Examples with TimeseriesQuery io.druid.query.timeseries.TimeseriesQuery used on opensource projects

Example 16 with TimeseriesQuery

use of io.druid.query.timeseries.TimeseriesQuery in project druid by druid-io.

the class AppendTest method testTimeSeries.

@Test
public void testTimeSeries() {
    List<Result<TimeseriesResultValue>> expectedResults = Arrays.asList(new Result<TimeseriesResultValue>(new DateTime("2011-01-12T00:00:00.000Z"), new TimeseriesResultValue(ImmutableMap.<String, Object>builder().put("rows", 8L).put("index", 700.0D).put("addRowsIndexConstant", 709.0D).put("uniques", 1.0002442201269182D).put("maxIndex", 100.0D).put("minIndex", 0.0D).build())));
    TimeseriesQuery query = makeTimeseriesQuery();
    QueryRunner runner = TestQueryRunners.makeTimeSeriesQueryRunner(segment);
    HashMap<String, Object> context = new HashMap<String, Object>();
    TestHelper.assertExpectedResults(expectedResults, runner.run(query, context));
}

Example 17 with TimeseriesQuery

use of io.druid.query.timeseries.TimeseriesQuery in project druid by druid-io.

the class AppendTest method testRowFiltering.

@Test
public void testRowFiltering() {
    List<Result<TimeseriesResultValue>> expectedResults = Arrays.asList(new Result<TimeseriesResultValue>(new DateTime("2011-01-12T00:00:00.000Z"), new TimeseriesResultValue(ImmutableMap.<String, Object>builder().put("rows", 5L).put("index", 500.0D).put("addRowsIndexConstant", 506.0D).put("uniques", 0.0D).put("maxIndex", 100.0D).put("minIndex", 100.0D).build())));
    TimeseriesQuery query = Druids.newTimeseriesQueryBuilder().dataSource(dataSource).granularity(allGran).intervals(fullOnInterval).filters(marketDimension, "breakstuff").aggregators(Lists.<AggregatorFactory>newArrayList(Iterables.concat(commonAggregators, Lists.newArrayList(new DoubleMaxAggregatorFactory("maxIndex", "index"), new DoubleMinAggregatorFactory("minIndex", "index"))))).postAggregators(Arrays.<PostAggregator>asList(addRowsIndexConstant)).build();
    QueryRunner runner = TestQueryRunners.makeTimeSeriesQueryRunner(segment3);
    HashMap<String, Object> context = new HashMap<String, Object>();
    TestHelper.assertExpectedResults(expectedResults, runner.run(query, context));
}

Example 18 with TimeseriesQuery

use of io.druid.query.timeseries.TimeseriesQuery in project druid by druid-io.

the class VarianceTimeseriesQueryTest method testTimeseriesWithNullFilterOnNonExistentDimension.

@Test
public void testTimeseriesWithNullFilterOnNonExistentDimension() {
    TimeseriesQuery query = Druids.newTimeseriesQueryBuilder().dataSource(VarianceTestHelper.dataSource).granularity(VarianceTestHelper.dayGran).filters("bobby", null).intervals(VarianceTestHelper.firstToThird).aggregators(VarianceTestHelper.commonPlusVarAggregators).postAggregators(Arrays.<PostAggregator>asList(VarianceTestHelper.addRowsIndexConstant, VarianceTestHelper.stddevOfIndexPostAggr)).descending(descending).build();
    List<Result<TimeseriesResultValue>> expectedResults = Arrays.asList(new Result<>(new DateTime("2011-04-01"), new TimeseriesResultValue(VarianceTestHelper.of("rows", 13L, "index", 6626.151596069336, "addRowsIndexConstant", 6640.151596069336, "uniques", VarianceTestHelper.UNIQUES_9, "index_var", descending ? 368885.6897238851 : 368885.689155086, "index_stddev", descending ? 607.3596049490657 : 607.35960448081))), new Result<>(new DateTime("2011-04-02"), new TimeseriesResultValue(VarianceTestHelper.of("rows", 13L, "index", 5833.2095947265625, "addRowsIndexConstant", 5847.2095947265625, "uniques", VarianceTestHelper.UNIQUES_9, "index_var", descending ? 259061.6037088883 : 259061.60216419376, "index_stddev", descending ? 508.9809463122252 : 508.98094479478675))));
    Iterable<Result<TimeseriesResultValue>> results = Sequences.toList(runner.run(query, new HashMap<String, Object>()), Lists.<Result<TimeseriesResultValue>>newArrayList());
    assertExpectedResults(expectedResults, results);
}

Example 19 with TimeseriesQuery

use of io.druid.query.timeseries.TimeseriesQuery in project druid by druid-io.

the class TimeseriesBenchmark method setupQueries.

