Java tutorial
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See the License for the * specific language governing permissions and limitations * under the License. */ package org.apache.druid.benchmark.query; import com.fasterxml.jackson.databind.ObjectMapper; import com.google.common.collect.Lists; import com.google.common.collect.Maps; import com.google.common.io.Files; import org.apache.commons.io.FileUtils; import org.apache.druid.benchmark.datagen.BenchmarkDataGenerator; import org.apache.druid.benchmark.datagen.BenchmarkSchemaInfo; import org.apache.druid.benchmark.datagen.BenchmarkSchemas; import org.apache.druid.data.input.InputRow; import org.apache.druid.hll.HyperLogLogHash; import org.apache.druid.jackson.DefaultObjectMapper; import org.apache.druid.java.util.common.Intervals; import org.apache.druid.java.util.common.concurrent.Execs; import org.apache.druid.java.util.common.granularity.Granularities; import org.apache.druid.java.util.common.guava.Sequence; import org.apache.druid.java.util.common.logger.Logger; import org.apache.druid.query.Druids; import org.apache.druid.query.FinalizeResultsQueryRunner; import org.apache.druid.query.Query; import org.apache.druid.query.QueryPlus; import org.apache.druid.query.QueryRunner; import org.apache.druid.query.QueryRunnerFactory; import org.apache.druid.query.QueryToolChest; import org.apache.druid.query.Result; import org.apache.druid.query.aggregation.AggregatorFactory; import org.apache.druid.query.aggregation.DoubleMinAggregatorFactory; import org.apache.druid.query.aggregation.DoubleSumAggregatorFactory; import org.apache.druid.query.aggregation.FilteredAggregatorFactory; import org.apache.druid.query.aggregation.LongMaxAggregatorFactory; import org.apache.druid.query.aggregation.LongSumAggregatorFactory; import org.apache.druid.query.aggregation.hyperloglog.HyperUniquesAggregatorFactory; import org.apache.druid.query.aggregation.hyperloglog.HyperUniquesSerde; import org.apache.druid.query.filter.BoundDimFilter; import org.apache.druid.query.filter.DimFilter; import org.apache.druid.query.filter.SelectorDimFilter; import org.apache.druid.query.ordering.StringComparators; import org.apache.druid.query.spec.MultipleIntervalSegmentSpec; import org.apache.druid.query.spec.QuerySegmentSpec; import org.apache.druid.query.timeseries.TimeseriesQuery; import org.apache.druid.query.timeseries.TimeseriesQueryEngine; import org.apache.druid.query.timeseries.TimeseriesQueryQueryToolChest; import org.apache.druid.query.timeseries.TimeseriesQueryRunnerFactory; import org.apache.druid.query.timeseries.TimeseriesResultValue; import org.apache.druid.segment.IncrementalIndexSegment; import org.apache.druid.segment.IndexIO; import org.apache.druid.segment.IndexMergerV9; import org.apache.druid.segment.IndexSpec; import org.apache.druid.segment.QueryableIndex; import org.apache.druid.segment.QueryableIndexSegment; import org.apache.druid.segment.column.ColumnConfig; import org.apache.druid.segment.column.ColumnHolder; import org.apache.druid.segment.incremental.IncrementalIndex; import org.apache.druid.segment.serde.ComplexMetrics; import org.apache.druid.segment.writeout.OffHeapMemorySegmentWriteOutMediumFactory; import org.openjdk.jmh.annotations.Benchmark; import org.openjdk.jmh.annotations.BenchmarkMode; import org.openjdk.jmh.annotations.Fork; import