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

Example 21 with TimeseriesQueryQueryToolChest

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

the class RetryQueryRunnerTest method testRunWithMissingSegments.

@Test
public void testRunWithMissingSegments() throws Exception {
    Map<String, Object> context = new MapMaker().makeMap();
    context.put(Result.MISSING_SEGMENTS_KEY, Lists.newArrayList());
    RetryQueryRunner<Result<TimeseriesResultValue>> runner = new RetryQueryRunner<>(new QueryRunner<Result<TimeseriesResultValue>>() {

        @Override
        public Sequence<Result<TimeseriesResultValue>> run(Query query, Map context) {
            ((List) context.get(Result.MISSING_SEGMENTS_KEY)).add(new SegmentDescriptor(new Interval(178888, 1999999), "test", 1));
            return Sequences.empty();
        }
    }, (QueryToolChest) new TimeseriesQueryQueryToolChest(QueryRunnerTestHelper.NoopIntervalChunkingQueryRunnerDecorator()), new RetryQueryRunnerConfig() {

        @Override
        public int getNumTries() {
            return 0;
        }

        @Override
        public boolean isReturnPartialResults() {
            return true;
        }
    }, jsonMapper);
    Iterable<Result<TimeseriesResultValue>> actualResults = Sequences.toList(runner.run(query, context), Lists.<Result<TimeseriesResultValue>>newArrayList());
    Assert.assertTrue("Should have one entry in the list of missing segments", ((List) context.get(Result.MISSING_SEGMENTS_KEY)).size() == 1);
    Assert.assertTrue("Should return an empty sequence as a result", ((List) actualResults).size() == 0);
}

Example 22 with TimeseriesQueryQueryToolChest

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

the class RetryQueryRunnerTest method testException.

@Test(expected = SegmentMissingException.class)
public void testException() throws Exception {
    Map<String, Object> context = new MapMaker().makeMap();
    context.put(Result.MISSING_SEGMENTS_KEY, Lists.newArrayList());
    RetryQueryRunner<Result<TimeseriesResultValue>> runner = new RetryQueryRunner<>(new QueryRunner<Result<TimeseriesResultValue>>() {

        @Override
        public Sequence<Result<TimeseriesResultValue>> run(Query<Result<TimeseriesResultValue>> query, Map<String, Object> context) {
            ((List) context.get(Result.MISSING_SEGMENTS_KEY)).add(new SegmentDescriptor(new Interval(178888, 1999999), "test", 1));
            return Sequences.empty();
        }
    }, (QueryToolChest) new TimeseriesQueryQueryToolChest(QueryRunnerTestHelper.NoopIntervalChunkingQueryRunnerDecorator()), new RetryQueryRunnerConfig() {

        private int numTries = 1;

        private boolean returnPartialResults = false;

        public int getNumTries() {
            return numTries;
        }

        public boolean returnPartialResults() {
            return returnPartialResults;
        }
    }, jsonMapper);
    Iterable<Result<TimeseriesResultValue>> actualResults = Sequences.toList(runner.run(query, context), Lists.<Result<TimeseriesResultValue>>newArrayList());
    Assert.assertTrue("Should have one entry in the list of missing segments", ((List) context.get(Result.MISSING_SEGMENTS_KEY)).size() == 1);
}

Example 23 with TimeseriesQueryQueryToolChest

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

the class RetryQueryRunnerTest method testRetry.

@Test
public void testRetry() throws Exception {
    Map<String, Object> context = new MapMaker().makeMap();
    context.put("count", 0);
    context.put(Result.MISSING_SEGMENTS_KEY, Lists.newArrayList());
    RetryQueryRunner<Result<TimeseriesResultValue>> runner = new RetryQueryRunner<>(new QueryRunner<Result<TimeseriesResultValue>>() {

