT returns the data type speciﬁed by the type argument. Note AWS Clean Rooms returns an error if you try to perform a problematic conversion, such as a DECIMAL conversion that loses precision, like the following: Data type formatting functions 521 AWS Clean Rooms SQL Reference SELECT CONVERT(decimal(2,1), 123.456); or an INTEGER conversion that causes an overﬂow: SELECT CONVERT(smallint, 12345678); Examples The following query uses the CONVERT function to convert a column of decimals into integers SELECT CONVERT(integer, pricepaid) FROM sales WHERE salesid=100; This example converts an integer into a character string. SELECT CONVERT(char(4), 2008); In this example, the current date and time is converted to a variable character data type: SELECT CONVERT(VARCHAR(30), GETDATE()); getdate --------- 2023-02-02 04:31:16 This example converts the saletime column into just the time, removing the dates from each row. SELECT CONVERT(time, saletime), salesid FROM sales order by salesid limit 10; The following example converts variable character data into a datetime object. SELECT CONVERT(datetime, '2008-02-18 02:36:48') as mysaletime; TO_CHAR TO_CHAR converts a timestamp or numeric expression to a character-string data format. Data type formatting functions 522 AWS Clean Rooms Syntax SQL Reference TO_CHAR (timestamp_expression \| numeric_expression , 'format') Arguments timestamp_expression An expression that results in a TIMESTAMP or TIMESTAMPTZ type value or a value that can implicitly be coerced to a timestamp. numeric_expression An expression that results in a numeric data type value or a value that can implicitly be coerced to a numeric type. For more information, see Numeric types. TO_CHAR inserts a space to the left of the numeral string. Note TO_CHAR doesn't support 128-bit DECIMAL values. format The format for the new value. For valid formats, see Datetime format strings and Numeric format strings. Return type VARCHAR Examples The following example converts a timestamp to a value with the date and time in a format with the name of the month padded to nine characters, the name of the day of the week, and the day number of the month. select to_char(timestamp '2009-12-31 23:15:59', 'MONTH-DY-DD-YYYY HH12:MIPM'); to_char ------------------------- Data type formatting functions 523 AWS Clean Rooms SQL Reference DECEMBER -THU-31-2009 11:15PM The following example converts a timestamp to a value with day number of the year. select to_char(timestamp '2009-12-31 23:15:59', 'DDD'); to_char ------------------------- 365 The following example converts a timestamp to an ISO day number of the week. select to_char(timestamp '2022-05-16 23:15:59', 'ID'); to_char ------------------------- 1 The following example extracts the month name from a date. select to_char(date '2009-12-31', 'MONTH'); to_char ------------------------- DECEMBER The following example converts each STARTTIME value in the EVENT table to a string that consists of hours, minutes, and seconds. select to_char(starttime, 'HH12:MI:SS') from event where eventid between 1 and 5 order by eventid; to_char ---------- 02:30:00 08:00:00 02:30:00 02:30:00 07:00:00 (5 rows) Data type formatting functions 524 AWS Clean Rooms SQL Reference The following example converts an entire timestamp value into a diﬀerent format. select starttime, to_char(starttime, 'MON-DD-YYYY HH12:MIPM') from event where eventid=1; starttime \| to_char ---------------------+--------------------- 2008-01-25 14:30:00 \| JAN-25-2008 02:30PM (1 row) The following example converts a timestamp literal to a character string. select to_char(timestamp '2009-12-31 23:15:59','HH24:MI:SS'); to_char ---------- 23:15:59 (1 row) The following example converts a number to a character string with the negative sign at the end. select to_char(-125.8, '999D99S'); to_char --------- 125.80- (1 row) The following example
sql-reference-144	sql-reference.pdf	144	eventid; to_char ---------- 02:30:00 08:00:00 02:30:00 02:30:00 07:00:00 (5 rows) Data type formatting functions 524 AWS Clean Rooms SQL Reference The following example converts an entire timestamp value into a diﬀerent format. select starttime, to_char(starttime, 'MON-DD-YYYY HH12:MIPM') from event where eventid=1; starttime \| to_char ---------------------+--------------------- 2008-01-25 14:30:00 \| JAN-25-2008 02:30PM (1 row) The following example converts a timestamp literal to a character string. select to_char(timestamp '2009-12-31 23:15:59','HH24:MI:SS'); to_char ---------- 23:15:59 (1 row) The following example converts a number to a character string with the negative sign at the end. select to_char(-125.8, '999D99S'); to_char --------- 125.80- (1 row) The following example converts a number to a character string with the currency symbol. select to_char(-125.88, '$S999D99'); to_char --------- $-125.88 (1 row) The following example converts a number to a character string using angle brackets for negative numbers. select to_char(-125.88, '$999D99PR'); to_char --------- $<125.88> Data type formatting functions 525 AWS Clean Rooms (1 row) SQL Reference The following example converts a number to a Roman numeral string. select to_char(125, 'RN'); to_char --------- CXXV (1 row) The following example displays the day of the week. SELECT to_char(current_timestamp, 'FMDay, FMDD HH12:MI:SS'); to_char ----------------------- Wednesday, 31 09:34:26 The following example displays the ordinal number suﬃx for a number. SELECT to_char(482, '999th'); to_char ----------------------- 482nd The following example subtracts the commission from the price paid in the sales table. The diﬀerence is then rounded up and converted to a roman numeral, shown in the to_char column: select salesid, pricepaid, commission, (pricepaid - commission) as difference, to_char(pricepaid - commission, 'rn') from sales group by sales.pricepaid, sales.commission, salesid order by salesid limit 10; salesid \| pricepaid \| commission \| difference \| to_char ---------+-----------+------------+------------+----------------- 1 \| 728.00 \| 109.20 \| 618.80 \| dcxix 2 \| 76.00 \| 11.40 \| 64.60 \| lxv 3 \| 350.00 \| 52.50 \| 297.50 \| ccxcviii 4 \| 175.00 \| 26.25 \| 148.75 \| cxlix 5 \| 154.00 \| 23.10 \| 130.90 \| cxxxi 6 \| 394.00 \| 59.10 \| 334.90 \| cccxxxv 7 \| 788.00 \| 118.20 \| 669.80 \| dclxx Data type formatting functions 526 AWS Clean Rooms SQL Reference 8 \| 197.00 \| 29.55 \| 167.45 \| clxvii 9 \| 591.00 \| 88.65 \| 502.35 \| dii 10 \| 65.00 \| 9.75 \| 55.25 \| lv (10 rows) The following example adds the currency symbol to the diﬀerence values shown in the to_char column: select salesid, pricepaid, commission, (pricepaid - commission) as difference, to_char(pricepaid - commission, 'l99999D99') from sales group by sales.pricepaid, sales.commission, salesid order by salesid limit 10; salesid \| pricepaid \| commission \| difference \| to_char --------+-----------+------------+------------+------------ 1 \| 728.00 \| 109.20 \| 618.80 \| $ 618.80 2 \| 76.00 \| 11.40 \| 64.60 \| $ 64.60 3 \| 350.00 \| 52.50 \| 297.50 \| $ 297.50 4 \| 175.00 \| 26.25 \| 148.75 \| $ 148.75 5 \| 154.00 \| 23.10 \| 130.90 \| $ 130.90 6 \| 394.00 \| 59.10 \| 334.90 \| $ 334.90 7 \| 788.00 \| 118.20 \| 669.80 \| $ 669.80 8 \| 197.00 \| 29.55 \| 167.45 \| $ 167.45 9 \| 591.00 \| 88.65 \| 502.35 \| $ 502.35 10 \| 65.00 \| 9.75 \| 55.25 \| $ 55.25 (10 rows) The following example lists the century in which each sale was made. select salesid, saletime, to_char(saletime, 'cc') from sales order by salesid limit 10; salesid \| saletime \| to_char ---------+---------------------+--------- 1 \| 2008-02-18 02:36:48 \| 21 2 \| 2008-06-06 05:00:16 \| 21 3 \| 2008-06-06 08:26:17 \| 21 4 \| 2008-06-09 08:38:52 \| 21 5 \| 2008-08-31 09:17:02 \| 21 6 \| 2008-07-16 11:59:24 \| 21 7 \| 2008-06-26 12:56:06 \| 21 8 \| 2008-07-10 02:12:36 \| 21 9 \| 2008-07-22 02:23:17 \| 21 Data type formatting functions 527 AWS Clean Rooms SQL Reference 10 \| 2008-08-06 02:51:55 \| 21 (10 rows) The following example converts each STARTTIME value in the EVENT table to a string that consists of hours, minutes, seconds, and time zone. select to_char(starttime, 'HH12:MI:SS TZ') from event where eventid between 1 and 5 order by eventid; to_char ---------- 02:30:00 UTC 08:00:00 UTC 02:30:00 UTC 02:30:00 UTC 07:00:00 UTC (5 rows) (10 rows) The following example shows formatting for seconds, milliseconds, and microseconds. select sysdate, to_char(sysdate, 'HH24:MI:SS') as seconds, to_char(sysdate, 'HH24:MI:SS.MS') as milliseconds, to_char(sysdate, 'HH24:MI:SS:US') as microseconds; timestamp \| seconds \| milliseconds \| microseconds --------------------+----------+--------------+---------------- 2015-04-10 18:45:09 \| 18:45:09 \| 18:45:09.325 \| 18:45:09:325143 TO_DATE function TO_DATE converts a date represented by a character string to a DATE data type. Syntax TO_DATE(string, format) TO_DATE(string, format, is_strict) Data type formatting functions 528 AWS Clean Rooms Arguments string A string to be converted. format SQL Reference A string literal that deﬁnes the format of the input string, in terms of its date parts. For a list of valid day, month, and year formats, see Datetime format strings. is_strict An optional Boolean
sql-reference-145	sql-reference.pdf	145	seconds, to_char(sysdate, 'HH24:MI:SS.MS') as milliseconds, to_char(sysdate, 'HH24:MI:SS:US') as microseconds; timestamp \| seconds \| milliseconds \| microseconds --------------------+----------+--------------+---------------- 2015-04-10 18:45:09 \| 18:45:09 \| 18:45:09.325 \| 18:45:09:325143 TO_DATE function TO_DATE converts a date represented by a character string to a DATE data type. Syntax TO_DATE(string, format) TO_DATE(string, format, is_strict) Data type formatting functions 528 AWS Clean Rooms Arguments string A string to be converted. format SQL Reference A string literal that deﬁnes the format of the input string, in terms of its date parts. For a list of valid day, month, and year formats, see Datetime format strings. is_strict An optional Boolean value that speciﬁes whether an error is returned if an input date value is out of range. When is_strict is set to TRUE, an error is returned if there is an out of range value. When is_strict is set to FALSE, which is the default, then overﬂow values are accepted. Return type TO_DATE returns a DATE, depending on the format value. If the conversion to format fails, then an error is returned. Examples The following SQL statement converts the date 02 Oct 2001 into a date data type. select to_date('02 Oct 2001', 'DD Mon YYYY'); to_date ------------ 2001-10-02 (1 row) The following SQL statement converts the string 20010631 to a date. select to_date('20010631', 'YYYYMMDD', FALSE); The result is July 1, 2001, because there are only 30 days in June. to_date ------------ 2001-07-01 Data type formatting functions 529 AWS Clean Rooms SQL Reference The following SQL statement converts the string 20010631 to a date: to_date('20010631', 'YYYYMMDD', TRUE); The result is an error because there are only 30 days in June. ERROR: date/time field date value out of range: 2001-6-31 TO_NUMBER TO_NUMBER converts a string to a numeric (decimal) value. Syntax to_number(string, format) Arguments string String to be converted. The format must be a literal value. format The second argument is a format string that indicates how the character string should be parsed to create the numeric value. For example, the format '99D999' speciﬁes that the string to be converted consists of ﬁve digits with the decimal point in the third position. For example, to_number('12.345','99D999') returns 12.345 as a numeric value. For a list of valid formats, see Numeric format strings. Return type TO_NUMBER returns a DECIMAL number. If the conversion to format fails, then an error is returned. Examples The following example converts the string 12,454.8- to a number: select to_number('12,454.8-', '99G999D9S'); Data type formatting functions 530 AWS Clean Rooms to_number ----------- -12454.8 SQL Reference The following example converts the string $ 12,454.88 to a number: select to_number('$ 12,454.88', 'L 99G999D99'); to_number ----------- 12454.88 The following example converts the string $ 2,012,454.88 to a number: select to_number('$ 2,012,454.88', 'L 9,999,999.99'); to_number ----------- 2012454.88 Datetime format strings The following datetime format strings apply to functions such as TO_CHAR. These strings can contain datetime separators (such as '-', '/', or ':') and the following "dateparts" and "timeparts". For examples of formatting dates as strings, see TO_CHAR. Datepart or timepart Meaning BC or B.C., AD or A.D., b.c. or bc, ad or a.d. Upper and lowercase era indicators CC Two-digit century number YYYY, YYY, YY, Y 4-digit, 3-digit, 2-digit, 1-digit year number Y,YYY IYYY, IYY, IY, I 4-digit year number with comma 4-digit, 3-digit, 2-digit, 1-digit International Organization for Standardization (ISO) year number Data type formatting functions 531 AWS Clean Rooms SQL Reference Datepart or timepart Meaning Q Quarter number (1 to 4) MONTH, Month, month MON, Mon, mon MM RM, rm W WW IW DAY, Day, day DY, Dy, dy DDD IDDD DD Month name (uppercase, mixed-case, lowercase, blank-padded to 9 characters) Abbreviated month name (uppercase, mixed- case, lowercase, blank-padded to 3 characters) Month number (01-12) Month number in Roman numerals (I–XII, with I being January, uppercase or lowercase) Week of month (1–5; the ﬁrst week starts on the ﬁrst day of the month.) Week number of year (1–53; the ﬁrst week starts on the ﬁrst day of the year.) ISO week number of year (the ﬁrst Thursday of the new year is in week 1.) Day name (uppercase, mixed-case, lowercase, blank-padded to 9 characters) Abbreviated day name (uppercase, mixed-case, lowercase, blank-padded to 3 characters) Day of year (001–366) Day of ISO 8601 week-numbering year (001-371; day 1 of the year is Monday of the ﬁrst ISO week) Day of month as a number (01–31) Data type formatting functions 532 AWS Clean Rooms SQL Reference Datepart or timepart Meaning D ID J HH24 HH or HH12 MI SS MS US Day of week (1–7; Sunday is 1) Note The D datepart behaves diﬀerently from the day of week (DOW) datepart used for the datetime functions DATE_PART and EXTRACT. DOW is based on integers 0–6, where Sunday is 0. For more information, see Date parts for date or timestamp functions. ISO 8601 day of
sql-reference-146	sql-reference.pdf	146	week-numbering year (001-371; day 1 of the year is Monday of the ﬁrst ISO week) Day of month as a number (01–31) Data type formatting functions 532 AWS Clean Rooms SQL Reference Datepart or timepart Meaning D ID J HH24 HH or HH12 MI SS MS US Day of week (1–7; Sunday is 1) Note The D datepart behaves diﬀerently from the day of week (DOW) datepart used for the datetime functions DATE_PART and EXTRACT. DOW is based on integers 0–6, where Sunday is 0. For more information, see Date parts for date or timestamp functions. ISO 8601 day of the week, Monday (1) to Sunday (7) Julian day (days since January 1, 4712 BC) Hour (24-hour clock, 00–23) Hour (12-hour clock, 01–12) Minutes (00–59) Seconds (00–59) Milliseconds (.000) Microseconds (.000000) AM or PM, A.M. or P.M., a.m. or p.m., am or pm Upper and lowercase meridian indicators (for TZ, tz OF 12-hour clock) Upper and lowercase time zone abbreviation; valid for TIMESTAMPTZ only Oﬀset from UTC; valid for TIMESTAMPTZ only Data type formatting functions 533 AWS Clean Rooms Note SQL Reference You must surround datetime separators (such as '-', '/' or ':') with single quotation marks, but you must surround the "dateparts" and "timeparts" listed in the preceding table with double quotation marks. Numeric format strings The following numeric format strings apply to functions such as TO_NUMBER and TO_CHAR. • For examples of formatting strings as numbers, see TO_NUMBER. • For examples of formatting numbers as strings, see TO_CHAR. Format Description 9 0 . (period), D , (comma) CC FM PR S L G Numeric value with the speciﬁed number of digits. Numeric value with leading zeros. Decimal point. Thousands separator. Century code. For example, the 21st century started on 2001-01-01 (supported for TO_CHAR only). Fill mode. Suppress padding blanks and zeroes. Negative value in angle brackets. Sign anchored to a number. Currency symbol in the speciﬁed position. Group separator. Data type formatting functions 534 AWS Clean Rooms Format MI PL SG RN TH or th SQL Reference Description Minus sign in the speciﬁed position for numbers that are less than 0. Plus sign in the speciﬁed position for numbers that are greater than 0. Plus or minus sign in the speciﬁed position. Roman numeral between 1 and 3999 (supported for TO_CHAR only). Ordinal number suﬃx. Does not convert fractional numbers or values that are less than zero. Teradata-style formatting characters for numeric data This topic shows you how the TEXT_TO_INT_ALT and TEXT_TO_NUMERIC_ALT functions interpret the characters in the input expression string. In the following table, you can also ﬁnd a list of the characters that you can specify in the format phrase. In addition, you can ﬁnd a description of the diﬀerences between Teradata-style formatting and AWS Clean Rooms for the format option. Format Description G D Not supported as a group separator in the input expression string. You can't specify this character in the format phrase. Radix symbol. You can specify this character in the format phrase. This character is equivalent to the . (period). The radix symbol can't appear in a format phrase that contains any of the following characters: Data type formatting functions 535 AWS Clean Rooms Format / , : % . B + - SQL Reference Description • . (period) • S (uppercase 's') • V (uppercase 'v') Insertion characters / (forward slash), comma (,), : (colon), and % (percent sign). You can't include these characters in the format phrase. AWS Clean Rooms ignores these characters in the input expression string. Period as a radix character, that is, a decimal point. This character can't appear in a format phrase that contains any of the following characters: • D (uppercase 'd') • S (uppercase 's') • V (uppercase 'v') You can't include the blank space character (B) in the format phrase. In the input expression string, leading and trailing spaces are ignored and spaces between digits aren't allowed. You can't include the plus sign (+) or minus sign (-) in the format phrase. However, the plus sign (+) and minus sign (-) are parsed implicitly as part of numeric value if they appear in the input expression string. Data type formatting functions 536 AWS Clean Rooms Format V Z 9 SQL Reference Description Decimal point position indicator. This character can't appear in a format phrase that contains any of the following characters: • D (uppercase 'd') • . (period) Zero-suppressed decimal digit. AWS Clean Rooms trims leading zeros. The Z character can't follow a 9 character. The Z character must be to the left of the radix character if the fraction part contains the 9 character. Decimal digit. CHAR(n) For this format, you can specify the following: • CHAR consists of Z or 9 characters. AWS Clean Rooms doesn't support a + (plus) or
sql-reference-147	sql-reference.pdf	147	Clean Rooms Format V Z 9 SQL Reference Description Decimal point position indicator. This character can't appear in a format phrase that contains any of the following characters: • D (uppercase 'd') • . (period) Zero-suppressed decimal digit. AWS Clean Rooms trims leading zeros. The Z character can't follow a 9 character. The Z character must be to the left of the radix character if the fraction part contains the 9 character. Decimal digit. CHAR(n) For this format, you can specify the following: • CHAR consists of Z or 9 characters. AWS Clean Rooms doesn't support a + (plus) or - (minus) in the CHAR value. • n is an integer constant, I, or F. For I, this is the number of characters necessary to display the integer portion of numeric or integer data. For F, this is the number of characters necessary to display the fractiona l portion of numeric data. - Hyphen (-) character. You can't include this character in the format phrase. AWS Clean Rooms ignores this character in the input expression string. Data type formatting functions 537 AWS Clean Rooms Format S E FN9 FNE SQL Reference Description Signed zoned decimal. The S character must follow the last decimal digit in the format phrase. The last character of the input expression string and the correspon ding numeric conversion are listed in Data formatting characters for signed zoned decimal, Teradata–style numeric data formatting . The S character can't appear in a format phrase that contains any of the following characters: • + (plus sign) • . (period) • D (uppercase 'd') • Z (uppercase 'z') • F (uppercase 'f') • E (uppercase 'e') Exponential notation. The input expression string can include the exponent character. You can't specify E as an exponent character in format phrase. Not supported in AWS Clean Rooms. Not supported in AWS Clean Rooms. Data type formatting functions 538 AWS Clean Rooms Format $, USD, US Dollars L C N O U A SQL Reference Description Dollar sign ($), ISO currency symbol (USD), and the currency name US Dollars. The ISO currency symbol USD and the currency name US Dollars are case-sensitive. AWS Clean Rooms supports only the USD currency. The input expression string can include spaces between the USD currency symbol and the numeric value, for example ‘$ 123E2’ or ‘123E2 $’. Currency symbol. This currency symbol character can only appear once in the format phrase. You can't specify repeated currency symbol characters. ISO currency symbol. This currency symbol character can only appear once in the format phrase. You can't specify repeated currency symbol characters. Full currency name. This currency symbol character can only appear once in the format phrase. You can't specify repeated currency symbol characters. Dual currency symbol. You can't specify this character in the format phrase. Dual ISO currency symbol. You can't specify this character in the format phrase. Full dual currency name. You can't specify this character in the format phrase. Data type formatting functions 539 AWS Clean Rooms SQL Reference Data formatting characters for signed zoned decimal, Teradata–style numeric data formatting You can use the following characters in the format phrase of the TEXT_TO_INT_ALT and TEXT_TO_NUMERIC_ALT functions for a signed zoned decimal value. Last character of the input string Numeric conversion { or 0 A or 1 B or 2 C or 3 D or 4 E or 5 F or 6 G or 7 H or 8 I or 9 } J K L M N O P n … 0 n … 1 n … 2 n … 3 n … 4 n … 5 n … 6 n … 7 n … 8 n … 9 -n … 0 -n … 1 -n … 2 -n … 3 -n … 4 -n … 5 -n … 6 -n … 7 Data type formatting functions 540 AWS Clean Rooms SQL Reference Last character of the input string Numeric conversion Q R -n … 8 -n … 9 Date and time functions Date and time functions allow you to perform a wide range of operations on date and time data, such as extracting parts of a date, performing date calculations, formatting dates and times, and working with the current date and time. These functions are essential for tasks such as data analysis, reporting, and data manipulation involving temporal data. AWS Clean Rooms SQL supports the following date and time functions: Topics • ADD_MONTHS function • CONVERT_TIMEZONE function • CURRENT_DATE function • DATEADD function • DATEDIFF function • DATE_PART function • DATE_TRUNC function • EXTRACT function • GETDATE function • SYSDATE function • TIMEOFDAY function • TO_TIMESTAMP function • Date and time functions in transactions • Date parts for date or timestamp functions Date and time functions 541 AWS Clean Rooms ADD_MONTHS function SQL Reference ADD_MONTHS adds the
sql-reference-148	sql-reference.pdf	148	the current date and time. These functions are essential for tasks such as data analysis, reporting, and data manipulation involving temporal data. AWS Clean Rooms SQL supports the following date and time functions: Topics • ADD_MONTHS function • CONVERT_TIMEZONE function • CURRENT_DATE function • DATEADD function • DATEDIFF function • DATE_PART function • DATE_TRUNC function • EXTRACT function • GETDATE function • SYSDATE function • TIMEOFDAY function • TO_TIMESTAMP function • Date and time functions in transactions • Date parts for date or timestamp functions Date and time functions 541 AWS Clean Rooms ADD_MONTHS function SQL Reference ADD_MONTHS adds the speciﬁed number of months to a date or timestamp value or expression. The DATEADD function provides similar functionality. Syntax ADD_MONTHS( {date \| timestamp}, integer) Arguments date \| timestamp A date or timestamp column or an expression that implicitly converts to a date or timestamp. If the date is the last day of the month, or if the resulting month is shorter, the function returns the last day of the month in the result. For other dates, the result contains the same day number as the date expression. integer A positive or negative integer. Use a negative number to subtract months from dates. Return type TIMESTAMP Example The following query uses the ADD_MONTHS function inside a TRUNC function. The TRUNC function removes the time of day from the result of ADD_MONTHS. The ADD_MONTHS function adds 12 months to each value from the CALDATE column. select distinct trunc(add_months(caldate, 12)) as calplus12, trunc(caldate) as cal from date order by 1 asc; calplus12 \| cal ------------+------------ 2009-01-01 \| 2008-01-01 2009-01-02 \| 2008-01-02 2009-01-03 \| 2008-01-03 Date and time functions 542 AWS Clean Rooms ... (365 rows) SQL Reference The following examples demonstrate the behavior when the ADD_MONTHS function operates on dates with months that have diﬀerent numbers of days. select add_months('2008-03-31',1); add_months --------------------- 2008-04-30 00:00:00 (1 row) select add_months('2008-04-30',1); add_months --------------------- 2008-05-31 00:00:00 (1 row) CONVERT_TIMEZONE function CONVERT_TIMEZONE converts a timestamp from one time zone to another. The function automatically adjusts for daylight saving time. Syntax CONVERT_TIMEZONE ( ['source_timezone',] 'target_timezone', 'timestamp') Arguments source_timezone (Optional) The time zone of the current timestamp. The default is UTC. target_timezone The time zone for the new timestamp. timestamp A timestamp column or an expression that implicitly converts to a timestamp. Date and time functions 543 AWS Clean Rooms Return type TIMESTAMP Examples SQL Reference The following example converts the timestamp value from the default UTC time zone to PST. select convert_timezone('PST', '2008-08-21 07:23:54'); convert_timezone ------------------------ 2008-08-20 23:23:54 The following example converts the timestamp value in the LISTTIME column from the default UTC time zone to PST. Though the timestamp is within the daylight time period, it's converted to standard time because the target time zone is speciﬁed as an abbreviation (PST). select listtime, convert_timezone('PST', listtime) from listing where listid = 16; listtime \| convert_timezone --------------------+------------------- 2008-08-24 09:36:12 2008-08-24 01:36:12 The following example converts a timestamp LISTTIME column from the default UTC time zone to US/Paciﬁc time zone. The target time zone uses a time zone name, and the timestamp is within the daylight time period, so the function returns the daylight time. select listtime, convert_timezone('US/Pacific', listtime) from listing where listid = 16; listtime \| convert_timezone --------------------+--------------------- 2008-08-24 09:36:12 \| 2008-08-24 02:36:12 The following example converts a timestamp string from EST to PST: select convert_timezone('EST', 'PST', '20080305 12:25:29'); convert_timezone Date and time functions 544 AWS Clean Rooms ------------------- 2008-03-05 09:25:29 SQL Reference The following example converts a timestamp to US Eastern Standard Time because the target time zone uses a time zone name (America/New_York) and the timestamp is within the standard time period. select convert_timezone('America/New_York', '2013-02-01 08:00:00'); convert_timezone --------------------- 2013-02-01 03:00:00 (1 row) The following example converts the timestamp to US Eastern Daylight Time because the target time zone uses a time zone name (America/New_York) and the timestamp is within the daylight time period. select convert_timezone('America/New_York', '2013-06-01 08:00:00'); convert_timezone --------------------- 2013-06-01 04:00:00 (1 row) The following example demonstrates the use of oﬀsets. SELECT CONVERT_TIMEZONE('GMT','NEWZONE +2','2014-05-17 12:00:00') as newzone_plus_2, CONVERT_TIMEZONE('GMT','NEWZONE-2:15','2014-05-17 12:00:00') as newzone_minus_2_15, CONVERT_TIMEZONE('GMT','America/Los_Angeles+2','2014-05-17 12:00:00') as la_plus_2, CONVERT_TIMEZONE('GMT','GMT+2','2014-05-17 12:00:00') as gmt_plus_2; newzone_plus_2 \| newzone_minus_2_15 \| la_plus_2 \| gmt_plus_2 ---------------------+---------------------+---------------------+--------------------- 2014-05-17 10:00:00 \| 2014-05-17 14:15:00 \| 2014-05-17 10:00:00 \| 2014-05-17 10:00:00 (1 row) CURRENT_DATE function CURRENT_DATE returns a date in the current session time zone (UTC by default) in the default format: YYYY-MM-DD. Date and time functions 545 AWS Clean Rooms Note SQL Reference CURRENT_DATE returns the start date for the current transaction, not for the start of the current statement. Consider the scenario where you start a transaction containing multiple statements on 10/01/08 23:59, and the statement containing CURRENT_DATE runs at 10/02/08 00:00. CURRENT_DATE returns 10/01/08, not 10/02/08. Syntax CURRENT_DATE Return type DATE Example The following example returns the current date (in the AWS Region where the function runs). select
sql-reference-149	sql-reference.pdf	149	(1 row) CURRENT_DATE function CURRENT_DATE returns a date in the current session time zone (UTC by default) in the default format: YYYY-MM-DD. Date and time functions 545 AWS Clean Rooms Note SQL Reference CURRENT_DATE returns the start date for the current transaction, not for the start of the current statement. Consider the scenario where you start a transaction containing multiple statements on 10/01/08 23:59, and the statement containing CURRENT_DATE runs at 10/02/08 00:00. CURRENT_DATE returns 10/01/08, not 10/02/08. Syntax CURRENT_DATE Return type DATE Example The following example returns the current date (in the AWS Region where the function runs). select current_date; date ------------ 2008-10-01 DATEADD function Increments a DATE, TIME, TIMETZ, or TIMESTAMP value by a speciﬁed interval. Syntax DATEADD( datepart, interval, {date\|time\|timetz\|timestamp} ) Arguments datepart The date part (year, month, day, or hour, for example) that the function operates on. For more information, see Date parts for date or timestamp functions. Date and time functions 546 AWS Clean Rooms interval SQL Reference An integer that speciﬁed the interval (number of days, for example) to add to the target expression. A negative integer subtracts the interval. date\|time\|timetz\|timestamp A DATE, TIME, TIMETZ, or TIMESTAMP column or an expression that implicitly converts to a DATE, TIME, TIMETZ, or TIMESTAMP. The DATE, TIME, TIMETZ, or TIMESTAMP expression must contain the speciﬁed date part. Return type TIMESTAMP or TIME or TIMETZ depending on the input data type. Examples with a DATE column The following example adds 30 days to each date in November that exists in the DATE table. select dateadd(day,30,caldate) as novplus30 from date where month='NOV' order by dateid; novplus30 --------------------- 2008-12-01 00:00:00 2008-12-02 00:00:00 2008-12-03 00:00:00 ... (30 rows) The following example adds 18 months to a literal date value. select dateadd(month,18,'2008-02-28'); date_add --------------------- 2009-08-28 00:00:00 (1 row) Date and time functions 547 AWS Clean Rooms SQL Reference The default column name for a DATEADD function is DATE_ADD. The default timestamp for a date value is 00:00:00. The following example adds 30 minutes to a date value that doesn't specify a timestamp. select dateadd(m,30,'2008-02-28'); date_add --------------------- 2008-02-28 00:30:00 (1 row) You can name date parts in full or abbreviate them. In this case, m stands for minutes, not months. Examples with a TIME column The following example table TIME_TEST has a column TIME_VAL (type TIME) with three values inserted. select time_val from time_test; time_val --------------------- 20:00:00 00:00:00.5550 00:58:00 The following example adds 5 minutes to each TIME_VAL in the TIME_TEST table. select dateadd(minute,5,time_val) as minplus5 from time_test; minplus5 --------------- 20:05:00 00:05:00.5550 01:03:00 The following example adds 8 hours to a literal time value. select dateadd(hour, 8, time '13:24:55'); date_add Date and time functions 548 AWS Clean Rooms --------------- 21:24:55 SQL Reference The following example shows when a time goes over 24:00:00 or under 00:00:00. select dateadd(hour, 12, time '13:24:55'); date_add --------------- 01:24:55 Examples with a TIMETZ column The output values in these examples are in UTC which is the default timezone. The following example table TIMETZ_TEST has a column TIMETZ_VAL (type TIMETZ) with three values inserted. select timetz_val from timetz_test; timetz_val ------------------ 04:00:00+00 00:00:00.5550+00 05:58:00+00 The following example adds 5 minutes to each TIMETZ_VAL in TIMETZ_TEST table. select dateadd(minute,5,timetz_val) as minplus5_tz from timetz_test; minplus5_tz --------------- 04:05:00+00 00:05:00.5550+00 06:03:00+00 The following example adds 2 hours to a literal timetz value. select dateadd(hour, 2, timetz '13:24:55 PST'); date_add --------------- Date and time functions 549 AWS Clean Rooms 23:24:55+00 Examples with a TIMESTAMP column SQL Reference The output values in these examples are in UTC which is the default timezone. The following example table TIMESTAMP_TEST has a column TIMESTAMP_VAL (type TIMESTAMP) with three values inserted. SELECT timestamp_val FROM timestamp_test; timestamp_val ------------------ 1988-05-15 10:23:31 2021-03-18 17:20:41 2023-06-02 18:11:12 The following example adds 20 years only to the TIMESTAMP_VAL values in TIMESTAMP_TEST from before the year 2000. SELECT dateadd(year,20,timestamp_val) FROM timestamp_test WHERE timestamp_val < to_timestamp('2000-01-01 00:00:00', 'YYYY-MM-DD HH:MI:SS'); date_add --------------- 2008-05-15 10:23:31 The following example adds 5 seconds to a literal timestamp value written without a seconds indicator. SELECT dateadd(second, 5, timestamp '2001-06-06'); date_add --------------- 2001-06-06 00:00:05 Usage notes The DATEADD(month, ...) and ADD_MONTHS functions handle dates that fall at the ends of months diﬀerently: Date and time functions 550 AWS Clean Rooms SQL Reference • ADD_MONTHS: If the date you are adding to is the last day of the month, the result is always the last day of the result month, regardless of the length of the month. For example, April 30 + 1 month is May 31. select add_months('2008-04-30',1); add_months --------------------- 2008-05-31 00:00:00 (1 row) • DATEADD: If there are fewer days in the date you are adding to than in the result month, the result is the corresponding day of the result month, not the last day of that month. For example, April 30 + 1 month is May 30. select dateadd(month,1,'2008-04-30'); date_add ---------------------
sql-reference-150	sql-reference.pdf	150	If the date you are adding to is the last day of the month, the result is always the last day of the result month, regardless of the length of the month. For example, April 30 + 1 month is May 31. select add_months('2008-04-30',1); add_months --------------------- 2008-05-31 00:00:00 (1 row) • DATEADD: If there are fewer days in the date you are adding to than in the result month, the result is the corresponding day of the result month, not the last day of that month. For example, April 30 + 1 month is May 30. select dateadd(month,1,'2008-04-30'); date_add --------------------- 2008-05-30 00:00:00 (1 row) The DATEADD function handles the leap year date 02-29 diﬀerently when using dateadd(month, 12,…) or dateadd(year, 1, …). select dateadd(month,12,'2016-02-29'); date_add --------------------- 2017-02-28 00:00:00 select dateadd(year, 1, '2016-02-29'); date_add --------------------- 2017-03-01 00:00:00 DATEDIFF function DATEDIFF returns the diﬀerence between the date parts of two date or time expressions. Date and time functions 551 AWS Clean Rooms Syntax SQL Reference DATEDIFF ( datepart, {date\|time\|timetz\|timestamp}, {date\|time\|timetz\|timestamp} ) Arguments datepart The speciﬁc part of the date or time value (year, month, or day, hour, minute, second, millisecond, or microsecond) that the function operates on. For more information, see Date parts for date or timestamp functions. Speciﬁcally, DATEDIFF determines the number of date part boundaries that are crossed between two expressions. For example, suppose that you're calculating the diﬀerence in years between two dates, 12-31-2008 and 01-01-2009. In this case, the function returns 1 year despite the fact that these dates are only one day apart. If you are ﬁnding the diﬀerence in hours between two timestamps, 01-01-2009 8:30:00 and 01-01-2009 10:00:00, the result is 2 hours. If you are ﬁnding the diﬀerence in hours between two timestamps, 8:30:00 and 10:00:00, the result is 2 hours. date\|time\|timetz\|timestamp A DATE, TIME, TIMETZ, or TIMESTAMP column or expressions that implicitly convert to a DATE, TIME, TIMETZ, or TIMESTAMP. The expressions must both contain the speciﬁed date or time part. If the second date or time is later than the ﬁrst date or time, the result is positive. If the second date or time is earlier than the ﬁrst date or time, the result is negative. Return type BIGINT Examples with a DATE column The following example ﬁnds the diﬀerence, in number of weeks, between two literal date values. select datediff(week,'2009-01-01','2009-12-31') as numweeks; numweeks ---------- 52 Date and time functions 552 AWS Clean Rooms (1 row) SQL Reference The following example ﬁnds the diﬀerence, in hours, between two literal date values. When you don't provide the time value for a date, it defaults to 00:00:00. select datediff(hour, '2023-01-01', '2023-01-03 05:04:03'); date_diff ---------- 53 (1 row) The following example ﬁnds the diﬀerence, in days, between two literal TIMESTAMETZ values. Select datediff(days, 'Jun 1,2008 09:59:59 EST', 'Jul 4,2008 09:59:59 EST') date_diff ---------- 33 The following example ﬁnds the diﬀerence, in days, between two dates in the same row of a table. select * from date_table; start_date \| end_date -----------+----------- 2009-01-01 \| 2009-03-23 2023-01-04 \| 2024-05-04 (2 rows) select datediff(day, start_date, end_date) as duration from date_table; duration --------- 81 486 (2 rows) The following example ﬁnds the diﬀerence, in number of quarters, between a literal value in the past and today's date. This example assumes that the current date is June 5, 2008. You can Date and time functions 553 AWS Clean Rooms SQL Reference name date parts in full or abbreviate them. The default column name for the DATEDIFF function is DATE_DIFF. select datediff(qtr, '1998-07-01', current_date); date_diff ----------- 40 (1 row) The following example joins the SALES and LISTING tables to calculate how many days after they were listed any tickets were sold for listings 1000 through 1005. The longest wait for sales of these listings was 15 days, and the shortest was less than one day (0 days). select priceperticket, datediff(day, listtime, saletime) as wait from sales, listing where sales.listid = listing.listid and sales.listid between 1000 and 1005 order by wait desc, priceperticket desc; priceperticket \| wait ---------------+------ 96.00 \| 15 123.00 \| 11 131.00 \| 9 123.00 \| 6 129.00 \| 4 96.00 \| 4 96.00 \| 0 (7 rows) This example calculates the average number of hours sellers waited for all ticket sales. select avg(datediff(hours, listtime, saletime)) as avgwait from sales, listing where sales.listid = listing.listid; avgwait --------- 465 (1 row) Date and time functions 554 AWS Clean Rooms Examples with a TIME column SQL Reference The following example table TIME_TEST has a column TIME_VAL (type TIME) with three values inserted. select time_val from time_test; time_val --------------------- 20:00:00 00:00:00.5550 00:58:00 The following example ﬁnds the diﬀerence in number of hours between the TIME_VAL column and a time literal. select datediff(hour, time_val, time '15:24:45') from time_test; date_diff ----------- -5 15 15 The following example ﬁnds the diﬀerence in number of minutes between two
sql-reference-151	sql-reference.pdf	151	sales. select avg(datediff(hours, listtime, saletime)) as avgwait from sales, listing where sales.listid = listing.listid; avgwait --------- 465 (1 row) Date and time functions 554 AWS Clean Rooms Examples with a TIME column SQL Reference The following example table TIME_TEST has a column TIME_VAL (type TIME) with three values inserted. select time_val from time_test; time_val --------------------- 20:00:00 00:00:00.5550 00:58:00 The following example ﬁnds the diﬀerence in number of hours between the TIME_VAL column and a time literal. select datediff(hour, time_val, time '15:24:45') from time_test; date_diff ----------- -5 15 15 The following example ﬁnds the diﬀerence in number of minutes between two literal time values. select datediff(minute, time '20:00:00', time '21:00:00') as nummins; nummins ---------- 60 Examples with a TIMETZ column The following example table TIMETZ_TEST has a column TIMETZ_VAL (type TIMETZ) with three values inserted. select timetz_val from timetz_test; timetz_val Date and time functions 555 AWS Clean Rooms ------------------ 04:00:00+00 00:00:00.5550+00 05:58:00+00 SQL Reference The following example ﬁnds the diﬀerences in number of hours, between a TIMETZ literal and timetz_val. select datediff(hours, timetz '20:00:00 PST', timetz_val) as numhours from timetz_test; numhours ---------- 0 -4 1 The following example ﬁnds the diﬀerence in number of hours, between two literal TIMETZ values. select datediff(hours, timetz '20:00:00 PST', timetz '00:58:00 EST') as numhours; numhours ---------- 1 DATE_PART function DATE_PART extracts date part values from an expression. DATE_PART is a synonym of the PGDATE_PART function. Syntax DATE_PART(datepart, {date\|timestamp}) Arguments datepart An identiﬁer literal or string of the speciﬁc part of the date value (for example, year, month, or day) that the function operates on. For more information, see Date parts for date or timestamp functions. Date and time functions 556 AWS Clean Rooms {date\|timestamp} SQL Reference A date column, timestamp column, or an expression that implicitly converts to a date or timestamp. The column or expression in date or timestamp must contain the date part speciﬁed in datepart. Return type DOUBLE Examples The default column name for the DATE_PART function is pgdate_part. The following example ﬁnds the minute from a timestamp literal. SELECT DATE_PART(minute, timestamp '20230104 04:05:06.789'); pgdate_part ----------- 5 The following example ﬁnds the week number from a timestamp literal. The week number calculation follows the ISO 8601 standard. For more information, see ISO 8601 in Wikipedia. SELECT DATE_PART(week, timestamp '20220502 04:05:06.789'); pgdate_part ----------- 18 The following example ﬁnds the day of the month from a timestamp literal. SELECT DATE_PART(day, timestamp '20220502 04:05:06.789'); pgdate_part ----------- 2 The following example ﬁnds the day of the week from a timestamp literal. The week number calculation follows the ISO 8601 standard. For more information, see ISO 8601 in Wikipedia. Date and time functions 557 AWS Clean Rooms SQL Reference SELECT DATE_PART(dayofweek, timestamp '20220502 04:05:06.789'); pgdate_part ----------- 1 The following example ﬁnds the century from a timestamp literal. The century calculation follows the ISO 8601 standard. For more information, see ISO 8601 in Wikipedia. SELECT DATE_PART(century, timestamp '20220502 04:05:06.789'); pgdate_part ----------- 21 The following example ﬁnds the millennium from a timestamp literal. The millennium calculation follows the ISO 8601 standard. For more information, see ISO 8601 in Wikipedia. SELECT DATE_PART(millennium, timestamp '20220502 04:05:06.789'); pgdate_part ----------- 3 The following example ﬁnds the microseconds from a timestamp literal. The microseconds calculation follows the ISO 8601 standard. For more information, see ISO 8601 in Wikipedia. SELECT DATE_PART(microsecond, timestamp '20220502 04:05:06.789'); pgdate_part ----------- 789000 The following example ﬁnds the month from a date literal. SELECT DATE_PART(month, date '20220502'); pgdate_part ----------- Date and time functions 558 AWS Clean Rooms 5 SQL Reference The following example applies the DATE_PART function to a column in a table. SELECT date_part(w, listtime) AS weeks, listtime FROM listing WHERE listid=10 weeks \| listtime ------+--------------------- 25 \| 2008-06-17 09:44:54 (1 row) You can name date parts in full or abbreviate them; in this case, w stands for weeks. The day of week date part returns an integer from 0-6, starting with Sunday. Use DATE_PART with dow (DAYOFWEEK) to view events on a Saturday. SELECT date_part(dow, starttime) AS dow, starttime FROM event WHERE date_part(dow, starttime)=6 ORDER BY 2,1; dow \| starttime -----+--------------------- 6 \| 2008-01-05 14:00:00 6 \| 2008-01-05 14:00:00 6 \| 2008-01-05 14:00:00 6 \| 2008-01-05 14:00:00 ... (1147 rows) DATE_TRUNC function The DATE_TRUNC function truncates a timestamp expression or literal based on the date part that you specify, such as hour, day, or month. Syntax DATE_TRUNC('datepart', timestamp) Date and time functions 559 AWS Clean Rooms Arguments datepart SQL Reference The date part to which to truncate the timestamp value. The input timestamp is truncated to the precision of the input datepart. For example, month truncates to the ﬁrst day of the month. Valid formats are as follows: • microsecond, microseconds • millisecond, milliseconds • second, seconds • minute, minutes • hour, hours • day, days • week, weeks • month, months • quarter, quarters • year, years •
