Examples with LinearShardSpec org.apache.druid.timeline.partition.LinearShardSpec used on opensource projects

Example 16 with LinearShardSpec

use of org.apache.druid.timeline.partition.LinearShardSpec in project druid by druid-io.

the class CachingClusteredClientBenchmark method setup.

@Setup(Level.Trial)
public void setup() {
    final String schemaName = "basic";
    parallelCombine = parallelism > 0;
    GeneratorSchemaInfo schemaInfo = GeneratorBasicSchemas.SCHEMA_MAP.get(schemaName);
    Map<DataSegment, QueryableIndex> queryableIndexes = Maps.newHashMapWithExpectedSize(numServers);
    for (int i = 0; i < numServers; i++) {
        final DataSegment dataSegment = DataSegment.builder().dataSource(DATA_SOURCE).interval(schemaInfo.getDataInterval()).version("1").shardSpec(new LinearShardSpec(i)).size(0).build();
        final SegmentGenerator segmentGenerator = closer.register(new SegmentGenerator());
        LOG.info("Starting benchmark setup using cacheDir[%s], rows[%,d].", segmentGenerator.getCacheDir(), rowsPerSegment);
        final QueryableIndex index = segmentGenerator.generate(dataSegment, schemaInfo, Granularities.NONE, rowsPerSegment);
        queryableIndexes.put(dataSegment, index);
    }
    final DruidProcessingConfig processingConfig = new DruidProcessingConfig() {

        @Override
        public String getFormatString() {
            return null;
        }

        @Override
        public int intermediateComputeSizeBytes() {
            return PROCESSING_BUFFER_SIZE;
        }

        @Override
        public int getNumMergeBuffers() {
            return 1;
        }

        @Override
        public int getNumThreads() {
            return numProcessingThreads;
        }

        @Override
        public boolean useParallelMergePool() {
            return true;
        }
    };
    conglomerate = new DefaultQueryRunnerFactoryConglomerate(ImmutableMap.<Class<? extends Query>, QueryRunnerFactory>builder().put(TimeseriesQuery.class, new TimeseriesQueryRunnerFactory(new TimeseriesQueryQueryToolChest(), new TimeseriesQueryEngine(), QueryRunnerTestHelper.NOOP_QUERYWATCHER)).put(TopNQuery.class, new TopNQueryRunnerFactory(new StupidPool<>("TopNQueryRunnerFactory-bufferPool", () -> ByteBuffer.allocate(PROCESSING_BUFFER_SIZE)), new TopNQueryQueryToolChest(new TopNQueryConfig()), QueryRunnerTestHelper.NOOP_QUERYWATCHER)).put(GroupByQuery.class, makeGroupByQueryRunnerFactory(GroupByQueryRunnerTest.DEFAULT_MAPPER, new GroupByQueryConfig() {

        @Override
        public String getDefaultStrategy() {
            return GroupByStrategySelector.STRATEGY_V2;
        }
    }, processingConfig)).build());
    toolChestWarehouse = new QueryToolChestWarehouse() {

        @Override
        public <T, QueryType extends Query<T>> QueryToolChest<T, QueryType> getToolChest(final QueryType query) {
            return conglomerate.findFactory(query).getToolchest();
        }
    };
    SimpleServerView serverView = new SimpleServerView();
    int serverSuffx = 1;
    for (Entry<DataSegment, QueryableIndex> entry : queryableIndexes.entrySet()) {
        serverView.addServer(createServer(serverSuffx++), entry.getKey(), entry.getValue());
    }
    processingPool = Execs.multiThreaded(processingConfig.getNumThreads(), "caching-clustered-client-benchmark");
    forkJoinPool = new ForkJoinPool((int) Math.ceil(Runtime.getRuntime().availableProcessors() * 0.75), ForkJoinPool.defaultForkJoinWorkerThreadFactory, null, true);
    cachingClusteredClient = new CachingClusteredClient(toolChestWarehouse, serverView, MapCache.create(0), JSON_MAPPER, new ForegroundCachePopulator(JSON_MAPPER, new CachePopulatorStats(), 0), new CacheConfig(), new DruidHttpClientConfig(), processingConfig, forkJoinPool, QueryStackTests.DEFAULT_NOOP_SCHEDULER, new MapJoinableFactory(ImmutableSet.of(), ImmutableMap.of()), new NoopServiceEmitter());
}

Example 17 with LinearShardSpec

use of org.apache.druid.timeline.partition.LinearShardSpec in project druid by druid-io.

the class SqlBenchmark method setup.

