feat: Early query cancellation based on per-segment sampling #19235
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,181 @@ | ||
| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one | ||
| * or more contributor license agreements. See the NOTICE file | ||
| * distributed with this work for additional information | ||
| * regarding copyright ownership. The ASF licenses this file | ||
| * to you under the Apache License, Version 2.0 (the | ||
| * "License"); you may not use this file except in compliance | ||
| * with the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, | ||
| * software distributed under the License is distributed on an | ||
| * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
| * KIND, either express or implied. See the License for the | ||
| * specific language governing permissions and limitations | ||
| * under the License. | ||
| */ | ||
|
|
||
| package org.apache.druid.query; | ||
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|
||
| import com.google.common.base.Throwables; | ||
| import org.apache.druid.java.util.common.concurrent.Execs; | ||
| import org.apache.druid.java.util.common.guava.Sequences; | ||
| import org.apache.druid.query.aggregation.CountAggregatorFactory; | ||
| import org.apache.druid.query.timeseries.TimeseriesQuery; | ||
| import org.easymock.EasyMock; | ||
| import org.junit.After; | ||
| import org.junit.Assert; | ||
| import org.junit.Before; | ||
| import org.junit.Test; | ||
|
|
||
| import java.util.Arrays; | ||
| import java.util.List; | ||
| import java.util.Map; | ||
|
|
||
| @SuppressWarnings({"unchecked", "rawtypes"}) | ||
| public class ChainedExecutionQueryRunnerExtrapolationTest | ||
| { | ||
| private QueryProcessingPool processingPool; | ||
|
|
||
| @Before | ||
| public void setup() | ||
| { | ||
| processingPool = new ForwardingQueryProcessingPool( | ||
| Execs.multiThreaded(2, "ExtrapolationTestExecutor-%d"), | ||
| Execs.scheduledSingleThreaded("ExtrapolationTestExecutor-Timeout-%d") | ||
| ); | ||
| } | ||
|
|
||
| @After | ||
| public void tearDown() | ||
| { | ||
| processingPool.shutdown(); | ||
| } | ||
|
|
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| @Test(timeout = 10_000L) | ||
| public void testExtrapolation_disabled_whenSamplingWindowZero() | ||
| { | ||
| QueryRunner slowRunner = sleepRunner(200); | ||
| QueryRunner fastRunner = (queryPlus, responseContext) -> Sequences.of(1); | ||
|
|
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| ChainedExecutionQueryRunner runner = makeRunner(slowRunner, fastRunner); | ||
| TimeseriesQuery query = makeQuery(Map.of( | ||
| QueryContexts.TIMEOUT_KEY, 10_000L | ||
| )); | ||
|
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| List results = runner.run(QueryPlus.wrap(query)).toList(); | ||
| Assert.assertNotNull(results); | ||
| Assert.assertEquals(2, results.size()); | ||
| } | ||
|
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| @Test(timeout = 10_000L) | ||
| public void testExtrapolation_cancelsQuery_whenProjectedTimeExceedsTimeout() | ||
| { | ||
| // 5 slow runners (300ms each on 2 threads), sampling window=2, timeout=500ms | ||
| // After 2 complete: elapsed ~300ms, extrapolated wall-clock = 300 * 5/2 = 750ms > 500ms → cancel | ||
| QueryRunner slowRunner = sleepRunner(300); | ||
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|
||
| ChainedExecutionQueryRunner runner = makeRunner( | ||
| slowRunner, | ||
| slowRunner, | ||
| slowRunner, | ||
| slowRunner, | ||
| slowRunner | ||
| ); | ||
| TimeseriesQuery query = makeQuery(Map.of( | ||
| QueryContexts.TIMEOUT_KEY, 500L, | ||
| QueryContexts.PER_SEGMENT_SAMPLING_WINDOW_KEY, 2 | ||
| )); | ||
|
|
||
| Exception thrown = null; | ||
| try { | ||
| runner.run(QueryPlus.wrap(query)).toList(); | ||
| } | ||
| catch (Exception e) { | ||
| thrown = e; | ||
| } | ||
|
|
||
| Assert.assertNotNull("Expected exception from extrapolation", thrown); | ||
