Examples with FloatDimensionSchema org.apache.druid.data.input.impl.FloatDimensionSchema used on opensource projects

Example 1 with FloatDimensionSchema

use of org.apache.druid.data.input.impl.FloatDimensionSchema in project druid by druid-io.

the class IndexMergerTestBase method getIndexWithNumericDims.

private IncrementalIndex getIndexWithNumericDims() throws Exception {
    IncrementalIndex index = getIndexWithDimsFromSchemata(Arrays.asList(new LongDimensionSchema("dimA"), new FloatDimensionSchema("dimB"), new StringDimensionSchema("dimC", MultiValueHandling.SORTED_ARRAY, useBitmapIndexes)));
    index.add(new MapBasedInputRow(1, Arrays.asList("dimA", "dimB", "dimC"), ImmutableMap.of("dimA", 100L, "dimB", 4000.567, "dimC", "Hello")));
    index.add(new MapBasedInputRow(1, Arrays.asList("dimA", "dimB", "dimC"), ImmutableMap.of("dimA", 72L, "dimB", 60000.789, "dimC", "World")));
    index.add(new MapBasedInputRow(1, Arrays.asList("dimA", "dimB", "dimC"), ImmutableMap.of("dimA", 3001L, "dimB", 1.2345, "dimC", "Foobar")));
    index.add(new MapBasedInputRow(1, Arrays.asList("dimA", "dimB", "dimC"), ImmutableMap.of("dimC", "Nully Row")));
    return index;
}

Example 2 with FloatDimensionSchema

use of org.apache.druid.data.input.impl.FloatDimensionSchema in project druid by druid-io.

the class QueryableIndexColumnCapabilitiesTest method setup.

@BeforeClass
public static void setup() throws IOException {
    MapInputRowParser parser = new MapInputRowParser(new TimeAndDimsParseSpec(new TimestampSpec("time", "auto", null), new DimensionsSpec(ImmutableList.<DimensionSchema>builder().addAll(DimensionsSpec.getDefaultSchemas(ImmutableList.of("d1", "d2"))).add(new DoubleDimensionSchema("d3")).add(new FloatDimensionSchema("d4")).add(new LongDimensionSchema("d5")).build())));
    AggregatorFactory[] metricsSpecs = new AggregatorFactory[] { new CountAggregatorFactory("cnt"), new DoubleSumAggregatorFactory("m1", "d3"), new FloatSumAggregatorFactory("m2", "d4"), new LongSumAggregatorFactory("m3", "d5"), new HyperUniquesAggregatorFactory("m4", "d1") };
    List<InputRow> rows = new ArrayList<>();
    Map<String, Object> event = ImmutableMap.<String, Object>builder().put("time", DateTimes.nowUtc().getMillis()).put("d1", "some string").put("d2", ImmutableList.of("some", "list")).put("d3", 1.234).put("d4", 1.234f).put("d5", 10L).build();
    rows.add(Iterables.getOnlyElement(parser.parseBatch(event)));
    IndexBuilder builder = IndexBuilder.create().rows(rows).schema(new IncrementalIndexSchema.Builder().withMetrics(metricsSpecs).withDimensionsSpec(parser).withRollup(false).build()).tmpDir(temporaryFolder.newFolder());
    INC_INDEX = builder.buildIncrementalIndex();
    MMAP_INDEX = builder.buildMMappedIndex();
    List<InputRow> rowsWithNulls = new ArrayList<>();
    rowsWithNulls.add(Iterables.getOnlyElement(parser.parseBatch(event)));
    Map<String, Object> eventWithNulls = new HashMap<>();
    eventWithNulls.put("time", DateTimes.nowUtc().getMillis());
    eventWithNulls.put("d1", null);
    eventWithNulls.put("d2", ImmutableList.of());
    eventWithNulls.put("d3", null);
    eventWithNulls.put("d4", null);
    eventWithNulls.put("d5", null);
    rowsWithNulls.add(Iterables.getOnlyElement(parser.parseBatch(eventWithNulls)));
    IndexBuilder builderWithNulls = IndexBuilder.create().rows(rowsWithNulls).schema(new IncrementalIndexSchema.Builder().withMetrics(metricsSpecs).withDimensionsSpec(parser).withRollup(false).build()).tmpDir(temporaryFolder.newFolder());
    INC_INDEX_WITH_NULLS = builderWithNulls.buildIncrementalIndex();
    MMAP_INDEX_WITH_NULLS = builderWithNulls.buildMMappedIndex();
}

Example 3 with FloatDimensionSchema

use of org.apache.druid.data.input.impl.FloatDimensionSchema in project druid by druid-io.

the class InputRowSerdeTest method testDimensionNullOrDefaultForNumerics.

