import abc import asyncio import datetime import hashlib import logging import os import random from asyncio import Queue from dataclasses import dataclass from typing import Any, List, Optional logger = logging.getLogger(__name__) @dataclass class WorkItem: """Represents a single work item in the queue""" hash: str work_paths: List[str] class WorkQueue(abc.ABC): """ Base class defining the interface for a work queue. """ @abc.abstractmethod async def populate_queue(self, work_paths: List[str], items_per_group: int) -> None: """ Add new items to the work queue. The specifics will vary depending on whether this is a local or S3-backed queue. Args: work_paths: Each individual path that we will process over items_per_group: Number of items to group together in a single work item """ pass @abc.abstractmethod async def initialize_queue(self) -> int: """ Load the work queue from the relevant store (local or remote) and initialize it for processing. For example, this might remove already completed work items and randomize the order before adding them to an internal queue. """ pass @abc.abstractmethod async def is_completed(self, work_hash: str) -> bool: """ Check if a work item has been completed. Args: work_hash: Hash of the work item to check Returns: True if the work is completed, False otherwise """ pass @abc.abstractmethod async def get_work(self, worker_lock_timeout_secs: int = 1800) -> Optional[WorkItem]: """ Get the next available work item that isn't completed or locked. Args: worker_lock_timeout_secs: Number of seconds before considering a worker lock stale (default 30 mins) Returns: WorkItem if work is available, None if queue is empty """ pass @abc.abstractmethod async def mark_done(self, work_item: WorkItem) -> None: """ Mark a work item as done by removing its lock file or performing any other cleanup. Args: work_item: The WorkItem to mark as done """ pass @property @abc.abstractmethod def size(self) -> int: """Get current size of work queue""" pass @staticmethod def _compute_workgroup_hash(work_paths: List[str]) -> str: """ Compute a deterministic hash for a group of paths. Args: work_paths: List of paths (local or S3) Returns: SHA1 hash of the sorted paths """ sha1 = hashlib.sha1() for path in sorted(work_paths): sha1.update(path.encode("utf-8")) return sha1.hexdigest() # -------------------------------------------------------------------------------------- # Local Helpers for reading/writing the index CSV (compressed with zstd) to disk # -------------------------------------------------------------------------------------- try: import zstandard except ImportError: zstandard = None def download_zstd_csv_local(local_path: str) -> List[str]: """ Download a zstd-compressed CSV from a local path. If the file doesn't exist, returns an empty list. """ if not os.path.exists(local_path): return [] if not zstandard: raise RuntimeError("zstandard package is required for local zstd CSV operations.") with open(local_path, "rb") as f: dctx = zstandard.ZstdDecompressor() data = dctx.decompress(f.read()) lines = data.decode("utf-8").splitlines() return lines def upload_zstd_csv_local(local_path: str, lines: List[str]) -> None: """ Upload a zstd-compressed CSV to a local path. """ if not zstandard: raise RuntimeError("zstandard package is required for local zstd CSV operations.") data = "\n".join(lines).encode("utf-8") cctx = zstandard.ZstdCompressor() compressed_data = cctx.compress(data) # Ensure parent directories exist os.makedirs(os.path.dirname(local_path), exist_ok=True) with open(local_path, "wb") as f: f.write(compressed_data) # -------------------------------------------------------------------------------------- # LocalWorkQueue Implementation # -------------------------------------------------------------------------------------- class LocalWorkQueue(WorkQueue): """ A local in-memory and on-disk WorkQueue implementation, which uses a local workspace directory to store the queue index, lock files, and completed results for persistent resumption across process restarts. """ def __init__(self, workspace_path: str): """ Initialize the local work queue. Args: workspace_path: Local directory path where the queue index, results, and locks are stored. """ self.workspace_path = os.path.abspath(workspace_path) os.makedirs(self.workspace_path, exist_ok=True) # Local index file (compressed) self._index_path = os.path.join(self.workspace_path, "work_index_list.csv.zstd") # Output directory for completed tasks self._results_dir = os.path.join(self.workspace_path, "results") os.makedirs(self._results_dir, exist_ok=True) # Directory for lock files self._locks_dir = os.path.join(self.workspace_path, "worker_locks") os.makedirs(self._locks_dir, exist_ok=True) # Internal queue self._queue: Queue[Any] = Queue() async def populate_queue(self, work_paths: List[str], items_per_group: int) -> None: """ Add new items to the work queue (local version). Args: work_paths: Each individual path (local in this context) that we will process over items_per_group: Number of items to group together in a single work item """ # Treat them as