mirror of https://github.com/apache/druid.git
add nested column query benchmarks (#12786)
This commit is contained in:
Clint Wylie
GitHub
parent
ee15c238cc
commit
1e0542626b
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@ -0,0 +1,317 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. 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,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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package org.apache.druid.benchmark.query;
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import com.google.common.collect.ImmutableList;
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import com.google.common.collect.ImmutableMap;
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import com.google.common.collect.ImmutableSet;
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import org.apache.druid.common.config.NullHandling;
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import org.apache.druid.data.input.impl.DimensionSchema;
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import org.apache.druid.data.input.impl.DimensionsSpec;
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import org.apache.druid.java.util.common.granularity.Granularities;
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import org.apache.druid.java.util.common.guava.Sequence;
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import org.apache.druid.java.util.common.io.Closer;
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import org.apache.druid.java.util.common.logger.Logger;
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import org.apache.druid.math.expr.ExpressionProcessing;
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import org.apache.druid.query.DruidProcessingConfig;
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import org.apache.druid.query.QueryContexts;
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import org.apache.druid.query.QueryRunnerFactoryConglomerate;
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import org.apache.druid.query.expression.TestExprMacroTable;
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import org.apache.druid.segment.NestedDataDimensionSchema;
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import org.apache.druid.segment.QueryableIndex;
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import org.apache.druid.segment.generator.GeneratorBasicSchemas;
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import org.apache.druid.segment.generator.GeneratorSchemaInfo;
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import org.apache.druid.segment.generator.SegmentGenerator;
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import org.apache.druid.segment.transform.ExpressionTransform;
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import org.apache.druid.segment.transform.TransformSpec;
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import org.apache.druid.server.QueryStackTests;
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import org.apache.druid.server.security.AuthTestUtils;
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import org.apache.druid.sql.calcite.SqlVectorizedExpressionSanityTest;
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import org.apache.druid.sql.calcite.planner.CalciteRulesManager;
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import org.apache.druid.sql.calcite.planner.Calcites;
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import org.apache.druid.sql.calcite.planner.DruidPlanner;
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import org.apache.druid.sql.calcite.planner.PlannerConfig;
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import org.apache.druid.sql.calcite.planner.PlannerFactory;
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import org.apache.druid.sql.calcite.planner.PlannerResult;
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import org.apache.druid.sql.calcite.schema.DruidSchemaCatalog;
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import org.apache.druid.sql.calcite.util.CalciteTests;
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import org.apache.druid.sql.calcite.util.SpecificSegmentsQuerySegmentWalker;
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import org.apache.druid.timeline.DataSegment;
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import org.apache.druid.timeline.partition.LinearShardSpec;
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import org.openjdk.jmh.annotations.Benchmark;
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import org.openjdk.jmh.annotations.BenchmarkMode;
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import org.openjdk.jmh.annotations.Fork;
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import org.openjdk.jmh.annotations.Level;
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import org.openjdk.jmh.annotations.Measurement;
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import org.openjdk.jmh.annotations.Mode;
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import org.openjdk.jmh.annotations.OutputTimeUnit;
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import org.openjdk.jmh.annotations.Param;
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import org.openjdk.jmh.annotations.Scope;
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import org.openjdk.jmh.annotations.Setup;
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import org.openjdk.jmh.annotations.State;
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import org.openjdk.jmh.annotations.TearDown;
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import org.openjdk.jmh.annotations.Warmup;
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import org.openjdk.jmh.infra.Blackhole;
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import javax.annotation.Nullable;
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import java.util.List;
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import java.util.Map;
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import java.util.concurrent.TimeUnit;
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@State(Scope.Benchmark)
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@Fork(value = 1)
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@Warmup(iterations = 3)
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@Measurement(iterations = 5)
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public class SqlNestedDataBenchmark
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{
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private static final Logger log = new Logger(SqlNestedDataBenchmark.class);
