zulip/zerver/lib/queue.py

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import logging
import random
import threading
import time
from abc import ABCMeta, abstractmethod
from collections import defaultdict
from typing import Any, Callable, Dict, Generic, List, Mapping, Optional, Set, TypeVar, Union
import orjson
import pika
import pika.adapters.tornado_connection
import pika.connection
import pika.exceptions
from django.conf import settings
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from pika.adapters.blocking_connection import BlockingChannel
from pika.channel import Channel
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from pika.spec import Basic
from tornado import ioloop
from zerver.lib.utils import assert_is_not_none, statsd
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MAX_REQUEST_RETRIES = 3
ChannelT = TypeVar("ChannelT", Channel, BlockingChannel)
Consumer = Callable[[ChannelT, Basic.Deliver, pika.BasicProperties, bytes], None]
# This simple queuing library doesn't expose much of the power of
# RabbitMQ/Pika's queuing system; its purpose is to just provide an
# interface for external files to put things into queues and take them
# out from bots without having to import pika code all over our codebase.
class QueueClient(Generic[ChannelT], metaclass=ABCMeta):
def __init__(
self,
# Disable RabbitMQ heartbeats by default because BlockingConnection can't process them
rabbitmq_heartbeat: Optional[int] = 0,
prefetch: int = 0,
) -> None:
self.log = logging.getLogger("zulip.queue")
python: Convert assignment type annotations to Python 3.6 style. This commit was split by tabbott; this piece covers the vast majority of files in Zulip, but excludes scripts/, tools/, and puppet/ to help ensure we at least show the right error messages for Xenial systems. We can likely further refine the remaining pieces with some testing. Generated by com2ann, with whitespace fixes and various manual fixes for runtime issues: - invoiced_through: Optional[LicenseLedger] = models.ForeignKey( + invoiced_through: Optional["LicenseLedger"] = models.ForeignKey( -_apns_client: Optional[APNsClient] = None +_apns_client: Optional["APNsClient"] = None - notifications_stream: Optional[Stream] = models.ForeignKey('Stream', related_name='+', null=True, blank=True, on_delete=CASCADE) - signup_notifications_stream: Optional[Stream] = models.ForeignKey('Stream', related_name='+', null=True, blank=True, on_delete=CASCADE) + notifications_stream: Optional["Stream"] = models.ForeignKey('Stream', related_name='+', null=True, blank=True, on_delete=CASCADE) + signup_notifications_stream: Optional["Stream"] = models.ForeignKey('Stream', related_name='+', null=True, blank=True, on_delete=CASCADE) - author: Optional[UserProfile] = models.ForeignKey('UserProfile', blank=True, null=True, on_delete=CASCADE) + author: Optional["UserProfile"] = models.ForeignKey('UserProfile', blank=True, null=True, on_delete=CASCADE) - bot_owner: Optional[UserProfile] = models.ForeignKey('self', null=True, on_delete=models.SET_NULL) + bot_owner: Optional["UserProfile"] = models.ForeignKey('self', null=True, on_delete=models.SET_NULL) - default_sending_stream: Optional[Stream] = models.ForeignKey('zerver.Stream', null=True, related_name='+', on_delete=CASCADE) - default_events_register_stream: Optional[Stream] = models.ForeignKey('zerver.Stream', null=True, related_name='+', on_delete=CASCADE) + default_sending_stream: Optional["Stream"] = models.ForeignKey('zerver.Stream', null=True, related_name='+', on_delete=CASCADE) + default_events_register_stream: Optional["Stream"] = models.ForeignKey('zerver.Stream', null=True, related_name='+', on_delete=CASCADE) -descriptors_by_handler_id: Dict[int, ClientDescriptor] = {} +descriptors_by_handler_id: Dict[int, "ClientDescriptor"] = {} -worker_classes: Dict[str, Type[QueueProcessingWorker]] = {} -queues: Dict[str, Dict[str, Type[QueueProcessingWorker]]] = {} +worker_classes: Dict[str, Type["QueueProcessingWorker"]] = {} +queues: Dict[str, Dict[str, Type["QueueProcessingWorker"]]] = {} -AUTH_LDAP_REVERSE_EMAIL_SEARCH: Optional[LDAPSearch] = None +AUTH_LDAP_REVERSE_EMAIL_SEARCH: Optional["LDAPSearch"] = None Signed-off-by: Anders Kaseorg <anders@zulipchat.com>