| Assert.assertTrue( | ||
| "Should be QueryTimeoutException", | ||
| Throwables.getRootCause(thrown) instanceof QueryTimeoutException | ||
| ); | ||
| Assert.assertTrue( | ||
| "Message should mention extrapolation", | ||
| thrown.getMessage().contains("extrapolated") | ||
| ); | ||
| } | ||
|
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||
| @Test(timeout = 10_000L) | ||
| public void testExtrapolation_doesNotCancel_whenProjectedTimeWithinTimeout() | ||
| { | ||
| QueryRunner fastRunner = (queryPlus, responseContext) -> Sequences.of(1); | ||
|
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||
| ChainedExecutionQueryRunner runner = makeRunner( | ||
| fastRunner, | ||
| fastRunner, | ||
| fastRunner | ||
| ); | ||
| TimeseriesQuery query = makeQuery(Map.of( | ||
| QueryContexts.TIMEOUT_KEY, 30_000L, | ||
| QueryContexts.PER_SEGMENT_SAMPLING_WINDOW_KEY, 2 | ||
| )); | ||
|
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| List results = runner.run(QueryPlus.wrap(query)).toList(); | ||
| Assert.assertNotNull(results); | ||
| Assert.assertEquals(3, results.size()); | ||
| } | ||
|
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| @Test(timeout = 10_000L) | ||
| public void testExtrapolation_skipped_whenFewerSegmentsThanSamplingWindow() | ||
| { | ||
| QueryRunner slowRunner = sleepRunner(200); | ||
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| ChainedExecutionQueryRunner runner = makeRunner(slowRunner, slowRunner); | ||
| TimeseriesQuery query = makeQuery(Map.of( | ||
| QueryContexts.TIMEOUT_KEY, 10_000L, | ||
| QueryContexts.PER_SEGMENT_SAMPLING_WINDOW_KEY, 5 | ||
| )); | ||
|
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| List results = runner.run(QueryPlus.wrap(query)).toList(); | ||
| Assert.assertNotNull(results); | ||
| Assert.assertEquals(2, results.size()); | ||
| } | ||
|
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| private QueryRunner sleepRunner(long sleepMs) | ||
| { | ||
| return (queryPlus, responseContext) -> { | ||
| try { | ||
| Thread.sleep(sleepMs); | ||
| return Sequences.of(1); | ||
| } | ||
| catch (InterruptedException e) { | ||
| throw new RuntimeException(e); | ||
| } | ||
| }; | ||
| } | ||
|
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| private ChainedExecutionQueryRunner makeRunner(QueryRunner... runners) | ||
| { | ||
| QueryWatcher watcher = EasyMock.createNiceMock(QueryWatcher.class); | ||
| EasyMock.replay(watcher); | ||
| return new ChainedExecutionQueryRunner<>( | ||
| processingPool, | ||
| watcher, | ||
| Arrays.asList(runners) | ||
| ); | ||
| } | ||
|
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| private TimeseriesQuery makeQuery(Map<String, Object> context) | ||
| { | ||
| return Druids.newTimeseriesQueryBuilder() | ||
| .dataSource("test") | ||
| .intervals("2014/2015") | ||
| .aggregators(List.of(new CountAggregatorFactory("count"))) | ||
| .context(context) | ||
| .queryId("test") | ||
| .build(); | ||
| } | ||
| } |
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I'm also worried about this calculation is not accounting for parallelism correctly and being "overkill." It's approximating of the time taken to complete a query as effectively avg segment time * total segments, which is not usually the case(it is assuming a purely serial execution). We have 100+ threads processing segments, and queries can sometimes query 100-1000s of segments per historical.
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For example, you could have a query that queries 300 segments each taking 1100ms with a total query timeout of 300s, we would preemptively kill this query even though assuming moderate contention it would complete in ~84s.
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We should instead incorporate the parallelism of the threadpool into the calculation to avoid killing ok queries. For example, something like (sum of extrapolated segment times) / (thread pool parallelism).