        @Override
        public Sequence<Result<TimeseriesResultValue>> run(Query<Result<TimeseriesResultValue>> query, Map<String, Object> context) {
            if ((int) context.get("count") == 0) {
                ((List) context.get(Result.MISSING_SEGMENTS_KEY)).add(new SegmentDescriptor(new Interval(178888, 1999999), "test", 1));
                context.put("count", 1);
                return Sequences.empty();
            } else {
                return Sequences.simple(Arrays.asList(new Result<>(new DateTime(), new TimeseriesResultValue(Maps.<String, Object>newHashMap()))));
            }
        }
    }, (QueryToolChest) new TimeseriesQueryQueryToolChest(QueryRunnerTestHelper.NoopIntervalChunkingQueryRunnerDecorator()), new RetryQueryRunnerConfig() {

        private int numTries = 1;

        private boolean returnPartialResults = true;

        public int getNumTries() {
            return numTries;
        }

        public boolean returnPartialResults() {
            return returnPartialResults;
        }
    }, jsonMapper);
    Iterable<Result<TimeseriesResultValue>> actualResults = Sequences.toList(runner.run(query, context), Lists.<Result<TimeseriesResultValue>>newArrayList());
    Assert.assertTrue("Should return a list with one element", ((List) actualResults).size() == 1);
    Assert.assertTrue("Should have nothing in missingSegment list", ((List) context.get(Result.MISSING_SEGMENTS_KEY)).size() == 0);
}

Example 24 with TimeseriesQueryQueryToolChest

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

the class FilteredAggregatorBenchmark method setup.

@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()));
    }
    schemaInfo = BenchmarkSchemas.SCHEMA_MAP.get(schema);
    BenchmarkDataGenerator gen = new BenchmarkDataGenerator(schemaInfo.getColumnSchemas(), RNG_SEED, schemaInfo.getDataInterval(), rowsPerSegment);
    incIndex = makeIncIndex(schemaInfo.getAggsArray());
    filter = new OrDimFilter(Arrays.asList(new BoundDimFilter("dimSequential", "-1", "-1", true, true, null, null, StringComparators.ALPHANUMERIC), new JavaScriptDimFilter("dimSequential", "function(x) { return false }", null, JavaScriptConfig.getEnabledInstance()), new RegexDimFilter("dimSequential", "X", null), new SearchQueryDimFilter("dimSequential", new ContainsSearchQuerySpec("X", false), null), new InDimFilter("dimSequential", Arrays.asList("X"), null)));
    filteredMetrics = new AggregatorFactory[1];
    filteredMetrics[0] = new FilteredAggregatorFactory(new CountAggregatorFactory("rows"), filter);
    incIndexFilteredAgg = makeIncIndex(filteredMetrics);
    inputRows = new ArrayList<>();
    for (int j = 0; j < rowsPerSegment; j++) {
        InputRow row = gen.nextRow();
        if (j % 10000 == 0) {
            log.info(j + " rows generated.");
        }
        incIndex.add(row);
        inputRows.add(row);
    }
    tmpDir = Files.createTempDir();
    log.info("Using temp dir: " + tmpDir.getAbsolutePath());
    indexFile = INDEX_MERGER_V9.persist(incIndex, tmpDir, new IndexSpec());
    qIndex = INDEX_IO.loadIndex(indexFile);
    factory = new TimeseriesQueryRunnerFactory(new TimeseriesQueryQueryToolChest(QueryBenchmarkUtil.NoopIntervalChunkingQueryRunnerDecorator()), new TimeseriesQueryEngine(), QueryBenchmarkUtil.NOOP_QUERYWATCHER);
    BenchmarkSchemaInfo basicSchema = BenchmarkSchemas.SCHEMA_MAP.get("basic");
    QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec(Arrays.asList(basicSchema.getDataInterval()));
    List<AggregatorFactory> queryAggs = new ArrayList<>();
    queryAggs.add(filteredMetrics[0]);
    query = Druids.newTimeseriesQueryBuilder().dataSource("blah").granularity(Granularities.ALL).intervals(intervalSpec).aggregators(queryAggs).descending(false).build();
}

Example 25 with TimeseriesQueryQueryToolChest

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

the class CachingClusteredClientTest method testSingleDimensionPruning.