org.openjdk.jmh.annotations.Measurement; import org.openjdk.jmh.annotations.Mode; import org.openjdk.jmh.annotations.OutputTimeUnit; import org.openjdk.jmh.annotations.Param; import org.openjdk.jmh.annotations.Scope; import org.openjdk.jmh.annotations.Setup; import org.openjdk.jmh.annotations.State; import org.openjdk.jmh.annotations.TearDown; import org.openjdk.jmh.annotations.Warmup; import org.openjdk.jmh.infra.Blackhole; import java.io.File; import java.io.IOException; import java.util.ArrayList; import java.util.Collections; import java.util.LinkedHashMap; import java.util.List; import java.util.Map; import java.util.concurrent.ExecutorService; import java.util.concurrent.TimeUnit; @State(Scope.Benchmark) @Fork(value = 1) @Warmup(iterations = 10) @Measurement(iterations = 25) public class TimeseriesBenchmark { @Param({ "1" }) private int numSegments; @Param({ "750000" }) private int rowsPerSegment; @Param({ "basic.A", "basic.timeFilterNumeric", "basic.timeFilterAlphanumeric", "basic.timeFilterByInterval" }) private String schemaAndQuery; private static final Logger log = new Logger(TimeseriesBenchmark.class); private static final int RNG_SEED = 9999; private static final IndexMergerV9 INDEX_MERGER_V9; private static final IndexIO INDEX_IO; public static final ObjectMapper JSON_MAPPER; private List<IncrementalIndex> incIndexes; private List<QueryableIndex> qIndexes; private File tmpDir; private QueryRunnerFactory factory; private BenchmarkSchemaInfo schemaInfo; private TimeseriesQuery query; private ExecutorService executorService; static { JSON_MAPPER = new DefaultObjectMapper(); INDEX_IO = new IndexIO(JSON_MAPPER, new ColumnConfig() { @Override public int columnCacheSizeBytes() { return 0; } }); INDEX_MERGER_V9 = new IndexMergerV9(JSON_MAPPER, INDEX_IO, OffHeapMemorySegmentWriteOutMediumFactory.instance()); } private static final Map<String, Map<String, TimeseriesQuery>> SCHEMA_QUERY_MAP = new LinkedHashMap<>(); private void setupQueries() { // queries for the basic schema Map<String, TimeseriesQuery> basicQueries = new LinkedHashMap<>(); BenchmarkSchemaInfo basicSchema = BenchmarkSchemas.SCHEMA_MAP.get("basic"); { // basic.A QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec( Collections.singletonList(basicSchema.getDataInterval())); List<AggregatorFactory> queryAggs = new ArrayList<>(); queryAggs.add(new LongSumAggregatorFactory("sumLongSequential", "sumLongSequential")); queryAggs.add(new LongMaxAggregatorFactory("maxLongUniform", "maxLongUniform")); queryAggs.add(new DoubleSumAggregatorFactory("sumFloatNormal", "sumFloatNormal")); queryAggs.add(new DoubleMinAggregatorFactory("minFloatZipf", "minFloatZipf")); queryAggs.add(new HyperUniquesAggregatorFactory("hyperUniquesMet", "hyper")); TimeseriesQuery queryA = Druids.newTimeseriesQueryBuilder().dataSource("blah") .granularity(Granularities.ALL).intervals(intervalSpec).aggregators(queryAggs).descending(false) .build(); basicQueries.put("A", queryA); } { QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec( Collections.singletonList(basicSchema.getDataInterval())); List<AggregatorFactory> queryAggs = new ArrayList<>(); LongSumAggregatorFactory lsaf = new LongSumAggregatorFactory("sumLongSequential", "sumLongSequential"); BoundDimFilter timeFilter = new BoundDimFilter(ColumnHolder.TIME_COLUMN_NAME, "200000", "300000", false, false, null, null, StringComparators.NUMERIC); queryAggs.add(new