diff --git a/swh/dataset/journalprocessor.py b/swh/dataset/journalprocessor.py index 0143eaf..d576d2a 100644 --- a/swh/dataset/journalprocessor.py +++ b/swh/dataset/journalprocessor.py @@ -1,397 +1,401 @@ # Copyright (C) 2020 The Software Heritage developers # See the AUTHORS file at the top-level directory of this distribution # License: GNU General Public License version 3, or any later version # See top-level LICENSE file for more information import collections import concurrent.futures from concurrent.futures import FIRST_EXCEPTION, ProcessPoolExecutor import contextlib from hashlib import sha1 import logging import multiprocessing from pathlib import Path import time from typing import Any, Dict, Mapping, Sequence, Tuple, Type from confluent_kafka import TopicPartition import tqdm from swh.dataset.exporter import Exporter from swh.dataset.utils import LevelDBSet from swh.journal.client import JournalClient from swh.journal.serializers import kafka_to_value from swh.model.identifiers import origin_identifier from swh.storage.fixer import fix_objects class JournalClientOffsetRanges(JournalClient): """ A subclass of JournalClient reading only inside some specific offset range. Partition assignments have to be manually given to the class. This client can only read a single topic at a time. """ def __init__( self, *args, offset_ranges: Mapping[int, Tuple[int, int]] = None, assignment: Sequence[int] = None, progress_queue: multiprocessing.Queue = None, refresh_every: int = 200, **kwargs, ): """ Args: offset_ranges: A mapping of partition_id -> (low, high) offsets that define the boundaries of the messages to consume. assignment: The list of partitions to assign to this client. progress_queue: a multiprocessing.Queue where the current progress will be reported. refresh_every: the refreshing rate of the progress reporting. """ self.offset_ranges = offset_ranges self.progress_queue = progress_queue self.refresh_every = refresh_every self.assignment = assignment self.count = None self.topic_name = None super().__init__(*args, **kwargs) def subscribe(self): self.topic_name = self.subscription[0] time.sleep(0.1) # https://github.com/edenhill/librdkafka/issues/1983 + logging.debug("Changing assignment to %s", str(self.assignment)) self.consumer.assign( [TopicPartition(self.topic_name, pid) for pid in self.assignment] ) def process(self, *args, **kwargs): self.count = 0 try: self.handle_committed_offsets() super().process(*args, **kwargs) except EOFError: pass finally: self.progress_queue.put(None) def handle_committed_offsets(self,): """ Handle already committed partition offsets before starting processing. """ committed = self.consumer.committed( [TopicPartition(self.topic_name, pid) for pid in self.assignment] ) for tp in committed: self.handle_offset(tp.partition, tp.offset) def handle_offset(self, partition_id, offset): """ Check whether the client has reached the end of the current partition, and trigger a reassignment if that is the case. Raise EOFError if all the partitions have reached the end. """ if offset < 0: # Uninitialized partition offset return if self.count % self.refresh_every == 0: self.progress_queue.put({partition_id: offset}) if offset >= self.offset_ranges[partition_id][1] - 1: - self.assignment = [pid for pid in self.assignment if pid != partition_id] - self.subscribe() + if partition_id in self.assignment: + self.assignment = [ + pid for pid in self.assignment if pid != partition_id + ] + self.subscribe() if not self.assignment: raise EOFError def deserialize_message(self, message): """ Override of the message deserialization to hook the handling of the message offset. We also return the raw objects instead of deserializing them because we will need the partition ID later. """ self.handle_offset(message.partition(), message.offset()) self.count += 1 return message class ParallelJournalProcessor: """ Reads the given object type from the journal in parallel. It creates one JournalExportWorker per process. """ def __init__( self, config, exporters: Sequence[Tuple[Type[Exporter], Dict[str, Any]]], export_id: str, obj_type: str, node_sets_path: Path, processes: int = 1, ): """ Args: config: the exporter config, which should also include the JournalClient configuration. exporters: a list of Exporter to process the objects export_id: a unique identifier for the export that will be used as part of a Kafka consumer group ID. obj_type: The type of SWH object to export. node_sets_path: A directory where to store the node sets. processes: The number of processes to run. """ self.config = config self.exporters = exporters self.export_id = "swh-dataset-export-{}".format(export_id) self.obj_type = obj_type self.processes = processes self.node_sets_path = node_sets_path self.offsets = None def get_offsets(self): """ First pass to fetch all the current low and high offsets of each partition to define the consumption boundaries. """ if self.offsets is None: client = JournalClient( **self.config["journal"], object_types=[self.obj_type], group_id=self.export_id, ) topic_name = client.subscription[0] topics = client.consumer.list_topics(topic_name).topics partitions = topics[topic_name].partitions self.offsets = {} def fetch_insert_partition_id(partition_id): tp = TopicPartition(topic_name, partition_id) (lo, hi) = client.consumer.get_watermark_offsets(tp) self.offsets[partition_id] = (lo, hi) with concurrent.futures.ThreadPoolExecutor( max_workers=self.processes ) as executor: list( tqdm.tqdm( executor.map(fetch_insert_partition_id, partitions.keys()), total=len(partitions), desc=" - Partition offsets", ) ) return self.offsets def run(self): """ Run the parallel export. """ offsets = self.get_offsets() to_assign = list(offsets.keys()) manager = multiprocessing.Manager() q = manager.Queue() with ProcessPoolExecutor(self.processes + 1) as pool: futures = [] for i in range(self.processes): futures.append( pool.submit( self.export_worker, assignment=to_assign[i :: self.processes], progress_queue=q, ) ) futures.append(pool.submit(self.progress_worker, queue=q)) concurrent.futures.wait(futures, return_when=FIRST_EXCEPTION) for f in futures: if f.running(): continue exc = f.exception() if exc: pool.shutdown(wait=False) f.result() raise exc def progress_worker(self, queue=None): """ An additional worker process that reports the current progress of the export between all the different parallel consumers and across all the partitions, by consuming the shared progress reporting Queue. """ d = {} active_workers = self.processes offset_diff = sum((hi - lo) for lo, hi in self.offsets.values()) with tqdm.tqdm(total=offset_diff, desc=" - Journal export") as pbar: while active_workers: item = queue.get() if item is None: active_workers -= 1 continue d.update(item) progress = sum(n - self.offsets[p][0] for p, n in d.items()) pbar.set_postfix( active_workers=active_workers, total_workers=self.processes ) pbar.update(progress - pbar.n) def export_worker(self, assignment, progress_queue): worker = JournalProcessorWorker( self.config, self.exporters, self.export_id, self.obj_type, self.offsets, assignment, progress_queue, self.node_sets_path, ) with worker: worker.run() class JournalProcessorWorker: """ Worker process that processes all the messages and calls the given exporters for each object read from the journal. """ def __init__( self, config, exporters: Sequence[Tuple[Type[Exporter], Dict[str, Any]]], export_id: str, obj_type: str, offsets: Dict[int, Tuple[int, int]], assignment: Sequence[int], progress_queue: multiprocessing.Queue, node_sets_path: Path, ): self.config = config self.export_id = export_id self.obj_type = obj_type self.offsets = offsets self.assignment = assignment self.progress_queue = progress_queue self.node_sets_path = node_sets_path self.node_sets_path.mkdir(exist_ok=True, parents=True) self.node_sets: Dict[Tuple[int, str], LevelDBSet] = {} self.exporters = [ exporter_class(config, **kwargs) for exporter_class, kwargs in exporters ] self.exit_stack: contextlib.ExitStack = contextlib.ExitStack() def __enter__(self): self.exit_stack.__enter__() for exporter in self.exporters: self.exit_stack.enter_context(exporter) return self def __exit__(self, exc_type, exc_value, traceback): self.exit_stack.__exit__(exc_type, exc_value, traceback) def get_node_set_for_object(self, partition_id: int, object_id: bytes): """ Return an on-disk set object, which stores the nodes that have already been processed. Node sets are sharded by partition ID (as each object is guaranteed to be assigned to a deterministic Kafka partition) then by object ID prefix. The sharding path of each file looks like: .node_sets/{origin..content}/part-{0..256}/nodes-{0..f}.sqlite """ # obj_id_prefix = "{:x}".format(object_id[0] % 16) obj_id_prefix = "all" # disable sharding for now shard_id = (partition_id, obj_id_prefix) if shard_id not in self.node_sets: node_set_dir = ( self.node_sets_path / self.obj_type / ("part-{}".format(str(partition_id))) ) node_set_dir.mkdir(exist_ok=True, parents=True) node_set_file = node_set_dir / "nodes-{}.db".format(obj_id_prefix) node_set = LevelDBSet(node_set_file) self.exit_stack.enter_context(node_set) self.node_sets[shard_id] = node_set return self.node_sets[shard_id] def run(self): """ Start a Journal client on the given assignment and process all the incoming messages. """ client = JournalClientOffsetRanges( **self.config["journal"], object_types=[self.obj_type], group_id=self.export_id, debug="cgrp,broker", offset_ranges=self.offsets, assignment=self.assignment, progress_queue=self.progress_queue, **{"message.max.bytes": str(500 * 1024 * 1024)}, ) client.process(self.process_messages) def process_messages(self, messages): """ Process the incoming Kafka messages. """ for object_type, message_list in messages.items(): fixed_objects_by_partition = collections.defaultdict(list) for message in message_list: fixed_objects_by_partition[message.partition()].extend( fix_objects(object_type, [kafka_to_value(message.value())]) ) for partition, objects in fixed_objects_by_partition.items(): for obj in objects: self.process_message(object_type, partition, obj) def process_message(self, object_type, partition, obj): """ Process a single incoming Kafka message if the object it refers to has not been processed yet. It uses an on-disk set to make sure that each object is only ever processed once. """ if object_type == "origin_visit": origin_id = origin_identifier({"url": obj["origin"]}) visit = obj["visit"] node_id = sha1(f"{origin_id}:{visit}".encode()).digest() elif object_type == "origin_visit_status": if obj["status"] not in ("partial", "full"): # Temporary visit object, not useful for the exports return origin_id = origin_identifier({"url": obj["origin"]}) visit = obj["visit"] ts = obj["date"].timestamp() node_id = sha1(f"{origin_id}:{visit}:{ts}".encode()).digest() elif object_type == "origin": node_id = sha1(obj["url"].encode()).digest() elif object_type in ("content", "skipped_content"): node_id = obj["sha1_git"] else: node_id = obj["id"] node_set = self.get_node_set_for_object(partition, node_id) if not node_set.add(node_id): # Node already processed, skipping. return for exporter in self.exporters: try: exporter.process_object(object_type, obj) except Exception: logging.exception( "Exporter %s: error while exporting the object: %s", exporter.__class__.__name__, str(obj), )