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static {
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NullHandling.initializeForTests();
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Calcites.setSystemProperties();
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ExpressionProcessing.initializeForStrictBooleansTests(true);
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}
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private static final DruidProcessingConfig PROCESSING_CONFIG = new DruidProcessingConfig()
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{
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@Override
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public int intermediateComputeSizeBytes()
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{
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return 512 * 1024 * 1024;
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}
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@Override
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public int getNumMergeBuffers()
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{
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return 3;
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}
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@Override
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public int getNumThreads()
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{
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return 1;
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}
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@Override
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public boolean useParallelMergePoolConfigured()
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{
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return true;
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}
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@Override
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public String getFormatString()
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{
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return "benchmarks-processing-%s";
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}
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};
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private static final List<String> QUERIES = ImmutableList.of(
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// ===========================
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// non-nested reference queries
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// ===========================
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// 0,1: timeseries, 1 columns
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"SELECT SUM(long1) FROM foo",
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"SELECT SUM(JSON_VALUE(nested, '$.long1' RETURNING BIGINT)) FROM foo",
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// 2,3: timeseries, 2 columns
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"SELECT SUM(long1), SUM(long2) FROM foo",
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"SELECT SUM(JSON_VALUE(nested, '$.long1' RETURNING BIGINT)), SUM(JSON_VALUE(nested, '$.nesteder.long2' RETURNING BIGINT)) FROM foo",
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// 4,5: timeseries, 3 columns
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"SELECT SUM(long1), SUM(long2), SUM(double3) FROM foo",
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"SELECT SUM(JSON_VALUE(nested, '$.long1' RETURNING BIGINT)), SUM(JSON_VALUE(nested, '$.nesteder.long2' RETURNING BIGINT)), SUM(JSON_VALUE(nested, '$.nesteder.double3' RETURNING DOUBLE)) FROM foo",
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// 6,7: group by string with 1 agg
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"SELECT string1, SUM(long1) FROM foo GROUP BY 1 ORDER BY 2",
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"SELECT JSON_VALUE(nested, '$.nesteder.string1'), SUM(JSON_VALUE(nested, '$.long1' RETURNING BIGINT)) FROM foo GROUP BY 1 ORDER BY 2",
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// 8,9: group by string with 2 agg
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"SELECT string1, SUM(long1), SUM(double3) FROM foo GROUP BY 1 ORDER BY 2",
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"SELECT JSON_VALUE(nested, '$.nesteder.string1'), SUM(JSON_VALUE(nested, '$.long1' RETURNING BIGINT)), SUM(JSON_VALUE(nested, '$.nesteder.double3' RETURNING DOUBLE)) FROM foo GROUP BY 1 ORDER BY 2",
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// 10,11: time-series filter string
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"SELECT SUM(long1) FROM foo WHERE string1 = '10000' OR string1 = '1000'",
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"SELECT SUM(JSON_VALUE(nested, '$.long1' RETURNING BIGINT)) FROM foo WHERE JSON_VALUE(nested, '$.nesteder.string1') = '10000' OR JSON_VALUE(nested, '$.nesteder.string1') = '1000'",
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// 12,13: time-series filter long
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"SELECT SUM(long1) FROM foo WHERE long2 = 10000 OR long2 = 1000",
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"SELECT SUM(JSON_VALUE(nested, '$.long1' RETURNING BIGINT)) FROM foo WHERE JSON_VALUE(nested, '$.nesteder.long2' RETURNING BIGINT) = 10000 OR JSON_VALUE(nested, '$.nesteder.long2' RETURNING BIGINT) = 1000",
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// 14,15: time-series filter double
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"SELECT SUM(long1) FROM foo WHERE double3 < 10000.0 AND double3 > 1000.0",
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"SELECT SUM(JSON_VALUE(nested, '$.long1' RETURNING BIGINT)) FROM foo WHERE JSON_VALUE(nested, '$.nesteder.double3' RETURNING DOUBLE) < 10000.0 AND JSON_VALUE(nested, '$.nesteder.double3' RETURNING DOUBLE) > 1000.0",
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// 16,17: group by long filter by string
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"SELECT long1, SUM(double3) FROM foo WHERE string1 = '10000' OR string1 = '1000' GROUP BY 1 ORDER BY 2",
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"SELECT JSON_VALUE(nested, '$.long1' RETURNING BIGINT), SUM(JSON_VALUE(nested, '$.nesteder.double3' RETURNING DOUBLE)) FROM foo WHERE JSON_VALUE(nested, '$.nesteder.string1') = '10000' OR JSON_VALUE(nested, '$.nesteder.string1') = '1000' GROUP BY 1 ORDER BY 2",