@Test
public void testDimensionNullOrDefaultForNumerics() {
    HashMap<String, Object> eventWithNulls = new HashMap<>();
    eventWithNulls.put("d1", null);
    eventWithNulls.put("d2", Arrays.asList("d2v1", "d2v2"));
    eventWithNulls.put("d3", null);
    eventWithNulls.put("d4", null);
    eventWithNulls.put("d5", null);
    InputRow in = new MapBasedInputRow(timestamp, dims, eventWithNulls);
    DimensionsSpec dimensionsSpec = new DimensionsSpec(Arrays.asList(new StringDimensionSchema("d1"), new StringDimensionSchema("d2"), new LongDimensionSchema("d3"), new FloatDimensionSchema("d4"), new DoubleDimensionSchema("d5")));
    byte[] result = InputRowSerde.toBytes(InputRowSerde.getTypeHelperMap(dimensionsSpec), in, new AggregatorFactory[0]).getSerializedRow();
    if (NullHandling.replaceWithDefault()) {
        long expected = 0;
        // timestamp bytes + dims length
        expected += 9;
        // dim_non_existing writes: 1 16 1 bytes
        expected += 18;
        // d1: writes 1 2 1 bytes
        expected += 4;
        // d2: writes 1 2 1 1 4 1 4 bytes
        expected += 14;
        // d3: writes 1 2 8 bytes
        expected += 11;
        // d4: writes 1 2 4 bytes
        expected += 7;
        // d5: writes 1 2 8 bytes
        expected += 11;
        // writes aggregator length
        expected += 1;
        Assert.assertEquals(expected, result.length);
        Assert.assertArrayEquals(new byte[] { 0, 0, 0, 0, 0, 0, 0, 0 }, Arrays.copyOfRange(result, 48, 56));
        Assert.assertArrayEquals(new byte[] { 0, 0, 0, 0 }, Arrays.copyOfRange(result, 59, 63));
        Assert.assertArrayEquals(new byte[] { 0, 0, 0, 0, 0, 0, 0, 0 }, Arrays.copyOfRange(result, 66, 74));
    } else {
        long expected = 9 + 18 + 4 + 14 + 4 + 4 + 4 + 1;
        Assert.assertEquals(expected, result.length);
        Assert.assertEquals(result[48], NullHandling.IS_NULL_BYTE);
        Assert.assertEquals(result[52], NullHandling.IS_NULL_BYTE);
        Assert.assertEquals(result[56], NullHandling.IS_NULL_BYTE);
    }
}

Example 4 with FloatDimensionSchema

use of org.apache.druid.data.input.impl.FloatDimensionSchema in project druid by druid-io.

the class InputRowSerdeTest method testDimensionParseExceptions.

@Test
public void testDimensionParseExceptions() {
    InputRowSerde.SerializeResult result;
    InputRow in = new MapBasedInputRow(timestamp, dims, event);
    AggregatorFactory[] aggregatorFactories = new AggregatorFactory[] { new LongSumAggregatorFactory("m2out", "m2") };
    DimensionsSpec dimensionsSpec = new DimensionsSpec(Collections.singletonList(new LongDimensionSchema("d1")));
    result = InputRowSerde.toBytes(InputRowSerde.getTypeHelperMap(dimensionsSpec), in, aggregatorFactories);
    Assert.assertEquals(Collections.singletonList("could not convert value [d1v] to long"), result.getParseExceptionMessages());
    dimensionsSpec = new DimensionsSpec(Collections.singletonList(new FloatDimensionSchema("d1")));
    result = InputRowSerde.toBytes(InputRowSerde.getTypeHelperMap(dimensionsSpec), in, aggregatorFactories);
    Assert.assertEquals(Collections.singletonList("could not convert value [d1v] to float"), result.getParseExceptionMessages());
    dimensionsSpec = new DimensionsSpec(Collections.singletonList(new DoubleDimensionSchema("d1")));
    result = InputRowSerde.toBytes(InputRowSerde.getTypeHelperMap(dimensionsSpec), in, aggregatorFactories);
    Assert.assertEquals(Collections.singletonList("could not convert value [d1v] to double"), result.getParseExceptionMessages());
}

Example 5 with FloatDimensionSchema

use of org.apache.druid.data.input.impl.FloatDimensionSchema in project druid by druid-io.

the class InputRowSerdeTest method testThrowParseExceptions.

@Test
public void testThrowParseExceptions() {
    InputRow in = new MapBasedInputRow(timestamp, dims, event);
    AggregatorFactory[] aggregatorFactories = new AggregatorFactory[] { new DoubleSumAggregatorFactory("agg_non_existing", "agg_non_existing_in"), new DoubleSumAggregatorFactory("m1out", "m1"), new LongSumAggregatorFactory("m2out", "m2"), new HyperUniquesAggregatorFactory("m3out", "m3"), // Unparseable from String to Long
    new LongSumAggregatorFactory("unparseable", "m3") };
    DimensionsSpec dimensionsSpec = new DimensionsSpec(Arrays.asList(new StringDimensionSchema("d1"), new StringDimensionSchema("d2"), new LongDimensionSchema("d3"), new FloatDimensionSchema("d4"), new DoubleDimensionSchema("d5")));
    InputRowSerde.SerializeResult result = InputRowSerde.toBytes(InputRowSerde.getTypeHelperMap(dimensionsSpec), in, aggregatorFactories);
    Assert.assertEquals(Collections.singletonList("Unable to parse value[m3v] for field[m3]"), result.getParseExceptionMessages());
}