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journalprocessor.py
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journalprocessor.py
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# 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
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 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)
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),
)

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