forked from MirrorHub/synapse
Add a batching queue implementation. (#10017)
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Erik Johnston
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1
changelog.d/10017.misc
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changelog.d/10017.misc
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Add a batching queue implementation.
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153
synapse/util/batching_queue.py
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153
synapse/util/batching_queue.py
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# 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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169
tests/util/test_batching_queue.py
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169
tests/util/test_batching_queue.py
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# 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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from twisted.internet import defer
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from synapse.logging.context import make_deferred_yieldable
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from synapse.util.batching_queue import BatchingQueue
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from tests.server import get_clock
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from tests.unittest import TestCase
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class BatchingQueueTestCase(TestCase):
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def setUp(self):
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self.clock, hs_clock = get_clock()
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self._pending_calls = []
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self.queue = BatchingQueue("test_queue", hs_clock, self._process_queue)
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async def _process_queue(self, values):
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d = defer.Deferred()
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self._pending_calls.append((values, d))
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return await make_deferred_yieldable(d)
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def test_simple(self):
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"""Tests the basic case of calling `add_to_queue` once and having
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`_process_queue` return.
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"""
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self.assertFalse(self._pending_calls)
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queue_d = defer.ensureDeferred(self.queue.add_to_queue("foo"))
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# The queue should wait a reactor tick before calling the processing
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# function.
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self.assertFalse(self._pending_calls)
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self.assertFalse(queue_d.called)
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# We should see a call to `_process_queue` after a reactor tick.
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self.clock.pump([0])
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self.assertEqual(len(self._pending_calls), 1)
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self.assertEqual(self._pending_calls[0][0], ["foo"])
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self.assertFalse(queue_d.called)
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# Return value of the `_process_queue` should be propagated back.
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self._pending_calls.pop()[1].callback("bar")
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self.assertEqual(self.successResultOf(queue_d), "bar")
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def test_batching(self):
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"""Test that multiple calls at the same time get batched up into one
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call to `_process_queue`.
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"""
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self.assertFalse(self._pending_calls)
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queue_d1 = defer.ensureDeferred(self.queue.add_to_queue("foo1"))
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queue_d2 = defer.ensureDeferred(self.queue.add_to_queue("foo2"))
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self.clock.pump([0])
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# We should see only *one* call to `_process_queue`
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self.assertEqual(len(self._pending_calls), 1)
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self.assertEqual(self._pending_calls[0][0], ["foo1", "foo2"])
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self.assertFalse(queue_d1.called)
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self.assertFalse(queue_d2.called)
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# Return value of the `_process_queue` should be propagated back to both.
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self._pending_calls.pop()[1].callback("bar")
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self.assertEqual(self.successResultOf(queue_d1), "bar")
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self.assertEqual(self.successResultOf(queue_d2), "bar")
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def test_queuing(self):
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"""Test that we queue up requests while a `_process_queue` is being
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called.
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"""
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self.assertFalse(self._pending_calls)
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queue_d1 = defer.ensureDeferred(self.queue.add_to_queue("foo1"))
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self.clock.pump([0])
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queue_d2 = defer.ensureDeferred(self.queue.add_to_queue("foo2"))
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# We should see only *one* call to `_process_queue`
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self.assertEqual(len(self._pending_calls), 1)
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self.assertEqual(self._pending_calls[0][0], ["foo1"])
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self.assertFalse(queue_d1.called)
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self.assertFalse(queue_d2.called)
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# Return value of the `_process_queue` should be propagated back to the
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# first.
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self._pending_calls.pop()[1].callback("bar1")
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self.assertEqual(self.successResultOf(queue_d1), "bar1")
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self.assertFalse(queue_d2.called)
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# We should now see a second call to `_process_queue`
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self.clock.pump([0])
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self.assertEqual(len(self._pending_calls), 1)
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self.assertEqual(self._pending_calls[0][0], ["foo2"])
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self.assertFalse(queue_d2.called)
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# Return value of the `_process_queue` should be propagated back to the
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# second.
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self._pending_calls.pop()[1].callback("bar2")
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self.assertEqual(self.successResultOf(queue_d2), "bar2")
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def test_different_keys(self):
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"""Test that calls to different keys get processed in parallel."""
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self.assertFalse(self._pending_calls)
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queue_d1 = defer.ensureDeferred(self.queue.add_to_queue("foo1", key=1))
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self.clock.pump([0])
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queue_d2 = defer.ensureDeferred(self.queue.add_to_queue("foo2", key=2))
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self.clock.pump([0])
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# We queue up another item with key=2 to check that we will keep taking
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# things off the queue.
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queue_d3 = defer.ensureDeferred(self.queue.add_to_queue("foo3", key=2))
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# We should see two calls to `_process_queue`
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self.assertEqual(len(self._pending_calls), 2)
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self.assertEqual(self._pending_calls[0][0], ["foo1"])
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self.assertEqual(self._pending_calls[1][0], ["foo2"])
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self.assertFalse(queue_d1.called)
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self.assertFalse(queue_d2.called)
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self.assertFalse(queue_d3.called)
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# Return value of the `_process_queue` should be propagated back to the
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# first.
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self._pending_calls.pop(0)[1].callback("bar1")
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self.assertEqual(self.successResultOf(queue_d1), "bar1")
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self.assertFalse(queue_d2.called)
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self.assertFalse(queue_d3.called)
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# Return value of the `_process_queue` should be propagated back to the
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# second.
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self._pending_calls.pop()[1].callback("bar2")
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self.assertEqual(self.successResultOf(queue_d2), "bar2")
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self.assertFalse(queue_d3.called)
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# We should now see a call `_pending_calls` for `foo3`
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self.clock.pump([0])
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self.assertEqual(len(self._pending_calls), 1)
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self.assertEqual(self._pending_calls[0][0], ["foo3"])
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self.assertFalse(queue_d3.called)
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# Return value of the `_process_queue` should be propagated back to the
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# third deferred.
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self._pending_calls.pop()[1].callback("bar4")
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self.assertEqual(self.successResultOf(queue_d3), "bar4")
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