local paths, but keep variable name for consistency all_paths = set(work_paths) logger.info(f"Found {len(all_paths):,} total paths") # Load existing work groups from local index existing_lines = await asyncio.to_thread(download_zstd_csv_local, self._index_path) existing_groups = {} for line in existing_lines: if line.strip(): parts = line.strip().split(",") group_hash = parts[0] group_paths = parts[1:] existing_groups[group_hash] = group_paths existing_path_set = {p for paths in existing_groups.values() for p in paths} new_paths = all_paths - existing_path_set logger.info(f"{len(new_paths):,} new paths to add to the workspace") if not new_paths: return # Create new work groups new_groups = [] current_group = [] for path in sorted(new_paths): current_group.append(path) if len(current_group) == items_per_group: group_hash = self._compute_workgroup_hash(current_group) new_groups.append((group_hash, current_group)) current_group = [] if current_group: group_hash = self._compute_workgroup_hash(current_group) new_groups.append((group_hash, current_group)) logger.info(f"Created {len(new_groups):,} new work groups") # Combine and save updated work groups combined_groups = existing_groups.copy() for group_hash, group_paths in new_groups: combined_groups[group_hash] = group_paths combined_lines = [",".join([group_hash] + group_paths) for group_hash, group_paths in combined_groups.items()] if new_groups: # Write the combined data back to disk in zstd CSV format await asyncio.to_thread(upload_zstd_csv_local, self._index_path, combined_lines) async def initialize_queue(self) -> int: """ Load the work queue from the local index file and initialize it for processing. Removes already completed work items and randomizes the order. """ # 1) Read the index work_queue_lines = await asyncio.to_thread(download_zstd_csv_local, self._index_path) work_queue = {parts[0]: parts[1:] for line in work_queue_lines if (parts := line.strip().split(",")) and line.strip()} # 2) Determine which items are completed by scanning local results/*.jsonl if not os.path.isdir(self._results_dir): os.makedirs(self._results_dir, exist_ok=True) done_work_items = [f for f in os.listdir(self._results_dir) if f.startswith("output_") and f.endswith(".jsonl")] done_work_hashes = {fn[len("output_") : -len(".jsonl")] for fn in done_work_items} # 3) Filter out completed items remaining_work_hashes = set(work_queue) - done_work_hashes remaining_items = [WorkItem(hash=hash_, work_paths=work_queue[hash_]) for hash_ in remaining_work_hashes] random.shuffle(remaining_items) # 4) Initialize our in-memory queue self._queue = asyncio.Queue() for item in remaining_items: await self._queue.put(item) logger.info(f"Initialized local queue with {self._queue.qsize()} work items") return self._queue.qsize() async def is_completed(self, work_hash: str) -> bool: """ Check if a work item has been completed locally by seeing if output_{work_hash}.jsonl is present in the results directory. Args: work_hash: Hash of the work item to check """ output_file = os.path.join(self._results_dir, f"output_{work_hash}.jsonl") return os.path.exists(output_file) async def get_work(self, worker_lock_timeout_secs: int = 1800) -> Optional[WorkItem]: """ Get the next available work item that isn't completed or locked. Args: worker_lock_timeout_secs: Number of seconds before considering a worker lock stale (default 30 mins) Returns: WorkItem if work is available, None if queue is empty """ while True: try: work_item = self._queue.get_nowait() except asyncio.QueueEmpty: return None # Check if work is already completed if await self.is_completed(work_item.hash): logger.debug(f"Work item {work_item.hash} already completed, skipping") self._queue.task_done() continue # Check for worker lock lock_file = os.path.join(self._locks_dir, f"output_{work_item.hash}.jsonl") if os.path.exists(lock_file): # Check modification time mtime = datetime.datetime.fromtimestamp(os.path.getmtime(lock_file), datetime.timezone.utc) if (datetime.datetime.now(datetime.timezone.utc) - mtime).total_seconds() > worker_lock_timeout_secs: # Lock is stale, we can take this work logger.debug(f"Found stale lock for {work_item.hash}, taking work item") else: # Lock is active, skip this work logger.debug(f"Work item {work_item.hash} is locked by another worker, skipping") self._queue.task_done() continue # Create our lock file (touch an empty file) try: with open(lock_file, "wb") as f: f.write(b"") except Exception as e: logger.warning(f"Failed to create lock file for {work_item.hash}: {e}") self._queue.task_done() continue return work_item async def mark_done(self, work_item: WorkItem) -> None: """ Mark a work item as done by removing its lock file. Args: work_item: The WorkItem to mark as done """ lock_file = os.path.join(self._locks_dir, f"output_{work_item.hash}.jsonl") if os.path.exists(lock_file): try: os.remove(lock_file) except Exception as e: logger.warning(f"Failed to delete lock file for {work_item.hash}: {e}") self._queue.task_done() @property def size(self) -> int: """Get current size of local work queue""" return self._queue.qsize()