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self.queues: Set[str] = set()
self.channel: Optional[ChannelT] = None
self.prefetch = prefetch
self.consumers: Dict[str, Set[Consumer[ChannelT]]] = defaultdict(set)
self.rabbitmq_heartbeat = rabbitmq_heartbeat
self.is_consuming = False
self._connect()
@abstractmethod
def _connect(self) -> None:
raise NotImplementedError
@abstractmethod
def _reconnect(self) -> None:
raise NotImplementedError
def _get_parameters(self) -> pika.ConnectionParameters:
credentials = pika.PlainCredentials(
settings.RABBITMQ_USERNAME, assert_is_not_none(settings.RABBITMQ_PASSWORD)
)
# With BlockingConnection, we are passed
# self.rabbitmq_heartbeat=0, which asks to explicitly disable
# the RabbitMQ heartbeat feature. This is correct since that
# heartbeat doesn't make sense with BlockingConnection (we do
# need it for TornadoConnection).
#
# Where we've disabled RabbitMQ's heartbeat, the only
# keepalive on this connection is the TCP keepalive (defaults:
# `/proc/sys/net/ipv4/tcp_keepalive_*`). On most Linux
# systems, the default is to start sending keepalive packets
# after TCP_KEEPIDLE (7200 seconds) of inactivity; after that
# point, it send them every TCP_KEEPINTVL (typically 75s).
# Some Kubernetes / Docker Swarm networks can kill "idle" TCP
# connections after as little as ~15 minutes of inactivity.
# To avoid this killing our RabbitMQ connections, we set
# TCP_KEEPIDLE to something significantly below 15 minutes.
tcp_options = None
if self.rabbitmq_heartbeat == 0:
tcp_options = dict(TCP_KEEPIDLE=60 * 5)
return pika.ConnectionParameters(
settings.RABBITMQ_HOST,
heartbeat=self.rabbitmq_heartbeat,
tcp_options=tcp_options,
credentials=credentials,
)
def _generate_ctag(self, queue_name: str) -> str:
return f"{queue_name}_{str(random.getrandbits(16))}"
def _reconnect_consumer_callback(self, queue: str, consumer: Consumer[ChannelT]) -> None:
self.log.info(f"Queue reconnecting saved consumer {consumer} to queue {queue}")
self.ensure_queue(
queue,
lambda channel: channel.basic_consume(
queue,
consumer,
consumer_tag=self._generate_ctag(queue),
),
)
def _reconnect_consumer_callbacks(self) -> None:
for queue, consumers in self.consumers.items():
for consumer in consumers:
self._reconnect_consumer_callback(queue, consumer)
def ready(self) -> bool:
return self.channel is not None
@abstractmethod
def ensure_queue(self, queue_name: str, callback: Callable[[ChannelT], object]) -> None:
raise NotImplementedError
def publish(self, queue_name: str, body: bytes) -> None:
def do_publish(channel: ChannelT) -> None:
channel.basic_publish(
exchange="",
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routing_key=queue_name,
properties=pika.BasicProperties(delivery_mode=2),
body=body,
)
statsd.incr(f"rabbitmq.publish.{queue_name}")
self.ensure_queue(queue_name, do_publish)
def json_publish(self, queue_name: str, body: Mapping[str, Any]) -> None:
data = orjson.dumps(body)
try:
self.publish(queue_name, data)
return
except pika.exceptions.AMQPConnectionError:
self.log.warning("Failed to send to rabbitmq, trying to reconnect and send again")
self._reconnect()
self.publish(queue_name, data)
class SimpleQueueClient(QueueClient[BlockingChannel]):
connection: Optional[pika.BlockingConnection]
def _connect(self) -> None:
start = time.time()
self.connection = pika.BlockingConnection(self._get_parameters())
self.channel = self.connection.channel()
self.log.info(f"SimpleQueueClient connected (connecting took {time.time() - start:.3f}s)")
def _reconnect(self) -> None:
self.connection = None
self.channel = None
self.queues = set()
self._connect()
def close(self) -> None:
if self.connection is not None:
self.connection.close()
def ensure_queue(self, queue_name: str, callback: Callable[[BlockingChannel], object]) -> None:
"""Ensure that a given queue has been declared, and then call
the callback with no arguments."""
