use of org.apache.druid.client.indexing.SamplerResponse in project druid by druid-io.
the class InputSourceSamplerTest method testWithTransformsDimensionsSpec.
@Test
public void testWithTransformsDimensionsSpec() throws IOException {
final TimestampSpec timestampSpec = new TimestampSpec("t", null, null);
final DimensionsSpec dimensionsSpec = new DimensionsSpec(ImmutableList.of(StringDimensionSchema.create("dim1PlusBar")));
final TransformSpec transformSpec = new TransformSpec(null, ImmutableList.of(new ExpressionTransform("dim1PlusBar", "concat(dim1 + 'bar')", TestExprMacroTable.INSTANCE)));
final AggregatorFactory[] aggregatorFactories = { new LongSumAggregatorFactory("met1", "met1") };
final GranularitySpec granularitySpec = new UniformGranularitySpec(Granularities.DAY, Granularities.HOUR, true, null);
final DataSchema dataSchema = createDataSchema(timestampSpec, dimensionsSpec, aggregatorFactories, granularitySpec, transformSpec);
final InputSource inputSource = createInputSource(getTestRows(), dataSchema);
final InputFormat inputFormat = createInputFormat();
SamplerResponse response = inputSourceSampler.sample(inputSource, inputFormat, dataSchema, null);
Assert.assertEquals(6, response.getNumRowsRead());
Assert.assertEquals(5, response.getNumRowsIndexed());
Assert.assertEquals(3, response.getData().size());
List<SamplerResponseRow> data = response.getData();
assertEqualsSamplerResponseRow(new SamplerResponseRow(getRawColumns().get(0), new SamplerTestUtils.MapAllowingNullValuesBuilder<String, Object>().put("__time", 1555934400000L).put("dim1PlusBar", "foobar").put("met1", 11L).build(), null, null), data.get(0));
assertEqualsSamplerResponseRow(new SamplerResponseRow(getRawColumns().get(3), new SamplerTestUtils.MapAllowingNullValuesBuilder<String, Object>().put("__time", 1555934400000L).put("dim1PlusBar", "foo2bar").put("met1", 4L).build(), null, null), data.get(1));
assertEqualsSamplerResponseRow(new SamplerResponseRow(getRawColumns().get(5), null, true, getUnparseableTimestampString()), data.get(2));
}
use of org.apache.druid.client.indexing.SamplerResponse in project druid by druid-io.
the class InputSourceSamplerTest method testIndexParseException.
@Test
public void testIndexParseException() throws IOException {
final TimestampSpec timestampSpec = new TimestampSpec("t", null, null);
final DimensionsSpec dimensionsSpec = new DimensionsSpec(ImmutableList.of(StringDimensionSchema.create("dim1PlusBar")));
final TransformSpec transformSpec = new TransformSpec(null, ImmutableList.of(new ExpressionTransform("dim1PlusBar", "concat(dim1 + 'bar')", TestExprMacroTable.INSTANCE)));
final AggregatorFactory[] aggregatorFactories = { new LongSumAggregatorFactory("met1", "met1") };
final GranularitySpec granularitySpec = new UniformGranularitySpec(Granularities.DAY, Granularities.HOUR, true, null);
final DataSchema dataSchema = createDataSchema(timestampSpec, dimensionsSpec, aggregatorFactories, granularitySpec, transformSpec);
//
// add a invalid row to cause parse exception when indexing
//
Map<String, Object> rawColumns4ParseExceptionRow = ImmutableMap.of("t", "2019-04-22T12:00", "dim1", "foo2", "met1", "invalidNumber");
final List<String> inputTestRows = Lists.newArrayList(getTestRows());
inputTestRows.add(ParserType.STR_CSV.equals(parserType) ? "2019-04-22T12:00,foo2,,invalidNumber" : OBJECT_MAPPER.writeValueAsString(rawColumns4ParseExceptionRow));
final InputSource inputSource = createInputSource(inputTestRows, dataSchema);
final InputFormat inputFormat = createInputFormat();