sql-reference-152	sql-reference.pdf	152	or literal based on the date part that you specify, such as hour, day, or month. Syntax DATE_TRUNC('datepart', timestamp) Date and time functions 559 AWS Clean Rooms Arguments datepart SQL Reference The date part to which to truncate the timestamp value. The input timestamp is truncated to the precision of the input datepart. For example, month truncates to the ﬁrst day of the month. Valid formats are as follows: • microsecond, microseconds • millisecond, milliseconds • second, seconds • minute, minutes • hour, hours • day, days • week, weeks • month, months • quarter, quarters • year, years • decade, decades • century, centuries • millennium, millennia For more information about abbreviations of some formats, see Date parts for date or timestamp functions timestamp A timestamp column or an expression that implicitly converts to a timestamp. Return type TIMESTAMP Examples Truncate the input timestamp to the second. SELECT DATE_TRUNC('second', TIMESTAMP '20200430 04:05:06.789'); Date and time functions 560 SQL Reference AWS Clean Rooms date_trunc 2020-04-30 04:05:06 Truncate the input timestamp to the minute. SELECT DATE_TRUNC('minute', TIMESTAMP '20200430 04:05:06.789'); date_trunc 2020-04-30 04:05:00 Truncate the input timestamp to the hour. SELECT DATE_TRUNC('hour', TIMESTAMP '20200430 04:05:06.789'); date_trunc 2020-04-30 04:00:00 Truncate the input timestamp to the day. SELECT DATE_TRUNC('day', TIMESTAMP '20200430 04:05:06.789'); date_trunc 2020-04-30 00:00:00 Truncate the input timestamp to the ﬁrst day of a month. SELECT DATE_TRUNC('month', TIMESTAMP '20200430 04:05:06.789'); date_trunc 2020-04-01 00:00:00 Truncate the input timestamp to the ﬁrst day of a quarter. SELECT DATE_TRUNC('quarter', TIMESTAMP '20200430 04:05:06.789'); date_trunc 2020-04-01 00:00:00 Truncate the input timestamp to the ﬁrst day of a year. SELECT DATE_TRUNC('year', TIMESTAMP '20200430 04:05:06.789'); date_trunc 2020-01-01 00:00:00 Truncate the input timestamp to the ﬁrst day of a century. Date and time functions 561 AWS Clean Rooms SQL Reference SELECT DATE_TRUNC('millennium', TIMESTAMP '20200430 04:05:06.789'); date_trunc 2001-01-01 00:00:00 Truncate the input timestamp to the Monday of a week. select date_trunc('week', TIMESTAMP '20220430 04:05:06.789'); date_trunc 2022-04-25 00:00:00 In the following example, the DATE_TRUNC function uses the 'week' date part to return the date for the Monday of each week. select date_trunc('week', saletime), sum(pricepaid) from sales where saletime like '2008-09%' group by date_trunc('week', saletime) order by 1; date_trunc \| sum ------------+------------- 2008-09-01 \| 2474899 2008-09-08 \| 2412354 2008-09-15 \| 2364707 2008-09-22 \| 2359351 2008-09-29 \| 705249 EXTRACT function The EXTRACT function returns a date or time part from a TIMESTAMP, TIMESTAMPTZ, TIME, or TIMETZ value. Examples include a day, month, year, hour, minute, second, millisecond, or microsecond from a timestamp. Syntax EXTRACT(datepart FROM source) Arguments datepart The subﬁeld of a date or time to extract, such as a day, month, year, hour, minute, second, millisecond, or microsecond. For possible values, see Date parts for date or timestamp functions. Date and time functions 562 AWS Clean Rooms source SQL Reference A column or expression that evaluates to a data type of TIMESTAMP, TIMESTAMPTZ, TIME, or TIMETZ. Return type INTEGER if the source value evaluates to data type TIMESTAMP, TIME, or TIMETZ. DOUBLE PRECISION if the source value evaluates to data type TIMESTAMPTZ. Examples with TIMESTAMP The following example determines the week numbers for sales in which the price paid was $10,000 or more. select salesid, extract(week from saletime) as weeknum from sales where pricepaid > 9999 order by 2; salesid \| weeknum --------+--------- 159073 \| 6 160318 \| 8 161723 \| 26 The following example returns the minute value from a literal timestamp value. select extract(minute from timestamp '2009-09-09 12:08:43'); date_part -- The following example returns the millisecond value from a literal timestamp value. select extract(ms from timestamp '2009-09-09 12:08:43.101'); date_part ----------- 101 Date and time functions 563 AWS Clean Rooms Examples with TIMESTAMPTZ SQL Reference The following example returns the year value from a literal timestamptz value. select extract(year from timestamptz '1.12.1997 07:37:16.00 PST'); date_part ----------- 1997 Examples with TIME The following example table TIME_TEST has a column TIME_VAL (type TIME) with three values inserted. select time_val from time_test; time_val --------------------- 20:00:00 00:00:00.5550 00:58:00 The following example extracts the minutes from each time_val. select extract(minute from time_val) as minutes from time_test; minutes ----------- 0 0 58 The following example extracts the hours from each time_val. select extract(hour from time_val) as hours from time_test; hours ----------- 20 0 Date and time functions 564 AWS Clean Rooms 0 SQL Reference The following example extracts milliseconds from a literal value. select extract(ms from time '18:25:33.123456'); date_part ----------- 123 Examples with TIMETZ The following example table TIMETZ_TEST has a column TIMETZ_VAL (type TIMETZ) with three values inserted. select timetz_val from timetz_test; timetz_val ------------------ 04:00:00+00 00:00:00.5550+00 05:58:00+00 The following example extracts the hours from each timetz_val. select extract(hour from timetz_val) as hours from time_test; hours ----------- 4 0 5 The following example extracts milliseconds from a literal value. Literals aren't converted to UTC before the extraction is processed. select extract(ms from timetz '18:25:33.123456 EST'); date_part ----------- 123 Date
sql-reference-153	sql-reference.pdf	153	Clean Rooms 0 SQL Reference The following example extracts milliseconds from a literal value. select extract(ms from time '18:25:33.123456'); date_part ----------- 123 Examples with TIMETZ The following example table TIMETZ_TEST has a column TIMETZ_VAL (type TIMETZ) with three values inserted. select timetz_val from timetz_test; timetz_val ------------------ 04:00:00+00 00:00:00.5550+00 05:58:00+00 The following example extracts the hours from each timetz_val. select extract(hour from timetz_val) as hours from time_test; hours ----------- 4 0 5 The following example extracts milliseconds from a literal value. Literals aren't converted to UTC before the extraction is processed. select extract(ms from timetz '18:25:33.123456 EST'); date_part ----------- 123 Date and time functions 565 AWS Clean Rooms SQL Reference The following example returns the timezone oﬀset hour from UTC from a literal timetz value. select extract(timezone_hour from timetz '1.12.1997 07:37:16.00 PDT'); date_part ----------- -7 GETDATE function The GETDATE function returns the current date and time in the current session time zone (UTC by default). It returns the start date or time of the current statement, even when it is within a transaction block. Syntax GETDATE() The parentheses are required. Return type TIMESTAMP Example The following example uses the GETDATE function to return the full timestamp for the current date. select getdate(); SYSDATE function SYSDATE returns the current date and time in the current session time zone (UTC by default). Note SYSDATE returns the start date and time for the current transaction, not for the start of the current statement. Date and time functions 566 SQL Reference AWS Clean Rooms Syntax SYSDATE This function requires no arguments. Return type TIMESTAMP Examples The following example uses the SYSDATE function to return the full timestamp for the current date. select sysdate; timestamp ---------------------------- 2008-12-04 16:10:43.976353 (1 row) The following example uses the SYSDATE function inside the TRUNC function to return the current date without the time. select trunc(sysdate); trunc ------------ 2008-12-04 (1 row) The following query returns sales information for dates that fall between the date when the query is issued and whatever date is 120 days earlier. select salesid, pricepaid, trunc(saletime) as saletime, trunc(sysdate) as now from sales where saletime between trunc(sysdate)-120 and trunc(sysdate) order by saletime asc; Date and time functions 567 AWS Clean Rooms SQL Reference salesid \| pricepaid \| saletime \| now ---------+-----------+------------+------------ 91535 \| 670.00 \| 2008-08-07 \| 2008-12-05 91635 \| 365.00 \| 2008-08-07 \| 2008-12-05 91901 \| 1002.00 \| 2008-08-07 \| 2008-12-05 ... TIMEOFDAY function TIMEOFDAY is a special alias used to return the weekday, date, and time as a string value. It returns the time of day string for the current statement, even when it is within a transaction block. Syntax TIMEOFDAY() Return type VARCHAR Examples The following example returns the current date and time by using the TIMEOFDAY function. select timeofday(); timeofday ------------ Thu Sep 19 22:53:50.333525 2013 UTC (1 row) TO_TIMESTAMP function TO_TIMESTAMP converts a TIMESTAMP string to TIMESTAMPTZ. Syntax to_timestamp (timestamp, format) to_timestamp (timestamp, format, is_strict) Date and time functions 568 AWS Clean Rooms Arguments timestamp SQL Reference A string that represents a timestamp value in the format speciﬁed by format. If this argument is left as empty, the timestamp value defaults to 0001-01-01 00:00:00. format A string literal that deﬁnes the format of the timestamp value. Formats that include a time zone (TZ, tz, or OF) are not supported as input. For valid timestamp formats, see Datetime format strings. is_strict An optional Boolean value that speciﬁes whether an error is returned if an input timestamp value is out of range. When is_strict is set to TRUE, an error is returned if there is an out of range value. When is_strict is set to FALSE, which is the default, then overﬂow values are accepted. Return type TIMESTAMPTZ Examples The following example demonstrates using the TO_TIMESTAMP function to convert a TIMESTAMP string to a TIMESTAMPTZ. select sysdate, to_timestamp(sysdate, 'YYYY-MM-DD HH24:MI:SS') as second; timestamp \| second -------------------------- ---------------------- 2021-04-05 19:27:53.281812 \| 2021-04-05 19:27:53+00 It's possible to pass TO_TIMESTAMP part of a date. The remaining date parts are set to default values. The time is included in the output: SELECT TO_TIMESTAMP('2017','YYYY'); to_timestamp -------------------------- Date and time functions 569 AWS Clean Rooms 2017-01-01 00:00:00+00 SQL Reference The following SQL statement converts the string '2011-12-18 24:38:15' to a TIMESTAMPTZ. The result is a TIMESTAMPTZ that falls on the next day because the number of hours is more than 24 hours: SELECT TO_TIMESTAMP('2011-12-18 24:38:15', 'YYYY-MM-DD HH24:MI:SS'); to_timestamp ---------------------- 2011-12-19 00:38:15+00 The following SQL statement converts the string '2011-12-18 24:38:15' to a TIMESTAMPTZ. The result is an error because the time value in the timestamp is more than 24 hours: SELECT TO_TIMESTAMP('2011-12-18 24:38:15', 'YYYY-MM-DD HH24:MI:SS', TRUE); ERROR: date/time field time value out of range: 24:38:15.0 Date and time functions in transactions When you run the following functions within a transaction block (BEGIN … END), the function returns the start date
sql-reference-154	sql-reference.pdf	154	TIMESTAMPTZ. The result is a TIMESTAMPTZ that falls on the next day because the number of hours is more than 24 hours: SELECT TO_TIMESTAMP('2011-12-18 24:38:15', 'YYYY-MM-DD HH24:MI:SS'); to_timestamp ---------------------- 2011-12-19 00:38:15+00 The following SQL statement converts the string '2011-12-18 24:38:15' to a TIMESTAMPTZ. The result is an error because the time value in the timestamp is more than 24 hours: SELECT TO_TIMESTAMP('2011-12-18 24:38:15', 'YYYY-MM-DD HH24:MI:SS', TRUE); ERROR: date/time field time value out of range: 24:38:15.0 Date and time functions in transactions When you run the following functions within a transaction block (BEGIN … END), the function returns the start date or time of the current transaction, not the start of the current statement. • SYSDATE • TIMESTAMP • CURRENT_DATE The following functions always return the start date or time of the current statement, even when they are within a transaction block. • GETDATE • TIMEOFDAY Date parts for date or timestamp functions The following table identiﬁes the date part and time part names and abbreviations that are accepted as arguments to the following functions: Date and time functions 570 AWS Clean Rooms • DATEADD • DATEDIFF • DATE_PART • EXTRACT SQL Reference Date part or time part Abbreviations millennium, millennia mil, mils century, centuries c, cent, cents decade, decades dec, decs epoch year, years quarter, quarters epoch (supported by the EXTRACT) y, yr, yrs qtr, qtrs month, months mon, mons week, weeks w day of week dayofweek, dow, dw, weekday (supported by the DATE_PART and the EXTRACT function) Returns an integer from 0–6, starting with Sunday. Note The DOW date part behaves diﬀerently from the day of week (D) date part used for datetime format strings. D is based on integers 1–7, where Sunday is 1. For more information, see Datetime format strings. day of year day, days dayofyear, doy, dy, yearday (supported by the EXTRACT) d Date and time functions 571 AWS Clean Rooms SQL Reference Date part or time part Abbreviations hour, hours h, hr, hrs minute, minutes m, min, mins second, seconds s, sec, secs millisecond, milliseconds ms, msec, msecs, msecond, mseconds, millisec, millisecs, millisecon microsecond, microseconds microsec, microsecs, microsecond, usecond, useconds, us, usec, usecs timezone, timezone_hour, timezone_minute Supported by the EXTRACT for timestamp with time zone (TIMESTAMPTZ) only. Variations in results with seconds, milliseconds, and microseconds Minor diﬀerences in query results occur when diﬀerent date functions specify seconds, milliseconds, or microseconds as date parts: • The EXTRACT function return integers for the speciﬁed date part only, ignoring higher- and lower-level date parts. If the speciﬁed date part is seconds, milliseconds and microseconds are not included in the result. If the speciﬁed date part is milliseconds, seconds and microseconds are not included. If the speciﬁed date part is microseconds, seconds and milliseconds are not included. • The DATE_PART function returns the complete seconds portion of the timestamp, regardless of the speciﬁed date part, returning either a decimal value or an integer as required. CENTURY, EPOCH, DECADE, and MIL notes CENTURY or CENTURIES AWS Clean Rooms interprets a CENTURY to start with year ###1 and end with year ###0: select extract (century from timestamp '2000-12-16 12:21:13'); date_part ----------- Date and time functions 572 AWS Clean Rooms 20 (1 row) SQL Reference select extract (century from timestamp '2001-12-16 12:21:13'); date_part ----------- 21 (1 row) EPOCH The AWS Clean Rooms implementation of EPOCH is relative to 1970-01-01 00:00:00.000000 independent of the time zone where the cluster resides. You might need to oﬀset the results by the diﬀerence in hours depending on the time zone where the cluster is located. DECADE or DECADES AWS Clean Rooms interprets the DECADE or DECADES DATEPART based on the common calendar. For example, because the common calendar starts from the year 1, the ﬁrst decade (decade 1) is 0001-01-01 through 0009-12-31, and the second decade (decade 2) is 0010-01-01 through 0019-12-31. For example, decade 201 spans from 2000-01-01 to 2009-12-31: select extract(decade from timestamp '1999-02-16 20:38:40'); date_part ----------- 200 (1 row) select extract(decade from timestamp '2000-02-16 20:38:40'); date_part ----------- 201 (1 row) select extract(decade from timestamp '2010-02-16 20:38:40'); date_part ----------- 202 (1 row) Date and time functions 573 AWS Clean Rooms MIL or MILS SQL Reference AWS Clean Rooms interprets a MIL to start with the ﬁrst day of year #001 and end with the last day of year #000: select extract (mil from timestamp '2000-12-16 12:21:13'); date_part ----------- 2 (1 row) select extract (mil from timestamp '2001-12-16 12:21:13'); date_part ----------- 3 (1 row) Hash functions A hash function is a mathematical function that converts a numerical input value into another value. AWS Clean Rooms SQL supports the following hash functions: Topics • MD5 function • SHA function • SHA1 function • SHA2 function • MURMUR3_32_HASH function MD5 function Uses the MD5 cryptographic hash function to convert a variable-length string into a 32-character string that
sql-reference-155	sql-reference.pdf	155	the ﬁrst day of year #001 and end with the last day of year #000: select extract (mil from timestamp '2000-12-16 12:21:13'); date_part ----------- 2 (1 row) select extract (mil from timestamp '2001-12-16 12:21:13'); date_part ----------- 3 (1 row) Hash functions A hash function is a mathematical function that converts a numerical input value into another value. AWS Clean Rooms SQL supports the following hash functions: Topics • MD5 function • SHA function • SHA1 function • SHA2 function • MURMUR3_32_HASH function MD5 function Uses the MD5 cryptographic hash function to convert a variable-length string into a 32-character string that is a text representation of the hexadecimal value of a 128-bit checksum. Syntax MD5(string) Hash functions 574 AWS Clean Rooms Arguments string A variable-length string. Return type SQL Reference The MD5 function returns a 32-character string that is a text representation of the hexadecimal value of a 128-bit checksum. Examples The following example shows the 128-bit value for the string 'AWS Clean Rooms': select md5('AWS Clean Rooms'); md5 ---------------------------------- f7415e33f972c03abd4f3fed36748f7a (1 row) SHA function Synonym of SHA1 function. See SHA1 function. SHA1 function The SHA1 function uses the SHA1 cryptographic hash function to convert a variable-length string into a 40-character string that is a text representation of the hexadecimal value of a 160-bit checksum. Syntax SHA1 is a synonym of SHA function. SHA1(string) Hash functions 575 AWS Clean Rooms Arguments string A variable-length string. Return type SQL Reference The SHA1 function returns a 40-character string that is a text representation of the hexadecimal value of a 160-bit checksum. Example The following example returns the 160-bit value for the word 'AWS Clean Rooms': select sha1('AWS Clean Rooms'); SHA2 function The SHA2 function uses the SHA2 cryptographic hash function to convert a variable-length string into a character string. The character string is a text representation of the hexadecimal value of the checksum with the speciﬁed number of bits. Syntax SHA2(string, bits) Arguments string A variable-length string. integer The number of bits in the hash functions. Valid values are 0 (same as 256), 224, 256, 384, and 512. Return type The SHA2 function returns a character string that is a text representation of the hexadecimal value of the checksum or an empty string if the number of bits is invalid. Hash functions 576 AWS Clean Rooms Example SQL Reference The following example returns the 256-bit value for the word 'AWS Clean Rooms': select sha2('AWS Clean Rooms', 256); MURMUR3_32_HASH function The MURMUR3_32_HASH function computes the 32-bit Murmur3A non-cryptographic hash for all common data types including numeric and string types. Syntax MURMUR3_32_HASH(value [, seed]) Arguments value The input value to hash. AWS Clean Rooms hashes the binary representation of the input value. This behavior is similar to FNV_HASH, but the value is converted to the binary representation speciﬁed by the Apache Iceberg 32-bit Murmur3 hash speciﬁcation. seed The INT seed of the hash function. This argument is optional. If not given, AWS Clean Rooms uses the default seed of 0. This enables combining the hash of multiple columns without any conversions or concatenations. Return type The function returns an INT. Example The following examples return the Murmur3 hash of a number, the string 'AWS Clean Rooms', and the concatenation of the two. select MURMUR3_32_HASH(1); MURMUR3_32_HASH ---------------------- Hash functions 577 AWS Clean Rooms -5968735742475085980 (1 row) select MURMUR3_32_HASH('AWS Clean Rooms'); MURMUR3_32_HASH ---------------------- 7783490368944507294 (1 row) SQL Reference select MURMUR3_32_HASH('AWS Clean Rooms', MURMUR3_32_HASH(1)); MURMUR3_32_HASH ---------------------- -2202602717770968555 (1 row) Usage notes To compute the hash of a table with multiple columns, you can compute the Murmur3 hash of the ﬁrst column and pass it as a seed to the hash of the second column. Then, it passes the Murmur3 hash of the second column as a seed to the hash of the third column. The following example creates seeds to hash a table with multiple columns. select MURMUR3_32_HASH(column_3, MURMUR3_32_HASH(column_2, MURMUR3_32_HASH(column_1))) from sample_table; The same property can be used to compute the hash of a concatenation of strings. select MURMUR3_32_HASH('abcd'); MURMUR3_32_HASH --------------------- -281581062704388899 (1 row) select MURMUR3_32_HASH('cd', MURMUR3_32_HASH('ab')); MURMUR3_32_HASH --------------------- Hash functions 578 AWS Clean Rooms -281581062704388899 (1 row) SQL Reference The hash function uses the type of the input to determine the number of bytes to hash. Use casting to enforce a speciﬁc type, if necessary. The following examples use diﬀerent input types to produce diﬀerent results. select MURMUR3_32_HASH(1::smallint); MURMUR3_32_HASH -------------------- 589727492704079044 (1 row) select MURMUR3_32_HASH(1); MURMUR3_32_HASH ---------------------- -5968735742475085980 (1 row) select MURMUR3_32_HASH(1::bigint); MURMUR3_32_HASH ---------------------- -8517097267634966620 (1 row) JSON functions When you need to store a relatively small set of key-value pairs, you might save space by storing the data in JSON format. Because JSON strings can be stored in a single column, using JSON might be more eﬃcient than storing your data in tabular format. Example For example, suppose you have a sparse table, where you need
sql-reference-156	sql-reference.pdf	156	casting to enforce a speciﬁc type, if necessary. The following examples use diﬀerent input types to produce diﬀerent results. select MURMUR3_32_HASH(1::smallint); MURMUR3_32_HASH -------------------- 589727492704079044 (1 row) select MURMUR3_32_HASH(1); MURMUR3_32_HASH ---------------------- -5968735742475085980 (1 row) select MURMUR3_32_HASH(1::bigint); MURMUR3_32_HASH ---------------------- -8517097267634966620 (1 row) JSON functions When you need to store a relatively small set of key-value pairs, you might save space by storing the data in JSON format. Because JSON strings can be stored in a single column, using JSON might be more eﬃcient than storing your data in tabular format. Example For example, suppose you have a sparse table, where you need to have many columns to fully represent all possible attributes. However, most of the column values are NULL for any given row or any given column. By using JSON for storage, you might be able to store the data for a row in key-value pairs in a single JSON string and eliminate the sparsely-populated table columns. JSON functions 579 AWS Clean Rooms SQL Reference In addition, you can easily modify JSON strings to store additional key:value pairs without needing to add columns to a table. We recommend using JSON sparingly. JSON isn't a good choice for storing larger datasets because, by storing disparate data in a single column, JSON doesn't use the AWS Clean Rooms column store architecture. JSON uses UTF-8 encoded text strings, so JSON strings can be stored as CHAR or VARCHAR data types. Use VARCHAR if the strings include multi-byte characters. JSON strings must be properly formatted JSON, according to the following rules: • The root level JSON can either be a JSON object or a JSON array. A JSON object is an unordered set of comma-separated key:value pairs enclosed by curly braces. For example, {"one":1, "two":2} • A JSON array is an ordered set of comma-separated values enclosed by brackets. An example is the following: ["first", {"one":1}, "second", 3, null] • JSON arrays use a zero-based index; the ﬁrst element in an array is at position 0. In a JSON key:value pair, the key is a string in double quotation marks. • A JSON value can be any of the following: • JSON object • JSON array • String in double quotation marks • Number (integer and ﬂoat) • Boolean • Null • Empty objects and empty arrays are valid JSON values. • JSON ﬁelds are case-sensitive. • White space between JSON structural elements (such as { }, [ ]) is ignored. The AWS Clean Rooms JSON functions and the AWS Clean Rooms COPY command use the same methods to work with JSON-formatted data. Topics JSON functions 580 SQL Reference AWS Clean Rooms • CAN_JSON_PARSE function • JSON_EXTRACT_ARRAY_ELEMENT_TEXT function • JSON_EXTRACT_PATH_TEXT function • JSON_PARSE function • JSON_SERIALIZE function • JSON_SERIALIZE_TO_VARBYTE function CAN_JSON_PARSE function The CAN_JSON_PARSE function parses data in JSON format and returns true if the result can be converted to a SUPER value using the JSON_PARSE function. Syntax CAN_JSON_PARSE(json_string) Arguments json_string An expression that returns serialized JSON in the VARBYTE or VARCHAR form. Return type BOOLEAN Example To see if the JSON array [10001,10002,"abc"] can be converted into the SUPER data type, use the following example. SELECT CAN_JSON_PARSE('[10001,10002,"abc"]'); +----------------+ \| can_json_parse \| +----------------+ \| true \| +----------------+ JSON functions 581 AWS Clean Rooms SQL Reference JSON_EXTRACT_ARRAY_ELEMENT_TEXT function The JSON_EXTRACT_ARRAY_ELEMENT_TEXT function returns a JSON array element in the outermost array of a JSON string, using a zero-based index. The ﬁrst element in an array is at position 0. If the index is negative or out of bound, JSON_EXTRACT_ARRAY_ELEMENT_TEXT returns empty string. If the null_if_invalid argument is set to true and the JSON string is invalid, the function returns NULL instead of returning an error. For more information, see JSON functions. Syntax json_extract_array_element_text('json string', pos [, null_if_invalid ] ) Arguments json_string A properly formatted JSON string. pos An integer representing the index of the array element to be returned, using a zero-based array index. null_if_invalid A Boolean value that speciﬁes whether to return NULL if the input JSON string is invalid instead of returning an error. To return NULL if the JSON is invalid, specify true (t). To return an error if the JSON is invalid, specify false (f). The default is false. Return type A VARCHAR string representing the JSON array element referenced by pos. Example The following example returns array element at position 2, which is the third element of a zero- based array index: select json_extract_array_element_text('[111,112,113]', 2); JSON functions 582 AWS Clean Rooms SQL Reference json_extract_array_element_text ------------------------------- 113 The following example returns an error because the JSON is invalid. select json_extract_array_element_text('["a",["b",1,["c",2,3,null,]]]',1); An error occurred when executing the SQL command: select json_extract_array_element_text('["a",["b",1,["c",2,3,null,]]]',1) The following example sets null_if_invalid to true, so the statement returns NULL instead of returning an error for invalid JSON. select json_extract_array_element_text('["a",["b",1,["c",2,3,null,]]]',1,true); json_extract_array_element_text ------------------------------- JSON_EXTRACT_PATH_TEXT function The JSON_EXTRACT_PATH_TEXT function returns the value for
sql-reference-157	sql-reference.pdf	157	string representing the JSON array element referenced by pos. Example The following example returns array element at position 2, which is the third element of a zero- based array index: select json_extract_array_element_text('[111,112,113]', 2); JSON functions 582 AWS Clean Rooms SQL Reference json_extract_array_element_text ------------------------------- 113 The following example returns an error because the JSON is invalid. select json_extract_array_element_text('["a",["b",1,["c",2,3,null,]]]',1); An error occurred when executing the SQL command: select json_extract_array_element_text('["a",["b",1,["c",2,3,null,]]]',1) The following example sets null_if_invalid to true, so the statement returns NULL instead of returning an error for invalid JSON. select json_extract_array_element_text('["a",["b",1,["c",2,3,null,]]]',1,true); json_extract_array_element_text ------------------------------- JSON_EXTRACT_PATH_TEXT function The JSON_EXTRACT_PATH_TEXT function returns the value for the key:value pair referenced by a series of path elements in a JSON string. The JSON path can be nested up to ﬁve levels deep. Path elements are case-sensitive. If a path element does not exist in the JSON string, JSON_EXTRACT_PATH_TEXT returns an empty string. If the null_if_invalid argument is set to true and the JSON string is invalid, the function returns NULL instead of returning an error. For information about additional JSON functions, see JSON functions. Syntax json_extract_path_text('json_string', 'path_elem' [,'path_elem'[, …] ] [, null_if_invalid ] ) Arguments json_string A properly formatted JSON string. JSON functions 583 AWS Clean Rooms path_elem SQL Reference A path element in a JSON string. One path element is required. Additional path elements can be speciﬁed, up to ﬁve levels deep. null_if_invalid A Boolean value that speciﬁes whether to return NULL if the input JSON string is invalid instead of returning an error. To return NULL if the JSON is invalid, specify true (t). To return an error if the JSON is invalid, specify false (f). The default is false. In a JSON string, AWS Clean Rooms recognizes \n as a newline character and \t as a tab character. To load a backslash, escape it with a backslash (\\). Return type VARCHAR string representing the JSON value referenced by the path elements. Example The following example returns the value for the path 'f4', 'f6'. select json_extract_path_text('{"f2":{"f3":1},"f4":{"f5":99,"f6":"star"}}','f4', 'f6'); json_extract_path_text ---------------------- star The following example returns an error because the JSON is invalid. select json_extract_path_text('{"f2":{"f3":1},"f4":{"f5":99,"f6":"star"}','f4', 'f6'); An error occurred when executing the SQL command: select json_extract_path_text('{"f2":{"f3":1},"f4":{"f5":99,"f6":"star"}','f4', 'f6') The following example sets null_if_invalid to true, so the statement returns NULL for invalid JSON instead of returning an error. select json_extract_path_text('{"f2":{"f3":1},"f4":{"f5":99,"f6":"star"}','f4', 'f6',true); JSON functions 584 AWS Clean Rooms SQL Reference json_extract_path_text ------------------------------- NULL The following example returns the value for the path 'farm', 'barn', 'color', where the value retrieved is at the third level. This sample is formatted with a JSON lint tool, to make it easier to read. select json_extract_path_text('{ "farm": { "barn": { "color": "red", "feed stocked": true } } }', 'farm', 'barn', 'color'); json_extract_path_text ---------------------- red The following example returns NULL because the 'color' element is missing. This sample is formatted with a JSON lint tool. select json_extract_path_text('{ "farm": { "barn": {} } }', 'farm', 'barn', 'color'); json_extract_path_text ---------------------- NULL If the JSON is valid, trying to extract an element that's missing returns NULL. The following example returns the value for the path 'house', 'appliances', 'washing machine', 'brand'. select json_extract_path_text('{ JSON functions 585 SQL Reference AWS Clean Rooms "house": { "address": { "street": "123 Any St.", "city": "Any Town", "state": "FL", "zip": "32830" }, "bathroom": { "color": "green", "shower": true }, "appliances": { "washing machine": { "brand": "Any Brand", "color": "beige" }, "dryer": { "brand": "Any Brand", "color": "white" } } } }', 'house', 'appliances', 'washing machine', 'brand'); json_extract_path_text ---------------------- Any Brand JSON_PARSE function The JSON_PARSE function parses data in JSON format and converts it into the SUPER representation. To ingest into SUPER data type using the INSERT or UPDATE command, use the JSON_PARSE function. When you use JSON_PARSE() to parse JSON strings into SUPER values, certain restrictions apply. Syntax JSON_PARSE(json_string) JSON functions 586 SQL Reference AWS Clean Rooms Arguments json_string An expression that returns serialized JSON as a varbyte or varchar type. Return type SUPER Example The following example is an example of the JSON_PARSE function. SELECT JSON_PARSE('[10001,10002,"abc"]'); json_parse -------------------------- [10001,10002,"abc"] (1 row) SELECT JSON_TYPEOF(JSON_PARSE('[10001,10002,"abc"]')); json_typeof ---------------- array (1 row) JSON_SERIALIZE function The JSON_SERIALIZE function serializes a SUPER expression into textual JSON representation to follow RFC 8259. For more information on that RFC, see The JavaScript Object Notation (JSON) Data Interchange Format. The SUPER size limit is approximately the same as the block limit, and the varchar limit is smaller than the SUPER size limit. Therefore, the JSON_SERIALIZE function returns an error when the JSON format exceeds the varchar limit of the system. Syntax JSON_SERIALIZE(super_expression) JSON functions 587 SQL Reference AWS Clean Rooms Arguments super_expression A super expression or column. Return type varchar Example The following example serializes a SUPER value to a string. SELECT JSON_SERIALIZE(JSON_PARSE('[10001,10002,"abc"]')); json_serialize --------------------- [10001,10002,"abc"] (1 row) JSON_SERIALIZE_TO_VARBYTE function The JSON_SERIALIZE_TO_VARBYTE function converts a SUPER value to a JSON string similar
sql-reference-158	sql-reference.pdf	158	JavaScript Object Notation (JSON) Data Interchange Format. The SUPER size limit is approximately the same as the block limit, and the varchar limit is smaller than the SUPER size limit. Therefore, the JSON_SERIALIZE function returns an error when the JSON format exceeds the varchar limit of the system. Syntax JSON_SERIALIZE(super_expression) JSON functions 587 SQL Reference AWS Clean Rooms Arguments super_expression A super expression or column. Return type varchar Example The following example serializes a SUPER value to a string. SELECT JSON_SERIALIZE(JSON_PARSE('[10001,10002,"abc"]')); json_serialize --------------------- [10001,10002,"abc"] (1 row) JSON_SERIALIZE_TO_VARBYTE function The JSON_SERIALIZE_TO_VARBYTE function converts a SUPER value to a JSON string similar to JSON_SERIALIZE(), but stored in a VARBYTE value instead. Syntax JSON_SERIALIZE_TO_VARBYTE(super_expression) Arguments super_expression A super expression or column. Return type varbyte Example The following example serializes a SUPER value and returns the result in VARBYTE format. JSON functions 588 AWS Clean Rooms SQL Reference SELECT JSON_SERIALIZE_TO_VARBYTE(JSON_PARSE('[10001,10002,"abc"]')); json_serialize_to_varbyte ---------------------------------------- 5b31303030312c31303030322c22616263225d The following example serializes a SUPER value and casts the result to VARCHAR format. SELECT JSON_SERIALIZE_TO_VARBYTE(JSON_PARSE('[10001,10002,"abc"]'))::VARCHAR; json_serialize_to_varbyte --------------------------- [10001,10002,"abc"] Math functions This section describes the mathematical operators and functions supported in AWS Clean Rooms. Topics • Mathematical operator symbols • ABS function • ACOS function • ASIN function • ATAN function • ATAN2 function • CBRT function • CEILING (or CEIL) function • COS function • COT function • DEGREES function • DEXP function • DLOG1 function • DLOG10 function • EXP function Math functions 589 SQL Reference AWS Clean Rooms • FLOOR function • LN function • LOG function • MOD function • PI function • POWER function • RADIANS function • RANDOM function • ROUND function • SIGN function • SIN function • SQRT function • TRUNC function Mathematical operator symbols The following table lists the supported mathematical operators. Supported operators Operator Description Example Result + - * / % ^ addition 2 + 3 subtraction 2 - 3 multiplic ation 2 * 3 division 4 / 2 modulo 5 % 4 exponenti ation 2.0 ^ 3.0 5 -1 6 2 1 8 Math functions 590 AWS Clean Rooms SQL Reference Operator Description Example Result \|/ \|\|/ @ square root \| / 25.0 cube root \|\| / 27.0 absolute value @ -5.0 5 3 5 Examples Calculate the commission paid plus a $2.00 handling fee for a given transaction: select commission, (commission + 2.00) as comm from sales where salesid=10000; commission \| comm -----------+------- 28.05 \| 30.05 (1 row) Calculate 20 percent of the sales price for a given transaction: select pricepaid, (pricepaid * .20) as twentypct from sales where salesid=10000; pricepaid \| twentypct ----------+----------- 187.00 \| 37.400 (1 row) Forecast ticket sales based on a continuous growth pattern. In this example, the subquery returns the number of tickets sold in 2008. That result is multiplied exponentially by a continuous growth rate of 5 percent over 10 years. select (select sum(qtysold) from sales, date where sales.dateid=date.dateid and year=2008) ^ ((5::float/100)*10) as qty10years; qty10years Math functions 591 AWS Clean Rooms ------------------ 587.664019657491 (1 row) SQL Reference Find the total price paid and commission for sales with a date ID that is greater than or equal to 2,000. Then subtract the total commission from the total price paid. select sum (pricepaid) as sum_price, dateid, sum (commission) as sum_comm, (sum (pricepaid) - sum (commission)) as value from sales where dateid >= 2000 group by dateid order by dateid limit 10; sum_price \| dateid \| sum_comm \| value -----------+--------+----------+----------- 364445.00 \| 2044 \| 54666.75 \| 309778.25 349344.00 \| 2112 \| 52401.60 \| 296942.40 343756.00 \| 2124 \| 51563.40 \| 292192.60 378595.00 \| 2116 \| 56789.25 \| 321805.75 328725.00 \| 2080 \| 49308.75 \| 279416.25 349554.00 \| 2028 \| 52433.10 \| 297120.90 249207.00 \| 2164 \| 37381.05 \| 211825.95 285202.00 \| 2064 \| 42780.30 \| 242421.70 320945.00 \| 2012 \| 48141.75 \| 272803.25 321096.00 \| 2016 \| 48164.40 \| 272931.60 (10 rows) ABS function ABS calculates the absolute value of a number, where that number can be a literal or an expression that evaluates to a number. Syntax ABS (number) Arguments number Number or expression that evaluates to a number. It can be the SMALLINT, INTEGER, BIGINT, DECIMAL, FLOAT4, or FLOAT8 type. Math functions 592 SQL Reference AWS Clean Rooms Return type ABS returns the same data type as its argument. Examples Calculate the absolute value of -38: select abs (-38); abs ------- 38 (1 row) Calculate the absolute value of (14-76): select abs (14-76); abs ------- 62 (1 row) ACOS function ACOS is a trigonometric function that returns the arc cosine of a number. The return value is in radians and is between 0 and PI. Syntax ACOS(number) Arguments number The input parameter is a DOUBLE PRECISION number. Return type DOUBLE PRECISION Math functions 593 AWS Clean Rooms Examples To return the arc cosine of -1, use the following example. SQL Reference SELECT
sql-reference-159	sql-reference.pdf	159	the same data type as its argument. Examples Calculate the absolute value of -38: select abs (-38); abs ------- 38 (1 row) Calculate the absolute value of (14-76): select abs (14-76); abs ------- 62 (1 row) ACOS function ACOS is a trigonometric function that returns the arc cosine of a number. The return value is in radians and is between 0 and PI. Syntax ACOS(number) Arguments number The input parameter is a DOUBLE PRECISION number. Return type DOUBLE PRECISION Math functions 593 AWS Clean Rooms Examples To return the arc cosine of -1, use the following example. SQL Reference SELECT ACOS(-1); +-------------------+ \| acos \| +-------------------+ \| 3.141592653589793 \| +-------------------+ ASIN function ASIN is a trigonometric function that returns the arc sine of a number. The return value is in radians and is between PI/2 and -PI/2. Syntax ASIN(number) Arguments number The input parameter is a DOUBLE PRECISION number. Return type DOUBLE PRECISION Examples To return the arc sine of 1, use the following example. SELECT ASIN(1) AS halfpi; +--------------------+ \| halfpi \| +--------------------+ \| 1.5707963267948966 \| Math functions 594 AWS Clean Rooms +--------------------+ ATAN function SQL Reference ATAN is a trigonometric function that returns the arc tangent of a number. The return value is in radians and is between -PI and PI. Syntax ATAN(number) Arguments number The input parameter is a DOUBLE PRECISION number. Return type DOUBLE PRECISION Examples To return the arc tangent of 1 and multiply it by 4, use the following example. SELECT ATAN(1) * 4 AS pi; +-------------------+ \| pi \| +-------------------+ \| 3.141592653589793 \| +-------------------+ ATAN2 function ATAN2 is a trigonometric function that returns the arc tangent of one number divided by another number. The return value is in radians and is between PI/2 and -PI/2. Syntax ATAN2(number1, number2) Math functions 595 SQL Reference AWS Clean Rooms Arguments number1 A DOUBLE PRECISION number. number2 A DOUBLE PRECISION number. Return type DOUBLE PRECISION Examples To return the arc tangent of 2/2 and multiply it by 4, use the following example. SELECT ATAN2(2,2) * 4 AS PI; +-------------------+ \| pi \| +-------------------+ \| 3.141592653589793 \| +-------------------+ CBRT function The CBRT function is a mathematical function that calculates the cube root of a number. Syntax CBRT (number) Argument CBRT takes a DOUBLE PRECISION number as an argument. Return type CBRT returns a DOUBLE PRECISION number. Math functions 596 AWS Clean Rooms Examples SQL Reference Calculate the cube root of the commission paid for a given transaction: select cbrt(commission) from sales where salesid=10000; cbrt ------------------ 3.03839539048843 (1 row) CEILING (or CEIL) function The CEILING or CEIL function is used to round a number up to the next whole number. (The FLOOR function rounds a number down to the next whole number.) Syntax CEIL \| CEILING(number) Arguments number The number or expression that evaluates to a number. It can be the SMALLINT, INTEGER, BIGINT, DECIMAL, FLOAT4, or FLOAT8 type. Return type CEILING and CEIL return the same data type as its argument. Example Calculate the ceiling of the commission paid for a given sales transaction: select ceiling(commission) from sales where salesid=10000; ceiling --------- 29 (1 row) Math functions 597 AWS Clean Rooms COS function SQL Reference COS is a trigonometric function that returns the cosine of a number. The return value is in radians and is between -1 and 1, inclusive. Syntax COS(double_precision) Argument number The input parameter is a double precision number. Return type The COS function returns a double precision number. Examples The following example returns cosine of 0: select cos(0); cos ----- 1 (1 row) The following example returns the cosine of PI: select cos(pi()); cos ----- -1 (1 row) COT function COT is a trigonometric function that returns the cotangent of a number. The input parameter must be nonzero. Math functions 598 SQL Reference AWS Clean Rooms Syntax COT(number) Argument number The input parameter is a DOUBLE PRECISION number. Return type DOUBLE PRECISION Examples To return the cotangent of 1, use the following example. SELECT COT(1); +--------------------+ \| cot \| +--------------------+ \| 0.6420926159343306 \| +--------------------+ DEGREES function Converts an angle in radians to its equivalent in degrees. Syntax DEGREES(number) Argument number The input parameter is a DOUBLE PRECISION number. Math functions 599 SQL Reference AWS Clean Rooms Return type DOUBLE PRECISION Example To return the degree equivalent of .5 radians, use the following example. SELECT DEGREES(.5); +-------------------+ \| degrees \| +-------------------+ \| 28.64788975654116 \| +-------------------+ To convert PI radians to degrees, use the following example. SELECT DEGREES(pi()); +---------+ \| degrees \| +---------+ \| 180 \| +---------+ DEXP function The DEXP function returns the exponential value in scientiﬁc notation for a double precision number. The only diﬀerence between the DEXP and EXP functions is that the parameter for DEXP must be a DOUBLE PRECISION. Syntax DEXP(number) Argument number The input parameter is a DOUBLE PRECISION number.