if self.connection is None or not self.connection.is_open:
self._connect()
assert self.channel is not None
self.channel.basic_qos(prefetch_count=self.prefetch)
if queue_name not in self.queues:
self.channel.queue_declare(queue=queue_name, durable=True)
self.queues.add(queue_name)
callback(self.channel)
def start_json_consumer(
self,
queue_name: str,
callback: Callable[[List[Dict[str, Any]]], None],
batch_size: int = 1,
timeout: Optional[int] = None,
) -> None:
if batch_size == 1:
timeout = None
def do_consume(channel: BlockingChannel) -> None:
events: List[Dict[str, Any]] = []
last_process = time.time()
max_processed: Optional[int] = None
self.is_consuming = True
# This iterator technique will iteratively collect up to
# batch_size events from the RabbitMQ queue (if present)
# before calling the callback with the batch. If not
# enough events are present, it will sleep for at most
# timeout seconds before calling the callback with the
# batch of events it has.
for method, properties, body in channel.consume(queue_name, inactivity_timeout=timeout):
if body is not None:
assert method is not None
events.append(orjson.loads(body))
max_processed = method.delivery_tag
now = time.time()
if len(events) >= batch_size or (timeout and now >= last_process + timeout):
if events:
assert max_processed is not None
try:
callback(events)
channel.basic_ack(max_processed, multiple=True)
except BaseException:
channel.basic_nack(max_processed, multiple=True)
raise
events = []
last_process = now
if not self.is_consuming:
break
self.ensure_queue(queue_name, do_consume)
queue: Rename queue_size, and update for all local queues. Despite its name, the `queue_size` method does not return the number of items in the queue; it returns the number of items that the local consumer has delivered but unprocessed. These are often, but not always, the same. RabbitMQ's queues maintain the queue of unacknowledged messages; when a consumer connects, it sends to the consumer some number of messages to handle, known as the "prefetch." This is a performance optimization, to ensure the consumer code does not need to wait for a network round-trip before having new data to consume. The default prefetch is 0, which means that RabbitMQ immediately dumps all outstanding messages to the consumer, which slowly processes and acknowledges them. If a second consumer were to connect to the same queue, they would receive no messages to process, as the first consumer has already been allocated them. If the first consumer disconnects or crashes, all prior events sent to it are then made available for other consumers on the queue. The consumer does not know the total size of the queue -- merely how many messages it has been handed. No change is made to the prefetch here; however, future changes may wish to limit the prefetch, either for memory-saving, or to allow multiple consumers to work the same queue. Rename the method to make clear that it only contains information about the local queue in the consumer, not the full RabbitMQ queue. Also include the waiting message count, which is used by the `consume()` iterator for similar purpose to the pending events list.