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// 18,19: group by string filter by long
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"SELECT string1, SUM(double3) FROM foo WHERE long2 < 10000 AND long2 > 1000 GROUP BY 1 ORDER BY 2",
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"SELECT JSON_VALUE(nested, '$.nesteder.string1'), SUM(JSON_VALUE(nested, '$.nesteder.double3' RETURNING DOUBLE)) FROM foo WHERE JSON_VALUE(nested, '$.nesteder.long2' RETURNING BIGINT) < 10000 AND JSON_VALUE(nested, '$.nesteder.long2' RETURNING BIGINT) > 1000 GROUP BY 1 ORDER BY 2",
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// 20,21: group by string filter by double
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"SELECT string1, SUM(double3) FROM foo WHERE double3 < 10000.0 AND double3 > 1000.0 GROUP BY 1 ORDER BY 2",
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"SELECT JSON_VALUE(nested, '$.nesteder.string1'), SUM(JSON_VALUE(nested, '$.nesteder.double3' RETURNING DOUBLE)) FROM foo WHERE JSON_VALUE(nested, '$.nesteder.double3' RETURNING DOUBLE) < 10000.0 AND JSON_VALUE(nested, '$.nesteder.double3' RETURNING DOUBLE) > 1000.0 GROUP BY 1 ORDER BY 2",
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// 22, 23:
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"SELECT long2 FROM foo WHERE long2 IN (1, 19, 21, 23, 25, 26, 46)",
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"SELECT JSON_VALUE(nested, '$.nesteder.long2' RETURNING BIGINT) FROM foo WHERE JSON_VALUE(nested, '$.nesteder.long2' RETURNING BIGINT) IN (1, 19, 21, 23, 25, 26, 46)",
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// 24, 25
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"SELECT long2 FROM foo WHERE long2 IN (1, 19, 21, 23, 25, 26, 46) GROUP BY 1",
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"SELECT JSON_VALUE(nested, '$.nesteder.long2' RETURNING BIGINT) FROM foo WHERE JSON_VALUE(nested, '$.nesteder.long2' RETURNING BIGINT) IN (1, 19, 21, 23, 25, 26, 46) GROUP BY 1"
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);
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@Param({"5000000"})
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private int rowsPerSegment;
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@Param({
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"false",
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"force"
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})
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private String vectorize;
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@Param({
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"0",
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"1",
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"2",
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"3",
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"4",
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"5",
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"6",
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"7",
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"8",
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"9",
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"10",
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"11",
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"12",
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"13",
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"14",
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"15",
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"16",
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"17",
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"18",
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"19",
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"20",
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"21",
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"22",
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"23",
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"24",
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"25"
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})
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private String query;
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@Nullable
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private PlannerFactory plannerFactory;
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private Closer closer = Closer.create();
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@Setup(Level.Trial)
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public void setup()
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{
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final GeneratorSchemaInfo schemaInfo = GeneratorBasicSchemas.SCHEMA_MAP.get("expression-testbench");
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final DataSegment dataSegment = DataSegment.builder()
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.dataSource("foo")
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.interval(schemaInfo.getDataInterval())
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.version("1")
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.shardSpec(new LinearShardSpec(0))
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.size(0)
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.build();
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final PlannerConfig plannerConfig = new PlannerConfig();
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final SegmentGenerator segmentGenerator = closer.register(new SegmentGenerator());
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log.info("Starting benchmark setup using cacheDir[%s], rows[%,d].", segmentGenerator.getCacheDir(), rowsPerSegment);
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TransformSpec transformSpec = new TransformSpec(
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null,
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ImmutableList.of(
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new ExpressionTransform(
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"nested",
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"json_object('long1', long1, 'nesteder', json_object('string1', string1, 'long2', long2, 'double3',double3))",
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TestExprMacroTable.INSTANCE
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)
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)
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);
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List<DimensionSchema> dims = ImmutableList.<DimensionSchema>builder()