@Setup(Level.Trial)
public void setup() {
    final GeneratorSchemaInfo schemaInfo = GeneratorBasicSchemas.SCHEMA_MAP.get("basic");
    final DataSegment dataSegment = DataSegment.builder().dataSource("foo").interval(schemaInfo.getDataInterval()).version("1").shardSpec(new LinearShardSpec(0)).size(0).build();
    final PlannerConfig plannerConfig = new PlannerConfig();
    final SegmentGenerator segmentGenerator = closer.register(new SegmentGenerator());
    log.info("Starting benchmark setup using cacheDir[%s], rows[%,d].", segmentGenerator.getCacheDir(), rowsPerSegment);
    final QueryableIndex index = segmentGenerator.generate(dataSegment, schemaInfo, Granularities.NONE, rowsPerSegment);
    final QueryRunnerFactoryConglomerate conglomerate = QueryStackTests.createQueryRunnerFactoryConglomerate(closer);
    final SpecificSegmentsQuerySegmentWalker walker = new SpecificSegmentsQuerySegmentWalker(conglomerate).add(dataSegment, index);
    closer.register(walker);
    final DruidSchemaCatalog rootSchema = CalciteTests.createMockRootSchema(conglomerate, walker, plannerConfig, AuthTestUtils.TEST_AUTHORIZER_MAPPER);
    plannerFactory = new PlannerFactory(rootSchema, CalciteTests.createMockQueryMakerFactory(walker, conglomerate), createOperatorTable(), CalciteTests.createExprMacroTable(), plannerConfig, AuthTestUtils.TEST_AUTHORIZER_MAPPER, CalciteTests.getJsonMapper(), CalciteTests.DRUID_SCHEMA_NAME);
}

Example 18 with LinearShardSpec

use of org.apache.druid.timeline.partition.LinearShardSpec in project druid by druid-io.

the class SqlVsNativeBenchmark method setup.

@Setup(Level.Trial)
public void setup() {
    this.closer = Closer.create();
    final GeneratorSchemaInfo schemaInfo = GeneratorBasicSchemas.SCHEMA_MAP.get("basic");
    final DataSegment dataSegment = DataSegment.builder().dataSource("foo").interval(schemaInfo.getDataInterval()).version("1").shardSpec(new LinearShardSpec(0)).size(0).build();
    final SegmentGenerator segmentGenerator = closer.register(new SegmentGenerator());
    log.info("Starting benchmark setup using tmpDir[%s], rows[%,d].", segmentGenerator.getCacheDir(), rowsPerSegment);
    final QueryableIndex index = segmentGenerator.generate(dataSegment, schemaInfo, Granularities.NONE, rowsPerSegment);
    final QueryRunnerFactoryConglomerate conglomerate = QueryStackTests.createQueryRunnerFactoryConglomerate(closer);
    final PlannerConfig plannerConfig = new PlannerConfig();
    this.walker = closer.register(new SpecificSegmentsQuerySegmentWalker(conglomerate).add(dataSegment, index));
    final DruidSchemaCatalog rootSchema = CalciteTests.createMockRootSchema(conglomerate, walker, plannerConfig, AuthTestUtils.TEST_AUTHORIZER_MAPPER);
    plannerFactory = new PlannerFactory(rootSchema, CalciteTests.createMockQueryMakerFactory(walker, conglomerate), CalciteTests.createOperatorTable(), CalciteTests.createExprMacroTable(), plannerConfig, AuthTestUtils.TEST_AUTHORIZER_MAPPER, CalciteTests.getJsonMapper(), CalciteTests.DRUID_SCHEMA_NAME);
    groupByQuery = GroupByQuery.builder().setDataSource("foo").setInterval(Intervals.ETERNITY).setDimensions(new DefaultDimensionSpec("dimZipf", "d0"), new DefaultDimensionSpec("dimSequential", "d1")).setAggregatorSpecs(new CountAggregatorFactory("c")).setGranularity(Granularities.ALL).build();
    sqlQuery = "SELECT\n" + "  dimZipf AS d0," + "  dimSequential AS d1,\n" + "  COUNT(*) AS c\n" + "FROM druid.foo\n" + "GROUP BY dimZipf, dimSequential";
}

Example 19 with LinearShardSpec

use of org.apache.druid.timeline.partition.LinearShardSpec in project druid by druid-io.

the class IndexedTableJoinCursorBenchmark method makeQueryableIndexSegment.