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def local_queue_size(self) -> int:
assert self.channel is not None
return self.channel.get_waiting_message_count() + len(
self.channel._pending_events # type: ignore[attr-defined] # private member missing from stubs
)
def stop_consuming(self) -> None:
assert self.channel is not None
assert self.is_consuming
self.is_consuming = False
self.channel.stop_consuming()
# Patch pika.adapters.tornado_connection.TornadoConnection so that a socket error doesn't
# throw an exception and disconnect the tornado process from the rabbitmq
# queue. Instead, just re-connect as usual
class ExceptionFreeTornadoConnection(pika.adapters.tornado_connection.TornadoConnection):
def _adapter_disconnect(self) -> None:
try:
super()._adapter_disconnect() # type: ignore[misc] # private method missing from stubs
except (
pika.exceptions.ProbableAuthenticationError,
pika.exceptions.ProbableAccessDeniedError,
pika.exceptions.IncompatibleProtocolError,
):
logging.warning(
"Caught exception in ExceptionFreeTornadoConnection when \
calling _adapter_disconnect, ignoring",
exc_info=True,
)
class TornadoQueueClient(QueueClient[Channel]):
connection: Optional[ExceptionFreeTornadoConnection]
# Based on:
# https://pika.readthedocs.io/en/0.9.8/examples/asynchronous_consumer_example.html
def __init__(self) -> None:
super().__init__(
# TornadoConnection can process heartbeats, so enable them.
queue_processors: Set a bounded prefetch size on rabbitmq queues. RabbitMQ clients have a setting called prefetch[1], which controls how many un-acknowledged events the server forwards to the local queue in the client. The default is 0; this means that when clients first connect, the server must send them every message in the queue. This itself may cause unbounded memory usage in the client, but also has other detrimental effects. While the client is attempting to process the head of the queue, it may be unable to read from the TCP socket at the rate that the server is sending to it -- filling the TCP buffers, and causing the server's writes to block. If the server blocks for more than 30 seconds, it times out the send, and closes the connection with: ``` closing AMQP connection <0.30902.126> (127.0.0.1:53870 -> 127.0.0.1:5672): {writer,send_failed,{error,timeout}} ``` This is https://github.com/pika/pika/issues/753#issuecomment-318119222. Set a prefetch limit of 100 messages, or the batch size, to better handle queues which start with large numbers of outstanding events. Setting prefetch=1 causes significant performance degradation in the no-op queue worker, to 30% of the prefetch=0 performance. Setting prefetch=100 achieves 90% of the prefetch=0 performance, and higher values offer only minor gains above that. For batch workers, their performance is not notably degraded by prefetch equal to their batch size, and they cannot function on smaller prefetches than their batch size. We also set a 100-count prefetch on Tornado workers, as they are potentially susceptible to the same effect. [1] https://www.rabbitmq.com/confirms.html#channel-qos-prefetch
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rabbitmq_heartbeat=None,
# Only ask for 100 un-acknowledged messages at once from
# the server, rather than an unbounded number.
prefetch=100,
)
self._on_open_cbs: List[Callable[[Channel], None]] = []
self._connection_failure_count = 0
def _connect(self) -> None:
self.log.info("Beginning TornadoQueueClient connection")
self.connection = ExceptionFreeTornadoConnection(
self._get_parameters(),
on_open_callback=self._on_open,
on_open_error_callback=self._on_connection_open_error,
on_close_callback=self._on_connection_closed,
)
def _reconnect(self) -> None:
self.connection = None
self.channel = None
self.queues = set()
self.log.warning("TornadoQueueClient attempting to reconnect to RabbitMQ")
self._connect()