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(Arrays.asList(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(Arrays.asList(basicSchema.getDataInterval()));
        List<AggregatorFactory> queryAggs = new ArrayList<>();
        LongSumAggregatorFactory lsaf = new LongSumAggregatorFactory("sumLongSequential", "sumLongSequential");
        BoundDimFilter timeFilter = new BoundDimFilter(Column.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(Arrays.asList(basicSchema.getDataInterval()));
        List<AggregatorFactory> queryAggs = new ArrayList<>();
        LongSumAggregatorFactory lsaf = new LongSumAggregatorFactory("sumLongSequential", "sumLongSequential");
        BoundDimFilter timeFilter = new BoundDimFilter(Column.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(Arrays.asList(new Interval(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);
}

Example 20 with TimeseriesQuery

use of io.druid.query.timeseries.TimeseriesQuery in project druid by druid-io.

the class IncrementalIndexTest method testSingleThreadedIndexingAndQuery.

@Test
public void testSingleThreadedIndexingAndQuery() throws Exception {
    final int dimensionCount = 5;
    final ArrayList<AggregatorFactory> ingestAggregatorFactories = new ArrayList<>();
    ingestAggregatorFactories.add(new CountAggregatorFactory("rows"));
    for (int i = 0; i < dimensionCount; ++i) {
        ingestAggregatorFactories.add(new LongSumAggregatorFactory(String.format("sumResult%s", i), String.format("Dim_%s", i)));
        ingestAggregatorFactories.add(new DoubleSumAggregatorFactory(String.format("doubleSumResult%s", i), String.format("Dim_%s", i)));
    }
    final IncrementalIndex index = closer.closeLater(indexCreator.createIndex(ingestAggregatorFactories.toArray(new AggregatorFactory[ingestAggregatorFactories.size()])));
    final long timestamp = System.currentTimeMillis();
    final int rows = 50;
    //ingesting same data twice to have some merging happening
    for (int i = 0; i < rows; i++) {
        index.add(getLongRow(timestamp + i, i, dimensionCount));
    }
    for (int i = 0; i < rows; i++) {
        index.add(getLongRow(timestamp + i, i, dimensionCount));
    }
    //run a timeseries query on the index and verify results
    final ArrayList<AggregatorFactory> queryAggregatorFactories = new ArrayList<>();
    queryAggregatorFactories.add(new CountAggregatorFactory("rows"));
    for (int i = 0; i < dimensionCount; ++i) {
        queryAggregatorFactories.add(new LongSumAggregatorFactory(String.format("sumResult%s", i), String.format("sumResult%s", i)));
        queryAggregatorFactories.add(new DoubleSumAggregatorFactory(String.format("doubleSumResult%s", i), String.format("doubleSumResult%s", i)));
    }
    TimeseriesQuery query = Druids.newTimeseriesQueryBuilder().dataSource("xxx").granularity(Granularities.ALL).intervals(ImmutableList.of(new Interval("2000/2030"))).aggregators(queryAggregatorFactories).build();
    final Segment incrementalIndexSegment = new IncrementalIndexSegment(index, null);
    final QueryRunnerFactory factory = new TimeseriesQueryRunnerFactory(new TimeseriesQueryQueryToolChest(QueryRunnerTestHelper.NoopIntervalChunkingQueryRunnerDecorator()), new TimeseriesQueryEngine(), QueryRunnerTestHelper.NOOP_QUERYWATCHER);
    final QueryRunner<Result<TimeseriesResultValue>> runner = new FinalizeResultsQueryRunner<Result<TimeseriesResultValue>>(factory.createRunner(incrementalIndexSegment), factory.getToolchest());
    List<Result<TimeseriesResultValue>> results = Sequences.toList(runner.run(query, new HashMap<String, Object>()), new LinkedList<Result<TimeseriesResultValue>>());
    Result<TimeseriesResultValue> result = Iterables.getOnlyElement(results);
    boolean isRollup = index.isRollup();
    Assert.assertEquals(rows * (isRollup ? 1 : 2), result.getValue().getLongMetric("rows").intValue());
    for (int i = 0; i < dimensionCount; ++i) {
        Assert.assertEquals(String.format("Failed long sum on dimension %d", i), 2 * rows, result.getValue().getLongMetric(String.format("sumResult%s", i)).intValue());
        Assert.assertEquals(String.format("Failed double sum on dimension %d", i), 2 * rows, result.getValue().getDoubleMetric(String.format("doubleSumResult%s", i)).intValue());
    }
}