SamplerResponse response = inputSourceSampler.sample(inputSource, inputFormat, dataSchema, null);
Assert.assertEquals(7, response.getNumRowsRead());
Assert.assertEquals(5, response.getNumRowsIndexed());
Assert.assertEquals(4, response.getData().size());
List<SamplerResponseRow> data = response.getData();
assertEqualsSamplerResponseRow(new SamplerResponseRow(getRawColumns().get(0), new SamplerTestUtils.MapAllowingNullValuesBuilder<String, Object>().put("__time", 1555934400000L).put("dim1PlusBar", "foobar").put("met1", 11L).build(), null, null), data.get(0));
assertEqualsSamplerResponseRow(new SamplerResponseRow(getRawColumns().get(3), new SamplerTestUtils.MapAllowingNullValuesBuilder<String, Object>().put("__time", 1555934400000L).put("dim1PlusBar", "foo2bar").put("met1", 4L).build(), null, null), data.get(1));
assertEqualsSamplerResponseRow(new SamplerResponseRow(getRawColumns().get(5), null, true, getUnparseableTimestampString()), data.get(2));
//
// the last row has parse exception when indexing, check if rawColumns and exception message match the expected
//
String indexParseExceptioMessage = ParserType.STR_CSV.equals(parserType) ? "Found unparseable columns in row: [SamplerInputRow{row=TransformedInputRow{row=MapBasedInputRow{timestamp=2019-04-22T12:00:00.000Z, event={t=2019-04-22T12:00, dim1=foo2, dim2=null, met1=invalidNumber}, dimensions=[dim1PlusBar]}}}], exceptions: [Unable to parse value[invalidNumber] for field[met1]]" : "Found unparseable columns in row: [SamplerInputRow{row=TransformedInputRow{row=MapBasedInputRow{timestamp=2019-04-22T12:00:00.000Z, event={t=2019-04-22T12:00, dim1=foo2, met1=invalidNumber}, dimensions=[dim1PlusBar]}}}], exceptions: [Unable to parse value[invalidNumber] for field[met1]]";
assertEqualsSamplerResponseRow(new SamplerResponseRow(rawColumns4ParseExceptionRow, null, true, indexParseExceptioMessage), data.get(3));
}
use of org.apache.druid.client.indexing.SamplerResponse in project druid by druid-io.
the class InputSourceSampler method sample.
public SamplerResponse sample(final InputSource inputSource, // inputFormat can be null only if inputSource.needsFormat() = false or parser is specified.
@Nullable final InputFormat inputFormat, @Nullable final DataSchema dataSchema, @Nullable final SamplerConfig samplerConfig) {
Preconditions.checkNotNull(inputSource, "inputSource required");
if (inputSource.needsFormat()) {
Preconditions.checkNotNull(inputFormat, "inputFormat required");
}
final DataSchema nonNullDataSchema = dataSchema == null ? DEFAULT_DATA_SCHEMA : dataSchema;
final SamplerConfig nonNullSamplerConfig = samplerConfig == null ? SamplerConfig.empty() : samplerConfig;
final Closer closer = Closer.create();
final File tempDir = FileUtils.createTempDir();
closer.register(() -> FileUtils.deleteDirectory(tempDir));
try {
final InputSourceReader reader = buildReader(nonNullSamplerConfig, nonNullDataSchema, inputSource, inputFormat, tempDir);
try (final CloseableIterator<InputRowListPlusRawValues> iterator = reader.sample();
final IncrementalIndex index = buildIncrementalIndex(nonNullSamplerConfig, nonNullDataSchema);
final Closer closer1 = closer) {
List<SamplerResponseRow> responseRows = new ArrayList<>(nonNullSamplerConfig.getNumRows());
int numRowsIndexed = 0;
while (responseRows.size() < nonNullSamplerConfig.getNumRows() && iterator.hasNext()) {
final InputRowListPlusRawValues inputRowListPlusRawValues = iterator.next();
final List<Map<String, Object>> rawColumnsList = inputRowListPlusRawValues.getRawValuesList();
final ParseException parseException = inputRowListPlusRawValues.getParseException();