public static QueryableIndexSegment makeQueryableIndexSegment(Closer closer, String dataSource, int rowsPerSegment) {
    final List<GeneratorColumnSchema> schemaColumnsInfo = ImmutableList.of(GeneratorColumnSchema.makeSequential("stringKey", ValueType.STRING, false, 1, null, 0, rowsPerSegment), GeneratorColumnSchema.makeSequential("longKey", ValueType.LONG, false, 1, null, 0, rowsPerSegment), GeneratorColumnSchema.makeLazyZipf("string1", ValueType.STRING, false, 1, 0.1, 0, rowsPerSegment, 2.0), GeneratorColumnSchema.makeLazyZipf("string2", ValueType.STRING, false, 1, 0.3, 0, 1000000, 1.5), GeneratorColumnSchema.makeLazyZipf("string3", ValueType.STRING, false, 1, 0.12, 0, 1000, 1.25), GeneratorColumnSchema.makeLazyZipf("string4", ValueType.STRING, false, 1, 0.22, 0, 12000, 3.0), GeneratorColumnSchema.makeLazyZipf("string5", ValueType.STRING, false, 1, 0.05, 0, 33333, 1.8), GeneratorColumnSchema.makeLazyZipf("long1", ValueType.LONG, false, 1, 0.1, 0, 1001, 2.0), GeneratorColumnSchema.makeLazyZipf("long2", ValueType.LONG, false, 1, 0.01, 0, 666666, 2.2), GeneratorColumnSchema.makeLazyZipf("long3", ValueType.LONG, false, 1, 0.12, 0, 1000000, 2.5), GeneratorColumnSchema.makeLazyZipf("long4", ValueType.LONG, false, 1, 0.4, 0, 23, 1.2), GeneratorColumnSchema.makeLazyZipf("long5", ValueType.LONG, false, 1, 0.33, 0, 9999, 1.5), GeneratorColumnSchema.makeLazyZipf("double1", ValueType.DOUBLE, false, 1, 0.1, 0, 333, 2.2), GeneratorColumnSchema.makeLazyZipf("double2", ValueType.DOUBLE, false, 1, 0.01, 0, 4021, 2.5), GeneratorColumnSchema.makeLazyZipf("double3", ValueType.DOUBLE, false, 1, 0.41, 0, 90210, 4.0), GeneratorColumnSchema.makeLazyZipf("double4", ValueType.DOUBLE, false, 1, 0.5, 0, 5555555, 1.2), GeneratorColumnSchema.makeLazyZipf("double5", ValueType.DOUBLE, false, 1, 0.23, 0, 80, 1.8), GeneratorColumnSchema.makeLazyZipf("float1", ValueType.FLOAT, false, 1, 0.11, 0, 1000000, 1.7), GeneratorColumnSchema.makeLazyZipf("float2", ValueType.FLOAT, false, 1, 0.4, 0, 10, 1.5), GeneratorColumnSchema.makeLazyZipf("float3", ValueType.FLOAT, false, 1, 0.8, 0, 5000, 2.3), GeneratorColumnSchema.makeLazyZipf("float4", ValueType.FLOAT, false, 1, 0.999, 0, 14440, 2.0), GeneratorColumnSchema.makeLazyZipf("float5", ValueType.FLOAT, false, 1, 0.001, 0, 1029, 1.5));
    final List<AggregatorFactory> aggs = new ArrayList<>();
    aggs.add(new CountAggregatorFactory("rows"));
    final Interval interval = Intervals.of("2000-01-01/P1D");
    final GeneratorSchemaInfo schema = new GeneratorSchemaInfo(schemaColumnsInfo, aggs, interval, false);
    final DataSegment dataSegment = DataSegment.builder().dataSource(dataSource).interval(schema.getDataInterval()).version("1").shardSpec(new LinearShardSpec(0)).size(0).build();
    final QueryableIndex index = closer.register(new SegmentGenerator()).generate(dataSegment, schema, Granularities.NONE, rowsPerSegment);
    return closer.register(new QueryableIndexSegment(index, SegmentId.dummy(dataSource)));
}

Example 20 with LinearShardSpec

use of org.apache.druid.timeline.partition.LinearShardSpec in project druid by druid-io.

the class ExpressionSelectorBenchmark method setup.

@Setup(Level.Trial)
public void setup() {
    this.closer = Closer.create();
    final GeneratorSchemaInfo schemaInfo = new GeneratorSchemaInfo(ImmutableList.of(GeneratorColumnSchema.makeZipf("n", ValueType.LONG, false, 1, 0d, 1000, 10000, 3d), GeneratorColumnSchema.makeZipf("s", ValueType.STRING, false, 1, 0d, 1000, 10000, 3d)), ImmutableList.of(), Intervals.of("2000/P1D"), false);
    final DataSegment dataSegment = DataSegment.builder().dataSource("foo").interval(schemaInfo.getDataInterval()).version("1").shardSpec(new LinearShardSpec(0)).size(0).build();
    final SegmentGenerator segmentGenerator = closer.register(new SegmentGenerator());
    this.index = closer.register(segmentGenerator.generate(dataSegment, schemaInfo, Granularities.HOUR, rowsPerSegment));
}