sql-reference-160	sql-reference.pdf	160	Clean Rooms Return type DOUBLE PRECISION Example To return the degree equivalent of .5 radians, use the following example. SELECT DEGREES(.5); +-------------------+ \| degrees \| +-------------------+ \| 28.64788975654116 \| +-------------------+ To convert PI radians to degrees, use the following example. SELECT DEGREES(pi()); +---------+ \| degrees \| +---------+ \| 180 \| +---------+ DEXP function The DEXP function returns the exponential value in scientiﬁc notation for a double precision number. The only diﬀerence between the DEXP and EXP functions is that the parameter for DEXP must be a DOUBLE PRECISION. Syntax DEXP(number) Argument number The input parameter is a DOUBLE PRECISION number. Math functions 600 SQL Reference AWS Clean Rooms Return type DOUBLE PRECISION Example SELECT (SELECT SUM(qtysold) FROM sales, date WHERE sales.dateid=date.dateid AND year=2008) * DEXP((7::FLOAT/100)10) qty2010; +-------------------+ \| qty2010 \| +-------------------+ \| 695447.4837722216 \| +-------------------+ DLOG1 function The DLOG1 function returns the natural logarithm of the input parameter. The DLOG1 function is a synonym of the LN function. DLOG10 function The DLOG10 returns the base 10 logarithm of the input parameter. The DLOG10 function is a synonym of the LOG function. Syntax DLOG10(number) Argument number The input parameter is a double precision number. Return type The DLOG10 function returns a double precision number. Math functions 601 AWS Clean Rooms Example SQL Reference The following example returns the base 10 logarithm of the number 100: select dlog10(100); dlog10 -------- 2 (1 row) EXP function The EXP function implements the exponential function for a numeric expression, or the base of the natural logarithm, e, raised to the power of expression. The EXP function is the inverse of LN function. Syntax EXP (expression) Argument expression The expression must be an INTEGER, DECIMAL, or DOUBLE PRECISION data type. Return type EXP returns a DOUBLE PRECISION number. Example Use the EXP function to forecast ticket sales based on a continuous growth pattern. In this example, the subquery returns the number of tickets sold in 2008. That result is multiplied by the result of the EXP function, which speciﬁes a continuous growth rate of 7% over 10 years. select (select sum(qtysold) from sales, date where sales.dateid=date.dateid Math functions 602 AWS Clean Rooms SQL Reference and year=2008) exp((7::float/100)*10) qty2018; qty2018 ------------------ 695447.483772222 (1 row) FLOOR function The FLOOR function rounds a number down to the next whole number. Syntax FLOOR (number) Argument number The number or expression that evaluates to a number. It can be the SMALLINT, INTEGER, BIGINT, DECIMAL, FLOAT4, or FLOAT8 type. Return type FLOOR returns the same data type as its argument. Example The example shows the value of the commission paid for a given sales transaction before and after using the FLOOR function. select commission from sales where salesid=10000; floor ------- 28.05 (1 row) select floor(commission) from sales Math functions 603 AWS Clean Rooms where salesid=10000; floor ------- 28 (1 row) LN function SQL Reference The LN function returns the natural logarithm of the input parameter. The LN function is a synonym of the DLOG1 function. Syntax LN(expression) Argument expression The target column or expression that the function operates on. Note This function returns an error for some data types if the expression references an AWS Clean Rooms user-created table or an AWS Clean Rooms STL or STV system table. Expressions with the following data types produce an error if they reference a user-created or system table. • BOOLEAN • CHAR • DATE • DECIMAL or NUMERIC • TIMESTAMP • VARCHAR Math functions 604 AWS Clean Rooms SQL Reference Expressions with the following data types run successfully on user-created tables and STL or STV system tables: • BIGINT • DOUBLE PRECISION • INTEGER • REAL • SMALLINT Return type The LN function returns the same type as the expression. Example The following example returns the natural logarithm, or base e logarithm, of the number 2.718281828: select ln(2.718281828); ln -------------------- 0.9999999998311267 (1 row) Note that the answer is nearly equal to 1. This example returns the natural logarithm of the values in the USERID column in the USERS table: select username, ln(userid) from users order by userid limit 10; username \| ln ----------+------------------- JSG99FHE \| 0 PGL08LJI \| 0.693147180559945 IFT66TXU \| 1.09861228866811 XDZ38RDD \| 1.38629436111989 AEB55QTM \| 1.6094379124341 NDQ15VBM \| 1.79175946922805 OWY35QYB \| 1.94591014905531 Math functions 605 AWS Clean Rooms SQL Reference AZG78YIP \| 2.07944154167984 MSD36KVR \| 2.19722457733622 WKW41AIW \| 2.30258509299405 (10 rows) LOG function Returns the base 10 logarithm of a number. Synonym of DLOG10 function. Syntax LOG(number) Argument number The input parameter is a double precision number. Return type The LOG function returns a double precision number. Example The following example returns the base 10 logarithm of the number 100: select log(100); dlog10 -------- 2 (1 row) MOD function Returns the remainder of two numbers, otherwise known as a modulo operation. To calculate the result, the ﬁrst parameter is divided by
sql-reference-161	sql-reference.pdf	161	1.94591014905531 Math functions 605 AWS Clean Rooms SQL Reference AZG78YIP \| 2.07944154167984 MSD36KVR \| 2.19722457733622 WKW41AIW \| 2.30258509299405 (10 rows) LOG function Returns the base 10 logarithm of a number. Synonym of DLOG10 function. Syntax LOG(number) Argument number The input parameter is a double precision number. Return type The LOG function returns a double precision number. Example The following example returns the base 10 logarithm of the number 100: select log(100); dlog10 -------- 2 (1 row) MOD function Returns the remainder of two numbers, otherwise known as a modulo operation. To calculate the result, the ﬁrst parameter is divided by the second. Math functions 606 AWS Clean Rooms Syntax MOD(number1, number2) Arguments number1 SQL Reference The ﬁrst input parameter is an INTEGER, SMALLINT, BIGINT, or DECIMAL number. If either parameter is a DECIMAL type, the other parameter must also be a DECIMAL type. If either parameter is an INTEGER, the other parameter can be an INTEGER, SMALLINT, or BIGINT. Both parameters can also be SMALLINT or BIGINT, but one parameter cannot be a SMALLINT if the other is a BIGINT. number2 The second parameter is an INTEGER, SMALLINT, BIGINT, or DECIMAL number. The same data type rules apply to number2 as to number1. Return type Valid return types are DECIMAL, INT, SMALLINT, and BIGINT. The return type of the MOD function is the same numeric type as the input parameters, if both input parameters are the same type. If either input parameter is an INTEGER, however, the return type will also be an INTEGER. Usage notes You can use % as a modulo operator. Examples The following example return the remainder when a number is divided by another: SELECT MOD(10, 4); mod ------ 2 The following example returns a decimal result: Math functions 607 SQL Reference AWS Clean Rooms SELECT MOD(10.5, 4); mod ------ 2.5 You can cast parameter values: SELECT MOD(CAST(16.4 as integer), 5); mod ------ 1 Check if the ﬁrst parameter is even by dividing it by 2: SELECT mod(5,2) = 0 as is_even; is_even -------- false You can use the % as a modulo operator: SELECT 11 % 4 as remainder; remainder ----------- 3 The following example returns information for odd-numbered categories in the CATEGORY table: select catid, catname from category where mod(catid,2)=1 order by 1,2; catid \| catname -------+----------- 1 \| MLB 3 \| NFL Math functions 608 SQL Reference AWS Clean Rooms 5 \| MLS 7 \| Plays 9 \| Pop 11 \| Classical (6 rows) PI function The PI function returns the value of pi to 14 decimal places. Syntax PI() Return type DOUBLE PRECISION Examples To return the value of pi, use the following example. SELECT PI(); +-------------------+ \| pi \| +-------------------+ \| 3.141592653589793 \| +-------------------+ POWER function The POWER function is an exponential function that raises a numeric expression to the power of a second numeric expression. For example, 2 to the third power is calculated as POWER(2,3), with a result of 8. Syntax {POWER(expression1, expression2) Math functions 609 AWS Clean Rooms Arguments expression1 SQL Reference Numeric expression to be raised. Must be an INTEGER, DECIMAL, or FLOAT data type. expression2 Power to raise expression1. Must be an INTEGER, DECIMAL, or FLOAT data type. Return type DOUBLE PRECISION Example SELECT (SELECT SUM(qtysold) FROM sales, date WHERE sales.dateid=date.dateid AND year=2008) * POW((1+7::FLOAT/100),10) qty2010; +-------------------+ \| qty2010 \| +-------------------+ \| 679353.7540885945 \| +-------------------+ RADIANS function The RADIANS function converts an angle in degrees to its equivalent in radians. Syntax RADIANS(number) Argument number The input parameter is a DOUBLE PRECISION number. Return type DOUBLE PRECISION Math functions 610 AWS Clean Rooms Example SQL Reference To return the radian equivalent of 180 degrees, use the following example. SELECT RADIANS(180); +-------------------+ \| radians \| +-------------------+ \| 3.141592653589793 \| +-------------------+ RANDOM function The RANDOM function generates a random value between 0.0 (inclusive) and 1.0 (exclusive). Syntax RANDOM() Return type RANDOM returns a DOUBLE PRECISION number. Examples 1. Compute a random value between 0 and 99. If the random number is 0 to 1, this query produces a random number from 0 to 100: select cast (random() * 100 as int); INTEGER ------ 24 (1 row) 2. Retrieve a uniform random sample of 10 items: select * from sales order by random() Math functions 611 AWS Clean Rooms limit 10; SQL Reference Now retrieve a random sample of 10 items, but choose the items in proportion to their prices. For example, an item that is twice the price of another would be twice as likely to appear in the query results: select * from sales order by log(1 - random()) / pricepaid limit 10; 3. This example uses the SET command to set a SEED value so that RANDOM generates a predictable sequence of numbers. First, return three RANDOM integers without setting the SEED value ﬁrst: select cast (random()
sql-reference-162	sql-reference.pdf	162	order by random() Math functions 611 AWS Clean Rooms limit 10; SQL Reference Now retrieve a random sample of 10 items, but choose the items in proportion to their prices. For example, an item that is twice the price of another would be twice as likely to appear in the query results: select * from sales order by log(1 - random()) / pricepaid limit 10; 3. This example uses the SET command to set a SEED value so that RANDOM generates a predictable sequence of numbers. First, return three RANDOM integers without setting the SEED value ﬁrst: select cast (random() * 100 as int); INTEGER ------ 6 (1 row) select cast (random() * 100 as int); INTEGER ------ 68 (1 row) select cast (random() * 100 as int); INTEGER ------ 56 (1 row) Now, set the SEED value to .25, and return three more RANDOM numbers: set seed to .25; select cast (random() * 100 as int); INTEGER ------ 21 (1 row) Math functions 612 AWS Clean Rooms SQL Reference select cast (random() * 100 as int); INTEGER ------ 79 (1 row) select cast (random() * 100 as int); INTEGER ------ 12 (1 row) Finally, reset the SEED value to .25, and verify that RANDOM returns the same results as the previous three calls: set seed to .25; select cast (random() * 100 as int); INTEGER ------ 21 (1 row) select cast (random() * 100 as int); INTEGER ------ 79 (1 row) select cast (random() * 100 as int); INTEGER ------ 12 (1 row) ROUND function The ROUND function rounds numbers to the nearest integer or decimal. The ROUND function can optionally include a second argument as an integer to indicate the number of decimal places for rounding, in either direction. When you don't provide the second Math functions 613 AWS Clean Rooms SQL Reference argument, the function rounds to the nearest whole number. When the second argument >n is speciﬁed, the function rounds to the nearest number with n decimal places of precision. Syntax ROUND (number [ , integer ] ) Argument number A number or expression that evaluates to a number. It can be the DECIMAL or FLOAT8 type. AWS Clean Rooms can convert other data types per the implicit conversion rules. integer (optional) An integer that indicates the number of decimal places for rounding in either directions. Return type ROUND returns the same numeric data type as the input argument(s). Examples Round the commission paid for a given transaction to the nearest whole number. select commission, round(commission) from sales where salesid=10000; commission \| round -----------+------- 28.05 \| 28 (1 row) Round the commission paid for a given transaction to the ﬁrst decimal place. select commission, round(commission, 1) from sales where salesid=10000; commission \| round Math functions 614 AWS Clean Rooms -----------+------- 28.05 \| 28.1 (1 row) For the same query, extend the precision in the opposite direction. select commission, round(commission, -1) from sales where salesid=10000; SQL Reference commission \| round -----------+------- 28.05 \| 30 (1 row) SIGN function The SIGN function returns the sign (positive or negative) of a number. The result of the SIGN function is 1, -1, or 0 indicating the sign of the argument. Syntax SIGN (number) Argument number Number or expression that evaluates to a number. It can be the DECIMALor FLOAT8 type. AWS Clean Rooms can convert other data types per the implicit conversion rules. Return type SIGN returns the same numeric data type as the input argument(s). If the input is DECIMAL, the output is DECIMAL(1,0). Examples To determine the sign of the commission paid for a given transaction from the SALES table, use the following example. Math functions 615 AWS Clean Rooms SQL Reference SELECT commission, SIGN(commission) FROM sales WHERE salesid=10000; +------------+------+ \| commission \| sign \| +------------+------+ \| 28.05 \| 1 \| +------------+------+ SIN function SIN is a trigonometric function that returns the sine of a number. The return value is between -1 and 1. Syntax SIN(number) Argument number A DOUBLE PRECISION number in radians. Return type DOUBLE PRECISION Example To return the sine of -PI, use the following example. SELECT SIN(-PI()); +-------------------------+ \| sin \| +-------------------------+ \| -0.00000000000000012246 \| +-------------------------+ Math functions 616 AWS Clean Rooms SQRT function SQL Reference The SQRT function returns the square root of a numeric value. The square root is a number multiplied by itself to get the given value. Syntax SQRT (expression) Argument expression The expression must have an integer, decimal, or ﬂoating-point data type. The expression can include functions. The system might perform implicit type conversions. Return type SQRT returns a DOUBLE PRECISION number. Examples The following example returns the square root of a number. select sqrt(16); sqrt --------------- 4 The following example performs an implicit type conversion. select sqrt('16'); sqrt --------------- 4 The following example nests functions to perform a
sql-reference-163	sql-reference.pdf	163	SQL Reference The SQRT function returns the square root of a numeric value. The square root is a number multiplied by itself to get the given value. Syntax SQRT (expression) Argument expression The expression must have an integer, decimal, or ﬂoating-point data type. The expression can include functions. The system might perform implicit type conversions. Return type SQRT returns a DOUBLE PRECISION number. Examples The following example returns the square root of a number. select sqrt(16); sqrt --------------- 4 The following example performs an implicit type conversion. select sqrt('16'); sqrt --------------- 4 The following example nests functions to perform a more complex task. Math functions 617 AWS Clean Rooms SQL Reference select sqrt(round(16.4)); sqrt --------------- 4 The following example results in the length of the radius when given the area of a circle. It calculates the radius in inches, for instance, when given the area in square inches. The area in the sample is 20. select sqrt(20/pi()); This returns the value 5.046265044040321. The following example returns the square root for COMMISSION values from the SALES table. The COMMISSION column is a DECIMAL column. This example shows how you can use the function in a query with more complex conditional logic. select sqrt(commission) from sales where salesid < 10 order by salesid; sqrt ------------------ 10.4498803820905 3.37638860322683 7.24568837309472 5.1234753829798 ... The following query returns the rounded square root for the same set of COMMISSION values. select salesid, commission, round(sqrt(commission)) from sales where salesid < 10 order by salesid; salesid \| commission \| round --------+------------+------- 1 \| 109.20 \| 10 2 \| 11.40 \| 3 3 \| 52.50 \| 7 4 \| 26.25 \| 5 Math functions 618 AWS Clean Rooms ... SQL Reference For more information about sample data in AWS Clean Rooms, see Sample database. TRUNC function The TRUNC function truncates numbers to the previous integer or decimal. The TRUNC function can optionally include a second argument as an integer to indicate the number of decimal places for rounding, in either direction. When you don't provide the second argument, the function rounds to the nearest whole number. When the second argument >nis speciﬁed, the function rounds to the nearest number with >n decimal places of precision. This function also truncates a timestamp and returns a date. Syntax TRUNC (number [ , integer ] \| timestamp ) Arguments number A number or expression that evaluates to a number. It can be the DECIMAL or FLOAT8 type. AWS Clean Rooms can convert other data types per the implicit conversion rules. integer (optional) An integer that indicates the number of decimal places of precision, in either direction. If no integer is provided, the number is truncated as a whole number; if an integer is speciﬁed, the number is truncated to the speciﬁed decimal place. timestamp The function can also return the date from a timestamp. (To return a timestamp value with 00:00:00 as the time, cast the function result to a timestamp.) Return type TRUNC returns the same data type as the ﬁrst input argument. For timestamps, TRUNC returns a date. Math functions 619 SQL Reference AWS Clean Rooms Examples Truncate the commission paid for a given sales transaction. select commission, trunc(commission) from sales where salesid=784; commission \| trunc -----------+------- 111.15 \| 111 (1 row) Truncate the same commission value to the ﬁrst decimal place. select commission, trunc(commission,1) from sales where salesid=784; commission \| trunc -----------+------- 111.15 \| 111.1 (1 row) Truncate the commission with a negative value for the second argument; 111.15 is rounded down to 110. select commission, trunc(commission,-1) from sales where salesid=784; commission \| trunc -----------+------- 111.15 \| 110 (1 row) Return the date portion from the result of the SYSDATE function (which returns a timestamp): select sysdate; timestamp ---------------------------- Math functions 620 AWS Clean Rooms 2011-07-21 10:32:38.248109 (1 row) select trunc(sysdate); trunc ------------ 2011-07-21 (1 row) SQL Reference Apply the TRUNC function to a TIMESTAMP column. The return type is a date. select trunc(starttime) from event order by eventid limit 1; trunc ------------ 2008-01-25 (1 row) String functions String functions process and manipulate character strings or expressions that evaluate to character strings. When the string argument in these functions is a literal value, it must be enclosed in single quotation marks. Supported data types include CHAR and VARCHAR. The following section provides the function names, syntax, and descriptions for supported functions. All oﬀsets into strings are one-based. Topics • \|\| (Concatenation) operator • BTRIM function • CHAR_LENGTH function • CHARACTER_LENGTH function • CHARINDEX function • CONCAT function • LEFT and RIGHT functions • LEN function String functions 621 SQL Reference AWS Clean Rooms • LENGTH function • LOWER function • LPAD and RPAD functions • LTRIM function • POSITION function • REGEXP_COUNT function • REGEXP_INSTR function • REGEXP_REPLACE function • REGEXP_SUBSTR function • REPEAT function • REPLACE function • REPLICATE
sql-reference-164	sql-reference.pdf	164	marks. Supported data types include CHAR and VARCHAR. The following section provides the function names, syntax, and descriptions for supported functions. All oﬀsets into strings are one-based. Topics • \|\| (Concatenation) operator • BTRIM function • CHAR_LENGTH function • CHARACTER_LENGTH function • CHARINDEX function • CONCAT function • LEFT and RIGHT functions • LEN function String functions 621 SQL Reference AWS Clean Rooms • LENGTH function • LOWER function • LPAD and RPAD functions • LTRIM function • POSITION function • REGEXP_COUNT function • REGEXP_INSTR function • REGEXP_REPLACE function • REGEXP_SUBSTR function • REPEAT function • REPLACE function • REPLICATE function • REVERSE function • RTRIM function • SOUNDEX function • SPLIT_PART function • STRPOS function • SUBSTR function • SUBSTRING function • TEXTLEN function • TRANSLATE function • TRIM function • UPPER function \|\| (Concatenation) operator Concatenates two expressions on either side of the \|\| symbol and returns the concatenated expression. The concatentation operator is similar to CONCAT function. String functions 622 AWS Clean Rooms Note SQL Reference For both the CONCAT function and the concatenation operator, if one or both expressions is null, the result of the concatenation is null. Syntax expression1 \|\| expression2 Arguments expression1, expression2 Both arguments can be ﬁxed-length or variable-length character strings or expressions. Return type The \|\| operator returns a string. The type of string is the same as the input arguments. Example The following example concatenates the FIRSTNAME and LASTNAME ﬁelds from the USERS table: select firstname \|\| ' ' \|\| lastname from users order by 1 limit 10; concat ----------------- Aaron Banks Aaron Booth Aaron Browning Aaron Burnett Aaron Casey Aaron Cash Aaron Castro Aaron Dickerson Aaron Dixon String functions 623 AWS Clean Rooms Aaron Dotson (10 rows) SQL Reference To concatenate columns that might contain nulls, use the NVL and COALESCE functions expression. The following example uses NVL to return a 0 whenever NULL is encountered. select venuename \|\| ' seats ' \|\| nvl(venueseats, 0) from venue where venuestate = 'NV' or venuestate = 'NC' order by 1 limit 10; seating ----------------------------------- Ballys Hotel seats 0 Bank of America Stadium seats 73298 Bellagio Hotel seats 0 Caesars Palace seats 0 Harrahs Hotel seats 0 Hilton Hotel seats 0 Luxor Hotel seats 0 Mandalay Bay Hotel seats 0 Mirage Hotel seats 0 New York New York seats 0 BTRIM function The BTRIM function trims a string by removing leading and trailing blanks or by removing leading and trailing characters that match an optional speciﬁed string. Syntax BTRIM(string [, trim_chars ] ) Arguments string The input VARCHAR string to be trimmed. trim_chars The VARCHAR string containing the characters to be matched. String functions 624 AWS Clean Rooms Return type The BTRIM function returns a VARCHAR string. Examples SQL Reference The following example trims leading and trailing blanks from the string ' abc ': select ' abc ' as untrim, btrim(' abc ') as trim; untrim \| trim ----------+------ abc \| abc The following example removes the leading and trailing 'xyz' strings from the string 'xyzaxyzbxyzcxyz'. The leading and trailing occurrences of 'xyz' are removed, but occurrences that are internal within the string are not removed. select 'xyzaxyzbxyzcxyz' as untrim, btrim('xyzaxyzbxyzcxyz', 'xyz') as trim; untrim \| trim -----------------+----------- xyzaxyzbxyzcxyz \| axyzbxyzc The following example removes the leading and trailing parts from the string 'setuphistorycassettes' that match any of the characters in the trim_chars list 'tes'. Any t, e, or s that occur before another character that is not in the trim_chars list at the beginning or ending of the input string are removed. SELECT btrim('setuphistorycassettes', 'tes'); btrim ----------------- uphistoryca CHAR_LENGTH function Synonym of the LEN function. See LEN function. String functions 625 AWS Clean Rooms SQL Reference CHARACTER_LENGTH function Synonym of the LEN function. See LEN function. CHARINDEX function Returns the location of the speciﬁed substring within a string. See POSITION function and STRPOS function for similar functions. Syntax CHARINDEX( substring, string ) Arguments substring The substring to search for within the string. string The string or column to be searched. Return type The CHARINDEX function returns an integer corresponding to the position of the substring (one- based, not zero-based). The position is based on the number of characters, not bytes, so that multi- byte characters are counted as single characters. Usage notes CHARINDEX returns 0 if the substring is not found within the string: select charindex('dog', 'fish'); charindex ---------- 0 String functions 626 AWS Clean Rooms (1 row) Examples SQL Reference The following example shows the position of the string fish within the word dogfish: select charindex('fish', 'dogfish'); charindex ---------- 4 (1 row) The following example returns the number of sales transactions with a COMMISSION over 999.00 from the SALES table: select distinct charindex('.', commission), count (charindex('.', commission)) from sales where charindex('.', commission) > 4 group by charindex('.', commission) order by 1,2;
sql-reference-165	sql-reference.pdf	165	multi- byte characters are counted as single characters. Usage notes CHARINDEX returns 0 if the substring is not found within the string: select charindex('dog', 'fish'); charindex ---------- 0 String functions 626 AWS Clean Rooms (1 row) Examples SQL Reference The following example shows the position of the string fish within the word dogfish: select charindex('fish', 'dogfish'); charindex ---------- 4 (1 row) The following example returns the number of sales transactions with a COMMISSION over 999.00 from the SALES table: select distinct charindex('.', commission), count (charindex('.', commission)) from sales where charindex('.', commission) > 4 group by charindex('.', commission) order by 1,2; charindex \| count ----------+------- 5 \| 629 (1 row) CONCAT function The CONCAT function concatenates two expressions and returns the resulting expression. To concatenate more than two expressions, use nested CONCAT functions. The concatenation operator (\|\|) between two expressions produces the same results as the CONCAT function. Note For both the CONCAT function and the concatenation operator, if one or both expressions is null, the result of the concatenation is null. Syntax CONCAT ( expression1, expression2 ) String functions 627 AWS Clean Rooms Arguments expression1, expression2 SQL Reference Both arguments can be a ﬁxed-length character string, a variable-length character string, a binary expression, or an expression that evaluates to one of these inputs. Return type CONCAT returns an expression. The data type of the expression is the same type as the input arguments. If the input expressions are of diﬀerent types, AWS Clean Rooms tries to implicitly type casts one of the expressions. If values can't be cast, an error is returned. Examples The following example concatenates two character literals: select concat('December 25, ', '2008'); concat ------------------- December 25, 2008 (1 row) The following query, using the \|\| operator instead of CONCAT, produces the same result: select 'December 25, '\|\|'2008'; concat ------------------- December 25, 2008 (1 row) The following example uses two CONCAT functions to concatenate three character strings: select concat('Thursday, ', concat('December 25, ', '2008')); concat ----------------------------- String functions 628 AWS Clean Rooms Thursday, December 25, 2008 (1 row) SQL Reference To concatenate columns that might contain nulls, use the NVL and COALESCE functions. The following example uses NVL to return a 0 whenever NULL is encountered. select concat(venuename, concat(' seats ', nvl(venueseats, 0))) as seating from venue where venuestate = 'NV' or venuestate = 'NC' order by 1 limit 5; seating ----------------------------------- Ballys Hotel seats 0 Bank of America Stadium seats 73298 Bellagio Hotel seats 0 Caesars Palace seats 0 Harrahs Hotel seats 0 (5 rows) The following query concatenates CITY and STATE values from the VENUE table: select concat(venuecity, venuestate) from venue where venueseats > 75000 order by venueseats; concat ------------------- DenverCO Kansas CityMO East RutherfordNJ LandoverMD (4 rows) The following query uses nested CONCAT functions. The query concatenates CITY and STATE values from the VENUE table but delimits the resulting string with a comma and a space: select concat(concat(venuecity,', '),venuestate) from venue where venueseats > 75000 order by venueseats; String functions 629 AWS Clean Rooms SQL Reference concat --------------------- Denver, CO Kansas City, MO East Rutherford, NJ Landover, MD (4 rows) LEFT and RIGHT functions These functions return the speciﬁed number of leftmost or rightmost characters from a character string. The number is based on the number of characters, not bytes, so that multibyte characters are counted as single characters. Syntax LEFT ( string, integer ) RIGHT ( string, integer ) Arguments string Any character string or any expression that evaluates to a character string. integer A positive integer. Return type LEFT and RIGHT return a VARCHAR string. Example The following example returns the leftmost 5 and rightmost 5 characters from event names that have IDs between 1000 and 1005: String functions 630 AWS Clean Rooms SQL Reference select eventid, eventname, left(eventname,5) as left_5, right(eventname,5) as right_5 from event where eventid between 1000 and 1005 order by 1; eventid \| eventname \| left_5 \| right_5 --------+----------------+--------+--------- 1000 \| Gypsy \| Gypsy \| Gypsy 1001 \| Chicago \| Chica \| icago 1002 \| The King and I \| The K \| and I 1003 \| Pal Joey \| Pal J \| Joey 1004 \| Grease \| Greas \| rease 1005 \| Chicago \| Chica \| icago (6 rows) LEN function Returns the length of the speciﬁed string as the number of characters. Syntax LEN is a synonym of LENGTH function, CHAR_LENGTH function, CHARACTER_LENGTH function, and TEXTLEN function. LEN(expression) Argument expression The input parameter is a CHAR or VARCHAR or an alias of one of the valid input types. Return type The LEN function returns an integer indicating the number of characters in the input string. If the input string is a character string, the LEN function returns the actual number of characters in multi-byte strings, not the number of bytes. For example, a VARCHAR(12) column is required
sql-reference-166	sql-reference.pdf	166	rows) LEN function Returns the length of the speciﬁed string as the number of characters. Syntax LEN is a synonym of LENGTH function, CHAR_LENGTH function, CHARACTER_LENGTH function, and TEXTLEN function. LEN(expression) Argument expression The input parameter is a CHAR or VARCHAR or an alias of one of the valid input types. Return type The LEN function returns an integer indicating the number of characters in the input string. If the input string is a character string, the LEN function returns the actual number of characters in multi-byte strings, not the number of bytes. For example, a VARCHAR(12) column is required to store three four-byte Chinese characters. The LEN function will return 3 for that same string. String functions 631 AWS Clean Rooms Usage notes SQL Reference Length calculations do not count trailing spaces for ﬁxed-length character strings but do count them for variable-length strings. Example The following example returns the number of bytes and the number of characters in the string français. select octet_length('français'), len('français'); octet_length \| len --------------+----- 9 \| 8 The following example returns the number of characters in the strings cat with no trailing spaces and cat with three trailing spaces: select len('cat'), len('cat '); len \| len -----+----- 3 \| 6 The following example returns the ten longest VENUENAME entries in the VENUE table: select venuename, len(venuename) from venue order by 2 desc, 1 limit 10; venuename \| len ----------------------------------------+----- Saratoga Springs Performing Arts Center \| 39 Lincoln Center for the Performing Arts \| 38 Nassau Veterans Memorial Coliseum \| 33 Jacksonville Municipal Stadium \| 30 Rangers BallPark in Arlington \| 29 University of Phoenix Stadium \| 29 Circle in the Square Theatre \| 28 Hubert H. Humphrey Metrodome \| 28 String functions 632 AWS Clean Rooms SQL Reference Oriole Park at Camden Yards \| 27 Dick's Sporting Goods Park \| 26 LENGTH function Synonym of the LEN function. See LEN function. LOWER function Converts a string to lowercase. LOWER supports UTF-8 multibyte characters, up to a maximum of four bytes per character. Syntax LOWER(string) Argument string The input parameter is a VARCHAR string (or any other data type, such as CHAR, that can be implicitly converted to VARCHAR). Return type The LOWER function returns a character string that is the same data type as the input string. Examples The following example converts the CATNAME ﬁeld to lowercase: select catname, lower(catname) from category order by 1,2; catname \| lower ----------+----------- Classical \| classical Jazz \| jazz MLB \| mlb MLS \| mls String functions 633 SQL Reference AWS Clean Rooms Musicals \| musicals NBA \| nba NFL \| nfl NHL \| nhl Opera \| opera Plays \| plays Pop \| pop (11 rows) LPAD and RPAD functions These functions prepend or append characters to a string, based on a speciﬁed length. Syntax LPAD (string1, length, [ string2 ]) RPAD (string1, length, [ string2 ]) Arguments string1 A character string or an expression that evaluates to a character string, such as the name of a character column. length An integer that deﬁnes the length of the result of the function. The length of a string is based on the number of characters, not bytes, so that multi-byte characters are counted as single characters. If string1 is longer than the speciﬁed length, it is truncated (on the right). If length is a negative number, the result of the function is an empty string. string2 One or more characters that are prepended or appended to string1. This argument is optional; if it is not speciﬁed, spaces are used. Return type These functions return a VARCHAR data type. String functions 634 AWS Clean Rooms Examples SQL Reference Truncate a speciﬁed set of event names to 20 characters and prepend the shorter names with spaces: select lpad(eventname,20) from event where eventid between 1 and 5 order by 1; lpad -------------------- Salome Il Trovatore Boris Godunov Gotterdammerung La Cenerentola (Cind (5 rows) Truncate the same set of event names to 20 characters but append the shorter names with 0123456789. select rpad(eventname,20,'0123456789') from event where eventid between 1 and 5 order by 1; rpad -------------------- Boris Godunov0123456 Gotterdammerung01234 Il Trovatore01234567 La Cenerentola (Cind Salome01234567890123 (5 rows) LTRIM function Trims characters from the beginning of a string. Removes the longest string containing only characters in the trim characters list. Trimming is complete when a trim character doesn't appear in the input string. Syntax LTRIM( string [, trim_chars] ) String functions 635 AWS Clean Rooms Arguments string SQL Reference A string column, expression, or string literal to be trimmed. trim_chars A string column, expression, or string literal that represents the characters to be trimmed from the beginning of string. If not speciﬁed, a space is used as the trim character. Return type The LTRIM function returns a character string that is the same data type
sql-reference-167	sql-reference.pdf	167	of a string. Removes the longest string containing only characters in the trim characters list. Trimming is complete when a trim character doesn't appear in the input string. Syntax LTRIM( string [, trim_chars] ) String functions 635 AWS Clean Rooms Arguments string SQL Reference A string column, expression, or string literal to be trimmed. trim_chars A string column, expression, or string literal that represents the characters to be trimmed from the beginning of string. If not speciﬁed, a space is used as the trim character. Return type The LTRIM function returns a character string that is the same data type as the input string (CHAR or VARCHAR). Examples The following example trims the year from the listime column. The trim characters in string literal '2008-' indicate the characters to be trimmed from the left. If you use the trim characters '028-', you achieve the same result. select listid, listtime, ltrim(listtime, '2008-') from listing order by 1, 2, 3 limit 10; listid \| listtime \| ltrim -------+---------------------+---------------- 1 \| 2008-01-24 06:43:29 \| 1-24 06:43:29 2 \| 2008-03-05 12:25:29 \| 3-05 12:25:29 3 \| 2008-11-01 07:35:33 \| 11-01 07:35:33 4 \| 2008-05-24 01:18:37 \| 5-24 01:18:37 5 \| 2008-05-17 02:29:11 \| 5-17 02:29:11 6 \| 2008-08-15 02:08:13 \| 15 02:08:13 7 \| 2008-11-15 09:38:15 \| 11-15 09:38:15 8 \| 2008-11-09 05:07:30 \| 11-09 05:07:30 9 \| 2008-09-09 08:03:36 \| 9-09 08:03:36 10 \| 2008-06-17 09:44:54 \| 6-17 09:44:54 LTRIM removes any of the characters in trim_chars when they appear at the beginning of string. The following example trims the characters 'C', 'D', and 'G' when they appear at the beginning of VENUENAME, which is a VARCHAR column. String functions 636 AWS Clean Rooms SQL Reference select venueid, venuename, ltrim(venuename, 'CDG') from venue where venuename like '%Park' order by 2 limit 7; venueid \| venuename \| btrim --------+----------------------------+-------------------------- 121 \| ATT Park \| ATT Park 109 \| Citizens Bank Park \| itizens Bank Park 102 \| Comerica Park \| omerica Park 9 \| Dick's Sporting Goods Park \| ick's Sporting Goods Park 97 \| Fenway Park \| Fenway Park 112 \| Great American Ball Park \| reat American Ball Park 114 \| Miller Park \| Miller Park The following example uses the trim character 2 which is retrieved from the venueid column. select ltrim('2008-01-24 06:43:29', venueid) from venue where venueid=2; ltrim ------------------ 008-01-24 06:43:29 The following example does not trim any characters because a 2 is found before the '0' trim character. select ltrim('2008-01-24 06:43:29', '0'); ltrim ------------------- 2008-01-24 06:43:29 The following example uses the default space trim character and trims the two spaces from the beginning of the string. select ltrim(' 2008-01-24 06:43:29'); ltrim String functions 637 AWS Clean Rooms SQL Reference ------------------- 2008-01-24 06:43:29 POSITION function Returns the location of the speciﬁed substring within a string. See CHARINDEX function and STRPOS function for similar functions. Syntax POSITION(substring IN string ) Arguments substring The substring to search for within the string. string The string or column to be searched. Return type The POSITION function returns an integer corresponding to the position of the substring (one- based, not zero-based). The position is based on the number of characters, not bytes, so that multi- byte characters are counted as single characters. Usage notes POSITION returns 0 if the substring is not found within the string: select position('dog' in 'fish'); position ---------- 0 (1 row) Examples The following example shows the position of the string fish within the word dogfish: String functions 638 AWS Clean Rooms SQL Reference select position('fish' in 'dogfish'); position ---------- 4 (1 row) The following example returns the number of sales transactions with a COMMISSION over 999.00 from the SALES table: select distinct position('.' in commission), count (position('.' in commission)) from sales where position('.' in commission) > 4 group by position('.' in commission) order by 1,2; position \| count ---------+------- 5 \| 629 (1 row) REGEXP_COUNT function Searches a string for a regular expression pattern and returns an integer that indicates the number of times the pattern occurs in the string. If no match is found, then the function returns 0. Syntax REGEXP_COUNT ( source_string, pattern [, position [, parameters ] ] ) Arguments source_string A string expression, such as a column name, to be searched. pattern A string literal that represents a regular expression pattern. position A positive integer that indicates the position within source_string to begin searching. The position is based on the number of characters, not bytes, so that multibyte characters are counted as single characters. The default is 1. If position is less than 1, the search begins at String functions 639 AWS Clean Rooms SQL Reference the ﬁrst character of source_string. If position is greater than the number of characters in source_string, the result is 0. parameters One or more string literals that indicate how the
sql-reference-168	sql-reference.pdf	168	column name, to be searched. pattern A string literal that represents a regular expression pattern. position A positive integer that indicates the position within source_string to begin searching. The position is based on the number of characters, not bytes, so that multibyte characters are counted as single characters. The default is 1. If position is less than 1, the search begins at String functions 639 AWS Clean Rooms SQL Reference the ﬁrst character of source_string. If position is greater than the number of characters in source_string, the result is 0. parameters One or more string literals that indicate how the function matches the pattern. The possible values are the following: • c – Perform case-sensitive matching. The default is to use case-sensitive matching. • i – Perform case-insensitive matching. • p – Interpret the pattern with Perl Compatible Regular Expression (PCRE) dialect. Return type Integer Example The following example counts the number of times a three-letter sequence occurs. SELECT regexp_count('abcdefghijklmnopqrstuvwxyz', '[a-z]{3}'); regexp_count -------------- 8 The following example counts the number of times the top-level domain name is either org or edu. SELECT email, regexp_count(email,'@[^.]\\.(org\|edu)')FROM users ORDER BY userid LIMIT 4; email \| regexp_count -----------------------------------------------+-------------- Etiam.laoreet.libero@sodalesMaurisblandit.edu \| 1 Suspendisse.tristique@nonnisiAenean.edu \| 1 amet.faucibus.ut@condimentumegetvolutpat.ca \| 0 sed@lacusUtnec.ca \| 0 The following example counts the occurrences of the string FOX, using case-insensitive matching. SELECT regexp_count('the fox', 'FOX', 1, 'i'); String functions 640 AWS Clean Rooms SQL Reference regexp_count -------------- 1 The following example uses a pattern written in the PCRE dialect to locate words containing at least one number and one lowercase letter. It uses the ?= operator, which has a speciﬁc look-ahead connotation in PCRE. This example counts the number of occurrences of such words, with case- sensitive matching. SELECT regexp_count('passwd7 plain A1234 a1234', '(?=[^ ][a-z])(?=[^ ][0-9])[^ ]+', 1, 'p'); regexp_count -------------- 2 The following example uses a pattern written in the PCRE dialect to locate words containing at least one number and one lowercase letter. It uses the ?= operator, which has a speciﬁc connotation in PCRE. This example counts the number of occurrences of such words, but diﬀers from the previous example in that it uses case-insensitive matching. SELECT regexp_count('passwd7 plain A1234 a1234', '(?=[^ ][a-z])(?=[^ ][0-9])[^ ]+', 1, 'ip'); regexp_count -------------- 3 REGEXP_INSTR function Searches a string for a regular expression pattern and returns an integer that indicates the beginning position or ending position of the matched substring. If no match is found, then the function returns 0. REGEXP_INSTR is similar to the POSITION function, but lets you search a string for a regular expression pattern. Syntax REGEXP_INSTR ( source_string, pattern [, position [, occurrence] [, option [, parameters ] ] ] ] ) String functions 641 AWS Clean Rooms Arguments source_string A string expression, such as a column name, to be searched. pattern A string literal that represents a regular expression pattern. position SQL Reference A positive integer that indicates the position within source_string to begin searching. The position is based on the number of characters, not bytes, so that multibyte characters are counted as single characters. The default is 1. If position is less than 1, the search begins at the ﬁrst character of source_string. If position is greater than the number of characters in source_string, the result is 0. occurrence A positive integer that indicates which occurrence of the pattern to use. REGEXP_INSTR skips the ﬁrst occurrence -1 matches. The default is 1. If occurrence is less than 1 or greater than the number of characters in source_string, the search is ignored and the result is 0. option A value that indicates whether to return the position of the ﬁrst character of the match (0) or the position of the ﬁrst character following the end of the match (1). A nonzero value is the same as 1. The default value is 0. parameters One or more string literals that indicate how the function matches the pattern. The possible values are the following: • c – Perform case-sensitive matching. The default is to use case-sensitive matching. • i – Perform case-insensitive matching. • e – Extract a substring using a subexpression. If pattern includes a subexpression, REGEXP_INSTR matches a substring using the ﬁrst subexpression in pattern. REGEXP_INSTR considers only the ﬁrst subexpression; additional subexpressions are ignored. If the pattern doesn't have a subexpression, REGEXP_INSTR ignores the 'e' parameter. • p – Interpret the pattern with Perl Compatible Regular Expression (PCRE) dialect. String functions 642 AWS Clean Rooms Return type Integer Example SQL Reference The following example searches for the @ character that begins a domain name and returns the starting position of the ﬁrst match. SELECT email, regexp_instr(email, '@[^.]') FROM users ORDER BY userid LIMIT 4; email \| regexp_instr -----------------------------------------------+-------------- Etiam.laoreet.libero@example.com \| 21 Suspendisse.tristique@nonnisiAenean.edu \| 22 amet.faucibus.ut@condimentumegetvolutpat.ca \| 17 sed@lacusUtnec.ca \| 4 The following example searches for variants of