if (parseException != null) {
if (rawColumnsList != null) {
// add all rows to response
responseRows.addAll(rawColumnsList.stream().map(rawColumns -> new SamplerResponseRow(rawColumns, null, true, parseException.getMessage())).collect(Collectors.toList()));
} else {
// no data parsed, add one response row
responseRows.add(new SamplerResponseRow(null, null, true, parseException.getMessage()));
}
continue;
}
List<InputRow> inputRows = inputRowListPlusRawValues.getInputRows();
if (inputRows == null) {
continue;
}
for (int i = 0; i < inputRows.size(); i++) {
// InputRowListPlusRawValues guarantees the size of rawColumnsList and inputRows are the same
Map<String, Object> rawColumns = rawColumnsList == null ? null : rawColumnsList.get(i);
InputRow row = inputRows.get(i);
// keep the index of the row to be added to responseRows for further use
final int rowIndex = responseRows.size();
IncrementalIndexAddResult addResult = index.add(new SamplerInputRow(row, rowIndex), true);
if (addResult.hasParseException()) {
responseRows.add(new SamplerResponseRow(rawColumns, null, true, addResult.getParseException().getMessage()));
} else {
// store the raw value; will be merged with the data from the IncrementalIndex later
responseRows.add(new SamplerResponseRow(rawColumns, null, null, null));
numRowsIndexed++;
}
}
}
final List<String> columnNames = index.getColumnNames();
columnNames.remove(SamplerInputRow.SAMPLER_ORDERING_COLUMN);
for (Row row : index) {
Map<String, Object> parsed = new LinkedHashMap<>();
parsed.put(ColumnHolder.TIME_COLUMN_NAME, row.getTimestampFromEpoch());
columnNames.forEach(k -> parsed.put(k, row.getRaw(k)));
Number sortKey = row.getMetric(SamplerInputRow.SAMPLER_ORDERING_COLUMN);
if (sortKey != null) {
responseRows.set(sortKey.intValue(), responseRows.get(sortKey.intValue()).withParsed(parsed));
}
}
// make sure size of responseRows meets the input
if (responseRows.size() > nonNullSamplerConfig.getNumRows()) {
responseRows = responseRows.subList(0, nonNullSamplerConfig.getNumRows());
}
int numRowsRead = responseRows.size();
return new SamplerResponse(numRowsRead, numRowsIndexed, responseRows.stream().filter(Objects::nonNull).filter(x -> x.getParsed() != null || x.isUnparseable() != null).collect(Collectors.toList()));
}
} catch (Exception e) {
throw new SamplerException(e, "Failed to sample data: %s", e.getMessage());
}
}
use of org.apache.druid.client.indexing.SamplerResponse in project druid by druid-io.
the class SamplerResponseTest method testSerde.
@Test
public void testSerde() throws IOException {
List<SamplerResponse.SamplerResponseRow> data = ImmutableList.of(new SamplerResponse.SamplerResponseRow(ImmutableMap.of("row1", "val1"), ImmutableMap.of("t", 123456, "dim1", "foo", "met1", 6), null, null), new SamplerResponse.SamplerResponseRow(ImmutableMap.of("row2", "val2"), ImmutableMap.of("t", 123457, "dim1", "foo2", "met1", 7), null, null), new SamplerResponse.SamplerResponseRow(ImmutableMap.of("row3", "val3"), null, true, "Could not parse"));
String out = MAPPER.writeValueAsString(new SamplerResponse(1123, 1112, data));
String expected = "{\"numRowsRead\":1123,\"numRowsIndexed\":1112,\"data\":[{\"input\":{\"row1\":\"val1\"},\"parsed\":{\"t\":123456,\"dim1\":\"foo\",\"met1\":6}},{\"input\":{\"row2\":\"val2\"},\"parsed\":{\"t\":123457,\"dim1\":\"foo2\",\"met1\":7}},{\"input\":{\"row3\":\"val3\"},\"unparseable\":true,\"error\":\"Could not parse\"}]}";
Assert.assertEquals(expected, out);
}
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