FilteredAggregatorFactory(lsaf, timeFilter)); TimeseriesQuery timeFilterQuery = Druids.newTimeseriesQueryBuilder().dataSource("blah") .granularity(Granularities.ALL).intervals(intervalSpec).aggregators(queryAggs).descending(false) .build(); basicQueries.put("timeFilterNumeric", timeFilterQuery); } { QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec( Collections.singletonList(basicSchema.getDataInterval())); List<AggregatorFactory> queryAggs = new ArrayList<>(); LongSumAggregatorFactory lsaf = new LongSumAggregatorFactory("sumLongSequential", "sumLongSequential"); BoundDimFilter timeFilter = new BoundDimFilter(ColumnHolder.TIME_COLUMN_NAME, "200000", "300000", false, false, null, null, StringComparators.ALPHANUMERIC); queryAggs.add(new FilteredAggregatorFactory(lsaf, timeFilter)); TimeseriesQuery timeFilterQuery = Druids.newTimeseriesQueryBuilder().dataSource("blah") .granularity(Granularities.ALL).intervals(intervalSpec).aggregators(queryAggs).descending(false) .build(); basicQueries.put("timeFilterAlphanumeric", timeFilterQuery); } { QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec( Collections.singletonList(Intervals.utc(200000, 300000))); List<AggregatorFactory> queryAggs = new ArrayList<>(); LongSumAggregatorFactory lsaf = new LongSumAggregatorFactory("sumLongSequential", "sumLongSequential"); queryAggs.add(lsaf); TimeseriesQuery timeFilterQuery = Druids.newTimeseriesQueryBuilder().dataSource("blah") .granularity(Granularities.ALL).intervals(intervalSpec).aggregators(queryAggs).descending(false) .build(); basicQueries.put("timeFilterByInterval", timeFilterQuery); } SCHEMA_QUERY_MAP.put("basic", basicQueries); } @Setup public void setup() throws IOException { log.info("SETUP CALLED AT " + System.currentTimeMillis()); if (ComplexMetrics.getSerdeForType("hyperUnique") == null) { ComplexMetrics.registerSerde("hyperUnique", new HyperUniquesSerde(HyperLogLogHash.getDefault())); } executorService = Execs.multiThreaded(numSegments, "TimeseriesThreadPool"); setupQueries(); String[] schemaQuery = schemaAndQuery.split("\\."); String schemaName = schemaQuery[0]; String queryName = schemaQuery[1]; schemaInfo = BenchmarkSchemas.SCHEMA_MAP.get(schemaName); query = SCHEMA_QUERY_MAP.get(schemaName).get(queryName); incIndexes = new ArrayList<>(); for (int i = 0; i < numSegments; i++) { log.info("Generating rows for segment " + i); BenchmarkDataGenerator gen = new BenchmarkDataGenerator(schemaInfo.getColumnSchemas(), RNG_SEED + i, schemaInfo.getDataInterval(), rowsPerSegment); IncrementalIndex incIndex = makeIncIndex(); for (int j = 0; j < rowsPerSegment; j++) { InputRow row = gen.nextRow(); if (j % 10000 == 0) { log.info(j + " rows generated."); } incIndex.add(row); } log.info(rowsPerSegment + " rows generated"); incIndexes.add(incIndex); } tmpDir = Files.createTempDir(); log.info("Using temp dir: " + tmpDir.getAbsolutePath()); qIndexes = new ArrayList<>(); for (int i = 0; i < numSegments; i++) { File indexFile = INDEX_MERGER_V9.persist(incIndexes.get(i), tmpDir, new IndexSpec(), null); QueryableIndex qIndex = INDEX_IO.loadIndex(indexFile); qIndexes.add(qIndex); } factory = new TimeseriesQueryRunnerFactory( new TimeseriesQueryQueryToolChest(QueryBenchmarkUtil.NoopIntervalChunkingQueryRunnerDecorator()), new TimeseriesQueryEngine(), QueryBenchmarkUtil.NOOP_QUERYWATCHER); } @TearDown public void