sql-reference-169	sql-reference.pdf	169	the ﬁrst subexpression; additional subexpressions are ignored. If the pattern doesn't have a subexpression, REGEXP_INSTR ignores the 'e' parameter. • p – Interpret the pattern with Perl Compatible Regular Expression (PCRE) dialect. String functions 642 AWS Clean Rooms Return type Integer Example SQL Reference The following example searches for the @ character that begins a domain name and returns the starting position of the ﬁrst match. SELECT email, regexp_instr(email, '@[^.]') FROM users ORDER BY userid LIMIT 4; email \| regexp_instr -----------------------------------------------+-------------- Etiam.laoreet.libero@example.com \| 21 Suspendisse.tristique@nonnisiAenean.edu \| 22 amet.faucibus.ut@condimentumegetvolutpat.ca \| 17 sed@lacusUtnec.ca \| 4 The following example searches for variants of the word Center and returns the starting position of the ﬁrst match. SELECT venuename, regexp_instr(venuename,'[cC]ent(er\|re)$') FROM venue WHERE regexp_instr(venuename,'[cC]ent(er\|re)$') > 0 ORDER BY venueid LIMIT 4; venuename \| regexp_instr -----------------------+-------------- The Home Depot Center \| 16 Izod Center \| 6 Wachovia Center \| 10 Air Canada Centre \| 12 The following example ﬁnds the starting position of the ﬁrst occurrence of the string FOX, using case-insensitive matching logic. SELECT regexp_instr('the fox', 'FOX', 1, 1, 0, 'i'); regexp_instr -------------- String functions 643 AWS Clean Rooms 5 SQL Reference The following example uses a pattern written in PCRE dialect to locate words containing at least one number and one lowercase letter. It uses the ?= operator, which has a speciﬁc look-ahead connotation in PCRE. This example ﬁnds the starting position of the second such word. SELECT regexp_instr('passwd7 plain A1234 a1234', '(?=[^ ][a-z])(?=[^ ][0-9])[^ ]+', 1, 2, 0, 'p'); regexp_instr -------------- 21 The following example uses a pattern written in PCRE dialect to locate words containing at least one number and one lowercase letter. It uses the ?= operator, which has a speciﬁc look-ahead connotation in PCRE. This example ﬁnds the starting position of the second such word, but diﬀers from the previous example in that it uses case-insensitive matching. SELECT regexp_instr('passwd7 plain A1234 a1234', '(?=[^ ][a-z])(?=[^ ][0-9])[^ ]+', 1, 2, 0, 'ip'); regexp_instr -------------- 15 REGEXP_REPLACE function Searches a string for a regular expression pattern and replaces every occurrence of the pattern with the speciﬁed string. REGEXP_REPLACE is similar to the REPLACE function, but lets you search a string for a regular expression pattern. r_REGEXP_REPLACE is similar to the TRANSLATE function and the REPLACE function, except that TRANSLATE makes multiple single-character substitutions and REPLACE substitutes one entire string with another string, while REGEXP_REPLACE lets you search a string for a regular expression pattern. Syntax REGEXP_REPLACE ( source_string, pattern [, replace_string [ , position [, parameters ] ] ] ) String functions 644 AWS Clean Rooms Arguments source_string A string expression, such as a column name, to be searched. pattern A string literal that represents a regular expression pattern. replace_string SQL Reference A string expression, such as a column name, that will replace each occurrence of pattern. The default is an empty string ( "" ). position A positive integer that indicates the position within source_string to begin searching. The position is based on the number of characters, not bytes, so that multibyte characters are counted as single characters. The default is 1. If position is less than 1, the search begins at the ﬁrst character of source_string. If position is greater than the number of characters in source_string, the result is source_string. parameters One or more string literals that indicate how the function matches the pattern. The possible values are the following: • c – Perform case-sensitive matching. The default is to use case-sensitive matching. • i – Perform case-insensitive matching. • p – Interpret the pattern with Perl Compatible Regular Expression (PCRE) dialect. Return type VARCHAR If either pattern or replace_string is NULL, the return is NULL. Example The following example deletes the @ and domain name from email addresses. SELECT email, regexp_replace(email, '@.\\.(org\|gov\|com\|edu\|ca)$') FROM users ORDER BY userid LIMIT 4; String functions 645 AWS Clean Rooms SQL Reference email \| regexp_replace -----------------------------------------------+---------------- Etiam.laoreet.libero@sodalesMaurisblandit.edu \| Etiam.laoreet.libero Suspendisse.tristique@nonnisiAenean.edu \| Suspendisse.tristique amet.faucibus.ut@condimentumegetvolutpat.ca \| amet.faucibus.ut sed@lacusUtnec.ca \| sed The following example replaces the domain names of email addresses with this value: internal.company.com. SELECT email, regexp_replace(email, '@.\\.[[:alpha:]]{2,3}', '@internal.company.com') FROM users ORDER BY userid LIMIT 4; email \| regexp_replace ----------------------------------------------- +-------------------------------------------- Etiam.laoreet.libero@sodalesMaurisblandit.edu \| Etiam.laoreet.libero@internal.company.com Suspendisse.tristique@nonnisiAenean.edu \| Suspendisse.tristique@internal.company.com amet.faucibus.ut@condimentumegetvolutpat.ca \| amet.faucibus.ut@internal.company.com sed@lacusUtnec.ca \| sed@internal.company.com The following example replaces all occurrences of the string FOX within the value quick brown fox, using case-insensitive matching. SELECT regexp_replace('the fox', 'FOX', 'quick brown fox', 1, 'i'); regexp_replace --------------------- the quick brown fox The following example uses a pattern written in the PCRE dialect to locate words containing at least one number and one lowercase letter. It uses the ?= operator, which has a speciﬁc look- ahead connotation in PCRE. This example replaces each occurrence of such a word with the value [hidden]. SELECT regexp_replace('passwd7 plain A1234 a1234', '(?=[^ ][a-z])(?=[^ ]*[0-9])[^ ]+', '[hidden]', 1, 'p'); String functions 646 AWS Clean Rooms SQL
sql-reference-170	sql-reference.pdf	170	replaces all occurrences of the string FOX within the value quick brown fox, using case-insensitive matching. SELECT regexp_replace('the fox', 'FOX', 'quick brown fox', 1, 'i'); regexp_replace --------------------- the quick brown fox The following example uses a pattern written in the PCRE dialect to locate words containing at least one number and one lowercase letter. It uses the ?= operator, which has a speciﬁc look- ahead connotation in PCRE. This example replaces each occurrence of such a word with the value [hidden]. SELECT regexp_replace('passwd7 plain A1234 a1234', '(?=[^ ][a-z])(?=[^ ][0-9])[^ ]+', '[hidden]', 1, 'p'); String functions 646 AWS Clean Rooms SQL Reference regexp_replace ------------------------------- [hidden] plain A1234 [hidden] The following example uses a pattern written in the PCRE dialect to locate words containing at least one number and one lowercase letter. It uses the ?= operator, which has a speciﬁc look- ahead connotation in PCRE. This example replaces each occurrence of such a word with the value [hidden], but diﬀers from the previous example in that it uses case-insensitive matching. SELECT regexp_replace('passwd7 plain A1234 a1234', '(?=[^ ][a-z])(?=[^ ][0-9])[^ ]+', '[hidden]', 1, 'ip'); regexp_replace ---------------------------------- [hidden] plain [hidden] [hidden] REGEXP_SUBSTR function Returns characters from a string by searching it for a regular expression pattern. REGEXP_SUBSTR is similar to the SUBSTRING function function, but lets you search a string for a regular expression pattern. If the function can't match the regular expression to any characters in the string, it returns an empty string. Syntax REGEXP_SUBSTR ( source_string, pattern [, position [, occurrence [, parameters ] ] ] ) Arguments source_string A string expression to be searched. pattern A string literal that represents a regular expression pattern. position A positive integer that indicates the position within source_string to begin searching. The position is based on the number of characters, not bytes, so that multi-byte characters are String functions 647 AWS Clean Rooms SQL Reference counted as single characters. The default is 1. If position is less than 1, the search begins at the ﬁrst character of source_string. If position is greater than the number of characters in source_string, the result is an empty string (""). occurrence A positive integer that indicates which occurrence of the pattern to use. REGEXP_SUBSTR skips the ﬁrst occurrence -1 matches. The default is 1. If occurrence is less than 1 or greater than the number of characters in source_string, the search is ignored and the result is NULL. parameters One or more string literals that indicate how the function matches the pattern. The possible values are the following: • c – Perform case-sensitive matching. The default is to use case-sensitive matching. • i – Perform case-insensitive matching. • e – Extract a substring using a subexpression. If pattern includes a subexpression, REGEXP_SUBSTR matches a substring using the ﬁrst subexpression in pattern. A subexpression is an expression within the pattern that is bracketed with parentheses. For example, for the pattern 'This is a (\\w+)' matches the ﬁrst expression with the string 'This is a ' followed by a word. Instead of returning pattern, REGEXP_SUBSTR with the e parameter returns only the string inside the subexpression. REGEXP_SUBSTR considers only the ﬁrst subexpression; additional subexpressions are ignored. If the pattern doesn't have a subexpression, REGEXP_SUBSTR ignores the 'e' parameter. • p – Interpret the pattern with Perl Compatible Regular Expression (PCRE) dialect. Return type VARCHAR Example The following example returns the portion of an email address between the @ character and the domain extension. SELECT email, regexp_substr(email,'@[^.]') String functions 648 AWS Clean Rooms FROM users ORDER BY userid LIMIT 4; SQL Reference email \| regexp_substr -----------------------------------------------+-------------------------- Etiam.laoreet.libero@sodalesMaurisblandit.edu \| @sodalesMaurisblandit Suspendisse.tristique@nonnisiAenean.edu \| @nonnisiAenean amet.faucibus.ut@condimentumegetvolutpat.ca \| @condimentumegetvolutpat sed@lacusUtnec.ca \| @lacusUtnec The following example returns the portion of the input corresponding to the ﬁrst occurrence of the string FOX, using case-insensitive matching. SELECT regexp_substr('the fox', 'FOX', 1, 1, 'i'); regexp_substr --------------- fox The following example returns the ﬁrst portion of the input that begins with lowercase letters. This is functionally identical to the same SELECT statement without the c parameter. SELECT regexp_substr('THE SECRET CODE IS THE LOWERCASE PART OF 1931abc0EZ.', '[a-z]+', 1, 1, 'c'); regexp_substr --------------- abc The following example uses a pattern written in the PCRE dialect to locate words containing at least one number and one lowercase letter. It uses the ?= operator, which has a speciﬁc look-ahead connotation in PCRE. This example returns the portion of the input corresponding to the second such word. SELECT regexp_substr('passwd7 plain A1234 a1234', '(?=[^ ][a-z])(?=[^ ]*[0-9])[^ ]+', 1, 2, 'p'); regexp_substr --------------- a1234 String functions 649 AWS Clean Rooms SQL Reference The following example uses a pattern written in the PCRE dialect to locate words containing at least one number and one lowercase letter. It uses the ?= operator, which has a speciﬁc look-ahead connotation in PCRE. This example returns the portion of the input corresponding
sql-reference-171	sql-reference.pdf	171	number and one lowercase letter. It uses the ?= operator, which has a speciﬁc look-ahead connotation in PCRE. This example returns the portion of the input corresponding to the second such word. SELECT regexp_substr('passwd7 plain A1234 a1234', '(?=[^ ][a-z])(?=[^ ][0-9])[^ ]+', 1, 2, 'p'); regexp_substr --------------- a1234 String functions 649 AWS Clean Rooms SQL Reference The following example uses a pattern written in the PCRE dialect to locate words containing at least one number and one lowercase letter. It uses the ?= operator, which has a speciﬁc look-ahead connotation in PCRE. This example returns the portion of the input corresponding to the second such word, but diﬀers from the previous example in that it uses case-insensitive matching. SELECT regexp_substr('passwd7 plain A1234 a1234', '(?=[^ ][a-z])(?=[^ ][0-9])[^ ]+', 1, 2, 'ip'); regexp_substr --------------- A1234 The following example uses a subexpression to ﬁnd the second string matching the pattern 'this is a (\\w+)' using case-insensitive matching. It returns the subexpression inside the parentheses. select regexp_substr( 'This is a cat, this is a dog. This is a mouse.', 'this is a (\\w+)', 1, 2, 'ie'); regexp_substr --------------- dog REPEAT function Repeats a string the speciﬁed number of times. If the input parameter is numeric, REPEAT treats it as a string. Synonym for REPLICATE function. Syntax REPEAT(string, integer) Arguments string The ﬁrst input parameter is the string to be repeated. String functions 650 AWS Clean Rooms integer SQL Reference The second parameter is an integer indicating the number of times to repeat the string. Return type The REPEAT function returns a string. Examples The following example repeats the value of the CATID column in the CATEGORY table three times: select catid, repeat(catid,3) from category order by 1,2; catid \| repeat -------+-------- 1 \| 111 2 \| 222 3 \| 333 4 \| 444 5 \| 555 6 \| 666 7 \| 777 8 \| 888 9 \| 999 10 \| 101010 11 \| 111111 (11 rows) REPLACE function Replaces all occurrences of a set of characters within an existing string with other speciﬁed characters. REPLACE is similar to the TRANSLATE function and the REGEXP_REPLACE function, except that TRANSLATE makes multiple single-character substitutions and REGEXP_REPLACE lets you search a string for a regular expression pattern, while REPLACE substitutes one entire string with another string. String functions 651 SQL Reference AWS Clean Rooms Syntax REPLACE(string1, old_chars, new_chars) Arguments string CHAR or VARCHAR string to be searched search old_chars CHAR or VARCHAR string to replace. new_chars New CHAR or VARCHAR string replacing the old_string. Return type VARCHAR If either old_chars or new_chars is NULL, the return is NULL. Examples The following example converts the string Shows to Theatre in the CATGROUP ﬁeld: select catid, catgroup, replace(catgroup, 'Shows', 'Theatre') from category order by 1,2,3; catid \| catgroup \| replace -------+----------+---------- 1 \| Sports \| Sports 2 \| Sports \| Sports 3 \| Sports \| Sports 4 \| Sports \| Sports 5 \| Sports \| Sports 6 \| Shows \| Theatre 7 \| Shows \| Theatre 8 \| Shows \| Theatre String functions 652 AWS Clean Rooms SQL Reference 9 \| Concerts \| Concerts 10 \| Concerts \| Concerts 11 \| Concerts \| Concerts (11 rows) REPLICATE function Synonym for the REPEAT function. See REPEAT function. REVERSE function The REVERSE function operates on a string and returns the characters in reverse order. For example, reverse('abcde') returns edcba. This function works on numeric and date data types as well as character data types; however, in most cases it has practical value for character strings. Syntax REVERSE ( expression ) Argument expression An expression with a character, date, timestamp, or numeric data type that represents the target of the character reversal. All expressions are implicitly converted to variable-length character strings. Trailing blanks in ﬁxed-width character strings are ignored. Return type REVERSE returns a VARCHAR. Examples Select ﬁve distinct city names and their corresponding reversed names from the USERS table: select distinct city as cityname, reverse(cityname) from users order by city limit 5; cityname \| reverse String functions 653 AWS Clean Rooms ---------+---------- Aberdeen \| needrebA Abilene \| enelibA Ada \| adA Agat \| tagA Agawam \| mawagA (5 rows) SQL Reference Select ﬁve sales IDs and their corresponding reversed IDs cast as character strings: select salesid, reverse(salesid)::varchar from sales order by salesid desc limit 5; salesid \| reverse --------+--------- 172456 \| 654271 172455 \| 554271 172454 \| 454271 172453 \| 354271 172452 \| 254271 (5 rows) RTRIM function The RTRIM function trims a speciﬁed set of characters from the end of a string. Removes the longest string containing only characters in the trim characters list. Trimming is complete when a trim character doesn't appear in the input string. Syntax RTRIM( string, trim_chars ) Arguments string A string column, expression, or string literal to be trimmed. trim_chars A string column, expression, or string
sql-reference-172	sql-reference.pdf	172	character strings: select salesid, reverse(salesid)::varchar from sales order by salesid desc limit 5; salesid \| reverse --------+--------- 172456 \| 654271 172455 \| 554271 172454 \| 454271 172453 \| 354271 172452 \| 254271 (5 rows) RTRIM function The RTRIM function trims a speciﬁed set of characters from the end of a string. Removes the longest string containing only characters in the trim characters list. Trimming is complete when a trim character doesn't appear in the input string. Syntax RTRIM( string, trim_chars ) Arguments string A string column, expression, or string literal to be trimmed. trim_chars A string column, expression, or string literal that represents the characters to be trimmed from the end of string. If not speciﬁed, a space is used as the trim character. String functions 654 AWS Clean Rooms Return type SQL Reference A string that is the same data type as the string argument. Example The following example trims leading and trailing blanks from the string ' abc ': select ' abc ' as untrim, rtrim(' abc ') as trim; untrim \| trim ----------+------ abc \| abc The following example removes the trailing 'xyz' strings from the string 'xyzaxyzbxyzcxyz'. The trailing occurrences of 'xyz' are removed, but occurrences that are internal within the string are not removed. select 'xyzaxyzbxyzcxyz' as untrim, rtrim('xyzaxyzbxyzcxyz', 'xyz') as trim; untrim \| trim -----------------+----------- xyzaxyzbxyzcxyz \| xyzaxyzbxyzc The following example removes the trailing parts from the string 'setuphistorycassettes' that match any of the characters in the trim_chars list 'tes'. Any t, e, or s that occur before another character that is not in the trim_chars list at the ending of the input string are removed. SELECT rtrim('setuphistorycassettes', 'tes'); rtrim ----------------- setuphistoryca The following example trims the characters 'Park' from the end of VENUENAME where present: select venueid, venuename, rtrim(venuename, 'Park') from venue order by 1, 2, 3 String functions 655 AWS Clean Rooms limit 10; SQL Reference venueid \| venuename \| rtrim --------+----------------------------+------------------------- 1 \| Toyota Park \| Toyota 2 \| Columbus Crew Stadium \| Columbus Crew Stadium 3 \| RFK Stadium \| RFK Stadium 4 \| CommunityAmerica Ballpark \| CommunityAmerica Ballp 5 \| Gillette Stadium \| Gillette Stadium 6 \| New York Giants Stadium \| New York Giants Stadium 7 \| BMO Field \| BMO Field 8 \| The Home Depot Center \| The Home Depot Cente 9 \| Dick's Sporting Goods Park \| Dick's Sporting Goods 10 \| Pizza Hut Park \| Pizza Hut Note that RTRIM removes any of the characters P, a, r, or k when they appear at the end of a VENUENAME. SOUNDEX function The SOUNDEX function returns the American Soundex value consisting of the ﬁrst letter followed by a 3–digit encoding of the sounds that represent the English pronunciation of the string that you specify. Syntax SOUNDEX(string) Arguments string You specify a CHAR or VARCHAR string that you want to convert to an American Soundex code value. Return type The SOUNDEX function returns a VARCHAR(4) string consisting of a capital letter followed by a three–digit encoding of the sounds that represent the English pronunciation. String functions 656 AWS Clean Rooms Usage notes SQL Reference The SOUNDEX function converts only English alphabetical lowercase and uppercase ASCII characters, including a–z and A–Z. SOUNDEX ignores other characters. SOUNDEX returns a single Soundex value for a string of multiple words separated by spaces. select soundex('AWS Amazon'); soundex --------- A252 SOUNDEX returns an empty string if the input string doesn't contain any English letters. select soundex('+-*/%'); soundex --------- Example The following example returns the Soundex A525 for the word Amazon. select soundex('Amazon'); soundex --------- A525 SPLIT_PART function Splits a string on the speciﬁed delimiter and returns the part at the speciﬁed position. Syntax SPLIT_PART(string, delimiter, position) String functions 657 AWS Clean Rooms Arguments string SQL Reference A string column, expression, or string literal to be split. The string can be CHAR or VARCHAR. delimiter The delimiter string indicating sections of the input string. If delimiter is a literal, enclose it in single quotation marks. position Position of the portion of string to return (counting from 1). Must be an integer greater than 0. If position is larger than the number of string portions, SPLIT_PART returns an empty string. If delimiter is not found in string, then the returned value contains the contents of the speciﬁed part, which might be the entire string or an empty value. Return type A CHAR or VARCHAR string, the same as the string parameter. Examples The following example splits a string literal into parts using the $ delimiter and returns the second part. select split_part('abc$def$ghi','$',2) split_part ---------- def The following example splits a string literal into parts using the $ delimiter. It returns an empty string because part 4 is not found. select split_part('abc$def$ghi','$',4) split_part ---------- String functions 658 AWS Clean Rooms SQL Reference The following
sql-reference-173	sql-reference.pdf	173	not found in string, then the returned value contains the contents of the speciﬁed part, which might be the entire string or an empty value. Return type A CHAR or VARCHAR string, the same as the string parameter. Examples The following example splits a string literal into parts using the $ delimiter and returns the second part. select split_part('abc$def$ghi','$',2) split_part ---------- def The following example splits a string literal into parts using the $ delimiter. It returns an empty string because part 4 is not found. select split_part('abc$def$ghi','$',4) split_part ---------- String functions 658 AWS Clean Rooms SQL Reference The following example splits a string literal into parts using the # delimiter. It returns the entire string, which is the ﬁrst part, because the delimiter is not found. select split_part('abc$def$ghi','#',1) split_part ------------ abc$def$ghi The following example splits the timestamp ﬁeld LISTTIME into year, month, and day components. select listtime, split_part(listtime,'-',1) as year, split_part(listtime,'-',2) as month, split_part(split_part(listtime,'-',3),' ',1) as day from listing limit 5; listtime \| year \| month \| day ---------------------+------+-------+------ 2008-03-05 12:25:29 \| 2008 \| 03 \| 05 2008-09-09 08:03:36 \| 2008 \| 09 \| 09 2008-09-26 05:43:12 \| 2008 \| 09 \| 26 2008-10-04 02:00:30 \| 2008 \| 10 \| 04 2008-01-06 08:33:11 \| 2008 \| 01 \| 06 The following example selects the LISTTIME timestamp ﬁeld and splits it on the '-' character to get the month (the second part of the LISTTIME string), then counts the number of entries for each month: select split_part(listtime,'-',2) as month, count(*) from listing group by split_part(listtime,'-',2) order by 1, 2; month \| count -------+------- 01 \| 18543 02 \| 16620 03 \| 17594 04 \| 16822 String functions 659 SQL Reference AWS Clean Rooms 05 \| 17618 06 \| 17158 07 \| 17626 08 \| 17881 09 \| 17378 10 \| 17756 11 \| 12912 12 \| 4589 STRPOS function Returns the position of a substring within a speciﬁed string. See CHARINDEX function and POSITION function for similar functions. Syntax STRPOS(string, substring ) Arguments string The ﬁrst input parameter is the string to be searched. substring The second parameter is the substring to search for within the string. Return type The STRPOS function returns an integer corresponding to the position of the substring (one-based, not zero-based). The position is based on the number of characters, not bytes, so that multi-byte characters are counted as single characters. Usage notes STRPOS returns 0 if the substring is not found within the string: select strpos('dogfish', 'fist'); strpos String functions 660 AWS Clean Rooms -------- 0 (1 row) Examples SQL Reference The following example shows the position of the string fish within the word dogfish: select strpos('dogfish', 'fish'); strpos -------- 4 (1 row) The following example returns the number of sales transactions with a COMMISSION over 999.00 from the SALES table: select distinct strpos(commission, '.'), count (strpos(commission, '.')) from sales where strpos(commission, '.') > 4 group by strpos(commission, '.') order by 1, 2; strpos \| count -------+------- 5 \| 629 (1 row) SUBSTR function Synonym of the SUBSTRING function. See SUBSTRING function. SUBSTRING function Returns the subset of a string based on the speciﬁed start position. If the input is a character string, the start position and number of characters extracted are based on characters, not bytes, so that multi-byte characters are counted as single characters. If the input String functions 661 AWS Clean Rooms SQL Reference is a binary expression, the start position and extracted substring are based on bytes. You can't specify a negative length, but you can specify a negative starting position. Syntax SUBSTRING(character_string FROM start_position [ FOR number_characters ] ) SUBSTRING(character_string, start_position, number_characters ) SUBSTRING(binary_expression, start_byte, number_bytes ) SUBSTRING(binary_expression, start_byte ) Arguments character_string The string to be searched. Non-character data types are treated like a string. start_position The position within the string to begin the extraction, starting at 1. The start_position is based on the number of characters, not bytes, so that multi-byte characters are counted as single characters. This number can be negative. number_characters The number of characters to extract (the length of the substring). The number_characters is based on the number of characters, not bytes, so that multi-byte characters are counted as single characters. This number cannot be negative. start_byte The position within the binary expression to begin the extraction, starting at 1. This number can be negative. number_bytes The number of bytes to extract, that is, the length of the substring. This number can't be negative. String functions 662 AWS Clean Rooms Return type VARCHAR Usage notes for character strings SQL Reference The following example returns a four-character string beginning with the sixth character. select substring('caterpillar',6,4); substring ----------- pill (1 row) If the start_position + number_characters exceeds the length of the string, SUBSTRING returns a substring starting from the start_position until the end of the string. For example:
sql-reference-174	sql-reference.pdf	174	start_byte The position within the binary expression to begin the extraction, starting at 1. This number can be negative. number_bytes The number of bytes to extract, that is, the length of the substring. This number can't be negative. String functions 662 AWS Clean Rooms Return type VARCHAR Usage notes for character strings SQL Reference The following example returns a four-character string beginning with the sixth character. select substring('caterpillar',6,4); substring ----------- pill (1 row) If the start_position + number_characters exceeds the length of the string, SUBSTRING returns a substring starting from the start_position until the end of the string. For example: select substring('caterpillar',6,8); substring ----------- pillar (1 row) If the start_position is negative or 0, the SUBSTRING function returns a substring beginning at the ﬁrst character of string with a length of start_position + number_characters -1. For example: select substring('caterpillar',-2,6); substring ----------- cat (1 row) If start_position + number_characters -1 is less than or equal to zero, SUBSTRING returns an empty string. For example: select substring('caterpillar',-5,4); substring ----------- String functions 663 AWS Clean Rooms (1 row) Examples SQL Reference The following example returns the month from the LISTTIME string in the LISTING table: select listid, listtime, substring(listtime, 6, 2) as month from listing order by 1, 2, 3 limit 10; listid \| listtime \| month --------+---------------------+------- 1 \| 2008-01-24 06:43:29 \| 01 2 \| 2008-03-05 12:25:29 \| 03 3 \| 2008-11-01 07:35:33 \| 11 4 \| 2008-05-24 01:18:37 \| 05 5 \| 2008-05-17 02:29:11 \| 05 6 \| 2008-08-15 02:08:13 \| 08 7 \| 2008-11-15 09:38:15 \| 11 8 \| 2008-11-09 05:07:30 \| 11 9 \| 2008-09-09 08:03:36 \| 09 10 \| 2008-06-17 09:44:54 \| 06 (10 rows) The following example is the same as above, but uses the FROM...FOR option: select listid, listtime, substring(listtime from 6 for 2) as month from listing order by 1, 2, 3 limit 10; listid \| listtime \| month --------+---------------------+------- 1 \| 2008-01-24 06:43:29 \| 01 2 \| 2008-03-05 12:25:29 \| 03 3 \| 2008-11-01 07:35:33 \| 11 4 \| 2008-05-24 01:18:37 \| 05 5 \| 2008-05-17 02:29:11 \| 05 6 \| 2008-08-15 02:08:13 \| 08 7 \| 2008-11-15 09:38:15 \| 11 String functions 664 AWS Clean Rooms SQL Reference 8 \| 2008-11-09 05:07:30 \| 11 9 \| 2008-09-09 08:03:36 \| 09 10 \| 2008-06-17 09:44:54 \| 06 (10 rows) You can't use SUBSTRING to predictably extract the preﬁx of a string that might contain multi-byte characters because you need to specify the length of a multi-byte string based on the number of bytes, not the number of characters. To extract the beginning segment of a string based on the length in bytes, you can CAST the string as VARCHAR(byte_length) to truncate the string, where byte_length is the required length. The following example extracts the ﬁrst 5 bytes from the string 'Fourscore and seven'. select cast('Fourscore and seven' as varchar(5)); varchar ------- Fours The following example returns the ﬁrst name Ana which appears after the last space in the input string Silva, Ana. select reverse(substring(reverse('Silva, Ana'), 1, position(' ' IN reverse('Silva, Ana')))) reverse ----------- Ana TEXTLEN function Synonym of LEN function. See LEN function. TRANSLATE function For a given expression, replaces all occurrences of speciﬁed characters with speciﬁed substitutes. Existing characters are mapped to replacement characters by their positions in the characters_to_replace and characters_to_substitute arguments. If more characters are speciﬁed String functions 665 AWS Clean Rooms SQL Reference in the characters_to_replace argument than in the characters_to_substitute argument, the extra characters from the characters_to_replace argument are omitted in the return value. TRANSLATE is similar to the REPLACE function and the REGEXP_REPLACE function, except that REPLACE substitutes one entire string with another string and REGEXP_REPLACE lets you search a string for a regular expression pattern, while TRANSLATE makes multiple single-character substitutions. If any argument is null, the return is NULL. Syntax TRANSLATE ( expression, characters_to_replace, characters_to_substitute ) Arguments expression The expression to be translated. characters_to_replace A string containing the characters to be replaced. characters_to_substitute A string containing the characters to substitute. Return type VARCHAR Examples The following example replaces several characters in a string: select translate('mint tea', 'inea', 'osin'); translate ----------- most tin String functions 666 AWS Clean Rooms SQL Reference The following example replaces the at sign (@) with a period for all values in a column: select email, translate(email, '@', '.') as obfuscated_email from users limit 10; email obfuscated_email ------------------------------------------------------------------------------------------- Etiam.laoreet.libero@sodalesMaurisblandit.edu Etiam.laoreet.libero.sodalesMaurisblandit.edu amet.faucibus.ut@condimentumegetvolutpat.ca amet.faucibus.ut.condimentumegetvolutpat.ca turpis@accumsanlaoreet.org turpis.accumsanlaoreet.org ullamcorper.nisl@Cras.edu ullamcorper.nisl.Cras.edu arcu.Curabitur@senectusetnetus.com arcu.Curabitur.senectusetnetus.com ac@velit.ca ac.velit.ca Aliquam.vulputate.ullamcorper@amalesuada.org Aliquam.vulputate.ullamcorper.amalesuada.org vel.est@velitegestas.edu vel.est.velitegestas.edu dolor.nonummy@ipsumdolorsit.ca dolor.nonummy.ipsumdolorsit.ca et@Nunclaoreet.ca et.Nunclaoreet.ca The following example replaces spaces with underscores and strips out periods for all values in a column: select city, translate(city, ' .', '_') from users where city like 'Sain%' or city like 'St%' group by city order by city; city translate --------------+------------------ Saint Albans Saint_Albans Saint Cloud Saint_Cloud Saint Joseph Saint_Joseph Saint Louis Saint_Louis Saint
sql-reference-175	sql-reference.pdf	175	sign (@) with a period for all values in a column: select email, translate(email, '@', '.') as obfuscated_email from users limit 10; email obfuscated_email ------------------------------------------------------------------------------------------- Etiam.laoreet.libero@sodalesMaurisblandit.edu Etiam.laoreet.libero.sodalesMaurisblandit.edu amet.faucibus.ut@condimentumegetvolutpat.ca amet.faucibus.ut.condimentumegetvolutpat.ca turpis@accumsanlaoreet.org turpis.accumsanlaoreet.org ullamcorper.nisl@Cras.edu ullamcorper.nisl.Cras.edu arcu.Curabitur@senectusetnetus.com arcu.Curabitur.senectusetnetus.com ac@velit.ca ac.velit.ca Aliquam.vulputate.ullamcorper@amalesuada.org Aliquam.vulputate.ullamcorper.amalesuada.org vel.est@velitegestas.edu vel.est.velitegestas.edu dolor.nonummy@ipsumdolorsit.ca dolor.nonummy.ipsumdolorsit.ca et@Nunclaoreet.ca et.Nunclaoreet.ca The following example replaces spaces with underscores and strips out periods for all values in a column: select city, translate(city, ' .', '_') from users where city like 'Sain%' or city like 'St%' group by city order by city; city translate --------------+------------------ Saint Albans Saint_Albans Saint Cloud Saint_Cloud Saint Joseph Saint_Joseph Saint Louis Saint_Louis Saint Paul Saint_Paul St. George St_George St. Marys St_Marys St. Petersburg St_Petersburg Stafford Stafford Stamford Stamford Stanton Stanton Starkville Starkville String functions 667 AWS Clean Rooms SQL Reference Statesboro Statesboro Staunton Staunton Steubenville Steubenville Stevens Point Stevens_Point Stillwater Stillwater Stockton Stockton Sturgis Sturgis TRIM function Trims a string by removing leading and trailing blanks or by removing leading and trailing characters that match an optional speciﬁed string. Syntax TRIM( [ BOTH ] [ trim_chars FROM ] string Arguments trim_chars (Optional) The characters to be trimmed from the string. If this parameter is omitted, blanks are trimmed. string The string to be trimmed. Return type The TRIM function returns a VARCHAR or CHAR string. If you use the TRIM function with a SQL command, AWS Clean Rooms implicitly converts the results to VARCHAR. If you use the TRIM function in the SELECT list for a SQL function, AWS Clean Rooms does not implicitly convert the results, and you might need to perform an explicit conversion to avoid a data type mismatch error. See the CAST function and CONVERT function functions for information about explicit conversions. Example The following example trims leading and trailing blanks from the string ' abc ': select ' abc ' as untrim, trim(' abc ') as trim; String functions 668 AWS Clean Rooms untrim \| trim ----------+------ abc \| abc SQL Reference The following example removes the double quotation marks that surround the string "dog": select trim('"' FROM '"dog"'); btrim ------- dog TRIM removes any of the characters in trim_chars when they appear at the beginning of string. The following example trims the characters 'C', 'D', and 'G' when they appear at the beginning of VENUENAME, which is a VARCHAR column. select venueid, venuename, trim(venuename, 'CDG') from venue where venuename like '%Park' order by 2 limit 7; venueid \| venuename \| btrim --------+----------------------------+-------------------------- 121 \| ATT Park \| ATT Park 109 \| Citizens Bank Park \| itizens Bank Park 102 \| Comerica Park \| omerica Park 9 \| Dick's Sporting Goods Park \| ick's Sporting Goods Park 97 \| Fenway Park \| Fenway Park 112 \| Great American Ball Park \| reat American Ball Park 114 \| Miller Park \| Miller Park UPPER function Converts a string to uppercase. UPPER supports UTF-8 multibyte characters, up to a maximum of four bytes per character. Syntax UPPER(string) String functions 669 AWS Clean Rooms Arguments string SQL Reference The input parameter is a VARCHAR string (or any other data type, such as CHAR, that can be implicitly converted to VARCHAR). Return type The UPPER function returns a character string that is the same data type as the input string. Examples The following example converts the CATNAME ﬁeld to uppercase: select catname, upper(catname) from category order by 1,2; catname \| upper ----------+----------- Classical \| CLASSICAL Jazz \| JAZZ MLB \| MLB MLS \| MLS Musicals \| MUSICALS NBA \| NBA NFL \| NFL NHL \| NHL Opera \| OPERA Plays \| PLAYS Pop \| POP (11 rows) SUPER type information functions This section describes the information functions for SQL to derive the dynamic information from inputs of the SUPER data type supported in AWS Clean Rooms. Topics • DECIMAL_PRECISION function • DECIMAL_SCALE function • IS_ARRAY function SUPER type information functions 670 SQL Reference AWS Clean Rooms • IS_BIGINT function • IS_CHAR function • IS_DECIMAL function • IS_FLOAT function • IS_INTEGER function • IS_OBJECT function • IS_SCALAR function • IS_SMALLINT function • IS_VARCHAR function • JSON_TYPEOF function DECIMAL_PRECISION function Checks the precision of the maximum total number of decimal digits to be stored. This number includes both the left and right digits of the decimal point. The range of the precision is from 1 to 38, with a default of 38. Syntax DECIMAL_PRECISION(super_expression) Arguments super_expression A SUPER expression or column. Return type INTEGER Example To apply the DECIMAL_PRECISION function to the table t, use the following example. CREATE TABLE t(s SUPER); SUPER type information functions 671 AWS Clean Rooms SQL Reference INSERT INTO t VALUES (3.14159); SELECT DECIMAL_PRECISION(s) FROM t; +-------------------+ \| decimal_precision \| +-------------------+ \| 6 \| +-------------------+ DECIMAL_SCALE function Checks the number of decimal digits to be stored to the right of the decimal point. The range of the scale is
sql-reference-176	sql-reference.pdf	176	the decimal point. The range of the precision is from 1 to 38, with a default of 38. Syntax DECIMAL_PRECISION(super_expression) Arguments super_expression A SUPER expression or column. Return type INTEGER Example To apply the DECIMAL_PRECISION function to the table t, use the following example. CREATE TABLE t(s SUPER); SUPER type information functions 671 AWS Clean Rooms SQL Reference INSERT INTO t VALUES (3.14159); SELECT DECIMAL_PRECISION(s) FROM t; +-------------------+ \| decimal_precision \| +-------------------+ \| 6 \| +-------------------+ DECIMAL_SCALE function Checks the number of decimal digits to be stored to the right of the decimal point. The range of the scale is from 0 to the precision point, with a default of 0. Syntax DECIMAL_SCALE(super_expression) Arguments super_expression A SUPER expression or column. Return type INTEGER Example To apply the DECIMAL_SCALE function to the table t, use the following example. CREATE TABLE t(s SUPER); INSERT INTO t VALUES (3.14159); SELECT DECIMAL_SCALE(s) FROM t; +---------------+ \| decimal_scale \| +---------------+ SUPER type information functions 672 AWS Clean Rooms \| 5 \| +---------------+ IS_ARRAY function SQL Reference Checks whether a variable is an array. The function returns true if the variable is an array. The function also includes empty arrays. Otherwise, the function returns false for all other values, including null. Syntax IS_ARRAY(super_expression) Arguments super_expression A SUPER expression or column. Return type BOOLEAN Example To check if [1,2] is an array using the IS_ARRAY function, use the following example. SELECT IS_ARRAY(JSON_PARSE('[1,2]')); +----------+ \| is_array \| +----------+ \| true \| +----------+ IS_BIGINT function Checks whether a value is a BIGINT. The IS_BIGINT function returns true for numbers of scale 0 in the 64-bit range. Otherwise, the function returns false for all other values, including null and ﬂoating point numbers. SUPER type information functions 673 AWS Clean Rooms SQL Reference The IS_BIGINT function is a superset of IS_INTEGER. Syntax IS_BIGINT(super_expression) Arguments super_expression A SUPER expression or column. Return type BOOLEAN Example To check if 5 is a BIGINT using the IS_BIGINT function, use the following example. CREATE TABLE t(s SUPER); INSERT INTO t VALUES (5); SELECT s, IS_BIGINT(s) FROM t; +---+-----------+ \| s \| is_bigint \| +---+-----------+ \| 5 \| true \| +---+-----------+ IS_CHAR function Checks whether a value is a CHAR. The IS_CHAR function returns true for strings that have only ASCII characters, because the CHAR type can store only characters that are in the ASCII format. The function returns false for any other values. Syntax IS_CHAR(super_expression) SUPER type information functions 674 SQL Reference AWS Clean Rooms Arguments super_expression A SUPER expression or column. Return type BOOLEAN Example To check if t is a CHAR using the IS_CHAR function, use the following example. CREATE TABLE t(s SUPER); INSERT INTO t VALUES ('t'); SELECT s, IS_CHAR(s) FROM t; +-----+---------+ \| s \| is_char \| +-----+---------+ \| "t" \| true \| +-----+---------+ IS_DECIMAL function Checks whether a value is a DECIMAL. The IS_DECIMAL function returns true for numbers that are not ﬂoating points. The function returns false for any other values, including null. The IS_DECIMAL function is a superset of IS_BIGINT. Syntax IS_DECIMAL(super_expression) Arguments super_expression A SUPER expression or column. SUPER type information functions 675 AWS Clean Rooms Return type BOOLEAN Example SQL Reference To check if 1.22 is a DECIMAL using the IS_DECIMAL function, use the following example. CREATE TABLE t(s SUPER); INSERT INTO t VALUES (1.22); SELECT s, IS_DECIMAL(s) FROM t; +------+------------+ \| s \| is_decimal \| +------+------------+ \| 1.22 \| true \| +------+------------+ IS_FLOAT function Checks whether a value is a ﬂoating point number. The IS_FLOAT function returns true for ﬂoating point numbers (FLOAT4 and FLOAT8). The function returns false for any other values. The set of IS_DECIMAL the set of IS_FLOAT are disjoint. Syntax IS_FLOAT(super_expression) Arguments super_expression A SUPER expression or column. Return type BOOLEAN SUPER type information functions 676 AWS Clean Rooms Example SQL Reference To check if 2.22::FLOAT is a FLOAT using the IS_FLOAT function, use the following example. CREATE TABLE t(s SUPER); INSERT INTO t VALUES(2.22::FLOAT); SELECT s, IS_FLOAT(s) FROM t; +---------+----------+ \| s \| is_float \| +---------+----------+ \| 2.22e+0 \| true \| +---------+----------+ IS_INTEGER function Returns true for numbers of scale 0 in the 32-bit range, and false for anything else (including null and ﬂoating point numbers). The IS_INTEGER function is a superset of the IS_SMALLINT function. Syntax IS_INTEGER(super_expression) Arguments super_expression A SUPER expression or column. Return type BOOLEAN Example To check if 5 is an INTEGER using the IS_INTEGER function, use the following example. CREATE TABLE t(s SUPER); SUPER type information functions 677 AWS Clean Rooms SQL Reference INSERT INTO t VALUES (5); SELECT s, IS_INTEGER(s) FROM t; +---+------------+ \| s \| is_integer \| +---+------------+ \| 5 \| true \| +---+------------+ IS_OBJECT function Checks whether a variable is an object. The IS_OBJECT function returns true for objects, including empty objects. The function returns false for any other values, including null.