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.addAll(schemaInfo.getDimensionsSpec().getDimensions())
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.add(new NestedDataDimensionSchema("nested"))
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.build();
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DimensionsSpec dimsSpec = new DimensionsSpec(dims);
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final QueryableIndex index = segmentGenerator.generate(
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dataSegment,
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schemaInfo,
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dimsSpec,
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transformSpec,
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Granularities.NONE,
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rowsPerSegment
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);
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final QueryRunnerFactoryConglomerate conglomerate = QueryStackTests.createQueryRunnerFactoryConglomerate(
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closer,
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PROCESSING_CONFIG
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);
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final SpecificSegmentsQuerySegmentWalker walker = new SpecificSegmentsQuerySegmentWalker(conglomerate).add(
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dataSegment,
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index
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);
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closer.register(walker);
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final DruidSchemaCatalog rootSchema =
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CalciteTests.createMockRootSchema(conglomerate, walker, plannerConfig, AuthTestUtils.TEST_AUTHORIZER_MAPPER);
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plannerFactory = new PlannerFactory(
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rootSchema,
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CalciteTests.createMockQueryMakerFactory(walker, conglomerate),
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CalciteTests.createOperatorTable(),
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CalciteTests.createExprMacroTable(),
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plannerConfig,
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AuthTestUtils.TEST_AUTHORIZER_MAPPER,
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CalciteTests.getJsonMapper(),
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CalciteTests.DRUID_SCHEMA_NAME,
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new CalciteRulesManager(ImmutableSet.of())
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);
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try {
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SqlVectorizedExpressionSanityTest.sanityTestVectorizedSqlQueries(
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plannerFactory,
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QUERIES.get(Integer.parseInt(query))
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);
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}
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catch (Throwable ignored) {
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// the show must go on
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}
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}
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@TearDown(Level.Trial)
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public void tearDown() throws Exception
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{
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closer.close();
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}
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@Benchmark
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@BenchmarkMode(Mode.AverageTime)
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@OutputTimeUnit(TimeUnit.MILLISECONDS)
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public void querySql(Blackhole blackhole) throws Exception
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{
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final Map<String, Object> context = ImmutableMap.of(
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QueryContexts.VECTORIZE_KEY, vectorize,
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QueryContexts.VECTORIZE_VIRTUAL_COLUMNS_KEY, vectorize
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);
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final String sql = QUERIES.get(Integer.parseInt(query));
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try (final DruidPlanner planner = plannerFactory.createPlannerForTesting(context, sql)) {
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final PlannerResult plannerResult = planner.plan();
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final Sequence<Object[]> resultSequence = plannerResult.run();
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final Object[] lastRow = resultSequence.accumulate(null, (accumulated, in) -> in);
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blackhole.consume(lastRow);
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}
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}
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}
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@ -22,6 +22,9 @@ package org.apache.druid.segment.generator;
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import com.google.common.hash.Hashing;
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import org.apache.druid.common.config.NullHandling;
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import org.apache.druid.data.input.InputRow;
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import org.apache.druid.data.input.MapBasedInputRow;
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import org.apache.druid.data.input.impl.DimensionsSpec;
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import org.apache.druid.guice.NestedDataModule;
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import org.apache.druid.java.util.common.FileUtils;
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import org.apache.druid.java.util.common.ISE;
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import org.apache.druid.java.util.common.StringUtils;
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@ -38,6 +41,8 @@ import org.apache.druid.segment.data.RoaringBitmapSerdeFactory;
