mirror of
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154 lines
5.4 KiB
Python
154 lines
5.4 KiB
Python
# Copyright 2021 The Matrix.org Foundation C.I.C.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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from typing import (
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Awaitable,
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Callable,
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Dict,
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Generic,
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Hashable,
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List,
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Set,
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Tuple,
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TypeVar,
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)
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from twisted.internet import defer
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from synapse.logging.context import PreserveLoggingContext, make_deferred_yieldable
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from synapse.metrics import LaterGauge
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from synapse.metrics.background_process_metrics import run_as_background_process
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from synapse.util import Clock
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logger = logging.getLogger(__name__)
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V = TypeVar("V")
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R = TypeVar("R")
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class BatchingQueue(Generic[V, R]):
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"""A queue that batches up work, calling the provided processing function
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with all pending work (for a given key).
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The provided processing function will only be called once at a time for each
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key. It will be called the next reactor tick after `add_to_queue` has been
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called, and will keep being called until the queue has been drained (for the
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given key).
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Note that the return value of `add_to_queue` will be the return value of the
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processing function that processed the given item. This means that the
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returned value will likely include data for other items that were in the
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batch.
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"""
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def __init__(
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self,
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name: str,
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clock: Clock,
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process_batch_callback: Callable[[List[V]], Awaitable[R]],
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):
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self._name = name
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self._clock = clock
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# The set of keys currently being processed.
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self._processing_keys = set() # type: Set[Hashable]
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# The currently pending batch of values by key, with a Deferred to call
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# with the result of the corresponding `_process_batch_callback` call.
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self._next_values = {} # type: Dict[Hashable, List[Tuple[V, defer.Deferred]]]
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# The function to call with batches of values.
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self._process_batch_callback = process_batch_callback
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LaterGauge(
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"synapse_util_batching_queue_number_queued",
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"The number of items waiting in the queue across all keys",
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labels=("name",),
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caller=lambda: sum(len(v) for v in self._next_values.values()),
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)
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LaterGauge(
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"synapse_util_batching_queue_number_of_keys",
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"The number of distinct keys that have items queued",
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labels=("name",),
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caller=lambda: len(self._next_values),
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)
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async def add_to_queue(self, value: V, key: Hashable = ()) -> R:
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"""Adds the value to the queue with the given key, returning the result
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of the processing function for the batch that included the given value.
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The optional `key` argument allows sharding the queue by some key. The
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queues will then be processed in parallel, i.e. the process batch
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function will be called in parallel with batched values from a single
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key.
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"""
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# First we create a defer and add it and the value to the list of
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# pending items.
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d = defer.Deferred()
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self._next_values.setdefault(key, []).append((value, d))
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# If we're not currently processing the key fire off a background
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# process to start processing.
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if key not in self._processing_keys:
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run_as_background_process(self._name, self._process_queue, key)
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return await make_deferred_yieldable(d)
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async def _process_queue(self, key: Hashable) -> None:
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"""A background task to repeatedly pull things off the queue for the
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given key and call the `self._process_batch_callback` with the values.
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"""
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try:
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if key in self._processing_keys:
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return
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self._processing_keys.add(key)
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while True:
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# We purposefully wait a reactor tick to allow us to batch
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# together requests that we're about to receive. A common
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# pattern is to call `add_to_queue` multiple times at once, and
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# deferring to the next reactor tick allows us to batch all of
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# those up.
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await self._clock.sleep(0)
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next_values = self._next_values.pop(key, [])
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if not next_values:
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# We've exhausted the queue.
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break
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try:
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values = [value for value, _ in next_values]
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results = await self._process_batch_callback(values)
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for _, deferred in next_values:
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with PreserveLoggingContext():
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deferred.callback(results)
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except Exception as e:
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for _, deferred in next_values:
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if deferred.called:
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continue
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with PreserveLoggingContext():
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deferred.errback(e)
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finally:
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self._processing_keys.discard(key)
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