sql-reference-177	sql-reference.pdf	177	a superset of the IS_SMALLINT function. Syntax IS_INTEGER(super_expression) Arguments super_expression A SUPER expression or column. Return type BOOLEAN Example To check if 5 is an INTEGER using the IS_INTEGER function, use the following example. CREATE TABLE t(s SUPER); SUPER type information functions 677 AWS Clean Rooms SQL Reference INSERT INTO t VALUES (5); SELECT s, IS_INTEGER(s) FROM t; +---+------------+ \| s \| is_integer \| +---+------------+ \| 5 \| true \| +---+------------+ IS_OBJECT function Checks whether a variable is an object. The IS_OBJECT function returns true for objects, including empty objects. The function returns false for any other values, including null. Syntax IS_OBJECT(super_expression) Arguments super_expression A SUPER expression or column. Return type BOOLEAN Example To check if {"name": "Joe"} is an object using the IS_OBJECT function, use the following example. CREATE TABLE t(s super); INSERT INTO t VALUES (JSON_PARSE('{"name": "Joe"}')); SELECT s, IS_OBJECT(s) FROM t; +----------------+-----------+ SUPER type information functions 678 AWS Clean Rooms SQL Reference \| s \| is_object \| +----------------+-----------+ \| {"name":"Joe"} \| true \| +----------------+-----------+ IS_SCALAR function Checks whether a variable is a scalar. The IS_SCALAR function returns true for any value that is not an array or an object. The function returns false for any other values, including null. The set of IS_ARRAY, IS_OBJECT, and IS_SCALAR cover all values except nulls. Syntax IS_SCALAR(super_expression) Arguments super_expression A SUPER expression or column. Return type BOOLEAN Example To check if {"name": "Joe"} is a scalar using the IS_SCALAR function, use the following example. CREATE TABLE t(s SUPER); INSERT INTO t VALUES (JSON_PARSE('{"name": "Joe"}')); SELECT s, IS_SCALAR(s.name) FROM t; +----------------+-----------+ \| s \| is_scalar \| +----------------+-----------+ \| {"name":"Joe"} \| true \| +----------------+-----------+ SUPER type information functions 679 AWS Clean Rooms IS_SMALLINT function SQL Reference Checks whether a variable is a SMALLINT. The IS_SMALLINT function returns true for numbers of scale 0 in the 16-bit range. The function returns false for any other values, including null and ﬂoating point numbers. Syntax IS_SMALLINT(super_expression) Arguments super_expression A SUPER expression or column. Return BOOLEAN Example To check if 5 is a SMALLINT using the IS_SMALLINT function, use the following example. CREATE TABLE t(s SUPER); INSERT INTO t VALUES (5); SELECT s, IS_SMALLINT(s) FROM t; +---+-------------+ \| s \| is_smallint \| +---+-------------+ \| 5 \| true \| +---+-------------+ IS_VARCHAR function Checks whether a variable is a VARCHAR. The IS_VARCHAR function returns true for all strings. The function returns false for any other values. SUPER type information functions 680 AWS Clean Rooms SQL Reference The IS_VARCHAR function is a superset of the IS_CHAR function. Syntax IS_VARCHAR(super_expression) Arguments super_expression A SUPER expression or column. Return type BOOLEAN Example To check if abc is a VARCHAR using the IS_VARCHAR function, use the following example. CREATE TABLE t(s SUPER); INSERT INTO t VALUES ('abc'); SELECT s, IS_VARCHAR(s) FROM t; +-------+------------+ \| s \| is_varchar \| +-------+------------+ \| "abc" \| true \| +-------+------------+ JSON_TYPEOF function The JSON_TYPEOF scalar function returns a VARCHAR with values boolean, number, string, object, array, or null, depending on the dynamic type of the SUPER value. Syntax JSON_TYPEOF(super_expression) SUPER type information functions 681 SQL Reference AWS Clean Rooms Arguments super_expression A SUPER expression or column. Return type VARCHAR Example To check the type of JSON for the array [1,2] using the JSON_TYPEOF function, use the following example. SELECT JSON_TYPEOF(ARRAY(1,2)); +-------------+ \| json_typeof \| +-------------+ \| array \| +-------------+ VARBYTE functions AWS Clean Rooms supports the following VARBYTE functions. Topics • FROM_HEX function • FROM_VARBYTE function • TO_HEX function • TO_VARBYTE function FROM_HEX function FROM_HEX converts a hexadecimal to a binary value. VARBYTE functions 682 AWS Clean Rooms Syntax FROM_HEX(hex_string) Arguments hex_string SQL Reference Hexadecimal string of data type VARCHAR or TEXT to be converted. The format must be a literal value. Return type VARBYTE Example To convert the hexadecimal representation of '6162' to a binary value, use the following example. The result is automatically shown as the hexadecimal representation of the binary value. SELECT FROM_HEX('6162'); +----------+ \| from_hex \| +----------+ \| 6162 \| +----------+ FROM_VARBYTE function FROM_VARBYTE converts a binary value to a character string in the speciﬁed format. Syntax FROM_VARBYTE(binary_value, format) Arguments binary_value A binary value of data type VARBYTE. VARBYTE functions 683 AWS Clean Rooms format SQL Reference The format of the returned character string. Case insensitive valid values are hex, binary, utf-8, and utf8. Return type VARCHAR Example To convert the binary value 'ab' to hexadecimal, use the following example. SELECT FROM_VARBYTE('ab', 'hex'); +--------------+ \| from_varbyte \| +--------------+ \| 6162 \| +--------------+ TO_HEX function TO_HEX converts a number or binary value to a hexadecimal representation. Syntax TO_HEX(value) Arguments value Either a number or binary value (VARBYTE) to be converted. Return type VARCHAR Example To convert a number to its hexadecimal representation, use the following example. VARBYTE functions 684 AWS Clean Rooms SQL Reference SELECT TO_HEX(2147676847); +----------+ \| to_hex \| +----------+ \| 8002f2af \| +----------+To
sql-reference-178	sql-reference.pdf	178	Case insensitive valid values are hex, binary, utf-8, and utf8. Return type VARCHAR Example To convert the binary value 'ab' to hexadecimal, use the following example. SELECT FROM_VARBYTE('ab', 'hex'); +--------------+ \| from_varbyte \| +--------------+ \| 6162 \| +--------------+ TO_HEX function TO_HEX converts a number or binary value to a hexadecimal representation. Syntax TO_HEX(value) Arguments value Either a number or binary value (VARBYTE) to be converted. Return type VARCHAR Example To convert a number to its hexadecimal representation, use the following example. VARBYTE functions 684 AWS Clean Rooms SQL Reference SELECT TO_HEX(2147676847); +----------+ \| to_hex \| +----------+ \| 8002f2af \| +----------+To create a table, insert the VARBYTE representation of 'abc' to a hexadecimal number, and select the column with the value, use the following example. TO_VARBYTE function TO_VARBYTE converts a string in a speciﬁed format to a binary value. Syntax TO_VARBYTE(string, format) Arguments string A CHAR or VARCHAR string. format The format of the input string. Case insensitive valid values are hex, binary, utf-8, and utf8. Return type VARBYTE Example To convert the hex 6162 to a binary value, use the following example. The result is automatically shown as the hexadecimal representation of the binary value. SELECT TO_VARBYTE('6162', 'hex'); +------------+ \| to_varbyte \| +------------+ VARBYTE functions 685 AWS Clean Rooms \| 6162 \| +------------+ Window functions SQL Reference By using window functions, you can create analytic business queries more eﬃciently. Window functions operate on a partition or "window" of a result set, and return a value for every row in that window. In contrast, non-windowed functions perform their calculations with respect to every row in the result set. Unlike group functions that aggregate result rows, window functions retain all rows in the table expression. The values returned are calculated by using values from the sets of rows in that window. For each row in the table, the window deﬁnes a set of rows that is used to compute additional attributes. A window is deﬁned using a window speciﬁcation (the OVER clause), and is based on three main concepts: • Window partitioning, which forms groups of rows (PARTITION clause) • Window ordering, which deﬁnes an order or sequence of rows within each partition (ORDER BY clause) • Window frames, which are deﬁned relative to each row to further restrict the set of rows (ROWS speciﬁcation) Window functions are the last set of operations performed in a query except for the ﬁnal ORDER BY clause. All joins and all WHERE, GROUP BY, and HAVING clauses are completed before the window functions are processed. Therefore, window functions can appear only in the select list or ORDER BY clause. You can use multiple window functions within a single query with diﬀerent frame clauses. You can also use window functions in other scalar expressions, such as CASE. Window function syntax summary Window functions follow a standard syntax, which is as follows. function (expression) OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list [ frame_clause ] ] ) Here, function is one of the functions described in this section. Window functions 686 SQL Reference AWS Clean Rooms The expr_list is as follows. expression \| column_name [, expr_list ] The order_list is as follows. expression \| column_name [ ASC \| DESC ] [ NULLS FIRST \| NULLS LAST ] [, order_list ] The frame_clause is as follows. ROWS { UNBOUNDED PRECEDING \| unsigned_value PRECEDING \| CURRENT ROW } \| { BETWEEN { UNBOUNDED PRECEDING \| unsigned_value { PRECEDING \| FOLLOWING } \| CURRENT ROW} AND { UNBOUNDED FOLLOWING \| unsigned_value { PRECEDING \| FOLLOWING } \| CURRENT ROW }} Arguments function The window function. For details, see the individual function descriptions. OVER The clause that deﬁnes the window speciﬁcation. The OVER clause is mandatory for window functions, and diﬀerentiates window functions from other SQL functions. PARTITION BY expr_list (Optional) The PARTITION BY clause subdivides the result set into partitions, much like the GROUP BY clause. If a partition clause is present, the function is calculated for the rows in each partition. If no partition clause is speciﬁed, a single partition contains the entire table, and the function is computed for that complete table. The ranking functions DENSE_RANK, NTILE, RANK, and ROW_NUMBER require a global comparison of all the rows in the result set. When a PARTITION BY clause is used, the query optimizer can run each aggregation in parallel by spreading the workload across multiple Window functions 687 AWS Clean Rooms SQL Reference slices according to the partitions. If the PARTITION BY clause is not present, the aggregation step must be run serially on a single slice, which can have a signiﬁcant negative impact on performance, especially for large clusters. AWS Clean Rooms doesn't support string literals in PARTITION BY clauses. ORDER BY order_list (Optional) The window function is applied to the rows within each partition
sql-reference-179	sql-reference.pdf	179	all the rows in the result set. When a PARTITION BY clause is used, the query optimizer can run each aggregation in parallel by spreading the workload across multiple Window functions 687 AWS Clean Rooms SQL Reference slices according to the partitions. If the PARTITION BY clause is not present, the aggregation step must be run serially on a single slice, which can have a signiﬁcant negative impact on performance, especially for large clusters. AWS Clean Rooms doesn't support string literals in PARTITION BY clauses. ORDER BY order_list (Optional) The window function is applied to the rows within each partition sorted according to the order speciﬁcation in ORDER BY. This ORDER BY clause is distinct from and completely unrelated to ORDER BY clauses in the frame_clause. The ORDER BY clause can be used without the PARTITION BY clause. For ranking functions, the ORDER BY clause identiﬁes the measures for the ranking values. For aggregation functions, the partitioned rows must be ordered before the aggregate function is computed for each frame. For more about window function types, see Window functions. Column identiﬁers or expressions that evaluate to column identiﬁers are required in the order list. Neither constants nor constant expressions can be used as substitutes for column names. NULLS values are treated as their own group, sorted and ranked according to the NULLS FIRST or NULLS LAST option. By default, NULL values are sorted and ranked last in ASC ordering, and sorted and ranked ﬁrst in DESC ordering. AWS Clean Rooms doesn't support string literals in ORDER BY clauses. If the ORDER BY clause is omitted, the order of the rows is nondeterministic. Note In any parallel system such as AWS Clean Rooms, when an ORDER BY clause doesn't produce a unique and total ordering of the data, the order of the rows is nondeterministic. That is, if the ORDER BY expression produces duplicate values (a partial ordering), the return order of those rows might vary from one run of AWS Clean Rooms to the next. In turn, window functions might return unexpected or inconsistent results. For more information, see Unique ordering of data for window functions. column_name Name of a column to be partitioned by or ordered by. Window functions 688 AWS Clean Rooms ASC \| DESC SQL Reference Option that deﬁnes the sort order for the expression, as follows: • ASC: ascending (for example, low to high for numeric values and 'A' to 'Z' for character strings). If no option is speciﬁed, data is sorted in ascending order by default. • DESC: descending (high to low for numeric values; 'Z' to 'A' for strings). NULLS FIRST \| NULLS LAST Option that speciﬁes whether NULLS should be ordered ﬁrst, before non-null values, or last, after non-null values. By default, NULLS are sorted and ranked last in ASC ordering, and sorted and ranked ﬁrst in DESC ordering. frame_clause For aggregate functions, the frame clause further reﬁnes the set of rows in a function's window when using ORDER BY. It enables you to include or exclude sets of rows within the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. The frame clause doesn't apply to ranking functions. Also, the frame clause isn't required when no ORDER BY clause is used in the OVER clause for an aggregate function. If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. When no ORDER BY clause is speciﬁed, the implied frame is unbounded, equivalent to ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING. ROWS This clause deﬁnes the window frame by specifying a physical oﬀset from the current row. This clause speciﬁes the rows in the current window or partition that the value in the current row is to be combined with. It uses arguments that specify row position, which can be before or after the current row. The reference point for all window frames is the current row. Each row becomes the current row in turn as the window frame slides forward in the partition. The frame can be a simple set of rows up to and including the current row. {UNBOUNDED PRECEDING \| offset PRECEDING \| CURRENT ROW} Or it can be a set of rows between two boundaries. Window functions 689 AWS Clean Rooms SQL Reference BETWEEN { UNBOUNDED PRECEDING \| offset { PRECEDING \| FOLLOWING } \| CURRENT ROW } AND { UNBOUNDED FOLLOWING \| offset { PRECEDING \| FOLLOWING } \| CURRENT ROW } UNBOUNDED PRECEDING indicates that the window starts at the ﬁrst row of the partition; oﬀset PRECEDING indicates that the window starts a number of rows equivalent to the value of oﬀset before the current row. UNBOUNDED PRECEDING is the default. CURRENT ROW indicates the window begins or ends at the current row. UNBOUNDED FOLLOWING indicates
sql-reference-180	sql-reference.pdf	180	a set of rows between two boundaries. Window functions 689 AWS Clean Rooms SQL Reference BETWEEN { UNBOUNDED PRECEDING \| offset { PRECEDING \| FOLLOWING } \| CURRENT ROW } AND { UNBOUNDED FOLLOWING \| offset { PRECEDING \| FOLLOWING } \| CURRENT ROW } UNBOUNDED PRECEDING indicates that the window starts at the ﬁrst row of the partition; oﬀset PRECEDING indicates that the window starts a number of rows equivalent to the value of oﬀset before the current row. UNBOUNDED PRECEDING is the default. CURRENT ROW indicates the window begins or ends at the current row. UNBOUNDED FOLLOWING indicates that the window ends at the last row of the partition; oﬀset FOLLOWING indicates that the window ends a number of rows equivalent to the value of oﬀset after the current row. oﬀset identiﬁes a physical number of rows before or after the current row. In this case, oﬀset must be a constant that evaluates to a positive numeric value. For example, 5 FOLLOWING ends the frame ﬁve rows after the current row. Where BETWEEN is not speciﬁed, the frame is implicitly bounded by the current row. For example, ROWS 5 PRECEDING is equal to ROWS BETWEEN 5 PRECEDING AND CURRENT ROW. Also, ROWS UNBOUNDED FOLLOWING is equal to ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING. Note You can't specify a frame in which the starting boundary is greater than the ending boundary. For example, you can't specify any of the following frames. between 5 following and 5 preceding between current row and 2 preceding between 3 following and current row Unique ordering of data for window functions If an ORDER BY clause for a window function doesn't produce a unique and total ordering of the data, the order of the rows is nondeterministic. If the ORDER BY expression produces duplicate Window functions 690 AWS Clean Rooms SQL Reference values (a partial ordering), the return order of those rows can vary in multiple runs. In this case, window functions can also return unexpected or inconsistent results. For example, the following query returns diﬀerent results over multiple runs. These diﬀerent results occur because order by dateid doesn't produce a unique ordering of the data for the SUM window function. select dateid, pricepaid, sum(pricepaid) over(order by dateid rows unbounded preceding) as sumpaid from sales group by dateid, pricepaid; dateid \| pricepaid \| sumpaid --------+-----------+------------- 1827 \| 1730.00 \| 1730.00 1827 \| 708.00 \| 2438.00 1827 \| 234.00 \| 2672.00 ... select dateid, pricepaid, sum(pricepaid) over(order by dateid rows unbounded preceding) as sumpaid from sales group by dateid, pricepaid; dateid \| pricepaid \| sumpaid --------+-----------+------------- 1827 \| 234.00 \| 234.00 1827 \| 472.00 \| 706.00 1827 \| 347.00 \| 1053.00 ... In this case, adding a second ORDER BY column to the window function can solve the problem. select dateid, pricepaid, sum(pricepaid) over(order by dateid, pricepaid rows unbounded preceding) as sumpaid from sales group by dateid, pricepaid; dateid \| pricepaid \| sumpaid --------+-----------+--------- 1827 \| 234.00 \| 234.00 1827 \| 337.00 \| 571.00 Window functions 691 AWS Clean Rooms 1827 \| 347.00 \| 918.00 ... Supported functions SQL Reference AWS Clean Rooms supports two types of window functions: aggregate and ranking. Following are the supported aggregate functions: • AVG window function • COUNT window function • CUME_DIST window function • DENSE_RANK window function • FIRST_VALUE window function • LAG window function • LAST_VALUE window function • LEAD window function • LISTAGG window function • MAX window function • MEDIAN window function • MIN window function • NTH_VALUE window function • PERCENTILE_CONT window function • PERCENTILE_DISC window function • RATIO_TO_REPORT window function • STDDEV_SAMP and STDDEV_POP window functions (STDDEV_SAMP and STDDEV are synonyms) • SUM window function • VAR_SAMP and VAR_POP window functions (VAR_SAMP and VARIANCE are synonyms) Following are the supported ranking functions: • DENSE_RANK window function Window functions 692 AWS Clean Rooms • NTILE window function • PERCENT_RANK window function • RANK window function • ROW_NUMBER window function SQL Reference Sample table for window function examples You can ﬁnd speciﬁc window function examples with each function description. Some of the examples use a table named WINSALES, which contains 11 rows, as shown in the following table. SALESID DATEID SELLERID BUYERID QTY QTY_SHIPP ED 30001 10001 10005 40001 10006 20001 40005 20002 30003 30004 30007 8/2/2003 12/24/2003 12/24/2003 1/9/2004 1/18/2004 2/12/2004 2/12/2004 2/16/2004 4/18/2004 4/18/2004 9/7/2004 3 1 1 4 1 2 4 2 3 3 3 B C A A C B A C B B C 10 10 20 10 20 10 10 30 40 10 20 10 20 15 20 30 AVG window function The AVG window function returns the average (arithmetic mean) of the input expression values. The AVG function works with numeric values and ignores NULL values. Window functions 693 SQL Reference AWS Clean Rooms Syntax AVG (
sql-reference-181	sql-reference.pdf	181	ED 30001 10001 10005 40001 10006 20001 40005 20002 30003 30004 30007 8/2/2003 12/24/2003 12/24/2003 1/9/2004 1/18/2004 2/12/2004 2/12/2004 2/16/2004 4/18/2004 4/18/2004 9/7/2004 3 1 1 4 1 2 4 2 3 3 3 B C A A C B A C B B C 10 10 20 10 20 10 10 30 40 10 20 10 20 15 20 30 AVG window function The AVG window function returns the average (arithmetic mean) of the input expression values. The AVG function works with numeric values and ignores NULL values. Window functions 693 SQL Reference AWS Clean Rooms Syntax AVG ( [ALL ] expression ) OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list frame_clause ] ) Arguments expression The target column or expression that the function operates on. ALL With the argument ALL, the function retains all duplicate values from the expression for counting. ALL is the default. DISTINCT is not supported. OVER Speciﬁes the window clauses for the aggregation functions. The OVER clause distinguishes window aggregation functions from normal set aggregation functions. PARTITION BY expr_list Deﬁnes the window for the AVG function in terms of one or more expressions. ORDER BY order_list Sorts the rows within each partition. If no PARTITION BY is speciﬁed, ORDER BY uses the entire table. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes the set of rows in a function's window, including or excluding sets of rows within the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. Data types The argument types supported by the AVG function are SMALLINT, INTEGER, BIGINT, NUMERIC, DECIMAL, REAL, and DOUBLE PRECISION. Window functions 694 AWS Clean Rooms SQL Reference The return types supported by the AVG function are: • BIGINT for SMALLINT or INTEGER arguments • NUMERIC for BIGINT arguments • DOUBLE PRECISION for ﬂoating point arguments Examples The following example computes a rolling average of quantities sold by date; order the results by date ID and sales ID: select salesid, dateid, sellerid, qty, avg(qty) over (order by dateid, salesid rows unbounded preceding) as avg from winsales order by 2,1; salesid \| dateid \| sellerid \| qty \| avg ---------+------------+----------+-----+----- 30001 \| 2003-08-02 \| 3 \| 10 \| 10 10001 \| 2003-12-24 \| 1 \| 10 \| 10 10005 \| 2003-12-24 \| 1 \| 30 \| 16 40001 \| 2004-01-09 \| 4 \| 40 \| 22 10006 \| 2004-01-18 \| 1 \| 10 \| 20 20001 \| 2004-02-12 \| 2 \| 20 \| 20 40005 \| 2004-02-12 \| 4 \| 10 \| 18 20002 \| 2004-02-16 \| 2 \| 20 \| 18 30003 \| 2004-04-18 \| 3 \| 15 \| 18 30004 \| 2004-04-18 \| 3 \| 20 \| 18 30007 \| 2004-09-07 \| 3 \| 30 \| 19 (11 rows) For a description of the WINSALES table, see Sample table for window function examples. COUNT window function The COUNT window function counts the rows deﬁned by the expression. The COUNT function has two variations. COUNT() counts all the rows in the target table whether they include nulls or not. COUNT(expression) computes the number of rows with non-NULL values in a speciﬁc column or expression. Window functions 695 SQL Reference AWS Clean Rooms Syntax COUNT ( \| [ ALL ] expression) OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list frame_clause ] ) Arguments expression The target column or expression that the function operates on. ALL With the argument ALL, the function retains all duplicate values from the expression for counting. ALL is the default. DISTINCT is not supported. OVER Speciﬁes the window clauses for the aggregation functions. The OVER clause distinguishes window aggregation functions from normal set aggregation functions. PARTITION BY expr_list Deﬁnes the window for the COUNT function in terms of one or more expressions. ORDER BY order_list Sorts the rows within each partition. If no PARTITION BY is speciﬁed, ORDER BY uses the entire table. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes the set of rows in a function's window, including or excluding sets of rows within the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. Data types The COUNT function supports all argument data types. Window functions 696 AWS Clean Rooms SQL Reference The return type supported by the COUNT function is BIGINT. Examples The following example shows the sales ID, quantity, and count of all rows from the beginning of the data window: select salesid, qty, count(*) over (order by salesid rows unbounded preceding) as count from winsales order by salesid; salesid \| qty \| count ---------+-----+----- 10001 \|
sql-reference-182	sql-reference.pdf	182	or excluding sets of rows within the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. Data types The COUNT function supports all argument data types. Window functions 696 AWS Clean Rooms SQL Reference The return type supported by the COUNT function is BIGINT. Examples The following example shows the sales ID, quantity, and count of all rows from the beginning of the data window: select salesid, qty, count(*) over (order by salesid rows unbounded preceding) as count from winsales order by salesid; salesid \| qty \| count ---------+-----+----- 10001 \| 10 \| 1 10005 \| 30 \| 2 10006 \| 10 \| 3 20001 \| 20 \| 4 20002 \| 20 \| 5 30001 \| 10 \| 6 30003 \| 15 \| 7 30004 \| 20 \| 8 30007 \| 30 \| 9 40001 \| 40 \| 10 40005 \| 10 \| 11 (11 rows) For a description of the WINSALES table, see Sample table for window function examples. The following example shows how the sales ID, quantity, and count of non-null rows from the beginning of the data window. (In the WINSALES table, the QTY_SHIPPED column contains some NULLs.) select salesid, qty, qty_shipped, count(qty_shipped) over (order by salesid rows unbounded preceding) as count from winsales order by salesid; salesid \| qty \| qty_shipped \| count ---------+-----+-------------+------- 10001 \| 10 \| 10 \| 1 Window functions 697 AWS Clean Rooms SQL Reference 10005 \| 30 \| \| 1 10006 \| 10 \| \| 1 20001 \| 20 \| 20 \| 2 20002 \| 20 \| 20 \| 3 30001 \| 10 \| 10 \| 4 30003 \| 15 \| \| 4 30004 \| 20 \| \| 4 30007 \| 30 \| \| 4 40001 \| 40 \| \| 4 40005 \| 10 \| 10 \| 5 (11 rows) CUME_DIST window function Calculates the cumulative distribution of a value within a window or partition. Assuming ascending ordering, the cumulative distribution is determined using this formula: count of rows with values <= x / count of rows in the window or partition where x equals the value in the current row of the column speciﬁed in the ORDER BY clause. The following dataset illustrates use of this formula: Row# Value Calculation CUME_DIST 1 2500 (1)/(5) 0.2 2 2600 (2)/(5) 0.4 3 2800 (3)/(5) 0.6 4 2900 (4)/(5) 0.8 5 3100 (5)/(5) 1.0 The return value range is >0 to 1, inclusive. Syntax CUME_DIST () OVER ( [ PARTITION BY partition_expression ] [ ORDER BY order_list ] ) Window functions 698 AWS Clean Rooms Arguments OVER SQL Reference A clause that speciﬁes the window partitioning. The OVER clause cannot contain a window frame speciﬁcation. PARTITION BY partition_expression Optional. An expression that sets the range of records for each group in the OVER clause. ORDER BY order_list The expression on which to calculate cumulative distribution. The expression must have either a numeric data type or be implicitly convertible to one. If ORDER BY is omitted, the return value is 1 for all rows. If ORDER BY doesn't produce a unique ordering, the order of the rows is nondeterministic. For more information, see Unique ordering of data for window functions. Return type FLOAT8 Examples The following example calculates the cumulative distribution of the quantity for each seller: select sellerid, qty, cume_dist() over (partition by sellerid order by qty) from winsales; sellerid qty cume_dist -------------------------------------------------- 1 10.00 0.33 1 10.64 0.67 1 30.37 1 3 10.04 0.25 3 15.15 0.5 3 20.75 0.75 3 30.55 1 2 20.09 0.5 2 20.12 1 4 10.12 0.5 Window functions 699 AWS Clean Rooms 4 40.23 1 SQL Reference For a description of the WINSALES table, see Sample table for window function examples. DENSE_RANK window function The DENSE_RANK window function determines the rank of a value in a group of values, based on the ORDER BY expression in the OVER clause. If the optional PARTITION BY clause is present, the rankings are reset for each group of rows. Rows with equal values for the ranking criteria receive the same rank. The DENSE_RANK function diﬀers from RANK in one respect: If two or more rows tie, there is no gap in the sequence of ranked values. For example, if two rows are ranked 1, the next rank is 2. You can have ranking functions with diﬀerent PARTITION BY and ORDER BY clauses in the same query. Syntax DENSE_RANK () OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list ] ) Arguments ( ) The function takes no arguments, but the empty parentheses are required. OVER The window clauses for the DENSE_RANK function. PARTITION BY expr_list Optional. One or more expressions that deﬁne the window. ORDER BY order_list Optional. The expression on which the ranking values are based. If
sql-reference-183	sql-reference.pdf	183	in the sequence of ranked values. For example, if two rows are ranked 1, the next rank is 2. You can have ranking functions with diﬀerent PARTITION BY and ORDER BY clauses in the same query. Syntax DENSE_RANK () OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list ] ) Arguments ( ) The function takes no arguments, but the empty parentheses are required. OVER The window clauses for the DENSE_RANK function. PARTITION BY expr_list Optional. One or more expressions that deﬁne the window. ORDER BY order_list Optional. The expression on which the ranking values are based. If no PARTITION BY is speciﬁed, ORDER BY uses the entire table. If ORDER BY is omitted, the return value is 1 for all rows. Window functions 700 AWS Clean Rooms SQL Reference If ORDER BY doesn't produce a unique ordering, the order of the rows is nondeterministic. For more information, see Unique ordering of data for window functions. Return type INTEGER Examples The following example orders the table by the quantity sold (in descending order), and assign both a dense rank and a regular rank to each row. The results are sorted after the window function results are applied. select salesid, qty, dense_rank() over(order by qty desc) as d_rnk, rank() over(order by qty desc) as rnk from winsales order by 2,1; salesid \| qty \| d_rnk \| rnk ---------+-----+-------+----- 10001 \| 10 \| 5 \| 8 10006 \| 10 \| 5 \| 8 30001 \| 10 \| 5 \| 8 40005 \| 10 \| 5 \| 8 30003 \| 15 \| 4 \| 7 20001 \| 20 \| 3 \| 4 20002 \| 20 \| 3 \| 4 30004 \| 20 \| 3 \| 4 10005 \| 30 \| 2 \| 2 30007 \| 30 \| 2 \| 2 40001 \| 40 \| 1 \| 1 (11 rows) Note the diﬀerence in rankings assigned to the same set of rows when the DENSE_RANK and RANK functions are used side by side in the same query. For a description of the WINSALES table, see Sample table for window function examples. The following example partitions the table by SELLERID and orders each partition by the quantity (in descending order) and assign a dense rank to each row. The results are sorted after the window function results are applied. Window functions 701 AWS Clean Rooms SQL Reference select salesid, sellerid, qty, dense_rank() over(partition by sellerid order by qty desc) as d_rnk from winsales order by 2,3,1; salesid \| sellerid \| qty \| d_rnk ---------+----------+-----+------- 10001 \| 1 \| 10 \| 2 10006 \| 1 \| 10 \| 2 10005 \| 1 \| 30 \| 1 20001 \| 2 \| 20 \| 1 20002 \| 2 \| 20 \| 1 30001 \| 3 \| 10 \| 4 30003 \| 3 \| 15 \| 3 30004 \| 3 \| 20 \| 2 30007 \| 3 \| 30 \| 1 40005 \| 4 \| 10 \| 2 40001 \| 4 \| 40 \| 1 (11 rows) For a description of the WINSALES table, see Sample table for window function examples. FIRST_VALUE window function Given an ordered set of rows, FIRST_VALUE returns the value of the speciﬁed expression with respect to the ﬁrst row in the window frame. For information about selecting the last row in the frame, see LAST_VALUE window function . Syntax FIRST_VALUE( expression )[ IGNORE NULLS \| RESPECT NULLS ] OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list frame_clause ] ) Arguments expression The target column or expression that the function operates on. Window functions 702 AWS Clean Rooms IGNORE NULLS SQL Reference When this option is used with FIRST_VALUE, the function returns the ﬁrst value in the frame that is not NULL (or NULL if all values are NULL). RESPECT NULLS Indicates that AWS Clean Rooms should include null values in the determination of which row to use. RESPECT NULLS is supported by default if you do not specify IGNORE NULLS. OVER Introduces the window clauses for the function. PARTITION BY expr_list Deﬁnes the window for the function in terms of one or more expressions. ORDER BY order_list Sorts the rows within each partition. If no PARTITION BY clause is speciﬁed, ORDER BY sorts the entire table. If you specify an ORDER BY clause, you must also specify a frame_clause. The results of the FIRST_VALUE function depends on the ordering of the data. The results are nondeterministic in the following cases: • When no ORDER BY clause is speciﬁed and a partition contains two diﬀerent values for an expression • When the expression evaluates to diﬀerent values that correspond to the same value in the ORDER BY list. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes
sql-reference-184	sql-reference.pdf	184	speciﬁed, ORDER BY sorts the entire table. If you specify an ORDER BY clause, you must also specify a frame_clause. The results of the FIRST_VALUE function depends on the ordering of the data. The results are nondeterministic in the following cases: • When no ORDER BY clause is speciﬁed and a partition contains two diﬀerent values for an expression • When the expression evaluates to diﬀerent values that correspond to the same value in the ORDER BY list. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes the set of rows in a function's window, including or excluding sets of rows in the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. Return type These functions support expressions that use primitive AWS Clean Rooms data types. The return type is the same as the data type of the expression. Window functions 703 AWS Clean Rooms Examples SQL Reference The following example returns the seating capacity for each venue in the VENUE table, with the results ordered by capacity (high to low). The FIRST_VALUE function is used to select the name of the venue that corresponds to the ﬁrst row in the frame: in this case, the row with the highest number of seats. The results are partitioned by state, so when the VENUESTATE value changes, a new ﬁrst value is selected. The window frame is unbounded so the same ﬁrst value is selected for each row in each partition. For California, Qualcomm Stadium has the highest number of seats (70561), so this name is the ﬁrst value for all of the rows in the CA partition. select venuestate, venueseats, venuename, first_value(venuename) over(partition by venuestate order by venueseats desc rows between unbounded preceding and unbounded following) from (select * from venue where venueseats >0) order by venuestate; venuestate \| venueseats \| venuename \| first_value -----------+------------+-------------------------------- +------------------------------ CA \| 70561 \| Qualcomm Stadium \| Qualcomm Stadium CA \| 69843 \| Monster Park \| Qualcomm Stadium CA \| 63026 \| McAfee Coliseum \| Qualcomm Stadium CA \| 56000 \| Dodger Stadium \| Qualcomm Stadium CA \| 45050 \| Angel Stadium of Anaheim \| Qualcomm Stadium CA \| 42445 \| PETCO Park \| Qualcomm Stadium CA \| 41503 \| AT&T Park \| Qualcomm Stadium CA \| 22000 \| Shoreline Amphitheatre \| Qualcomm Stadium CO \| 76125 \| INVESCO Field \| INVESCO Field CO \| 50445 \| Coors Field \| INVESCO Field DC \| 41888 \| Nationals Park \| Nationals Park FL \| 74916 \| Dolphin Stadium \| Dolphin Stadium FL \| 73800 \| Jacksonville Municipal Stadium \| Dolphin Stadium FL \| 65647 \| Raymond James Stadium \| Dolphin Stadium FL \| 36048 \| Tropicana Field \| Dolphin Stadium ... Window functions 704 AWS Clean Rooms LAG window function SQL Reference The LAG window function returns the values for a row at a given oﬀset above (before) the current row in the partition. Syntax LAG (value_expr [, offset ]) [ IGNORE NULLS \| RESPECT NULLS ] OVER ( [ PARTITION BY window_partition ] ORDER BY window_ordering ) Arguments value_expr The target column or expression that the function operates on. oﬀset An optional parameter that speciﬁes the number of rows before the current row to return values for. The oﬀset can be a constant integer or an expression that evaluates to an integer. If you do not specify an oﬀset, AWS Clean Rooms uses 1 as the default value. An oﬀset of 0 indicates the current row. IGNORE NULLS An optional speciﬁcation that indicates that AWS Clean Rooms should skip null values in the determination of which row to use. Null values are included if IGNORE NULLS is not listed. Note You can use an NVL or COALESCE expression to replace the null values with another value. RESPECT NULLS Indicates that AWS Clean Rooms should include null values in the determination of which row to use. RESPECT NULLS is supported by default if you do not specify IGNORE NULLS. Window functions 705 AWS Clean Rooms OVER SQL Reference Speciﬁes the window partitioning and ordering. The OVER clause cannot contain a window frame speciﬁcation. PARTITION BY window_partition An optional argument that sets the range of records for each group in the OVER clause. ORDER BY window_ordering Sorts the rows within each partition. The LAG window function supports expressions that use any of the AWS Clean Rooms data types. The return type is the same as the type of the value_expr. Examples The following example shows the quantity of tickets sold to the buyer with a buyer ID of 3 and the time that buyer 3 bought the tickets. To compare each sale with the previous sale for buyer 3, the query returns the previous quantity
sql-reference-185	sql-reference.pdf	185	BY window_partition An optional argument that sets the range of records for each group in the OVER clause. ORDER BY window_ordering Sorts the rows within each partition. The LAG window function supports expressions that use any of the AWS Clean Rooms data types. The return type is the same as the type of the value_expr. Examples The following example shows the quantity of tickets sold to the buyer with a buyer ID of 3 and the time that buyer 3 bought the tickets. To compare each sale with the previous sale for buyer 3, the query returns the previous quantity sold for each sale. Since there is no purchase before 1/16/2008, the ﬁrst previous quantity sold value is null: select buyerid, saletime, qtysold, lag(qtysold,1) over (order by buyerid, saletime) as prev_qtysold from sales where buyerid = 3 order by buyerid, saletime; buyerid \| saletime \| qtysold \| prev_qtysold ---------+---------------------+---------+-------------- 3 \| 2008-01-16 01:06:09 \| 1 \| 3 \| 2008-01-28 02:10:01 \| 1 \| 1 3 \| 2008-03-12 10:39:53 \| 1 \| 1 3 \| 2008-03-13 02:56:07 \| 1 \| 1 3 \| 2008-03-29 08:21:39 \| 2 \| 1 3 \| 2008-04-27 02:39:01 \| 1 \| 2 3 \| 2008-08-16 07:04:37 \| 2 \| 1 3 \| 2008-08-22 11:45:26 \| 2 \| 2 3 \| 2008-09-12 09:11:25 \| 1 \| 2 3 \| 2008-10-01 06:22:37 \| 1 \| 1 3 \| 2008-10-20 01:55:51 \| 2 \| 1 3 \| 2008-10-28 01:30:40 \| 1 \| 2 (12 rows) Window functions 706 AWS Clean Rooms SQL Reference LAST_VALUE window function Given an ordered set of rows, The LAST_VALUE function returns the value of the expression with respect to the last row in the frame. For information about selecting the ﬁrst row in the frame, see FIRST_VALUE window function . Syntax LAST_VALUE( expression )[ IGNORE NULLS \| RESPECT NULLS ] OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list frame_clause ] ) Arguments expression The target column or expression that the function operates on. IGNORE NULLS The function returns the last value in the frame that is not NULL (or NULL if all values are NULL). RESPECT NULLS Indicates that AWS Clean Rooms should include null values in the determination of which row to use. RESPECT NULLS is supported by default if you do not specify IGNORE NULLS. OVER Introduces the window clauses for the function. PARTITION BY expr_list Deﬁnes the window for the function in terms of one or more expressions. ORDER BY order_list Sorts the rows within each partition. If no PARTITION BY clause is speciﬁed, ORDER BY sorts the entire table. If you specify an ORDER BY clause, you must also specify a frame_clause. Window functions 707 AWS Clean Rooms SQL Reference The results depend on the ordering of the data. The results are nondeterministic in the following cases: • When no ORDER BY clause is speciﬁed and a partition contains two diﬀerent values for an expression • When the expression evaluates to diﬀerent values that correspond to the same value in the ORDER BY list. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes the set of rows in a function's window, including or excluding sets of rows in the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. Return type These functions support expressions that use primitive AWS Clean Rooms data types. The return type is the same as the data type of the expression. Examples The following example returns the seating capacity for each venue in the VENUE table, with the results ordered by capacity (high to low). The LAST_VALUE function is used to select the name of the venue that corresponds to the last row in the frame: in this case, the row with the least number of seats. The results are partitioned by state, so when the VENUESTATE value changes, a new last value is selected. The window frame is unbounded so the same last value is selected for each row in each partition. For California, Shoreline Amphitheatre is returned for every row in the partition because it has the lowest number of seats (22000). select venuestate, venueseats, venuename, last_value(venuename) over(partition by venuestate order by venueseats desc rows between unbounded preceding and unbounded following) from (select * from venue where venueseats >0) order by venuestate; venuestate \| venueseats \| venuename \| last_value Window functions 708 AWS Clean Rooms SQL Reference -----------+------------+-------------------------------- +------------------------------ CA \| 70561 \| Qualcomm Stadium \| Shoreline Amphitheatre CA \| 69843 \| Monster Park \| Shoreline Amphitheatre CA \| 63026 \| McAfee Coliseum \| Shoreline Amphitheatre CA \| 56000 \| Dodger Stadium \| Shoreline Amphitheatre CA \| 45050 \| Angel Stadium of Anaheim \| Shoreline Amphitheatre CA \| 42445
sql-reference-186	sql-reference.pdf	186	of seats (22000). select venuestate, venueseats, venuename, last_value(venuename) over(partition by venuestate order by venueseats desc rows between unbounded preceding and unbounded following) from (select * from venue where venueseats >0) order by venuestate; venuestate \| venueseats \| venuename \| last_value Window functions 708 AWS Clean Rooms SQL Reference -----------+------------+-------------------------------- +------------------------------ CA \| 70561 \| Qualcomm Stadium \| Shoreline Amphitheatre CA \| 69843 \| Monster Park \| Shoreline Amphitheatre CA \| 63026 \| McAfee Coliseum \| Shoreline Amphitheatre CA \| 56000 \| Dodger Stadium \| Shoreline Amphitheatre CA \| 45050 \| Angel Stadium of Anaheim \| Shoreline Amphitheatre CA \| 42445 \| PETCO Park \| Shoreline Amphitheatre CA \| 41503 \| AT&T Park \| Shoreline Amphitheatre CA \| 22000 \| Shoreline Amphitheatre \| Shoreline Amphitheatre CO \| 76125 \| INVESCO Field \| Coors Field CO \| 50445 \| Coors Field \| Coors Field DC \| 41888 \| Nationals Park \| Nationals Park FL \| 74916 \| Dolphin Stadium \| Tropicana Field FL \| 73800 \| Jacksonville Municipal Stadium \| Tropicana Field FL \| 65647 \| Raymond James Stadium \| Tropicana Field FL \| 36048 \| Tropicana Field \| Tropicana Field ... LEAD window function The LEAD window function returns the values for a row at a given oﬀset below (after) the current row in the partition. Syntax LEAD (value_expr [, offset ]) [ IGNORE NULLS \| RESPECT NULLS ] OVER ( [ PARTITION BY window_partition ] ORDER BY window_ordering ) Arguments value_expr The target column or expression that the function operates on. oﬀset An optional parameter that speciﬁes the number of rows below the current row to return values for. The oﬀset can be a constant integer or an expression that evaluates to an integer. If you do not specify an oﬀset, AWS Clean Rooms uses 1 as the default value. An oﬀset of 0 indicates the current row. Window functions 709 AWS Clean Rooms IGNORE NULLS SQL Reference An optional speciﬁcation that indicates that AWS Clean Rooms should skip null values in the determination of which row to use. Null values are included if IGNORE NULLS is not listed. Note You can use an NVL or COALESCE expression to replace the null values with another value. RESPECT NULLS Indicates that AWS Clean Rooms should include null values in the determination of which row to use. RESPECT NULLS is supported by default if you do not specify IGNORE NULLS. OVER Speciﬁes the window partitioning and ordering. The OVER clause cannot contain a window frame speciﬁcation. PARTITION BY window_partition An optional argument that sets the range of records for each group in the OVER clause. ORDER BY window_ordering Sorts the rows within each partition. The LEAD window function supports expressions that use any of the AWS Clean Rooms data types. The return type is the same as the type of the value_expr. Examples The following example provides the commission for events in the SALES table for which tickets were sold on January 1, 2008 and January 2, 2008 and the commission paid for ticket sales for the subsequent sale. select eventid, commission, saletime, lead(commission, 1) over (order by saletime) as next_comm from sales where saletime between '2008-01-01 00:00:00' and '2008-01-02 12:59:59' order by saletime; Window functions 710 AWS Clean Rooms SQL Reference eventid \| commission \| saletime \| next_comm ---------+------------+---------------------+----------- 6213 \| 52.05 \| 2008-01-01 01:00:19 \| 106.20 7003 \| 106.20 \| 2008-01-01 02:30:52 \| 103.20 8762 \| 103.20 \| 2008-01-01 03:50:02 \| 70.80 1150 \| 70.80 \| 2008-01-01 06:06:57 \| 50.55 1749 \| 50.55 \| 2008-01-01 07:05:02 \| 125.40 8649 \| 125.40 \| 2008-01-01 07:26:20 \| 35.10 2903 \| 35.10 \| 2008-01-01 09:41:06 \| 259.50 6605 \| 259.50 \| 2008-01-01 12:50:55 \| 628.80 6870 \| 628.80 \| 2008-01-01 12:59:34 \| 74.10 6977 \| 74.10 \| 2008-01-02 01:11:16 \| 13.50 4650 \| 13.50 \| 2008-01-02 01:40:59 \| 26.55 4515 \| 26.55 \| 2008-01-02 01:52:35 \| 22.80 5465 \| 22.80 \| 2008-01-02 02:28:01 \| 45.60 5465 \| 45.60 \| 2008-01-02 02:28:02 \| 53.10 7003 \| 53.10 \| 2008-01-02 02:31:12 \| 70.35 4124 \| 70.35 \| 2008-01-02 03:12:50 \| 36.15 1673 \| 36.15 \| 2008-01-02 03:15:00 \| 1300.80 ... (39 rows) LISTAGG window function For each group in a query, the LISTAGG window function orders the rows for that group according to the ORDER BY expression, then concatenates the values into a single string. LISTAGG is a compute-node only function. The function returns an error if the query doesn't reference a user-deﬁned table or AWS Clean Rooms system table. Syntax LISTAGG( [DISTINCT] expression [, 'delimiter' ] ) [ WITHIN GROUP (ORDER BY order_list) ] OVER ( [PARTITION BY partition_expression] ) Arguments DISTINCT (Optional) A clause that eliminates duplicate values from the speciﬁed expression before concatenating. Trailing spaces are ignored, so the strings 'a' and 'a ' are treated as Window functions 711 AWS Clean
sql-reference-187	sql-reference.pdf	187	orders the rows for that group according to the ORDER BY expression, then concatenates the values into a single string. LISTAGG is a compute-node only function. The function returns an error if the query doesn't reference a user-deﬁned table or AWS Clean Rooms system table. Syntax LISTAGG( [DISTINCT] expression [, 'delimiter' ] ) [ WITHIN GROUP (ORDER BY order_list) ] OVER ( [PARTITION BY partition_expression] ) Arguments DISTINCT (Optional) A clause that eliminates duplicate values from the speciﬁed expression before concatenating. Trailing spaces are ignored, so the strings 'a' and 'a ' are treated as Window functions 711 AWS Clean Rooms SQL Reference duplicates. LISTAGG uses the ﬁrst value encountered. For more information, see Signiﬁcance of trailing blanks. aggregate_expression Any valid expression (such as a column name) that provides the values to aggregate. NULL values and empty strings are ignored. delimiter (Optional) The string constant to separate the concatenated values. The default is NULL. AWS Clean Rooms supports any amount of leading or trailing whitespace around an optional comma or colon as well as an empty string or any number of spaces. Examples of valid values are: ", " ": " " " WITHIN GROUP (ORDER BY order_list) (Optional) A clause that speciﬁes the sort order of the aggregated values. Deterministic only if ORDER BY provides unique ordering. The default is to aggregate all rows and return a single value. OVER A clause that speciﬁes the window partitioning. The OVER clause cannot contain a window ordering or window frame speciﬁcation. PARTITION BY partition_expression (Optional) Sets the range of records for each group in the OVER clause. Returns VARCHAR(MAX). If the result set is larger than the maximum VARCHAR size (64K – 1, or 65535), then LISTAGG returns the following error: Invalid operation: Result size exceeds LISTAGG limit Window functions 712 AWS Clean Rooms Examples SQL Reference The following examples uses the WINSALES table. For a description of the WINSALES table, see Sample table for window function examples. The following example returns a list of seller IDs, ordered by seller ID. select listagg(sellerid) within group (order by sellerid) over() from winsales; listagg ------------ 11122333344 ... ... 11122333344 11122333344 (11 rows) The following example returns a list of seller IDs for buyer B, ordered by date. select listagg(sellerid) within group (order by dateid) over () as seller from winsales where buyerid = 'b' ; seller --------- 3233 3233 3233 3233 (4 rows) The following example returns a comma-separated list of sales dates for buyer B. select listagg(dateid,',') within group (order by sellerid desc,salesid asc) over () as dates Window functions 713 SQL Reference AWS Clean Rooms from winsales where buyerid = 'b'; dates ------------------------------------------- 2003-08-02,2004-04-18,2004-04-18,2004-02-12 2003-08-02,2004-04-18,2004-04-18,2004-02-12 2003-08-02,2004-04-18,2004-04-18,2004-02-12 2003-08-02,2004-04-18,2004-04-18,2004-02-12 (4 rows) The following example uses DISTINCT to return a list of unique sales dates for buyer B. select listagg(distinct dateid,',') within group (order by sellerid desc,salesid asc) over () as dates from winsales where buyerid = 'b'; dates -------------------------------- 2003-08-02,2004-04-18,2004-02-12 2003-08-02,2004-04-18,2004-02-12 2003-08-02,2004-04-18,2004-02-12 2003-08-02,2004-04-18,2004-02-12 (4 rows) The following example returns a comma-separated list of sales IDs for each buyer ID. select buyerid, listagg(salesid,',') within group (order by salesid) over (partition by buyerid) as sales_id from winsales order by buyerid; buyerid \| sales_id -----------+------------------------ a \|10005,40001,40005 a \|10005,40001,40005 a \|10005,40001,40005 Window functions 714 AWS Clean Rooms SQL Reference b \|20001,30001,30004,30003 b \|20001,30001,30004,30003 b \|20001,30001,30004,30003 b \|20001,30001,30004,30003 c \|10001,20002,30007,10006 c \|10001,20002,30007,10006 c \|10001,20002,30007,10006 c \|10001,20002,30007,10006 (11 rows) MAX window function The MAX window function returns the maximum of the input expression values. The MAX function works with numeric values and ignores NULL values. Syntax MAX ( [ ALL ] expression ) OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list frame_clause ] ) Arguments expression The target column or expression that the function operates on. ALL With the argument ALL, the function retains all duplicate values from the expression. ALL is the default. DISTINCT is not supported. OVER A clause that speciﬁes the window clauses for the aggregation functions. The OVER clause distinguishes window aggregation functions from normal set aggregation functions. PARTITION BY expr_list Deﬁnes the window for the MAX function in terms of one or more expressions. Window functions 715 AWS Clean Rooms ORDER BY order_list SQL Reference Sorts the rows within each partition. If no PARTITION BY is speciﬁed, ORDER BY uses the entire table. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes the set of rows in a function's window, including or excluding sets of rows within the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. Data types Accepts any data type as input. Returns the same data type as expression. Examples The following example shows the sales ID, quantity, and maximum quantity from the beginning of
sql-reference-188	sql-reference.pdf	188	partition. If no PARTITION BY is speciﬁed, ORDER BY uses the entire table. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes the set of rows in a function's window, including or excluding sets of rows within the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. Data types Accepts any data type as input. Returns the same data type as expression. Examples The following example shows the sales ID, quantity, and maximum quantity from the beginning of the data window: select salesid, qty, max(qty) over (order by salesid rows unbounded preceding) as max from winsales order by salesid; salesid \| qty \| max ---------+-----+----- 10001 \| 10 \| 10 10005 \| 30 \| 30 10006 \| 10 \| 30 20001 \| 20 \| 30 20002 \| 20 \| 30 30001 \| 10 \| 30 30003 \| 15 \| 30 30004 \| 20 \| 30 30007 \| 30 \| 30 40001 \| 40 \| 40 40005 \| 10 \| 40 (11 rows) For a description of the WINSALES table, see Sample table for window function examples. Window functions 716 AWS Clean Rooms SQL Reference The following example shows the salesid, quantity, and maximum quantity in a restricted frame: select salesid, qty, max(qty) over (order by salesid rows between 2 preceding and 1 preceding) as max from winsales order by salesid; salesid \| qty \| max ---------+-----+----- 10001 \| 10 \| 10005 \| 30 \| 10 10006 \| 10 \| 30 20001 \| 20 \| 30 20002 \| 20 \| 20 30001 \| 10 \| 20 30003 \| 15 \| 20 30004 \| 20 \| 15 30007 \| 30 \| 20 40001 \| 40 \| 30 40005 \| 10 \| 40 (11 rows) MEDIAN window function Calculates the median value for the range of values in a window or partition. NULL values in the range are ignored. MEDIAN is an inverse distribution function that assumes a continuous distribution model. MEDIAN is a compute-node only function. The function returns an error if the query doesn't reference a user-deﬁned table or AWS Clean Rooms system table. Syntax MEDIAN ( median_expression ) OVER ( [ PARTITION BY partition_expression ] ) Window functions 717 AWS Clean Rooms Arguments median_expression SQL Reference An expression, such as a column name, that provides the values for which to determine the median. The expression must have either a numeric or datetime data type or be implicitly convertible to one. OVER A clause that speciﬁes the window partitioning. The OVER clause cannot contain a window ordering or window frame speciﬁcation. PARTITION BY partition_expression Optional. An expression that sets the range of records for each group in the OVER clause. Data types The return type is determined by the data type of median_expression. The following table shows the return type for each median_expression data type. Input Type Return Type NUMERIC, DECIMAL FLOAT, DOUBLE DATE Usage notes DECIMAL DOUBLE DATE If the median_expression argument is a DECIMAL data type deﬁned with the maximum precision of 38 digits, it is possible that MEDIAN will return either an inaccurate result or an error. If the return value of the MEDIAN function exceeds 38 digits, the result is truncated to ﬁt, which causes a loss of precision. If, during interpolation, an intermediate result exceeds the maximum precision, a numeric overﬂow occurs and the function returns an error. To avoid these conditions, we recommend either using a data type with lower precision or casting the median_expression argument to a lower precision. Window functions 718 AWS Clean Rooms SQL Reference For example, a SUM function with a DECIMAL argument returns a default precision of 38 digits. The scale of the result is the same as the scale of the argument. So, for example, a SUM of a DECIMAL(5,2) column returns a DECIMAL(38,2) data type. The following example uses a SUM function in the median_expression argument of a MEDIAN function. The data type of the PRICEPAID column is DECIMAL (8,2), so the SUM function returns DECIMAL(38,2). select salesid, sum(pricepaid), median(sum(pricepaid)) over() from sales where salesid < 10 group by salesid; To avoid a potential loss of precision or an overﬂow error, cast the result to a DECIMAL data type with lower precision, as the following example shows. select salesid, sum(pricepaid), median(sum(pricepaid)::decimal(30,2)) over() from sales where salesid < 10 group by salesid; Examples The following example calculates the median sales quantity for each seller: select sellerid, qty, median(qty) over (partition by sellerid) from winsales order by sellerid; sellerid qty median --------------------------- 1 10 10.0 1 10 10.0 1 30 10.0 2 20 20.0 2 20 20.0 3 10 17.5 3 15 17.5 3 20 17.5 3 30 17.5 4 10 25.0 4 40 25.0 Window functions 719
sql-reference-189	sql-reference.pdf	189	precision or an overﬂow error, cast the result to a DECIMAL data type with lower precision, as the following example shows. select salesid, sum(pricepaid), median(sum(pricepaid)::decimal(30,2)) over() from sales where salesid < 10 group by salesid; Examples The following example calculates the median sales quantity for each seller: select sellerid, qty, median(qty) over (partition by sellerid) from winsales order by sellerid; sellerid qty median --------------------------- 1 10 10.0 1 10 10.0 1 30 10.0 2 20 20.0 2 20 20.0 3 10 17.5 3 15 17.5 3 20 17.5 3 30 17.5 4 10 25.0 4 40 25.0 Window functions 719 AWS Clean Rooms SQL Reference For a description of the WINSALES table, see Sample table for window function examples. MIN window function The MIN window function returns the minimum of the input expression values. The MIN function works with numeric values and ignores NULL values. Syntax MIN ( [ ALL ] expression ) OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list frame_clause ] ) Arguments expression The target column or expression that the function operates on. ALL With the argument ALL, the function retains all duplicate values from the expression. ALL is the default. DISTINCT is not supported. OVER Speciﬁes the window clauses for the aggregation functions. The OVER clause distinguishes window aggregation functions from normal set aggregation functions. PARTITION BY expr_list Deﬁnes the window for the MIN function in terms of one or more expressions. ORDER BY order_list Sorts the rows within each partition. If no PARTITION BY is speciﬁed, ORDER BY uses the entire table. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes the set of rows in a function's window, including or excluding sets of Window functions 720 AWS Clean Rooms SQL Reference rows within the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. Data types Accepts any data type as input. Returns the same data type as expression. Examples The following example shows the sales ID, quantity, and minimum quantity from the beginning of the data window: select salesid, qty, min(qty) over (order by salesid rows unbounded preceding) from winsales order by salesid; salesid \| qty \| min ---------+-----+----- 10001 \| 10 \| 10 10005 \| 30 \| 10 10006 \| 10 \| 10 20001 \| 20 \| 10 20002 \| 20 \| 10 30001 \| 10 \| 10 30003 \| 15 \| 10 30004 \| 20 \| 10 30007 \| 30 \| 10 40001 \| 40 \| 10 40005 \| 10 \| 10 (11 rows) For a description of the WINSALES table, see Sample table for window function examples. The following example shows the sales ID, quantity, and minimum quantity in a restricted frame: select salesid, qty, min(qty) over (order by salesid rows between 2 preceding and 1 preceding) as min from winsales Window functions 721 SQL Reference AWS Clean Rooms order by salesid; salesid \| qty \| min ---------+-----+----- 10001 \| 10 \| 10005 \| 30 \| 10 10006 \| 10 \| 10 20001 \| 20 \| 10 20002 \| 20 \| 10 30001 \| 10 \| 20 30003 \| 15 \| 10 30004 \| 20 \| 10 30007 \| 30 \| 15 40001 \| 40 \| 20 40005 \| 10 \| 30 (11 rows) NTH_VALUE window function The NTH_VALUE window function returns the expression value of the speciﬁed row of the window frame relative to the ﬁrst row of the window. Syntax NTH_VALUE (expr, offset) [ IGNORE NULLS \| RESPECT NULLS ] OVER ( [ PARTITION BY window_partition ] [ ORDER BY window_ordering frame_clause ] ) Arguments expr The target column or expression that the function operates on. oﬀset Determines the row number relative to the ﬁrst row in the window for which to return the expression. The oﬀset can be a constant or an expression and must be a positive integer that is greater than 0. Window functions 722 AWS Clean Rooms IGNORE NULLS SQL Reference An optional speciﬁcation that indicates that AWS Clean Rooms should skip null values in the determination of which row to use. Null values are included if IGNORE NULLS is not listed. RESPECT NULLS Indicates that AWS Clean Rooms should include null values in the determination of which row to use. RESPECT NULLS is supported by default if you do not specify IGNORE NULLS. OVER Speciﬁes the window partitioning, ordering, and window frame. PARTITION BY window_partition Sets the range of records for each group in the OVER clause. ORDER BY window_ordering Sorts the rows within each partition. If ORDER BY is omitted, the default frame consists of all rows in the partition. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required.
sql-reference-190	sql-reference.pdf	190	not listed. RESPECT NULLS Indicates that AWS Clean Rooms should include null values in the determination of which row to use. RESPECT NULLS is supported by default if you do not specify IGNORE NULLS. OVER Speciﬁes the window partitioning, ordering, and window frame. PARTITION BY window_partition Sets the range of records for each group in the OVER clause. ORDER BY window_ordering Sorts the rows within each partition. If ORDER BY is omitted, the default frame consists of all rows in the partition. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes the set of rows in a function's window, including or excluding sets of rows in the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. The NTH_VALUE window function supports expressions that use any of the AWS Clean Rooms data types. The return type is the same as the type of the expr. Examples The following example shows the number of seats in the third largest venue in California, Florida, and New York compared to the number of seats in the other venues in those states: select venuestate, venuename, venueseats, nth_value(venueseats, 3) ignore nulls over(partition by venuestate order by venueseats desc rows between unbounded preceding and unbounded following) as third_most_seats Window functions 723 AWS Clean Rooms SQL Reference from (select * from venue where venueseats > 0 and venuestate in('CA', 'FL', 'NY')) order by venuestate; venuestate \| venuename \| venueseats \| third_most_seats ------------+--------------------------------+------------+------------------ CA \| Qualcomm Stadium \| 70561 \| 63026 CA \| Monster Park \| 69843 \| 63026 CA \| McAfee Coliseum \| 63026 \| 63026 CA \| Dodger Stadium \| 56000 \| 63026 CA \| Angel Stadium of Anaheim \| 45050 \| 63026 CA \| PETCO Park \| 42445 \| 63026 CA \| AT&T Park \| 41503 \| 63026 CA \| Shoreline Amphitheatre \| 22000 \| 63026 FL \| Dolphin Stadium \| 74916 \| 65647 FL \| Jacksonville Municipal Stadium \| 73800 \| 65647 FL \| Raymond James Stadium \| 65647 \| 65647 FL \| Tropicana Field \| 36048 \| 65647 NY \| Ralph Wilson Stadium \| 73967 \| 20000 NY \| Yankee Stadium \| 52325 \| 20000 NY \| Madison Square Garden \| 20000 \| 20000 (15 rows) NTILE window function The NTILE window function divides ordered rows in the partition into the speciﬁed number of ranked groups of as equal size as possible and returns the group that a given row falls into. Syntax NTILE (expr) OVER ( [ PARTITION BY expression_list ] [ ORDER BY order_list ] ) Arguments expr The number of ranking groups and must result in a positive integer value (greater than 0) for each partition. The expr argument must not be nullable. Window functions 724 AWS Clean Rooms OVER SQL Reference A clause that speciﬁes the window partitioning and ordering. The OVER clause cannot contain a window frame speciﬁcation. PARTITION BY window_partition Optional. The range of records for each group in the OVER clause. ORDER BY window_ordering Optional. An expression that sorts the rows within each partition. If the ORDER BY clause is omitted, the ranking behavior is the same. If ORDER BY does not produce a unique ordering, the order of the rows is nondeterministic. For more information, see Unique ordering of data for window functions. Return type BIGINT Examples The following example ranks into four ranking groups the price paid for Hamlet tickets on August 26, 2008. The result set is 17 rows, divided almost evenly among the rankings 1 through 4: select eventname, caldate, pricepaid, ntile(4) over(order by pricepaid desc) from sales, event, date where sales.eventid=event.eventid and event.dateid=date.dateid and eventname='Hamlet' and caldate='2008-08-26' order by 4; eventname \| caldate \| pricepaid \| ntile -----------+------------+-----------+------- Hamlet \| 2008-08-26 \| 1883.00 \| 1 Hamlet \| 2008-08-26 \| 1065.00 \| 1 Hamlet \| 2008-08-26 \| 589.00 \| 1 Hamlet \| 2008-08-26 \| 530.00 \| 1 Hamlet \| 2008-08-26 \| 472.00 \| 1 Hamlet \| 2008-08-26 \| 460.00 \| 2 Hamlet \| 2008-08-26 \| 355.00 \| 2 Hamlet \| 2008-08-26 \| 334.00 \| 2 Hamlet \| 2008-08-26 \| 296.00 \| 2 Window functions 725 AWS Clean Rooms SQL Reference Hamlet \| 2008-08-26 \| 230.00 \| 3 Hamlet \| 2008-08-26 \| 216.00 \| 3 Hamlet \| 2008-08-26 \| 212.00 \| 3 Hamlet \| 2008-08-26 \| 106.00 \| 3 Hamlet \| 2008-08-26 \| 100.00 \| 4 Hamlet \| 2008-08-26 \| 94.00 \| 4 Hamlet \| 2008-08-26 \| 53.00 \| 4 Hamlet \| 2008-08-26 \| 25.00 \| 4 (17 rows) PERCENT_RANK window function Calculates the percent rank of a given row. The percent rank is determined using this formula: (x - 1) / (the number of rows in the window or partition - 1) where x is the rank of the current row.