CONNECTION_RETRY_SECS = 2
# When the RabbitMQ server is restarted, it's normal for it to
# take a few seconds to come back; we'll retry a few times and all
# will be well. So for the first few failures, we report only at
# "warning" level, avoiding an email to the server admin.
#
# A loss of an existing connection starts a retry loop just like a
# failed connection attempt, so it counts as the first failure.
#
# On an unloaded test system, a RabbitMQ restart takes about 6s,
# potentially causing 4 failures. We add some headroom above that.
CONNECTION_FAILURES_BEFORE_NOTIFY = 10
def _on_connection_open_error(
self, connection: pika.connection.Connection, reason: Union[str, Exception]
) -> None:
self._connection_failure_count += 1
retry_secs = self.CONNECTION_RETRY_SECS
self.log.log(
logging.CRITICAL
if self._connection_failure_count > self.CONNECTION_FAILURES_BEFORE_NOTIFY
else logging.WARNING,
"TornadoQueueClient couldn't connect to RabbitMQ, retrying in %d secs...",
retry_secs,
)
ioloop.IOLoop.instance().call_later(retry_secs, self._reconnect)
def _on_connection_closed(
self, connection: pika.connection.Connection, reason: Exception
) -> None:
self._connection_failure_count = 1
retry_secs = self.CONNECTION_RETRY_SECS
self.log.warning(
"TornadoQueueClient lost connection to RabbitMQ, reconnecting in %d secs...",
retry_secs,
)
ioloop.IOLoop.instance().call_later(retry_secs, self._reconnect)
def _on_open(self, connection: pika.connection.Connection) -> None:
assert self.connection is not None
self._connection_failure_count = 0
try:
self.connection.channel(on_open_callback=self._on_channel_open)
except pika.exceptions.ConnectionClosed:
# The connection didn't stay open long enough for this code to get to it.
# Let _on_connection_closed deal with trying again.
self.log.warning("TornadoQueueClient couldn't open channel: connection already closed")
def _on_channel_open(self, channel: Channel) -> None:
self.channel = channel
for callback in self._on_open_cbs:
callback(channel)
self._reconnect_consumer_callbacks()
self.log.info("TornadoQueueClient connected")
def close(self) -> None:
if self.connection is not None:
self.connection.close()
def ensure_queue(self, queue_name: str, callback: Callable[[Channel], object]) -> None:
def set_qos(frame: Any) -> None:
assert self.channel is not None
self.queues.add(queue_name)
self.channel.basic_qos(prefetch_count=self.prefetch, callback=finish)
def finish(frame: Any) -> None:
assert self.channel is not None
callback(self.channel)
if queue_name not in self.queues:
# If we're not connected yet, send this message
# once we have created the channel
if not self.ready():
self._on_open_cbs.append(lambda channel: self.ensure_queue(queue_name, callback))
return
assert self.channel is not None
self.channel.queue_declare(queue=queue_name, durable=True, callback=set_qos)
else:
assert self.channel is not None
callback(self.channel)
def start_json_consumer(
self,
queue_name: str,
callback: Callable[[List[Dict[str, Any]]], None],
batch_size: int = 1,
timeout: Optional[int] = None,
) -> None:
def wrapped_consumer(
ch: Channel,
method: Basic.Deliver,
properties: pika.BasicProperties,
body: bytes,
) -> None:
assert method.delivery_tag is not None
callback([orjson.loads(body)])
ch.basic_ack(delivery_tag=method.delivery_tag)
assert batch_size == 1
assert timeout is None
self.consumers[queue_name].add(wrapped_consumer)
if not self.ready():
return
self.ensure_queue(
queue_name,
lambda channel: channel.basic_consume(
queue_name,
wrapped_consumer,
consumer_tag=self._generate_ctag(queue_name),
),
)
thread_data = threading.local()
def get_queue_client() -> Union[SimpleQueueClient, TornadoQueueClient]:
if not hasattr(thread_data, "queue_client"):
if not settings.USING_RABBITMQ:
raise RuntimeError("Cannot get a queue client without USING_RABBITMQ")
thread_data.queue_client = SimpleQueueClient()
return thread_data.queue_client
def set_queue_client(queue_client: Union[SimpleQueueClient, TornadoQueueClient]) -> None:
thread_data.queue_client = queue_client
def queue_json_publish(
queue_name: str,
event: Dict[str, Any],
processor: Optional[Callable[[Any], None]] = None,
) -> None:
if settings.USING_RABBITMQ:
get_queue_client().json_publish(queue_name, event)
elif processor:
processor(event)
else:
# Must be imported here: A top section import leads to circular imports
from zerver.worker.queue_processors import get_worker
get_worker(queue_name).consume_single_event(event)
def retry_event(
queue_name: str, event: Dict[str, Any], failure_processor: Callable[[Dict[str, Any]], None]
) -> None:
if "failed_tries" not in event:
event["failed_tries"] = 0
event["failed_tries"] += 1
if event["failed_tries"] > MAX_REQUEST_RETRIES:
failure_processor(event)
else:
queue_json_publish(queue_name, event, lambda x: None)