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import org.apache.druid.segment.incremental.IncrementalIndex;
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import org.apache.druid.segment.incremental.IncrementalIndexSchema;
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import org.apache.druid.segment.serde.ComplexMetrics;
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import org.apache.druid.segment.transform.TransformSpec;
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import org.apache.druid.segment.transform.Transformer;
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import org.apache.druid.segment.writeout.OffHeapMemorySegmentWriteOutMediumFactory;
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import org.apache.druid.timeline.DataSegment;
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import org.apache.druid.timeline.SegmentId;
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|
@ -48,7 +53,9 @@ import java.io.File;
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import java.io.IOException;
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import java.nio.charset.StandardCharsets;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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public class SegmentGenerator implements Closeable
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{
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@ -98,6 +105,7 @@ public class SegmentGenerator implements Closeable
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return cacheDir;
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}
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public QueryableIndex generate(
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final DataSegment dataSegment,
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final GeneratorSchemaInfo schemaInfo,
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@ -105,14 +113,28 @@ public class SegmentGenerator implements Closeable
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final int numRows
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)
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{
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// In case we need to generate hyperUniques.
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return generate(dataSegment, schemaInfo, schemaInfo.getDimensionsSpec(), TransformSpec.NONE, granularity, numRows);
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}
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public QueryableIndex generate(
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final DataSegment dataSegment,
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final GeneratorSchemaInfo schemaInfo,
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final DimensionsSpec dimensionsSpec,
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final TransformSpec transformSpec,
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final Granularity queryGranularity,
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final int numRows
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)
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{
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// In case we need to generate hyperUniques or json
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ComplexMetrics.registerSerde("hyperUnique", new HyperUniquesSerde());
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NestedDataModule.registerHandlersAndSerde();
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final String dataHash = Hashing.sha256()
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.newHasher()
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.putString(dataSegment.getId().toString(), StandardCharsets.UTF_8)
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.putString(schemaInfo.toString(), StandardCharsets.UTF_8)
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.putString(granularity.toString(), StandardCharsets.UTF_8)
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.putString(dimensionsSpec.toString(), StandardCharsets.UTF_8)
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.putString(queryGranularity.toString(), StandardCharsets.UTF_8)
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.putInt(numRows)
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.hash()
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.toString();
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|
|
@ -139,18 +161,25 @@ public class SegmentGenerator implements Closeable
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);
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final IncrementalIndexSchema indexSchema = new IncrementalIndexSchema.Builder()
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.withDimensionsSpec(schemaInfo.getDimensionsSpec())
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.withDimensionsSpec(dimensionsSpec)
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.withMetrics(schemaInfo.getAggsArray())
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.withRollup(schemaInfo.isWithRollup())
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.withQueryGranularity(granularity)
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.withQueryGranularity(queryGranularity)
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.build();
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final List<InputRow> rows = new ArrayList<>();
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final List<QueryableIndex> indexes = new ArrayList<>();
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||||
Transformer transformer = transformSpec.toTransformer();
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for (int i = 0; i < numRows; i++) {
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final InputRow row = dataGenerator.nextRow();
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rows.add(row);
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final InputRow row = transformer.transform(dataGenerator.nextRow());
|
||||
Map<String, Object> evaluated = new HashMap<>();
|
||||
for (String dimension : dimensionsSpec.getDimensionNames()) {
|
||||
evaluated.put(dimension, row.getRaw(dimension));
|
||||
}
|
||||
MapBasedInputRow transformedRow = new MapBasedInputRow(row.getTimestamp(), dimensionsSpec.getDimensionNames(), evaluated);
|
||||
rows.add(transformedRow);
|
||||
|
||||
if ((i + 1) % 20000 == 0) {
|
||||
log.info("%,d/%,d rows generated for[%s].", i + 1, numRows, dataSegment);
|
||||
|
|
|
|||
Loading…
Reference in New Issue