sql-reference-191	sql-reference.pdf	191	\| 2008-08-26 \| 230.00 \| 3 Hamlet \| 2008-08-26 \| 216.00 \| 3 Hamlet \| 2008-08-26 \| 212.00 \| 3 Hamlet \| 2008-08-26 \| 106.00 \| 3 Hamlet \| 2008-08-26 \| 100.00 \| 4 Hamlet \| 2008-08-26 \| 94.00 \| 4 Hamlet \| 2008-08-26 \| 53.00 \| 4 Hamlet \| 2008-08-26 \| 25.00 \| 4 (17 rows) PERCENT_RANK window function Calculates the percent rank of a given row. The percent rank is determined using this formula: (x - 1) / (the number of rows in the window or partition - 1) where x is the rank of the current row. The following dataset illustrates use of this formula: Row# Value Rank Calculation PERCENT_RANK 1 15 1 (1-1)/(7-1) 0.0000 2 20 2 (2-1)/(7-1) 0.1666 3 20 2 (2-1)/(7-1) 0.1666 4 20 2 (2-1)/(7-1) 0.1666 5 30 5 (5-1)/(7-1) 0.6666 6 30 5 (5-1)/(7-1) 0.6666 7 40 7 (7-1)/(7-1) 1.0000 The return value range is 0 to 1, inclusive. The ﬁrst row in any set has a PERCENT_RANK of 0. Syntax PERCENT_RANK () OVER ( [ PARTITION BY partition_expression ] [ ORDER BY order_list ] ) Arguments ( ) The function takes no arguments, but the empty parentheses are required. Window functions 726 AWS Clean Rooms OVER SQL Reference A clause that speciﬁes the window partitioning. The OVER clause cannot contain a window frame speciﬁcation. PARTITION BY partition_expression Optional. An expression that sets the range of records for each group in the OVER clause. ORDER BY order_list Optional. The expression on which to calculate percent rank. The expression must have either a numeric data type or be implicitly convertible to one. If ORDER BY is omitted, the return value is 0 for all rows. If ORDER BY does not produce a unique ordering, the order of the rows is nondeterministic. For more information, see Unique ordering of data for window functions. Return type FLOAT8 Examples The following example calculates the percent rank of the sales quantities for each seller: select sellerid, qty, percent_rank() over (partition by sellerid order by qty) from winsales; sellerid qty percent_rank ---------------------------------------- 1 10.00 0.0 1 10.64 0.5 1 30.37 1.0 3 10.04 0.0 3 15.15 0.33 3 20.75 0.67 3 30.55 1.0 2 20.09 0.0 2 20.12 1.0 4 10.12 0.0 4 40.23 1.0 Window functions 727 AWS Clean Rooms SQL Reference For a description of the WINSALES table, see Sample table for window function examples. PERCENTILE_CONT window function PERCENTILE_CONT is an inverse distribution function that assumes a continuous distribution model. It takes a percentile value and a sort speciﬁcation, and returns an interpolated value that would fall into the given percentile value with respect to the sort speciﬁcation. PERCENTILE_CONT computes a linear interpolation between values after ordering them. Using the percentile value (P) and the number of not null rows (N) in the aggregation group, the function computes the row number after ordering the rows according to the sort speciﬁcation. This row number (RN) is computed according to the formula RN = (1+ (P(N-1)). The ﬁnal result of the aggregate function is computed by linear interpolation between the values from rows at row numbers CRN = CEILING(RN) and FRN = FLOOR(RN). The ﬁnal result will be as follows. If (CRN = FRN = RN) then the result is (value of expression from row at RN) Otherwise the result is as follows: (CRN - RN) (value of expression for row at FRN) + (RN - FRN) * (value of expression for row at CRN). You can specify only the PARTITION clause in the OVER clause. If PARTITION is speciﬁed, for each row, PERCENTILE_CONT returns the value that would fall into the speciﬁed percentile among a set of values within a given partition. PERCENTILE_CONT is a compute-node only function. The function returns an error if the query doesn't reference a user-deﬁned table or AWS Clean Rooms system table. Syntax PERCENTILE_CONT ( percentile ) WITHIN GROUP (ORDER BY expr) OVER ( [ PARTITION BY expr_list ] ) Arguments percentile Numeric constant between 0 and 1. Nulls are ignored in the calculation. Window functions 728 AWS Clean Rooms WITHIN GROUP ( ORDER BY expr) SQL Reference Speciﬁes numeric or date/time values to sort and compute the percentile over. OVER Speciﬁes the window partitioning. The OVER clause cannot contain a window ordering or window frame speciﬁcation. PARTITION BY expr Optional argument that sets the range of records for each group in the OVER clause. Returns The return type is determined by the data type of the ORDER BY expression in the WITHIN GROUP clause. The following table shows the return type for each ORDER BY expression data type. Input Type Return Type SMALLINTINTEGERBIGINTNUMERIC, DECIMAL DECIMAL FLOAT, DOUBLE DATE TIMESTAMP Usage notes DOUBLE DATE TIMESTAMP If the ORDER BY expression is a DECIMAL data type deﬁned with the maximum precision of
sql-reference-192	sql-reference.pdf	192	Speciﬁes the window partitioning. The OVER clause cannot contain a window ordering or window frame speciﬁcation. PARTITION BY expr Optional argument that sets the range of records for each group in the OVER clause. Returns The return type is determined by the data type of the ORDER BY expression in the WITHIN GROUP clause. The following table shows the return type for each ORDER BY expression data type. Input Type Return Type SMALLINTINTEGERBIGINTNUMERIC, DECIMAL DECIMAL FLOAT, DOUBLE DATE TIMESTAMP Usage notes DOUBLE DATE TIMESTAMP If the ORDER BY expression is a DECIMAL data type deﬁned with the maximum precision of 38 digits, it is possible that PERCENTILE_CONT will return either an inaccurate result or an error. If the return value of the PERCENTILE_CONT function exceeds 38 digits, the result is truncated to ﬁt, which causes a loss of precision. If, during interpolation, an intermediate result exceeds the maximum precision, a numeric overﬂow occurs and the function returns an error. To avoid these conditions, we recommend either using a data type with lower precision or casting the ORDER BY expression to a lower precision. For example, a SUM function with a DECIMAL argument returns a default precision of 38 digits. The scale of the result is the same as the scale of the argument. So, for example, a SUM of a DECIMAL(5,2) column returns a DECIMAL(38,2) data type. Window functions 729 AWS Clean Rooms SQL Reference The following example uses a SUM function in the ORDER BY clause of a PERCENTILE_CONT function. The data type of the PRICEPAID column is DECIMAL (8,2), so the SUM function returns DECIMAL(38,2). select salesid, sum(pricepaid), percentile_cont(0.6) within group (order by sum(pricepaid) desc) over() from sales where salesid < 10 group by salesid; To avoid a potential loss of precision or an overﬂow error, cast the result to a DECIMAL data type with lower precision, as the following example shows. select salesid, sum(pricepaid), percentile_cont(0.6) within group (order by sum(pricepaid)::decimal(30,2) desc) over() from sales where salesid < 10 group by salesid; Examples The following examples uses the WINSALES table. For a description of the WINSALES table, see Sample table for window function examples. select sellerid, qty, percentile_cont(0.5) within group (order by qty) over() as median from winsales; sellerid \| qty \| median ----------+-----+-------- 1 \| 10 \| 20.0 1 \| 10 \| 20.0 3 \| 10 \| 20.0 4 \| 10 \| 20.0 3 \| 15 \| 20.0 2 \| 20 \| 20.0 3 \| 20 \| 20.0 2 \| 20 \| 20.0 3 \| 30 \| 20.0 1 \| 30 \| 20.0 4 \| 40 \| 20.0 (11 rows) select sellerid, qty, percentile_cont(0.5) Window functions 730 AWS Clean Rooms SQL Reference within group (order by qty) over(partition by sellerid) as median from winsales; sellerid \| qty \| median ----------+-----+-------- 2 \| 20 \| 20.0 2 \| 20 \| 20.0 4 \| 10 \| 25.0 4 \| 40 \| 25.0 1 \| 10 \| 10.0 1 \| 10 \| 10.0 1 \| 30 \| 10.0 3 \| 10 \| 17.5 3 \| 15 \| 17.5 3 \| 20 \| 17.5 3 \| 30 \| 17.5 (11 rows) The following example calculates the PERCENTILE_CONT and PERCENTILE_DISC of the ticket sales for sellers in Washington state. SELECT sellerid, state, sum(qtysoldpricepaid) sales, percentile_cont(0.6) within group (order by sum(qtysoldpricepaid::decimal(14,2) ) desc) over(), percentile_disc(0.6) within group (order by sum(qtysold*pricepaid::decimal(14,2) ) desc) over() from sales s, users u where s.sellerid = u.userid and state = 'WA' and sellerid < 1000 group by sellerid, state; sellerid \| state \| sales \| percentile_cont \| percentile_disc ----------+-------+---------+-----------------+----------------- 127 \| WA \| 6076.00 \| 2044.20 \| 1531.00 787 \| WA \| 6035.00 \| 2044.20 \| 1531.00 381 \| WA \| 5881.00 \| 2044.20 \| 1531.00 777 \| WA \| 2814.00 \| 2044.20 \| 1531.00 33 \| WA \| 1531.00 \| 2044.20 \| 1531.00 800 \| WA \| 1476.00 \| 2044.20 \| 1531.00 1 \| WA \| 1177.00 \| 2044.20 \| 1531.00 (7 rows) Window functions 731 AWS Clean Rooms SQL Reference PERCENTILE_DISC window function PERCENTILE_DISC is an inverse distribution function that assumes a discrete distribution model. It takes a percentile value and a sort speciﬁcation and returns an element from the given set. For a given percentile value P, PERCENTILE_DISC sorts the values of the expression in the ORDER BY clause and returns the value with the smallest cumulative distribution value (with respect to the same sort speciﬁcation) that is greater than or equal to P. You can specify only the PARTITION clause in the OVER clause. PERCENTILE_DISC is a compute-node only function. The function returns an error if the query doesn't reference a user-deﬁned table or AWS Clean Rooms system table. Syntax PERCENTILE_DISC ( percentile ) WITHIN GROUP (ORDER BY expr) OVER ( [ PARTITION BY expr_list ] ) Arguments percentile Numeric constant between 0 and
sql-reference-193	sql-reference.pdf	193	value P, PERCENTILE_DISC sorts the values of the expression in the ORDER BY clause and returns the value with the smallest cumulative distribution value (with respect to the same sort speciﬁcation) that is greater than or equal to P. You can specify only the PARTITION clause in the OVER clause. PERCENTILE_DISC is a compute-node only function. The function returns an error if the query doesn't reference a user-deﬁned table or AWS Clean Rooms system table. Syntax PERCENTILE_DISC ( percentile ) WITHIN GROUP (ORDER BY expr) OVER ( [ PARTITION BY expr_list ] ) Arguments percentile Numeric constant between 0 and 1. Nulls are ignored in the calculation. WITHIN GROUP ( ORDER BY expr) Speciﬁes numeric or date/time values to sort and compute the percentile over. OVER Speciﬁes the window partitioning. The OVER clause cannot contain a window ordering or window frame speciﬁcation. PARTITION BY expr Optional argument that sets the range of records for each group in the OVER clause. Returns The same data type as the ORDER BY expression in the WITHIN GROUP clause. Window functions 732 AWS Clean Rooms Examples SQL Reference The following examples uses the WINSALES table. For a description of the WINSALES table, see Sample table for window function examples. select sellerid, qty, percentile_disc(0.5) within group (order by qty) over() as median from winsales; sellerid \| qty \| median ----------+-----+-------- 1 \| 10 \| 20 3 \| 10 \| 20 1 \| 10 \| 20 4 \| 10 \| 20 3 \| 15 \| 20 2 \| 20 \| 20 2 \| 20 \| 20 3 \| 20 \| 20 1 \| 30 \| 20 3 \| 30 \| 20 4 \| 40 \| 20 (11 rows) select sellerid, qty, percentile_disc(0.5) within group (order by qty) over(partition by sellerid) as median from winsales; sellerid \| qty \| median ----------+-----+-------- 2 \| 20 \| 20 2 \| 20 \| 20 4 \| 10 \| 10 4 \| 40 \| 10 1 \| 10 \| 10 1 \| 10 \| 10 1 \| 30 \| 10 3 \| 10 \| 15 3 \| 15 \| 15 3 \| 20 \| 15 3 \| 30 \| 15 (11 rows) Window functions 733 AWS Clean Rooms RANK window function SQL Reference The RANK window function determines the rank of a value in a group of values, based on the ORDER BY expression in the OVER clause. If the optional PARTITION BY clause is present, the rankings are reset for each group of rows. Rows with equal values for the ranking criteria receive the same rank. AWS Clean Rooms adds the number of tied rows to the tied rank to calculate the next rank and thus the ranks might not be consecutive numbers. For example, if two rows are ranked 1, the next rank is 3. RANK diﬀers from the DENSE_RANK window function in one respect: For DENSE_RANK, if two or more rows tie, there is no gap in the sequence of ranked values. For example, if two rows are ranked 1, the next rank is 2. You can have ranking functions with diﬀerent PARTITION BY and ORDER BY clauses in the same query. Syntax RANK () OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list ] ) Arguments ( ) The function takes no arguments, but the empty parentheses are required. OVER The window clauses for the RANK function. PARTITION BY expr_list Optional. One or more expressions that deﬁne the window. ORDER BY order_list Optional. Deﬁnes the columns on which the ranking values are based. If no PARTITION BY is speciﬁed, ORDER BY uses the entire table. If ORDER BY is omitted, the return value is 1 for all rows. Window functions 734 AWS Clean Rooms SQL Reference If ORDER BY does not produce a unique ordering, the order of the rows is nondeterministic. For more information, see Unique ordering of data for window functions. Return type INTEGER Examples The following example orders the table by the quantity sold (default ascending), and assign a rank to each row. A rank value of 1 is the highest ranked value. The results are sorted after the window function results are applied: select salesid, qty, rank() over (order by qty) as rnk from winsales order by 2,1; salesid \| qty \| rnk --------+-----+----- 10001 \| 10 \| 1 10006 \| 10 \| 1 30001 \| 10 \| 1 40005 \| 10 \| 1 30003 \| 15 \| 5 20001 \| 20 \| 6 20002 \| 20 \| 6 30004 \| 20 \| 6 10005 \| 30 \| 9 30007 \| 30 \| 9 40001 \| 40 \| 11 (11 rows) Note that the outer ORDER BY clause in this example includes columns 2 and 1 to make sure that AWS Clean Rooms returns consistently sorted results each time this query
sql-reference-194	sql-reference.pdf	194	as rnk from winsales order by 2,1; salesid \| qty \| rnk --------+-----+----- 10001 \| 10 \| 1 10006 \| 10 \| 1 30001 \| 10 \| 1 40005 \| 10 \| 1 30003 \| 15 \| 5 20001 \| 20 \| 6 20002 \| 20 \| 6 30004 \| 20 \| 6 10005 \| 30 \| 9 30007 \| 30 \| 9 40001 \| 40 \| 11 (11 rows) Note that the outer ORDER BY clause in this example includes columns 2 and 1 to make sure that AWS Clean Rooms returns consistently sorted results each time this query is run. For example, rows with sales IDs 10001 and 10006 have identical QTY and RNK values. Ordering the ﬁnal result set by column 1 ensures that row 10001 always falls before 10006. For a description of the WINSALES table, see Sample table for window function examples. In the following example, the ordering is reversed for the window function (order by qty desc). Now the highest rank value applies to the largest QTY value. Window functions 735 AWS Clean Rooms SQL Reference select salesid, qty, rank() over (order by qty desc) as rank from winsales order by 2,1; salesid \| qty \| rank ---------+-----+----- 10001 \| 10 \| 8 10006 \| 10 \| 8 30001 \| 10 \| 8 40005 \| 10 \| 8 30003 \| 15 \| 7 20001 \| 20 \| 4 20002 \| 20 \| 4 30004 \| 20 \| 4 10005 \| 30 \| 2 30007 \| 30 \| 2 40001 \| 40 \| 1 (11 rows) For a description of the WINSALES table, see Sample table for window function examples. The following example partitions the table by SELLERID and order each partition by the quantity (in descending order) and assign a rank to each row. The results are sorted after the window function results are applied. select salesid, sellerid, qty, rank() over (partition by sellerid order by qty desc) as rank from winsales order by 2,3,1; salesid \| sellerid \| qty \| rank --------+----------+-----+----- 10001 \| 1 \| 10 \| 2 10006 \| 1 \| 10 \| 2 10005 \| 1 \| 30 \| 1 20001 \| 2 \| 20 \| 1 20002 \| 2 \| 20 \| 1 30001 \| 3 \| 10 \| 4 30003 \| 3 \| 15 \| 3 30004 \| 3 \| 20 \| 2 30007 \| 3 \| 30 \| 1 Window functions 736 AWS Clean Rooms SQL Reference 40005 \| 4 \| 10 \| 2 40001 \| 4 \| 40 \| 1 (11 rows) RATIO_TO_REPORT window function Calculates the ratio of a value to the sum of the values in a window or partition. The ratio to report value is determined using the formula: value of ratio_expression argument for the current row / sum of ratio_expression argument for the window or partition The following dataset illustrates use of this formula: Row# Value Calculation RATIO_TO_REPORT 1 2500 (2500)/(13900) 0.1798 2 2600 (2600)/(13900) 0.1870 3 2800 (2800)/(13900) 0.2014 4 2900 (2900)/(13900) 0.2086 5 3100 (3100)/(13900) 0.2230 The return value range is 0 to 1, inclusive. If ratio_expression is NULL, then the return value is NULL. Syntax RATIO_TO_REPORT ( ratio_expression ) OVER ( [ PARTITION BY partition_expression ] ) Arguments ratio_expression An expression, such as a column name, that provides the value for which to determine the ratio. The expression must have either a numeric data type or be implicitly convertible to one. You cannot use any other analytic function in ratio_expression. OVER A clause that speciﬁes the window partitioning. The OVER clause cannot contain a window ordering or window frame speciﬁcation. Window functions 737 AWS Clean Rooms SQL Reference PARTITION BY partition_expression Optional. An expression that sets the range of records for each group in the OVER clause. Return type FLOAT8 Examples The following example calculates the ratios of the sales quantities for each seller: select sellerid, qty, ratio_to_report(qty) over (partition by sellerid) from winsales; sellerid qty ratio_to_report ------------------------------------------- 2 20.12312341 0.5 2 20.08630000 0.5 4 10.12414400 0.2 4 40.23000000 0.8 1 30.37262000 0.6 1 10.64000000 0.21 1 10.00000000 0.2 3 10.03500000 0.13 3 15.14660000 0.2 3 30.54790000 0.4 3 20.74630000 0.27 For a description of the WINSALES table, see Sample table for window function examples. ROW_NUMBER window function Determines the ordinal number of the current row within a group of rows, counting from 1, based on the ORDER BY expression in the OVER clause. If the optional PARTITION BY clause is present, the ordinal numbers are reset for each group of rows. Rows with equal values for the ORDER BY expressions receive the diﬀerent row numbers nondeterministically. Syntax ROW_NUMBER () OVER Window functions 738 AWS Clean Rooms ( [ PARTITION BY expr_list ] [ ORDER BY order_list ] ) Arguments ( ) SQL Reference The function takes no arguments,
sql-reference-195	sql-reference.pdf	195	Sample table for window function examples. ROW_NUMBER window function Determines the ordinal number of the current row within a group of rows, counting from 1, based on the ORDER BY expression in the OVER clause. If the optional PARTITION BY clause is present, the ordinal numbers are reset for each group of rows. Rows with equal values for the ORDER BY expressions receive the diﬀerent row numbers nondeterministically. Syntax ROW_NUMBER () OVER Window functions 738 AWS Clean Rooms ( [ PARTITION BY expr_list ] [ ORDER BY order_list ] ) Arguments ( ) SQL Reference The function takes no arguments, but the empty parentheses are required. OVER The window clauses for the ROW_NUMBER function. PARTITION BY expr_list Optional. One or more expressions that deﬁne the ROW_NUMBER function. ORDER BY order_list Optional. The expression that deﬁnes the columns on which the row numbers are based. If no PARTITION BY is speciﬁed, ORDER BY uses the entire table. If ORDER BY does not produce a unique ordering or is omitted, the order of the rows is nondeterministic. For more information, see Unique ordering of data for window functions. Return type BIGINT Examples The following example partitions the table by SELLERID and orders each partition by QTY (in ascending order), then assigns a row number to each row. The results are sorted after the window function results are applied. select salesid, sellerid, qty, row_number() over (partition by sellerid order by qty asc) as row from winsales Window functions 739 SQL Reference AWS Clean Rooms order by 2,4; salesid \| sellerid \| qty \| row ---------+----------+-----+----- 10006 \| 1 \| 10 \| 1 10001 \| 1 \| 10 \| 2 10005 \| 1 \| 30 \| 3 20001 \| 2 \| 20 \| 1 20002 \| 2 \| 20 \| 2 30001 \| 3 \| 10 \| 1 30003 \| 3 \| 15 \| 2 30004 \| 3 \| 20 \| 3 30007 \| 3 \| 30 \| 4 40005 \| 4 \| 10 \| 1 40001 \| 4 \| 40 \| 2 (11 rows) For a description of the WINSALES table, see Sample table for window function examples. STDDEV_SAMP and STDDEV_POP window functions The STDDEV_SAMP and STDDEV_POP window functions return the sample and population standard deviation of a set of numeric values (integer, decimal, or ﬂoating-point). See also STDDEV_SAMP and STDDEV_POP functions. STDDEV_SAMP and STDDEV are synonyms for the same function. Syntax STDDEV_SAMP \| STDDEV \| STDDEV_POP ( [ ALL ] expression ) OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list frame_clause ] ) Arguments expression The target column or expression that the function operates on. Window functions 740 AWS Clean Rooms ALL SQL Reference With the argument ALL, the function retains all duplicate values from the expression. ALL is the default. DISTINCT is not supported. OVER Speciﬁes the window clauses for the aggregation functions. The OVER clause distinguishes window aggregation functions from normal set aggregation functions. PARTITION BY expr_list Deﬁnes the window for the function in terms of one or more expressions. ORDER BY order_list Sorts the rows within each partition. If no PARTITION BY is speciﬁed, ORDER BY uses the entire table. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes the set of rows in a function's window, including or excluding sets of rows within the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. Data types The argument types supported by the STDDEV functions are SMALLINT, INTEGER, BIGINT, NUMERIC, DECIMAL, REAL, and DOUBLE PRECISION. Regardless of the data type of the expression, the return type of a STDDEV function is a double precision number. Examples The following example shows how to use STDDEV_POP and VAR_POP functions as window functions. The query computes the population variance and population standard deviation for PRICEPAID values in the SALES table. select salesid, dateid, pricepaid, round(stddev_pop(pricepaid) over (order by dateid, salesid rows unbounded preceding)) as stddevpop, round(var_pop(pricepaid) over Window functions 741 AWS Clean Rooms SQL Reference (order by dateid, salesid rows unbounded preceding)) as varpop from sales order by 2,1; salesid \| dateid \| pricepaid \| stddevpop \| varpop --------+--------+-----------+-----------+--------- 33095 \| 1827 \| 234.00 \| 0 \| 0 65082 \| 1827 \| 472.00 \| 119 \| 14161 88268 \| 1827 \| 836.00 \| 248 \| 61283 97197 \| 1827 \| 708.00 \| 230 \| 53019 110328 \| 1827 \| 347.00 \| 223 \| 49845 110917 \| 1827 \| 337.00 \| 215 \| 46159 150314 \| 1827 \| 688.00 \| 211 \| 44414 157751 \| 1827 \| 1730.00 \| 447 \| 199679 165890 \| 1827 \| 4192.00 \| 1185 \| 1403323 ... The sample standard deviation and variance functions can be used in the same way. SUM window function
sql-reference-196	sql-reference.pdf	196	--------+--------+-----------+-----------+--------- 33095 \| 1827 \| 234.00 \| 0 \| 0 65082 \| 1827 \| 472.00 \| 119 \| 14161 88268 \| 1827 \| 836.00 \| 248 \| 61283 97197 \| 1827 \| 708.00 \| 230 \| 53019 110328 \| 1827 \| 347.00 \| 223 \| 49845 110917 \| 1827 \| 337.00 \| 215 \| 46159 150314 \| 1827 \| 688.00 \| 211 \| 44414 157751 \| 1827 \| 1730.00 \| 447 \| 199679 165890 \| 1827 \| 4192.00 \| 1185 \| 1403323 ... The sample standard deviation and variance functions can be used in the same way. SUM window function The SUM window function returns the sum of the input column or expression values. The SUM function works with numeric values and ignores NULL values. Syntax SUM ( [ ALL ] expression ) OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list frame_clause ] ) Arguments expression The target column or expression that the function operates on. ALL With the argument ALL, the function retains all duplicate values from the expression. ALL is the default. DISTINCT is not supported. Window functions 742 AWS Clean Rooms OVER SQL Reference Speciﬁes the window clauses for the aggregation functions. The OVER clause distinguishes window aggregation functions from normal set aggregation functions. PARTITION BY expr_list Deﬁnes the window for the SUM function in terms of one or more expressions. ORDER BY order_list Sorts the rows within each partition. If no PARTITION BY is speciﬁed, ORDER BY uses the entire table. frame_clause If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes the set of rows in a function's window, including or excluding sets of rows within the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. Data types The argument types supported by the SUM function are SMALLINT, INTEGER, BIGINT, NUMERIC, DECIMAL, REAL, and DOUBLE PRECISION. The return types supported by the SUM function are: • BIGINT for SMALLINT or INTEGER arguments • NUMERIC for BIGINT arguments • DOUBLE PRECISION for ﬂoating-point arguments Examples The following example creates a cumulative (rolling) sum of sales quantities ordered by date and sales ID: select salesid, dateid, sellerid, qty, sum(qty) over (order by dateid, salesid rows unbounded preceding) as sum from winsales order by 2,1; Window functions 743 AWS Clean Rooms SQL Reference salesid \| dateid \| sellerid \| qty \| sum ---------+------------+----------+-----+----- 30001 \| 2003-08-02 \| 3 \| 10 \| 10 10001 \| 2003-12-24 \| 1 \| 10 \| 20 10005 \| 2003-12-24 \| 1 \| 30 \| 50 40001 \| 2004-01-09 \| 4 \| 40 \| 90 10006 \| 2004-01-18 \| 1 \| 10 \| 100 20001 \| 2004-02-12 \| 2 \| 20 \| 120 40005 \| 2004-02-12 \| 4 \| 10 \| 130 20002 \| 2004-02-16 \| 2 \| 20 \| 150 30003 \| 2004-04-18 \| 3 \| 15 \| 165 30004 \| 2004-04-18 \| 3 \| 20 \| 185 30007 \| 2004-09-07 \| 3 \| 30 \| 215 (11 rows) For a description of the WINSALES table, see Sample table for window function examples. The following example creates a cumulative (rolling) sum of sales quantities by date, partition the results by seller ID, and order the results by date and sales ID within the partition: select salesid, dateid, sellerid, qty, sum(qty) over (partition by sellerid order by dateid, salesid rows unbounded preceding) as sum from winsales order by 2,1; salesid \| dateid \| sellerid \| qty \| sum ---------+------------+----------+-----+----- 30001 \| 2003-08-02 \| 3 \| 10 \| 10 10001 \| 2003-12-24 \| 1 \| 10 \| 10 10005 \| 2003-12-24 \| 1 \| 30 \| 40 40001 \| 2004-01-09 \| 4 \| 40 \| 40 10006 \| 2004-01-18 \| 1 \| 10 \| 50 20001 \| 2004-02-12 \| 2 \| 20 \| 20 40005 \| 2004-02-12 \| 4 \| 10 \| 50 20002 \| 2004-02-16 \| 2 \| 20 \| 40 30003 \| 2004-04-18 \| 3 \| 15 \| 25 30004 \| 2004-04-18 \| 3 \| 20 \| 45 30007 \| 2004-09-07 \| 3 \| 30 \| 75 (11 rows) The following example numbers all of the rows sequentially in the result set, ordered by the SELLERID and SALESID columns: Window functions 744 AWS Clean Rooms SQL Reference select salesid, sellerid, qty, sum(1) over (order by sellerid, salesid rows unbounded preceding) as rownum from winsales order by 2,1; salesid \| sellerid \| qty \| rownum --------+----------+------+-------- 10001 \| 1 \| 10 \| 1 10005 \| 1 \| 30 \| 2 10006 \| 1 \| 10 \| 3 20001 \| 2 \| 20 \| 4 20002 \| 2 \| 20 \| 5 30001 \| 3 \| 10 \| 6 30003 \| 3 \| 15 \| 7 30004 \| 3 \| 20 \|
sql-reference-197	sql-reference.pdf	197	result set, ordered by the SELLERID and SALESID columns: Window functions 744 AWS Clean Rooms SQL Reference select salesid, sellerid, qty, sum(1) over (order by sellerid, salesid rows unbounded preceding) as rownum from winsales order by 2,1; salesid \| sellerid \| qty \| rownum --------+----------+------+-------- 10001 \| 1 \| 10 \| 1 10005 \| 1 \| 30 \| 2 10006 \| 1 \| 10 \| 3 20001 \| 2 \| 20 \| 4 20002 \| 2 \| 20 \| 5 30001 \| 3 \| 10 \| 6 30003 \| 3 \| 15 \| 7 30004 \| 3 \| 20 \| 8 30007 \| 3 \| 30 \| 9 40001 \| 4 \| 40 \| 10 40005 \| 4 \| 10 \| 11 (11 rows) For a description of the WINSALES table, see Sample table for window function examples. The following example numbers all rows sequentially in the result set, partition the results by SELLERID, and order the results by SELLERID and SALESID within the partition: select salesid, sellerid, qty, sum(1) over (partition by sellerid order by sellerid, salesid rows unbounded preceding) as rownum from winsales order by 2,1; salesid \| sellerid \| qty \| rownum ---------+----------+-----+-------- 10001 \| 1 \| 10 \| 1 10005 \| 1 \| 30 \| 2 10006 \| 1 \| 10 \| 3 20001 \| 2 \| 20 \| 1 20002 \| 2 \| 20 \| 2 30001 \| 3 \| 10 \| 1 30003 \| 3 \| 15 \| 2 30004 \| 3 \| 20 \| 3 30007 \| 3 \| 30 \| 4 40001 \| 4 \| 40 \| 1 Window functions 745 AWS Clean Rooms SQL Reference 40005 \| 4 \| 10 \| 2 (11 rows) VAR_SAMP and VAR_POP window functions A The VAR_SAMP and VAR_POP window functions return the sample and population variance of a set of numeric values (integer, decimal, or ﬂoating-point). See also VAR_SAMP and VAR_POP functions. VAR_SAMP and VARIANCE are synonyms for the same function. Syntax VAR_SAMP \| VARIANCE \| VAR_POP ( [ ALL ] expression ) OVER ( [ PARTITION BY expr_list ] [ ORDER BY order_list frame_clause ] ) Arguments expression The target column or expression that the function operates on. ALL With the argument ALL, the function retains all duplicate values from the expression. ALL is the default. DISTINCT is not supported. OVER Speciﬁes the window clauses for the aggregation functions. The OVER clause distinguishes window aggregation functions from normal set aggregation functions. PARTITION BY expr_list Deﬁnes the window for the function in terms of one or more expressions. ORDER BY order_list Sorts the rows within each partition. If no PARTITION BY is speciﬁed, ORDER BY uses the entire table. Window functions 746 AWS Clean Rooms frame_clause SQL Reference If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause reﬁnes the set of rows in a function's window, including or excluding sets of rows within the ordered result. The frame clause consists of the ROWS keyword and associated speciﬁers. See Window function syntax summary. Data types The argument types supported by the VARIANCE functions are SMALLINT, INTEGER, BIGINT, NUMERIC, DECIMAL, REAL, and DOUBLE PRECISION. Regardless of the data type of the expression, the return type of a VARIANCE function is a double precision number. AWS Clean Rooms SQL conditions Conditions are statements of one or more expressions and logical operators that evaluate to true, false, or unknown. Conditions are also sometimes referred to as predicates. Note All string comparisons and LIKE pattern matches are case-sensitive. For example, 'A' and 'a' do not match. However, you can do a case-insensitive pattern match by using the ILIKE predicate. The following SQL conditions are supported in AWS Clean Rooms. Topics • Comparison conditions • Logical conditions • Pattern-matching conditions • BETWEEN range condition • Null condition • EXISTS condition SQL conditions 747 AWS Clean Rooms • IN condition • Syntax Comparison conditions SQL Reference Comparison conditions state logical relationships between two values. All comparison conditions are binary operators with a Boolean return type. AWS Clean Rooms SQL supports the comparison operators described in the following table. Operator < > <= >= Syntax a < b a > b a <= b a >= b Description The less than comparison operator. Used to compare two values and determine if the value on the left is less than the value on the right. The greater than compariso n operator. Used to compare two values and determine if the value on the left is greater than the value on the right. The less than or equal to comparison operator. Used to compare two values and returns true if the value on the left is less than or equal to the value on the right, and false otherwise. The greater than or equal
sql-reference-198	sql-reference.pdf	198	<= b a >= b Description The less than comparison operator. Used to compare two values and determine if the value on the left is less than the value on the right. The greater than compariso n operator. Used to compare two values and determine if the value on the left is greater than the value on the right. The less than or equal to comparison operator. Used to compare two values and returns true if the value on the left is less than or equal to the value on the right, and false otherwise. The greater than or equal to comparison operator. Used to compare two values and determine if the value on the Comparison conditions 748 AWS Clean Rooms Operator Syntax Description SQL Reference = a = b <> or != a <> b or a != b left is greater than or equal to the value on the right. The equality comparison operator, which compares two values and returns true if they're equal, and false otherwise. The not equal to comparison operator, which compares two values and returns true if they're not equal, and false otherwise. Examples Here are some simple examples of comparison conditions: a = 5 a < b min(x) >= 5 qtysold = any (select qtysold from sales where dateid = 1882 The following query returns the id values for all the squirrels that are not currently foraging. SELECT id FROM squirrels WHERE !is_foraging The following query returns venues with more than 10,000 seats from the VENUE table: select venueid, venuename, venueseats from venue where venueseats > 10000 order by venueseats desc; venueid \| venuename \| venueseats Comparison conditions 749 AWS Clean Rooms SQL Reference ---------+--------------------------------+------------ 83 \| FedExField \| 91704 6 \| New York Giants Stadium \| 80242 79 \| Arrowhead Stadium \| 79451 78 \| INVESCO Field \| 76125 69 \| Dolphin Stadium \| 74916 67 \| Ralph Wilson Stadium \| 73967 76 \| Jacksonville Municipal Stadium \| 73800 89 \| Bank of America Stadium \| 73298 72 \| Cleveland Browns Stadium \| 73200 86 \| Lambeau Field \| 72922 ... (57 rows) This example selects the users (USERID) from the USERS table who like rock music: select userid from users where likerock = 't' order by 1 limit 5; userid -------- 3 5 6 13 16 (5 rows) This example selects the users (USERID) from the USERS table where it is unknown whether they like rock music: select firstname, lastname, likerock from users where likerock is unknown order by userid limit 10; firstname \| lastname \| likerock ----------+----------+---------- Rafael \| Taylor \| Vladimir \| Humphrey \| Barry \| Roy \| Tamekah \| Juarez \| Mufutau \| Watkins \| Naida \| Calderon \| Comparison conditions 750 AWS Clean Rooms Anika \| Huff \| Bruce \| Beck \| Mallory \| Farrell \| Scarlett \| Mayer \| (10 rows Examples with a TIME column SQL Reference The following example table TIME_TEST has a column TIME_VAL (type TIME) with three values inserted. select time_val from time_test; time_val --------------------- 20:00:00 00:00:00.5550 00:58:00 The following example extracts the hours from each timetz_val. select time_val from time_test where time_val < '3:00'; time_val --------------- 00:00:00.5550 00:58:00 The following example compares two time literals. select time '18:25:33.123456' = time '18:25:33.123456'; ?column? ---------- t Examples with a TIMETZ column The following example table TIMETZ_TEST has a column TIMETZ_VAL (type TIMETZ) with three values inserted. select timetz_val from timetz_test; Comparison conditions 751 AWS Clean Rooms SQL Reference timetz_val ------------------ 04:00:00+00 00:00:00.5550+00 05:58:00+00 The following example selects only the TIMETZ values less than 3:00:00 UTC. The comparison is made after converting the value to UTC. select timetz_val from timetz_test where timetz_val < '3:00:00 UTC'; timetz_val --------------- 00:00:00.5550+00 The following example compares two TIMETZ literals. The time zone is ignored for the comparison. select time '18:25:33.123456 PST' < time '19:25:33.123456 EST'; ?column? ---------- t Logical conditions Logical conditions combine the result of two conditions to produce a single result. All logical conditions are binary operators with a Boolean return type. Syntax expression { AND \| OR } expression NOT expression Logical conditions use a three-valued Boolean logic where the null value represents an unknown relationship. The following table describes the results for logical conditions, where E1 and E2 represent expressions: Logical conditions 752 AWS Clean Rooms SQL Reference E1 TRUE TRUE TRUE FALSE FALSE FALSE E1 AND E2 E1 OR E2 NOT E2 E2 TRUE FALSE TRUE FALSE UNKNOWN UNKNOWN TRUE FALSE FALSE FALSE FALSE TRUE UNKNOWN TRUE TRUE TRUE TRUE FALSE UNKNOWN FALSE UNKNOWN UNKNOWN TRUE UNKNOWN TRUE UNKNOWN FALSE FALSE UNKNOWN UNKNOWN UNKNOWN UNKNOWN UNKNOWN The NOT operator is evaluated before AND, and the AND operator is evaluated before the OR operator. Any parentheses used may override this default order of evaluation. Examples The following example returns USERID and USERNAME from the
sql-reference-199	sql-reference.pdf	199	where E1 and E2 represent expressions: Logical conditions 752 AWS Clean Rooms SQL Reference E1 TRUE TRUE TRUE FALSE FALSE FALSE E1 AND E2 E1 OR E2 NOT E2 E2 TRUE FALSE TRUE FALSE UNKNOWN UNKNOWN TRUE FALSE FALSE FALSE FALSE TRUE UNKNOWN TRUE TRUE TRUE TRUE FALSE UNKNOWN FALSE UNKNOWN UNKNOWN TRUE UNKNOWN TRUE UNKNOWN FALSE FALSE UNKNOWN UNKNOWN UNKNOWN UNKNOWN UNKNOWN The NOT operator is evaluated before AND, and the AND operator is evaluated before the OR operator. Any parentheses used may override this default order of evaluation. Examples The following example returns USERID and USERNAME from the USERS table where the user likes both Las Vegas and sports: select userid, username from users where likevegas = 1 and likesports = 1 order by userid; userid \| username --------+---------- 1 \| JSG99FHE 67 \| TWU10MZT 87 \| DUF19VXU 92 \| HYP36WEQ 109 \| FPL38HZK 120 \| DMJ24GUZ 123 \| QZR22XGQ Logical conditions 753 AWS Clean Rooms 130 \| ZQC82ALK 133 \| LBN45WCH 144 \| UCX04JKN 165 \| TEY68OEB 169 \| AYQ83HGO 184 \| TVX65AZX ... (2128 rows) SQL Reference The next example returns the USERID and USERNAME from the USERS table where the user likes Las Vegas, or sports, or both. This query returns all of the output from the previous example plus the users who like only Las Vegas or sports. select userid, username from users where likevegas = 1 or likesports = 1 order by userid; userid \| username --------+---------- 1 \| JSG99FHE 2 \| PGL08LJI 3 \| IFT66TXU 5 \| AEB55QTM 6 \| NDQ15VBM 9 \| MSD36KVR 10 \| WKW41AIW 13 \| QTF33MCG 15 \| OWU78MTR 16 \| ZMG93CDD 22 \| RHT62AGI 27 \| KOY02CVE 29 \| HUH27PKK ... (18968 rows) The following query uses parentheses around the OR condition to ﬁnd venues in New York or California where Macbeth was performed: select distinct venuename, venuecity from venue join event on venue.venueid=event.venueid where (venuestate = 'NY' or venuestate = 'CA') and eventname='Macbeth' order by 2,1; Logical conditions 754 AWS Clean Rooms SQL Reference venuename \| venuecity ----------------------------------------+--------------- Geffen Playhouse \| Los Angeles Greek Theatre \| Los Angeles Royce Hall \| Los Angeles American Airlines Theatre \| New York City August Wilson Theatre \| New York City Belasco Theatre \| New York City Bernard B. Jacobs Theatre \| New York City ... Removing the parentheses in this example changes the logic and results of the query. The following example uses the NOT operator: select * from category where not catid=1 order by 1; catid \| catgroup \| catname \| catdesc -------+----------+-----------+-------------------------------------------- 2 \| Sports \| NHL \| National Hockey League 3 \| Sports \| NFL \| National Football League 4 \| Sports \| NBA \| National Basketball Association 5 \| Sports \| MLS \| Major League Soccer ... The following example uses a NOT condition followed by an AND condition: select * from category where (not catid=1) and catgroup='Sports' order by catid; catid \| catgroup \| catname \| catdesc -------+----------+---------+--------------------------------- 2 \| Sports \| NHL \| National Hockey League 3 \| Sports \| NFL \| National Football League 4 \| Sports \| NBA \| National Basketball Association 5 \| Sports \| MLS \| Major League Soccer (4 rows) Logical conditions 755 AWS Clean Rooms SQL Reference Pattern-matching conditions A pattern-matching operator searches a string for a pattern speciﬁed in the conditional expression and returns true or false depending on whether it ﬁnds a match. AWS Clean Rooms uses the following methods for pattern matching: • LIKE expressions The LIKE operator compares a string expression, such as a column name, with a pattern that uses the wildcard characters % (percent) and _ (underscore). LIKE pattern matching always covers the entire string. LIKE performs a case-sensitive match and ILIKE performs a case-insensitive match. • SIMILAR TO regular expressions The SIMILAR TO operator matches a string expression with a SQL standard regular expression pattern, which can include a set of pattern-matching metacharacters that includes the two supported by the LIKE operator. SIMILAR TO matches the entire string and performs a case- sensitive match. Topics • LIKE • SIMILAR TO LIKE The LIKE operator compares a string expression, such as a column name, with a pattern that uses the wildcard characters % (percent) and _ (underscore). LIKE pattern matching always covers the entire string. To match a sequence anywhere within a string, the pattern must start and end with a percent sign. LIKE is case-sensitive; ILIKE is case-insensitive. Syntax expression [ NOT ] LIKE \| ILIKE pattern [ ESCAPE 'escape_char' ] Pattern-matching conditions 756 AWS Clean Rooms Arguments expression SQL Reference A valid UTF-8 character expression, such as a column name. LIKE \| ILIKE LIKE performs a case-sensitive pattern match. ILIKE performs a case-insensitive pattern match for single-byte UTF-8 (ASCII) characters. To perform a case-insensitive pattern match for multibyte characters,
sql-reference-200	sql-reference.pdf	200	characters % (percent) and _ (underscore). LIKE pattern matching always covers the entire string. To match a sequence anywhere within a string, the pattern must start and end with a percent sign. LIKE is case-sensitive; ILIKE is case-insensitive. Syntax expression [ NOT ] LIKE \| ILIKE pattern [ ESCAPE 'escape_char' ] Pattern-matching conditions 756 AWS Clean Rooms Arguments expression SQL Reference A valid UTF-8 character expression, such as a column name. LIKE \| ILIKE LIKE performs a case-sensitive pattern match. ILIKE performs a case-insensitive pattern match for single-byte UTF-8 (ASCII) characters. To perform a case-insensitive pattern match for multibyte characters, use the LOWER function on expression and pattern with a LIKE condition. In contrast to comparison predicates, such as = and <>, LIKE and ILIKE predicates do not implicitly ignore trailing spaces. To ignore trailing spaces, use RTRIM or explicitly cast a CHAR column to VARCHAR. The ~~ operator is equivalent to LIKE, and ~~* is equivalent to ILIKE. Also the !~~ and !~~* operators are equivalent to NOT LIKE and NOT ILIKE. pattern A valid UTF-8 character expression with the pattern to be matched. escape_char A character expression that will escape metacharacters characters in the pattern. The default is two backslashes ('\\'). If pattern does not contain metacharacters, then the pattern only represents the string itself; in that case LIKE acts the same as the equals operator. Either of the character expressions can be CHAR or VARCHAR data types. If they diﬀer, AWS Clean Rooms converts pattern to the data type of expression. LIKE supports the following pattern-matching metacharacters: Operator Description % _ Matches any sequence of zero or more characters. Matches any single character. Pattern-matching conditions 757 AWS Clean Rooms Examples SQL Reference The following table shows examples of pattern matching using LIKE: Expression Returns 'abc' LIKE 'abc' 'abc' LIKE 'a%' 'abc' LIKE '_B_' 'abc' ILIKE '_B_' 'abc' LIKE 'c%' True True False True False The following example ﬁnds all cities whose names start with "E": select distinct city from users where city like 'E%' order by city; city --------------- East Hartford East Lansing East Rutherford East St. Louis Easthampton Easton Eatontown Eau Claire ... The following example ﬁnds users whose last name contains "ten" : select distinct lastname from users where lastname like '%ten%' order by lastname; lastname ------------- Christensen Wooten ... Pattern-matching conditions 758 AWS Clean Rooms SQL Reference The following example ﬁnds cities whose third and fourth characters are "ea". The command uses ILIKE to demonstrate case insensitivity: select distinct city from users where city ilike '__EA%' order by city; city ------------- Brea Clearwater Great Falls Ocean City Olean Wheaton (6 rows) The following example uses the default escape string (\\) to search for strings that include "start_" (the text start followed by an underscore _): select tablename, "column" from my_table_def where "column" like '%start\\_%' limit 5; tablename \| column -------------------+--------------- my_s3client \| start_time my_tr_conflict \| xact_start_ts my_undone \| undo_start_ts my_unload_log \| start_time my_vacuum_detail \| start_row (5 rows) The following example speciﬁes '^' as the escape character, then uses the escape character to search for strings that include "start_" (the text start followed by an underscore _): select tablename, "column" from my_table_def where "column" like '%start^_%' escape '^' limit 5; tablename \| column -------------------+--------------- Pattern-matching conditions 759 AWS Clean Rooms SQL Reference my_s3client \| start_time my_tr_conflict \| xact_start_ts my_undone \| undo_start_ts my_unload_log \| start_time my_vacuum_detail \| start_row (5 rows) The following example uses the ~~* operator to do a case-insensitive (ILIKE) search for cities that start with "Ag". select distinct city from users where city ~~* 'Ag%' order by city; city ------------ Agat Agawam Agoura Hills Aguadilla SIMILAR TO The SIMILAR TO operator matches a string expression, such as a column name, with a SQL standard regular expression pattern. A SQL regular expression pattern can include a set of pattern-matching metacharacters, including the two supported by the LIKE operator. The SIMILAR TO operator returns true only if its pattern matches the entire string, unlike POSIX regular expression behavior, where the pattern can match any portion of the string. SIMILAR TO performs a case-sensitive match. Note Regular expression matching using SIMILAR TO is computationally expensive. We recommend using LIKE whenever possible, especially when processing a very large number of rows. For example, the following queries are functionally identical, but the query that uses LIKE runs several times faster than the query that uses a regular expression: select count() from event where eventname SIMILAR TO '%(Ring\|Die)%'; select count() from event where eventname LIKE '%Ring%' OR eventname LIKE '%Die %'; Pattern-matching conditions 760 AWS Clean Rooms Syntax SQL Reference expression [ NOT ] SIMILAR TO pattern [ ESCAPE 'escape_char' ] Arguments expression A valid UTF-8 character expression, such as a column name. SIMILAR TO SIMILAR TO performs a case-sensitive pattern match for the entire string in expression. pattern
