use of io.druid.query.spec.QuerySegmentSpec in project druid by druid-io.
the class TopNBenchmark method setupQueries.
private void setupQueries() {
// queries for the basic schema
Map<String, TopNQueryBuilder> 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"));
TopNQueryBuilder queryBuilderA = new TopNQueryBuilder().dataSource("blah").granularity(Granularities.ALL).dimension("dimSequential").metric("sumFloatNormal").intervals(intervalSpec).aggregators(queryAggs);
basicQueries.put("A", queryBuilderA);
}
{
// basic.numericSort
QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec(Arrays.asList(basicSchema.getDataInterval()));
List<AggregatorFactory> queryAggs = new ArrayList<>();
queryAggs.add(new LongSumAggregatorFactory("sumLongSequential", "sumLongSequential"));
TopNQueryBuilder queryBuilderA = new TopNQueryBuilder().dataSource("blah").granularity(Granularities.ALL).dimension("dimUniform").metric(new DimensionTopNMetricSpec(null, StringComparators.NUMERIC)).intervals(intervalSpec).aggregators(queryAggs);
basicQueries.put("numericSort", queryBuilderA);
}
{
// basic.alphanumericSort
QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec(Arrays.asList(basicSchema.getDataInterval()));
List<AggregatorFactory> queryAggs = new ArrayList<>();
queryAggs.add(new LongSumAggregatorFactory("sumLongSequential", "sumLongSequential"));
TopNQueryBuilder queryBuilderA = new TopNQueryBuilder().dataSource("blah").granularity(Granularities.ALL).dimension("dimUniform").metric(new DimensionTopNMetricSpec(null, StringComparators.ALPHANUMERIC)).intervals(intervalSpec).aggregators(queryAggs);
basicQueries.put("alphanumericSort", queryBuilderA);
}
SCHEMA_QUERY_MAP.put("basic", basicQueries);
}
use of io.druid.query.spec.QuerySegmentSpec in project druid by druid-io.
the class GroupByBenchmark method setupQueries.
private void setupQueries() {
// queries for the basic schema
Map<String, GroupByQuery> 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"));
GroupByQuery queryA = GroupByQuery.builder().setDataSource("blah").setQuerySegmentSpec(intervalSpec).setDimensions(Lists.<DimensionSpec>newArrayList(new DefaultDimensionSpec("dimSequential", null), new DefaultDimensionSpec("dimZipf", null))).setAggregatorSpecs(queryAggs).setGranularity(Granularity.fromString(queryGranularity)).build();
basicQueries.put("A", queryA);
}
{
// basic.nested
QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec(Arrays.asList(basicSchema.getDataInterval()));
List<AggregatorFactory> queryAggs = new ArrayList<>();
queryAggs.add(new LongSumAggregatorFactory("sumLongSequential", "sumLongSequential"));
GroupByQuery subqueryA = GroupByQuery.builder().setDataSource("blah").setQuerySegmentSpec(intervalSpec).setDimensions(Lists.<DimensionSpec>newArrayList(new DefaultDimensionSpec("dimSequential", null), new DefaultDimensionSpec("dimZipf", null))).setAggregatorSpecs(queryAggs).setGranularity(Granularities.DAY).build();
GroupByQuery queryA = GroupByQuery.builder().setDataSource(subqueryA).setQuerySegmentSpec(intervalSpec).setDimensions(Lists.<DimensionSpec>newArrayList(new DefaultDimensionSpec("dimSequential", null))).setAggregatorSpecs(queryAggs).setGranularity(Granularities.WEEK).build();
basicQueries.put("nested", queryA);
}
SCHEMA_QUERY_MAP.put("basic", basicQueries);
// simple one column schema, for testing performance difference between querying on numeric values as Strings and
// directly as longs
Map<String, GroupByQuery> simpleQueries = new LinkedHashMap<>();
BenchmarkSchemaInfo simpleSchema = BenchmarkSchemas.SCHEMA_MAP.get("simple");
{
// simple.A
QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec(Arrays.asList(simpleSchema.getDataInterval()));
List<AggregatorFactory> queryAggs = new ArrayList<>();
queryAggs.add(new LongSumAggregatorFactory("rows", "rows"));
GroupByQuery queryA = GroupByQuery.builder().setDataSource("blah").setQuerySegmentSpec(intervalSpec).setDimensions(Lists.<DimensionSpec>newArrayList(new DefaultDimensionSpec("dimSequential", "dimSequential", ValueType.STRING))).setAggregatorSpecs(queryAggs).setGranularity(Granularity.fromString(queryGranularity)).build();
simpleQueries.put("A", queryA);
}
SCHEMA_QUERY_MAP.put("simple", simpleQueries);
Map<String, GroupByQuery> simpleLongQueries = new LinkedHashMap<>();
BenchmarkSchemaInfo simpleLongSchema = BenchmarkSchemas.SCHEMA_MAP.get("simpleLong");
{
// simpleLong.A
QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec(Arrays.asList(simpleLongSchema.getDataInterval()));
List<AggregatorFactory> queryAggs = new ArrayList<>();
queryAggs.add(new LongSumAggregatorFactory("rows", "rows"));
GroupByQuery queryA = GroupByQuery.builder().setDataSource("blah").setQuerySegmentSpec(intervalSpec).setDimensions(Lists.<DimensionSpec>newArrayList(new DefaultDimensionSpec("dimSequential", "dimSequential", ValueType.LONG))).setAggregatorSpecs(queryAggs).setGranularity(Granularity.fromString(queryGranularity)).build();
simpleLongQueries.put("A", queryA);
}
SCHEMA_QUERY_MAP.put("simpleLong", simpleLongQueries);
Map<String, GroupByQuery> simpleFloatQueries = new LinkedHashMap<>();
BenchmarkSchemaInfo simpleFloatSchema = BenchmarkSchemas.SCHEMA_MAP.get("simpleFloat");
{
// simpleFloat.A
QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec(Arrays.asList(simpleFloatSchema.getDataInterval()));
List<AggregatorFactory> queryAggs = new ArrayList<>();
queryAggs.add(new LongSumAggregatorFactory("rows", "rows"));
GroupByQuery queryA = GroupByQuery.builder().setDataSource("blah").setQuerySegmentSpec(intervalSpec).setDimensions(Lists.<DimensionSpec>newArrayList(new DefaultDimensionSpec("dimSequential", "dimSequential", ValueType.FLOAT))).setAggregatorSpecs(queryAggs).setGranularity(Granularity.fromString(queryGranularity)).build();
simpleFloatQueries.put("A", queryA);
}
SCHEMA_QUERY_MAP.put("simpleFloat", simpleFloatQueries);
}
use of io.druid.query.spec.QuerySegmentSpec in project druid by druid-io.