tearDown() throws IOException { FileUtils.deleteDirectory(tmpDir); } private IncrementalIndex makeIncIndex() { return new IncrementalIndex.Builder().setSimpleTestingIndexSchema(schemaInfo.getAggsArray()) .setReportParseExceptions(false).setMaxRowCount(rowsPerSegment).buildOnheap(); } private static <T> List<T> runQuery(QueryRunnerFactory factory, QueryRunner runner, Query<T> query) { QueryToolChest toolChest = factory.getToolchest(); QueryRunner<T> theRunner = new FinalizeResultsQueryRunner<>( toolChest.mergeResults(toolChest.preMergeQueryDecoration(runner)), toolChest); Sequence<T> queryResult = theRunner.run(QueryPlus.wrap(query), Maps.newHashMap()); return queryResult.toList(); } @Benchmark @BenchmarkMode(Mode.AverageTime) @OutputTimeUnit(TimeUnit.MICROSECONDS) public void querySingleIncrementalIndex(Blackhole blackhole) { QueryRunner<Result<TimeseriesResultValue>> runner = QueryBenchmarkUtil.makeQueryRunner(factory, "incIndex", new IncrementalIndexSegment(incIndexes.get(0), "incIndex")); List<Result<TimeseriesResultValue>> results = TimeseriesBenchmark.runQuery(factory, runner, query); for (Result<TimeseriesResultValue> result : results) { blackhole.consume(result); } } @Benchmark @BenchmarkMode(Mode.AverageTime) @OutputTimeUnit(TimeUnit.MICROSECONDS) public void querySingleQueryableIndex(Blackhole blackhole) { final QueryRunner<Result<TimeseriesResultValue>> runner = QueryBenchmarkUtil.makeQueryRunner(factory, "qIndex", new QueryableIndexSegment("qIndex", qIndexes.get(0))); List<Result<TimeseriesResultValue>> results = TimeseriesBenchmark.runQuery(factory, runner, query); for (Result<TimeseriesResultValue> result : results) { blackhole.consume(result); } } @Benchmark @BenchmarkMode(Mode.AverageTime) @OutputTimeUnit(TimeUnit.MICROSECONDS) public void queryFilteredSingleQueryableIndex(Blackhole blackhole) { final QueryRunner<Result<TimeseriesResultValue>> runner = QueryBenchmarkUtil.makeQueryRunner(factory, "qIndex", new QueryableIndexSegment("qIndex", qIndexes.get(0))); DimFilter filter = new SelectorDimFilter("dimSequential", "399", null); Query filteredQuery = query.withDimFilter(filter); List<Result<TimeseriesResultValue>> results = TimeseriesBenchmark.runQuery(factory, runner, filteredQuery); for (Result<TimeseriesResultValue> result : results) { blackhole.consume(result); } } @Benchmark @BenchmarkMode(Mode.AverageTime) @OutputTimeUnit(TimeUnit.MICROSECONDS) public void queryMultiQueryableIndex(Blackhole blackhole) { List<QueryRunner<Result<TimeseriesResultValue>>> singleSegmentRunners = Lists.newArrayList(); QueryToolChest toolChest = factory.getToolchest(); for (int i = 0; i < numSegments; i++) { String segmentName = "qIndex" + i; QueryRunner<Result<TimeseriesResultValue>> runner = QueryBenchmarkUtil.makeQueryRunner(factory, segmentName, new QueryableIndexSegment(segmentName, qIndexes.get(i))); singleSegmentRunners.add(toolChest.preMergeQueryDecoration(runner)); } QueryRunner theRunner = toolChest.postMergeQueryDecoration(new FinalizeResultsQueryRunner<>( toolChest.mergeResults(factory.mergeRunners(executorService, singleSegmentRunners)), toolChest)); Sequence<Result<TimeseriesResultValue>> queryResult = theRunner.run(QueryPlus.wrap(query), Maps.newHashMap()); List<Result<TimeseriesResultValue>> results = queryResult.toList(); for (Result<TimeseriesResultValue> result : results) { blackhole.consume(result); } } }