sql-reference-201	sql-reference.pdf	201	large number of rows. For example, the following queries are functionally identical, but the query that uses LIKE runs several times faster than the query that uses a regular expression: select count() from event where eventname SIMILAR TO '%(Ring\|Die)%'; select count() from event where eventname LIKE '%Ring%' OR eventname LIKE '%Die %'; Pattern-matching conditions 760 AWS Clean Rooms Syntax SQL Reference expression [ NOT ] SIMILAR TO pattern [ ESCAPE 'escape_char' ] Arguments expression A valid UTF-8 character expression, such as a column name. SIMILAR TO SIMILAR TO performs a case-sensitive pattern match for the entire string in expression. pattern A valid UTF-8 character expression representing a SQL standard regular expression pattern. escape_char A character expression that will escape metacharacters in the pattern. The default is two backslashes ('\\'). If pattern does not contain metacharacters, then the pattern only represents the string itself. Either of the character expressions can be CHAR or VARCHAR data types. If they diﬀer, AWS Clean Rooms converts pattern to the data type of expression. SIMILAR TO supports the following pattern-matching metacharacters: Operator Description % _ \| * + ? Matches any sequence of zero or more characters. Matches any single character. Denotes alternation (either of two alternatives). Repeat the previous item zero or more times. Repeat the previous item one or more times. Repeat the previous item zero or one time. Pattern-matching conditions 761 AWS Clean Rooms SQL Reference Operator Description Repeat the previous item exactly m times. Repeat the previous item m or more times. Repeat the previous item at least m and not more than n times. Parentheses group items into a single logical item. A bracket expression speciﬁes a character class, just as in POSIX regular expressions. {m} {m,} {m,n} () [...] Examples The following table shows examples of pattern matching using SIMILAR TO: Expression Returns 'abc' SIMILAR TO 'abc' 'abc' SIMILAR TO '_b_' 'abc' SIMILAR TO '_A_' 'abc' SIMILAR TO '%(b\|d)%' 'abc' SIMILAR TO '(b\|c)%' True True False True False 'AbcAbcdefgefg12efgefg12' SIMILAR True TO '((Ab)?c)+d((efg)+(12))+' 'aaaaaab11111xy' SIMILAR TO 'a{6}_ True [0-9]{5}(x\|y){2}' '$0.87' SIMILAR TO '$[0-9]+(.[0-9] True [0-9])?' The following example ﬁnds cities whose names contain "E" or "H": Pattern-matching conditions 762 AWS Clean Rooms SQL Reference SELECT DISTINCT city FROM users WHERE city SIMILAR TO '%E%\|%H%' ORDER BY city LIMIT 5; city ----------------- Agoura Hills Auburn Hills Benton Harbor Beverly Hills Chicago Heights The following example uses the default escape string ('\\') to search for strings that include "_": SELECT tablename, "column" FROM my_table_def WHERE "column" SIMILAR TO '%start\\_%' ORDER BY tablename, "column" LIMIT 5; tablename \| column --------------------------+--------------------- my_abort_idle \| idle_start_time my_abort_idle \| txn_start_time my_analyze_compression \| start_time my_auto_worker_levels \| start_level my_auto_worker_levels \| start_wlm_occupancy The following example speciﬁes '^' as the escape string, then uses the escape string to search for strings that include "_": SELECT tablename, "column" FROM my_table_def WHERE "column" SIMILAR TO '%start^_%' ESCAPE '^' ORDER BY tablename, "column" LIMIT 5; tablename \| column --------------------------+--------------------- stcs_abort_idle \| idle_start_time stcs_abort_idle \| txn_start_time stcs_analyze_compression \| start_time stcs_auto_worker_levels \| start_level stcs_auto_worker_levels \| start_wlm_occupancy Pattern-matching conditions 763 AWS Clean Rooms SQL Reference BETWEEN range condition A BETWEEN condition tests expressions for inclusion in a range of values, using the keywords BETWEEN and AND. Syntax expression [ NOT ] BETWEEN expression AND expression Expressions can be numeric, character, or datetime data types, but they must be compatible. The range is inclusive. Examples The ﬁrst example counts how many transactions registered sales of either 2, 3, or 4 tickets: select count() from sales where qtysold between 2 and 4; count -------- 104021 (1 row) The range condition includes the begin and end values. select min(dateid), max(dateid) from sales where dateid between 1900 and 1910; min \| max -----+----- 1900 \| 1910 The ﬁrst expression in a range condition must be the lesser value and the second expression the greater value. The following example will always return zero rows due to the values of the expressions: select count() from sales where qtysold between 4 and 2; BETWEEN range condition 764 AWS Clean Rooms count ------- 0 (1 row) SQL Reference However, applying the NOT modiﬁer will invert the logic and produce a count of all rows: select count(*) from sales where qtysold not between 4 and 2; count -------- 172456 (1 row) The following query returns a list of venues with 20000 to 50000 seats: select venueid, venuename, venueseats from venue where venueseats between 20000 and 50000 order by venueseats desc; venueid \| venuename \| venueseats ---------+-------------------------------+------------ 116 \| Busch Stadium \| 49660 106 \| Rangers BallPark in Arlington \| 49115 96 \| Oriole Park at Camden Yards \| 48876 ... (22 rows) The following example demonstrates using BETWEEN for date values: select salesid, qtysold, pricepaid, commission, saletime from sales where eventid between 1000 and 2000 and saletime between '2008-01-01' and '2008-01-03' order by saletime asc;
sql-reference-202	sql-reference.pdf	202	2; count -------- 172456 (1 row) The following query returns a list of venues with 20000 to 50000 seats: select venueid, venuename, venueseats from venue where venueseats between 20000 and 50000 order by venueseats desc; venueid \| venuename \| venueseats ---------+-------------------------------+------------ 116 \| Busch Stadium \| 49660 106 \| Rangers BallPark in Arlington \| 49115 96 \| Oriole Park at Camden Yards \| 48876 ... (22 rows) The following example demonstrates using BETWEEN for date values: select salesid, qtysold, pricepaid, commission, saletime from sales where eventid between 1000 and 2000 and saletime between '2008-01-01' and '2008-01-03' order by saletime asc; salesid \| qtysold \| pricepaid \| commission \| saletime --------+---------+-----------+------------+--------------- 65082 \| 4 \| 472 \| 70.8 \| 1/1/2008 06:06 110917 \| 1 \| 337 \| 50.55 \| 1/1/2008 07:05 BETWEEN range condition 765 AWS Clean Rooms SQL Reference 112103 \| 1 \| 241 \| 36.15 \| 1/2/2008 03:15 137882 \| 3 \| 1473 \| 220.95 \| 1/2/2008 05:18 40331 \| 2 \| 58 \| 8.7 \| 1/2/2008 05:57 110918 \| 3 \| 1011 \| 151.65 \| 1/2/2008 07:17 96274 \| 1 \| 104 \| 15.6 \| 1/2/2008 07:18 150499 \| 3 \| 135 \| 20.25 \| 1/2/2008 07:20 68413 \| 2 \| 158 \| 23.7 \| 1/2/2008 08:12 Note that although BETWEEN's range is inclusive, dates default to having a time value of 00:00:00. The only valid January 3 row for the sample query would be a row with a saletime of 1/3/2008 00:00:00. Null condition The NULL condition tests for nulls, when a value is missing or unknown. Syntax expression IS [ NOT ] NULL Arguments expression Any expression such as a column. IS NULL Is true when the expression's value is null and false when it has a value. IS NOT NULL Is false when the expression's value is null and true when it has a value. Example This example indicates how many times the SALES table contains null in the QTYSOLD ﬁeld: select count() from sales where qtysold is null; count ------- Null condition 766 AWS Clean Rooms 0 (1 row) EXISTS condition SQL Reference EXISTS conditions test for the existence of rows in a subquery, and return true if a subquery returns at least one row. If NOT is speciﬁed, the condition returns true if a subquery returns no rows. Syntax [ NOT ] EXISTS (table_subquery) Arguments EXISTS Is true when the table_subquery returns at least one row. NOT EXISTS Is true when the table_subquery returns no rows. table_subquery A subquery that evaluates to a table with one or more columns and one or more rows. Example This example returns all date identiﬁers, one time each, for each date that had a sale of any kind: select dateid from date where exists ( select 1 from sales where date.dateid = sales.dateid ) order by dateid; dateid -------- 1827 1828 1829 EXISTS condition 767 AWS Clean Rooms ... IN condition SQL Reference An IN condition tests a value for membership in a set of values or in a subquery. Syntax expression [ NOT ] IN (expr_list \| table_subquery) Arguments expression A numeric, character, or datetime expression that is evaluated against the expr_list or table_subquery and must be compatible with the data type of that list or subquery. expr_list One or more comma-delimited expressions, or one or more sets of comma-delimited expressions bounded by parentheses. table_subquery A subquery that evaluates to a table with one or more rows, but is limited to only one column in its select list. IN \| NOT IN IN returns true if the expression is a member of the expression list or query. NOT IN returns true if the expression is not a member. IN and NOT IN return NULL and no rows are returned in the following cases: If expression yields null; or if there are no matching expr_list or table_subquery values and at least one of these comparison rows yields null. Examples The following conditions are true only for those values listed: qtysold in (2, 4, 5) date.day in ('Mon', 'Tues') date.month not in ('Oct', 'Nov', 'Dec') IN condition 768 AWS Clean Rooms SQL Reference Optimization for Large IN Lists To optimize query performance, an IN list that includes more than 10 values is internally evaluated as a scalar array. IN lists with fewer than 10 values are evaluated as a series of OR predicates. This optimization is supported for SMALLINT, INTEGER, BIGINT, REAL, DOUBLE PRECISION, BOOLEAN, CHAR, VARCHAR, DATE, TIMESTAMP, and TIMESTAMPTZ data types. Look at the EXPLAIN output for the query to see the eﬀect of this optimization. For example: explain select from sales QUERY PLAN -------------------------------------------------------------------- XN Seq Scan on sales (cost=0.00..6035.96 rows=86228 width=53) Filter: (salesid = ANY ('{1,2,3,4,5,6,7,8,9,10,11}'::integer[])) (2 rows) Syntax comparison_condition \| logical_condition \| range_condition \| pattern_matching_condition \| null_condition \| EXISTS_condition \| IN_condition Syntax
sql-reference-203	sql-reference.pdf	203	than 10 values is internally evaluated as a scalar array. IN lists with fewer than 10 values are evaluated as a series of OR predicates. This optimization is supported for SMALLINT, INTEGER, BIGINT, REAL, DOUBLE PRECISION, BOOLEAN, CHAR, VARCHAR, DATE, TIMESTAMP, and TIMESTAMPTZ data types. Look at the EXPLAIN output for the query to see the eﬀect of this optimization. For example: explain select * from sales QUERY PLAN -------------------------------------------------------------------- XN Seq Scan on sales (cost=0.00..6035.96 rows=86228 width=53) Filter: (salesid = ANY ('{1,2,3,4,5,6,7,8,9,10,11}'::integer[])) (2 rows) Syntax comparison_condition \| logical_condition \| range_condition \| pattern_matching_condition \| null_condition \| EXISTS_condition \| IN_condition Syntax 769 AWS Clean Rooms SQL Reference Querying nested data AWS Clean Rooms oﬀers SQL-compatible access to relational and nested data. AWS Clean Rooms uses dotted notation and array subscript for path navigation when accessing nested data. It also enables the FROM clause items to iterate over arrays and use for unnest operations. The following topics provide descriptions of the diﬀerent query patterns that combine the use of the array/struct/map data type with path and array navigation, unnesting, and joins. Topics • Navigation • Unnesting queries • Lax semantics • Types of introspection Navigation AWS Clean Rooms enables navigation into arrays and structures using the [...] bracket and dot notation respectively. Furthermore, you can mix navigation into structures using the dot notation and arrays using the bracket notation. Example For example, the following example query assumes that the c_orders array data column is an array with a structure and an attribute is named o_orderkey. SELECT cust.c_orders[0].o_orderkey FROM customer_orders_lineitem AS cust; You can use the dot and bracket notations in all types of queries, such as ﬁltering, join, and aggregation. You can use these notations in a query in which there are normally column references. Example The following example uses a SELECT statement that ﬁlters results. SELECT count() FROM customer_orders_lineitem WHERE c_orders[0].o_orderkey IS NOT NULL; Navigation 770 AWS Clean Rooms Example SQL Reference The following example uses the bracket and dot navigation in both GROUP BY and ORDER BY clauses. SELECT c_orders[0].o_orderdate, c_orders[0].o_orderstatus, count() FROM customer_orders_lineitem WHERE c_orders[0].o_orderkey IS NOT NULL GROUP BY c_orders[0].o_orderstatus, c_orders[0].o_orderdate ORDER BY c_orders[0].o_orderdate; Unnesting queries To unnest queries, AWS Clean Rooms enables iteration over arrays. It does this by navigating the array using the FROM clause of a query. Example Using the previous example, the following example iterates over the attribute values for c_orders. SELECT o FROM customer_orders_lineitem c, c.c_orders o; The unnesting syntax is an extension of the FROM clause. In standard SQL, the FROM clause x (AS) y means that y iterates over each tuple in relation x. In this case, x refers to a relation and y refers to an alias for relation x. Similarly, the syntax of unnesting using the FROM clause item x (AS) y means that y iterates over each value in array expression x. In this case, x is an array expression and y is an alias for x. The left operand can also use the dot and bracket notation for regular navigation. Example In the previous example: • customer_orders_lineitem c is the iteration over the customer_order_lineitem base table • c.c_orders o is the iteration over the c.c_orders array Unnesting queries 771 AWS Clean Rooms SQL Reference To iterate over the o_lineitems attribute, which is an array within an array, you add multiple clauses. SELECT o, l FROM customer_orders_lineitem c, c.c_orders o, o.o_lineitems l; AWS Clean Rooms also supports an array index when iterating over the array using the AT keyword. The clause x AS y AT z iterates over array x and generates the ﬁeld z, which is the array index. Example The following example shows how an array index works. SELECT c_name, orders.o_orderkey AS orderkey, index AS orderkey_index FROM customer_orders_lineitem c, c.c_orders AS orders AT index ORDER BY orderkey_index; c_name \| orderkey \| orderkey_index -------------------+----------+---------------- Customer#000008251 \| 3020007 \| 0 Customer#000009452 \| 4043971 \| 0 (2 rows) Example The following example iterates over a scalar array. CREATE TABLE bar AS SELECT json_parse('{"scalar_array": [1, 2.3, 45000000]}') AS data; SELECT index, element FROM bar AS b, b.data.scalar_array AS element AT index; index \| element -------+---------- 0 \| 1 1 \| 2.3 2 \| 45000000 (3 rows) Example The following example iterates over an array of multiple levels. The example uses multiple unnest clauses to iterate into the innermost arrays. The f.multi_level_array AS array Unnesting queries 772 AWS Clean Rooms SQL Reference iterates over multi_level_array. The array AS element is the iteration over the arrays within multi_level_array. CREATE TABLE foo AS SELECT json_parse('[[1.1, 1.2], [2.1, 2.2], [3.1, 3.2]]') AS multi_level_array; SELECT array, element FROM foo AS f, f.multi_level_array AS array, array AS element; array \| element -----------+--------- [1.1,1.2] \| 1.1 [1.1,1.2] \| 1.2 [2.1,2.2] \| 2.1 [2.1,2.2] \| 2.2 [3.1,3.2] \| 3.1 [3.1,3.2] \| 3.2 (6 rows) Lax semantics By default,
sql-reference-204	sql-reference.pdf	204	iterates over an array of multiple levels. The example uses multiple unnest clauses to iterate into the innermost arrays. The f.multi_level_array AS array Unnesting queries 772 AWS Clean Rooms SQL Reference iterates over multi_level_array. The array AS element is the iteration over the arrays within multi_level_array. CREATE TABLE foo AS SELECT json_parse('[[1.1, 1.2], [2.1, 2.2], [3.1, 3.2]]') AS multi_level_array; SELECT array, element FROM foo AS f, f.multi_level_array AS array, array AS element; array \| element -----------+--------- [1.1,1.2] \| 1.1 [1.1,1.2] \| 1.2 [2.1,2.2] \| 2.1 [2.1,2.2] \| 2.2 [3.1,3.2] \| 3.1 [3.1,3.2] \| 3.2 (6 rows) Lax semantics By default, navigation operations on nested data values return null instead of returning an error out when the navigation is invalid. Object navigation is invalid if the nested data value is not an object or if the nested data value is an object but doesn't contain the attribute name used in the query. Example For example, the following query accesses an invalid attribute name in the nested data column c_orders: SELECT c.c_orders.something FROM customer_orders_lineitem c; Array navigation returns null if the nested data value is not an array or the array index is out of bounds. Example The following query returns null because c_orders[1][1] is out of bounds. SELECT c.c_orders[1][1] FROM customer_orders_lineitem c; Lax semantics 773 AWS Clean Rooms SQL Reference Types of introspection Nested data type columns support inspection functions that return the type and other type information about the value. AWS Clean Rooms supports the following boolean functions for nested data columns: • DECIMAL_PRECISION • DECIMAL_SCALE • IS_ARRAY • IS_BIGINT • IS_CHAR • IS_DECIMAL • IS_FLOAT • IS_INTEGER • IS_OBJECT • IS_SCALAR • IS_SMALLINT • IS_VARCHAR • JSON_TYPEOF All these functions return false if the input value is null. IS_SCALAR, IS_OBJECT, and IS_ARRAY are mutually exclusive and cover all possible values except for null. To infer the types corresponding to the data, AWS Clean Rooms uses the JSON_TYPEOF function that returns the type of (the top level of) the nested data value as shown in the following example: SELECT JSON_TYPEOF(r_nations) FROM region_nations; json_typeof ------------- array (1 row) SELECT JSON_TYPEOF(r_nations[0].n_nationkey) FROM region_nations; json_typeof ------------- number Types of introspection 774 AWS Clean Rooms SQL Reference Document history for the AWS Clean Rooms SQL Reference The following table describes the documentation releases for the AWS Clean Rooms SQL Reference. For notiﬁcation about updates to this documentation, you can subscribe to the RSS feed. To subscribe to RSS updates, you must have an RSS plug-in enabled for the browser you are using. Change Description Date AWS Clean Rooms Spark SQL October 29, 2024 Customers can now run queries using some SQL conditions, functions, commands, and conventions supported with the Spark SQL analytics engine. SQL commands and SQL functions – update Examples have been added for the JOIN clause, EXCEPT February 28, 2024 SQL functions - update set operator, CASE conditional expression, and the following functions: ANY_VALUE, NVL and COALESCE, NULLIF, CAST, CONVERT, CONVERT_T IMEZONE, EXTRACT, MOD, SIGN, CONCAT, FIRST_VALUE, and LAST_VALUE. AWS Clean Rooms now supports the following SQL functions: Array, SUPER, and VARBYTE. The following math functions are now supported: ACOS, ASIN, ATAN, ATAN2, COT, DEXP, PI, October 6, 2023 775 AWS Clean Rooms SQL Reference POW, RADIANS, and SIN. The following JSON functions are now supported: CAN_JSON_ PARSE, JSON_PARSE, and JSON_SERIALIZE. Nested data type support AWS Clean Rooms now supports nested data types. August 30, 2023 SQL naming rules - update Documentation-only change to clarify reserved column August 16, 2023 General availability names. The AWS Clean Rooms SQL Reference is now generally available. July 31, 2023 776
sql-server-ec2-001	sql-server-ec2.pdf	1	User Guide Microsoft SQL Server on Amazon EC2 Copyright © 2025 Amazon Web Services, Inc. and/or its aﬃliates. All rights reserved. Microsoft SQL Server on Amazon EC2 User Guide Microsoft SQL Server on Amazon EC2: User Guide Copyright © 2025 Amazon Web Services, Inc. and/or its aﬃliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be aﬃliated with, connected to, or sponsored by Amazon. Microsoft SQL Server on Amazon EC2 Table of Contents User Guide What is Microsoft SQL Server on Amazon EC2? ............................................................................ 1 Features .......................................................................................................................................................... 1 Pricing ............................................................................................................................................................. 3 Options to run SQL Server ......................................................................................................................... 3 SQL Server on Amazon EC2 ................................................................................................................. 4 RDS for SQL Server ................................................................................................................................ 4 Amazon RDS Custom ............................................................................................................................. 5 Concepts and terminology ......................................................................................................................... 6 SQL Clustering best practices .................................................................................................................... 9 Assign IP addresses .............................................................................................................................. 10 Cluster properties ................................................................................................................................. 11 Cluster quorum votes and 50/50 splits in a multi-site cluster .................................................... 11 DNS registration .................................................................................................................................... 12 Elastic Network Adapters (ENAs) ....................................................................................................... 13 Multi-site clusters and EC2 instance placement ............................................................................. 13 Instance type selection ........................................................................................................................ 13 Assign elastic network interfaces and IPs to the instance ........................................................... 13 Heartbeat network ............................................................................................................................... 14 Conﬁgure the network adapter in the OS ....................................................................................... 14 IPv6 .......................................................................................................................................................... 14 Host record TTL for SQL Availability Group Listeners .................................................................. 14 Logging ................................................................................................................................................... 15 NetBIOS over TCP ................................................................................................................................. 15 NetFT Virtual Adapter .......................................................................................................................... 15 Set possible owners ............................................................................................................................. 16 Tune the failover thresholds .............................................................................................................. 17 Witness importance and Dynamic Quorum Architecture ............................................................. 18 Troubleshoot .......................................................................................................................................... 18 Set up SQL Server on Amazon EC2 .............................................................................................. 20 Prerequisites ................................................................................................................................................ 20 Sign up for an AWS account .............................................................................................................. 20 Create a user with administrative access ......................................................................................... 21 Create a key pair ................................................................................................................................... 22 Create a security group ....................................................................................................................... 23 iii Microsoft SQL Server on Amazon EC2 User Guide Permissions .................................................................................................................................................. 26 Licensing options and considerations .......................................................................................... 27 Licensing options ........................................................................................................................................ 27 License-included .................................................................................................................................... 27 BYOL ........................................................................................................................................................ 28 Licensing considerations ........................................................................................................................... 28 Choose a SQL Server edition ............................................................................................................. 29 Purchase SQL Server from AWS ........................................................................................................ 29 Use BYOL for SQL Server on AWS .................................................................................................... 30 Quantify license requirements ........................................................................................................... 30 License Mobility with SQL Server ...................................................................................................... 30 Track BYOL license consumption ....................................................................................................... 31 SQL Server CALs ................................................................................................................................... 31 Licensing for passive failover ............................................................................................................. 31 Find a SQL Server license-included AMI ...................................................................................... 33 Methods to ﬁnd a SQL Server license-included AMI .......................................................................... 33 Deploy SQL Server on Amazon EC2 ............................................................................................. 37 Considerations ............................................................................................................................................. 37 Deployment options .................................................................................................................................. 38 Connect to SQL Server on Amazon EC2 ....................................................................................... 46 SSMS ............................................................................................................................................................. 46 Conﬁguration Manager ............................................................................................................................. 47 VSS based database backup ......................................................................................................... 48 Snapshot prerequisites .............................................................................................................................. 48 Create AWS VSS solution based EBS snapshots .................................................................................. 49 VSS based database restore with automation runbook .............................................................. 50 Pricing ........................................................................................................................................................... 50 Prerequisites ................................................................................................................................................ 51 Grant IAM permissions for the restore process .............................................................................. 52 Restore from VSS snapshots ................................................................................................................... 57 Runbook parameters ............................................................................................................................ 57 Run the restore process ....................................................................................................................... 59 Troubleshoot VSS snapshot restore ....................................................................................................... 63 Debug failures in AWSEC2-RestoreSqlServerDatabaseWithVss from the Systems Manager console .................................................................................................................................................... 63 Evaluate downgrading your SQL Server edition ......................................................................... 65 iv Microsoft SQL Server on Amazon EC2 User Guide Downgrade requirements ......................................................................................................................... 65 Downgrade your SQL Server Enterprise edition .................................................................................. 67 Migrating an on-premises database to Amazon EC2 ................................................................... 69 Automated SQL Server backup and restore ......................................................................................... 69 Manual SQL Server backup and restore ................................................................................................ 69 Prerequisites ........................................................................................................................................... 70 Step 1: Backing up your database .................................................................................................... 70 Step 2: Uploading your database backup ﬁles ............................................................................... 70 Step 3: Downloading your database backup ﬁles .......................................................................... 71 Step 4: Restoring your database backup ﬁles ................................................................................ 71 Server rehost ............................................................................................................................................... 71 Migrate Microsoft SQL Server from Windows to Linux ............................................................... 73 Concepts ....................................................................................................................................................... 73 Related services .......................................................................................................................................... 74 How Windows to Linux replatforming assistant for Microsoft SQL Server works ......................... 74 Components ................................................................................................................................................ 75 Replatforming script prerequisites ......................................................................................................... 75 Prerequisites to run the replatforming script ................................................................................. 75 Prerequisites for replatforming to an existing EC2 instance ....................................................... 77 Run the replatforming script ................................................................................................................... 77 Replatforming script examples .......................................................................................................... 78 Replatforming script parameters ...................................................................................................... 79 Security .......................................................................................................................................... 84 Document history .......................................................................................................................... 85 v Microsoft SQL Server on Amazon EC2 User Guide What is Microsoft SQL Server on Amazon EC2? You can run Microsoft SQL Server on Amazon EC2. Microsoft SQL Server is a relational database management system (RDBMS) whose primary purpose is to store and retrieve data. SQL Server includes additional services, such as Analysis Services (SSAS), Reporting Services (SSRS), Integration Services (SSIS), and Machine Learning (ML). AWS provides a comprehensive set of services and tools to deploy Microsoft SQL Server
sql-server-ec2-002	sql-server-ec2.pdf	2	replatforming script ................................................................................................................... 77 Replatforming script examples .......................................................................................................... 78 Replatforming script parameters ...................................................................................................... 79 Security .......................................................................................................................................... 84 Document history .......................................................................................................................... 85 v Microsoft SQL Server on Amazon EC2 User Guide What is Microsoft SQL Server on Amazon EC2? You can run Microsoft SQL Server on Amazon EC2. Microsoft SQL Server is a relational database management system (RDBMS) whose primary purpose is to store and retrieve data. SQL Server includes additional services, such as Analysis Services (SSAS), Reporting Services (SSRS), Integration Services (SSIS), and Machine Learning (ML). AWS provides a comprehensive set of services and tools to deploy Microsoft SQL Server on the reliable and secure AWS Cloud infrastructure. The beneﬁts of running SQL Server on AWS include cost savings, scalability, high availability and disaster recovery, improved performance, and ease of management. For more information, see Learn why AWS is the best cloud to run Microsoft Windows Server and SQL Server workloads on the AWS Compute blog. Amazon Elastic Compute Cloud (Amazon EC2) supports a self-managed SQL Server. That is, it gives you full control over the setup of the infrastructure and the database environment. Running SQL Server on Amazon EC2 is very similar to running SQL Server on your own server. You have full control of the database and operating system-level access, so you can use your choice of tools to manage the operating system, database software, patches, data replication, backup, and restoration. You are responsible for data replication and recovery across your instances in the same or diﬀerent AWS Regions. For more information, refer to the AWS Shared Responsibility Model. Overview topics • Microsoft SQL Server on Amazon EC2 features • Microsoft SQL Server on Amazon EC2 pricing • Options to run SQL Server on the AWS Cloud • Microsoft SQL Server on Amazon EC2 concepts and terminology • Best practices and recommendations for SQL Server clustering on Amazon EC2 Microsoft SQL Server on Amazon EC2 features SQL Server on Amazon EC2 provides the following features: • Flexible licensing options — When you use Amazon EC2 instances with the license included, you are using instances with fully-compliant Windows Server and SQL Server that are licensed through AWS. Flexible BYOL options include default tenant EC2 for products that are eligible for Microsoft License Mobility through Software Assurance, as well as Amazon EC2 Dedicated Hosts Features 1 Microsoft SQL Server on Amazon EC2 User Guide and Amazon EC2 Dedicated Instances. You can use AWS License Manager to track the usage of software licenses and reduce the risk of non-compliance. For more information, see Licensing in the Amazon Web Services and Microsoft Frequently Asked Questions. • High performance block storage — Amazon Elastic Block Store provides multiple options for high-performance block storage for Microsoft SQL Server. EC2 Instances using io2 Block Express give you the highest block storage performance with a single storage volume. Other SSD-backed Amazon EBS options include io2 volumes for business-critical applications and gp3 volumes for general purpose applications. Amazon EBS also oﬀers crash-consistent snapshots, and enables application-consistent snapshots through Windows VSS (Volume Shadow Copy Services) to help protect your SQL Server deployments. • Fully-managed shared storage — Amazon FSx for Windows File Server and Amazon FSx for NetApp ONTAP oﬀer fully-managed shared storage for high-availability SQL Server failover cluster instances (FCI) workloads. • Windows-based services — AWS Directory Service oﬀers managed Microsoft Active Directory with identity and access management. • Scalable processors — Intel Xeon Scalable Processors on AWS provide you with better data protection, faster processing of more data volumes, and increased service ﬂexibility for Amazon EC2. • Migration programs — AWS oﬀers programs for migration for customers looking to migrate SQL Server workloads to AWS. AWS Migration Acceleration Program (MAP) for Windows provides services, best practices, and tools to help you save costs and accelerate your migration on AWS. • Windows workload optimization — After you move your SQL Server workloads to AWS, you can continue to optimize costs, usage, and licenses to suit your business requirements. With Cost Explorer Service, you can visualize, understand, and manage your AWS costs and usage over time. AWS Compute Optimizer recommends optimal AWS compute resources for your workloads so that you can reduce costs up to 25% by analyzing historical utilization data. AWS Trusted Advisor can check that your EC2 instances have the required amount of SQL Server licenses and that the EC2 instance vCPU count doesn’t exceed what is permitted for the SQL Server edition. AWS Managed Services can help operate your cloud environment post-migration by analyzing alerts and responding to incidents, reducing operational overhead and risk. You can use AWS Systems Manager to automate operational tasks across your AWS resources and better manage your infrastructure at scale. AWS can help you to modernize you Windows-based applications with AWS open source services if you want to reduce the
sql-server-ec2-003	sql-server-ec2.pdf	3	historical utilization data. AWS Trusted Advisor can check that your EC2 instances have the required amount of SQL Server licenses and that the EC2 instance vCPU count doesn’t exceed what is permitted for the SQL Server edition. AWS Managed Services can help operate your cloud environment post-migration by analyzing alerts and responding to incidents, reducing operational overhead and risk. You can use AWS Systems Manager to automate operational tasks across your AWS resources and better manage your infrastructure at scale. AWS can help you to modernize you Windows-based applications with AWS open source services if you want to reduce the high cost of commercial licensing. Options include running SQL Server database applications on Linux, moving workloads to Amazon Aurora, containerizing Features 2 Microsoft SQL Server on Amazon EC2 User Guide your Windows applications with Amazon EKS, going serverless with AWS Lambda, or taking advantage of micro-services based architecture. For more features speciﬁc to Amazon EC2, see Features of Amazon EC2. Microsoft SQL Server on Amazon EC2 pricing For information about pricing for Amazon EC2, see the Amazon EC2 pricing page. For information about creating a price estimate for Microsoft Windows Server and Microsoft SQL Server, see Tutorial: Using Windows Server and SQL Server on Amazon EC2 calculator in the AWS Pricing Calculator User Guide. Options to run SQL Server on the AWS Cloud AWS provides the option to run Microsoft SQL Server in a cloud environment. For developers and database administrators, running SQL Server in the AWS Cloud is similar to running SQL Server databases in a data center. There are three primary options to run SQL Server on AWS: • Microsoft SQL Server on Amazon EC2 • Amazon RDS for Microsoft SQL Server • Amazon RDS Custom for Microsoft SQL Server Your application requirements, database features, functionality, growth capacity, and overall architecture complexity will determine which option to choose. Many AWS customers run multiple SQL Server database workloads across Amazon RDS and Amazon EC2. For more information on how to choose how to run SQL Server on the AWS Cloud, see Decision matrix on the AWS Prescriptive Guidance website. If you are migrating multiple SQL Server databases to AWS, some of them might be a great ﬁt for Amazon RDS, whereas others might be better suited to run directly on Amazon EC2. You might have databases that are running on SQL Server Enterprise edition but are a good ﬁt for SQL Server Standard edition. You may also want to modernize your SQL Server database running on Windows to run on a Linux operating system to save on cost and licenses. Options Pricing 3 Microsoft SQL Server on Amazon EC2 User Guide • Microsoft SQL Server on Amazon EC2 • Amazon RDS for Microsoft SQL Server • Amazon RDS Custom for SQL Server Microsoft SQL Server on Amazon EC2 When to choose Microsoft SQL Server on Amazon EC2: • You want full control over the database and access to its underlying operating system, database installation, and conﬁguration. • You want to administer your database, including backups and recovery, patching the operating system and the database, tuning the operating system and database parameters, managing security, and conﬁguring high availability or replication. • You want to use features and options that aren’t currently supported by Amazon RDS. For more information, see Features not supported and features with limited support in the Amazon RDS documentation. • You require a speciﬁc SQL Server version that isn’t supported by Amazon RDS. For a list of supported versions and editions, see SQL Server versions on Amazon RDS in the RDS for Microsoft SQL Server User Guide. • Your database size and performance requirements exceed the current RDS for Microsoft SQL Server oﬀerings. For more information, see Amazon RDS DB instance storage in the Amazon RDS User Guide. • You want to avoid automatic software patches that might not be compliant with your applications. • You want to bring your own license instead of using the RDS for Microsoft SQL Server license- included model. • You want to achieve higher IOPS and storage capacity than the current limits. For more information, see Amazon RDS DB instance storage in the Amazon RDS User Guide. Amazon RDS for Microsoft SQL Server RDS for Microsoft SQL Server is a managed database service that simpliﬁes the provisioning and management of SQL Server on AWS. With Amazon RDS, you can quickly deploy multiple versions and editions of SQL Server , with cost-eﬃcient and resizeable compute capacity. You can provision SQL Server on Amazon EC2 4 Microsoft SQL Server on Amazon EC2 User Guide Amazon RDS for SQL Server DB instances with either General Purpose SSD or Provisioned IOPS SSD storage. Provisioned IOPS SSD is optimized for I/O-intensive, transactional (OLTP) database workloads. Amazon RDS manages database administration tasks, including provisioning, backups, software patching,
sql-server-ec2-004	sql-server-ec2.pdf	4	SQL Server RDS for Microsoft SQL Server is a managed database service that simpliﬁes the provisioning and management of SQL Server on AWS. With Amazon RDS, you can quickly deploy multiple versions and editions of SQL Server , with cost-eﬃcient and resizeable compute capacity. You can provision SQL Server on Amazon EC2 4 Microsoft SQL Server on Amazon EC2 User Guide Amazon RDS for SQL Server DB instances with either General Purpose SSD or Provisioned IOPS SSD storage. Provisioned IOPS SSD is optimized for I/O-intensive, transactional (OLTP) database workloads. Amazon RDS manages database administration tasks, including provisioning, backups, software patching, monitoring, and hardware scaling. Amazon RDS also oﬀers Multi-AZ deployments and read replicas (for SQL Server Enterprise edition) to provide high availability, performance, scalability, and reliability for production workloads. For more information, see Amazon RDS for Microsoft SQL Server. When to choose RDS for Microsoft SQL Server: • You want to focus on your business and applications, and you want AWS to take care of undiﬀerentiated heavy lifting tasks, such as the provisioning of the database, management of backup and recovery tasks, management of security patches, minor SQL Server version upgrades, and storage management. • You want a highly available database solution, and you want to take advantage of the push- button, synchronous Multi-AZ replication oﬀered by Amazon RDS, without having to manually set up and maintain database mirroring, failover clusters, or Always On availability groups. • You want to pay for the SQL Server license as part of the instance cost on an hourly basis, instead of making a large, up front investment. • Your database size and IOPS requirements are supported by Amazon RDS for SQL Server. See Amazon RDS DB Instance Storage in the AWS documentation for the current maximum limits. • You don’t want to manage backups or point-in-time recoveries of your database. • You want to focus on high-level tasks, such as performance tuning and schema optimization, instead of the daily administration of the database. • You want to scale the instance type up or down based on your workload patterns without being concerned about licensing complexities. Amazon RDS Custom for SQL Server Amazon RDS Custom for SQL Server is a managed database service for legacy, custom, and packaged applications that require access to the underlying operating system and database environment. Amazon RDS Custom for SQL Server automates setup, operation, and scaling of databases in the AWS Cloud while granting you access to the database and underlying operating system on Amazon EC2 to conﬁgure settings, install patches, and enable native features to meet Amazon RDS Custom 5 Microsoft SQL Server on Amazon EC2 User Guide the dependent application's requirements. For more information, see Working with RDS Custom for SQL Server in the Amazon Relational Database Service User Guide. When to choose Amazon RDS Custom for SQL Server: • You want the beneﬁts of Amazon RDS, but your requirements include the need to customize the underlying operating system and database environment for legacy, custom, and packaged applications. • You need administrative rights to the database and underlying operating system. • You need to install custom database and OS patches and packages. • You need to conﬁgure ﬁle systems to share ﬁles directly with their applications. Microsoft SQL Server on Amazon EC2 concepts and terminology The following concepts introduce you to the fundamental terminology used when working with Microsoft SQL Server on Amazon EC2 instances: • Amazon Machine Images (AMIs) • Backup • Billing • High availability and disaster recovery (HADR) • Instance • Instance types • Launching • Security • Storage Amazon Machine Images (AMIs) SQL Server on Amazon EC2 instances are created from Amazon Machine Images (AMIs). AMIs are similar to templates. SQL Server on Amazon EC2 AMIs are pre-installed with an operating system, typically Microsoft Windows Server, and other software. Together, these determine the operating Concepts and terminology 6 Microsoft SQL Server on Amazon EC2 User Guide environment. You can select an AMI provided by AWS, create your own AMI, or select an AMI from the AWS Marketplace. To ﬁnd a SQL Server on Amazon EC2 AMI, see the options under Find a Windows AMI in the Amazon EC2 User Guide. Backup Your backup and recovery design for SQL Server on Amazon EC2 is ﬂexible, depending on your RTO and RPO requirements. AWS provides the ability to perform server-level backups using Windows Volume Shadow Copy Service (VSS)-enabled Amazon Elastic Block Store (Amazon EBS) snapshots and with AWS Backup. You can also perform database-level backups using native backup and restore procedures for SQL Server databases. Database-level backups can be stored on Amazon EBS, FSx for Windows File Server, or Amazon Simple Storage Service using AWS Storage Gateway. For more information about backing up SQL Server on Amazon EC2, see Backup and restore options for SQL

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