@Test
public void testSingleDimensionPruning() throws Exception {
    DimFilter filter = Druids.newAndDimFilterBuilder().fields(Arrays.asList(Druids.newOrDimFilterBuilder().fields(Arrays.asList(new SelectorDimFilter("dim1", "a", null), new BoundDimFilter("dim1", "from", "to", false, false, false, null, StringComparators.LEXICOGRAPHIC))).build(), Druids.newAndDimFilterBuilder().fields(Arrays.asList(new InDimFilter("dim2", Arrays.asList("a", "c", "e", "g"), null), new BoundDimFilter("dim2", "aaa", "hi", false, false, false, null, StringComparators.LEXICOGRAPHIC), new BoundDimFilter("dim2", "e", "zzz", true, true, false, null, StringComparators.LEXICOGRAPHIC))).build())).build();
    final Druids.TimeseriesQueryBuilder builder = Druids.newTimeseriesQueryBuilder().dataSource(DATA_SOURCE).filters(filter).granularity(GRANULARITY).intervals(SEG_SPEC).context(CONTEXT).intervals("2011-01-05/2011-01-10").aggregators(RENAMED_AGGS).postAggregators(RENAMED_POST_AGGS);
    TimeseriesQuery query = builder.build();
    Map<String, List> context = new HashMap<>();
    final Interval interval1 = new Interval("2011-01-06/2011-01-07");
    final Interval interval2 = new Interval("2011-01-07/2011-01-08");
    final Interval interval3 = new Interval("2011-01-08/2011-01-09");
    QueryRunner runner = new FinalizeResultsQueryRunner(client, new TimeseriesQueryQueryToolChest(QueryRunnerTestHelper.NoopIntervalChunkingQueryRunnerDecorator()));
    final DruidServer lastServer = servers[random.nextInt(servers.length)];
    ServerSelector selector1 = makeMockSingleDimensionSelector(lastServer, "dim1", null, "b", 1);
    ServerSelector selector2 = makeMockSingleDimensionSelector(lastServer, "dim1", "e", "f", 2);
    ServerSelector selector3 = makeMockSingleDimensionSelector(lastServer, "dim1", "hi", "zzz", 3);
    ServerSelector selector4 = makeMockSingleDimensionSelector(lastServer, "dim2", "a", "e", 4);
    ServerSelector selector5 = makeMockSingleDimensionSelector(lastServer, "dim2", null, null, 5);
    ServerSelector selector6 = makeMockSingleDimensionSelector(lastServer, "other", "b", null, 6);
    timeline.add(interval1, "v", new StringPartitionChunk<>(null, "a", 1, selector1));
    timeline.add(interval1, "v", new StringPartitionChunk<>("a", "b", 2, selector2));
    timeline.add(interval1, "v", new StringPartitionChunk<>("b", null, 3, selector3));
    timeline.add(interval2, "v", new StringPartitionChunk<>(null, "d", 4, selector4));
    timeline.add(interval2, "v", new StringPartitionChunk<>("d", null, 5, selector5));
    timeline.add(interval3, "v", new StringPartitionChunk<>(null, null, 6, selector6));
    final Capture<TimeseriesQuery> capture = Capture.newInstance();
    final Capture<Map<String, List>> contextCap = Capture.newInstance();
    QueryRunner mockRunner = EasyMock.createNiceMock(QueryRunner.class);
    EasyMock.expect(mockRunner.run(EasyMock.capture(capture), EasyMock.capture(contextCap))).andReturn(Sequences.empty()).anyTimes();
    EasyMock.expect(serverView.getQueryRunner(lastServer)).andReturn(mockRunner).anyTimes();
    EasyMock.replay(serverView);
    EasyMock.replay(mockRunner);
    List<SegmentDescriptor> descriptors = new ArrayList<>();
    descriptors.add(new SegmentDescriptor(interval1, "v", 1));
    descriptors.add(new SegmentDescriptor(interval1, "v", 3));
    descriptors.add(new SegmentDescriptor(interval2, "v", 5));
    descriptors.add(new SegmentDescriptor(interval3, "v", 6));
    MultipleSpecificSegmentSpec expected = new MultipleSpecificSegmentSpec(descriptors);
    Sequences.toList(runner.run(query, context), Lists.newArrayList());
    Assert.assertEquals(expected, capture.getValue().getQuerySegmentSpec());
}