the class SearchBenchmark method basicD.
private static SearchQueryBuilder basicD(final BenchmarkSchemaInfo basicSchema) {
final QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec(Arrays.asList(basicSchema.getDataInterval()));
final List<String> dimUniformFilterVals = Lists.newArrayList();
final int resultNum = (int) (100000 * 0.1);
final int step = 100000 / resultNum;
for (int i = 1; i < 100001 && dimUniformFilterVals.size() < resultNum; i += step) {
dimUniformFilterVals.add(String.valueOf(i));
}
final String dimName = "dimUniform";
final List<DimFilter> dimFilters = Lists.newArrayList();
dimFilters.add(new InDimFilter(dimName, dimUniformFilterVals, null));
dimFilters.add(new SelectorDimFilter(dimName, "3", null));
dimFilters.add(new BoundDimFilter(dimName, "100", "10000", true, true, true, null, null));
dimFilters.add(new InDimFilter(dimName, dimUniformFilterVals, null));
dimFilters.add(new InDimFilter(dimName, dimUniformFilterVals, null));
dimFilters.add(new InDimFilter(dimName, dimUniformFilterVals, null));
return Druids.newSearchQueryBuilder().dataSource("blah").granularity(Granularities.ALL).intervals(intervalSpec).query("").dimensions(Lists.newArrayList("dimUniform")).filters(new AndDimFilter(dimFilters));
}
use of io.druid.query.spec.QuerySegmentSpec 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();
}
use of io.druid.query.spec.QuerySegmentSpec in project druid by druid-io.
the class TopNTypeInterfaceBenchmark method setupQueries.
private void setupQueries() {
// queries for the basic schema
Map<String, TopNQueryBuilder> 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"));
// Use an IdentityExtractionFn to force usage of DimExtractionTopNAlgorithm
TopNQueryBuilder queryBuilderString = new TopNQueryBuilder().dataSource("blah").granularity(Granularities.ALL).dimension(new ExtractionDimensionSpec("dimSequential", "dimSequential", IdentityExtractionFn.getInstance())).metric("sumFloatNormal").intervals(intervalSpec).aggregators(queryAggs);
// DimExtractionTopNAlgorithm is always used for numeric columns
TopNQueryBuilder queryBuilderLong = new TopNQueryBuilder().dataSource("blah").granularity(Granularities.ALL).dimension("metLongUniform").metric("sumFloatNormal").intervals(intervalSpec).aggregators(queryAggs);
TopNQueryBuilder queryBuilderFloat = new TopNQueryBuilder().dataSource("blah").granularity(Granularities.ALL).dimension("metFloatNormal").metric("sumFloatNormal").intervals(intervalSpec).aggregators(queryAggs);
basicQueries.put("string", queryBuilderString);
basicQueries.put("long", queryBuilderLong);
basicQueries.put("float", queryBuilderFloat);
}
{
// basic.numericSort
QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec(Arrays.asList(basicSchema.getDataInterval()));
List<AggregatorFactory> queryAggs = new ArrayList<>();
queryAggs.add(new LongSumAggregatorFactory("sumLongSequential", "sumLongSequential"));
TopNQueryBuilder queryBuilderA = new TopNQueryBuilder().dataSource("blah").granularity(Granularities.ALL).dimension("dimUniform").metric(new DimensionTopNMetricSpec(null, StringComparators.NUMERIC)).intervals(intervalSpec).aggregators(queryAggs);
basicQueries.put("numericSort", queryBuilderA);
}
{
// basic.alphanumericSort
QuerySegmentSpec intervalSpec = new MultipleIntervalSegmentSpec(Arrays.asList(basicSchema.getDataInterval()));
List<AggregatorFactory> queryAggs = new ArrayList<>();
queryAggs.add(new LongSumAggregatorFactory("sumLongSequential", "sumLongSequential"));
TopNQueryBuilder queryBuilderA = new TopNQueryBuilder().dataSource("blah").granularity(Granularities.ALL).dimension("dimUniform").metric(new DimensionTopNMetricSpec(null, StringComparators.ALPHANUMERIC)).intervals(intervalSpec).aggregators(queryAggs);
basicQueries.put("alphanumericSort", queryBuilderA);
}
SCHEMA_QUERY_MAP.put("basic", basicQueries);
}
Aggregations