use of org.apache.druid.timeline.CompactionState in project druid by druid-io.
the class NewestSegmentFirstPolicyTest method testIteratorReturnsSegmentsAsSegmentsWasCompactedAndHaveDifferentRollup.
@Test
public void testIteratorReturnsSegmentsAsSegmentsWasCompactedAndHaveDifferentRollup() {
// Same indexSpec as what is set in the auto compaction config
Map<String, Object> indexSpec = mapper.convertValue(new IndexSpec(), new TypeReference<Map<String, Object>>() {
});
// Same partitionsSpec as what is set in the auto compaction config
PartitionsSpec partitionsSpec = NewestSegmentFirstIterator.findPartitionsSpecFromConfig(ClientCompactionTaskQueryTuningConfig.from(null, null));
// Create segments that were compacted (CompactionState != null) and have
// rollup=false for interval 2017-10-01T00:00:00/2017-10-02T00:00:00,
// rollup=true for interval 2017-10-02T00:00:00/2017-10-03T00:00:00,
// and rollup=null for interval 2017-10-03T00:00:00/2017-10-04T00:00:00 (queryGranularity was not set during last compaction)
final VersionedIntervalTimeline<String, DataSegment> timeline = createTimeline(new SegmentGenerateSpec(Intervals.of("2017-10-01T00:00:00/2017-10-02T00:00:00"), new Period("P1D"), null, new CompactionState(partitionsSpec, null, null, null, indexSpec, ImmutableMap.of("rollup", "false"))), new SegmentGenerateSpec(Intervals.of("2017-10-02T00:00:00/2017-10-03T00:00:00"), new Period("P1D"), null, new CompactionState(partitionsSpec, null, null, null, indexSpec, ImmutableMap.of("rollup", "true"))), new SegmentGenerateSpec(Intervals.of("2017-10-03T00:00:00/2017-10-04T00:00:00"), new Period("P1D"), null, new CompactionState(partitionsSpec, null, null, null, indexSpec, ImmutableMap.of())));
// Auto compaction config sets rollup=true
final CompactionSegmentIterator iterator = policy.reset(ImmutableMap.of(DATA_SOURCE, createCompactionConfig(130000, new Period("P0D"), new UserCompactionTaskGranularityConfig(null, null, true))), ImmutableMap.of(DATA_SOURCE, timeline), Collections.emptyMap());
// We should get interval 2017-10-01T00:00:00/2017-10-02T00:00:00 and interval 2017-10-03T00:00:00/2017-10-04T00:00:00.
Assert.assertTrue(iterator.hasNext());
List<DataSegment> expectedSegmentsToCompact = new ArrayList<>(timeline.findNonOvershadowedObjectsInInterval(Intervals.of("2017-10-03T00:00:00/2017-10-04T00:00:00"), Partitions.ONLY_COMPLETE));
Assert.assertEquals(ImmutableSet.copyOf(expectedSegmentsToCompact), ImmutableSet.copyOf(iterator.next()));
Assert.assertTrue(iterator.hasNext());
expectedSegmentsToCompact = new ArrayList<>(timeline.findNonOvershadowedObjectsInInterval(Intervals.of("2017-10-01T00:00:00/2017-10-02T00:00:00"), Partitions.ONLY_COMPLETE));
Assert.assertEquals(ImmutableSet.copyOf(expectedSegmentsToCompact), ImmutableSet.copyOf(iterator.next()));
// No more
Assert.assertFalse(iterator.hasNext());
}
use of org.apache.druid.timeline.CompactionState in project druid by druid-io.
the class CompactionTaskParallelRunTest method testRunParallelWithHashPartitioningMatchCompactionState.
@Test
public void testRunParallelWithHashPartitioningMatchCompactionState() throws Exception {
// Hash partitioning is not supported with segment lock yet
Assume.assumeFalse(lockGranularity == LockGranularity.SEGMENT);
runIndexTask(null, true);
final Builder builder = new Builder(DATA_SOURCE, getSegmentCacheManagerFactory(), RETRY_POLICY_FACTORY);
final CompactionTask compactionTask = builder.inputSpec(new CompactionIntervalSpec(INTERVAL_TO_INDEX, null)).tuningConfig(newTuningConfig(new HashedPartitionsSpec(null, 3, null), 2, true)).build();
final Set<DataSegment> compactedSegments = runTask(compactionTask);
for (DataSegment segment : compactedSegments) {
// Expect compaction state to exist as store compaction state by default
Map<String, String> expectedLongSumMetric = new HashMap<>();
expectedLongSumMetric.put("type", "longSum");
expectedLongSumMetric.put("name", "val");
expectedLongSumMetric.put("fieldName", "val");
expectedLongSumMetric.put("expression", null);
Assert.assertSame(HashBasedNumberedShardSpec.class, segment.getShardSpec().getClass());
CompactionState expectedState = new CompactionState(new HashedPartitionsSpec(null, 3, null), new DimensionsSpec(DimensionsSpec.getDefaultSchemas(ImmutableList.of("ts", "dim"))), ImmutableList.of(expectedLongSumMetric), null, compactionTask.getTuningConfig().getIndexSpec().asMap(getObjectMapper()), getObjectMapper().readValue(getObjectMapper().writeValueAsString(new UniformGranularitySpec(Granularities.HOUR, Granularities.MINUTE, true, ImmutableList.of(segment.getInterval()))), Map.class));
Assert.assertEquals(expectedState, segment.getLastCompactionState());
}
}
use of org.apache.druid.timeline.CompactionState in project druid by druid-io.
the class CompactionTaskParallelRunTest method testRunParallelWithMultiDimensionRangePartitioningWithSingleTask.
@Test
public void testRunParallelWithMultiDimensionRangePartitioningWithSingleTask() throws Exception {
// Range partitioning is not supported with segment lock yet
Assume.assumeFalse(lockGranularity == LockGranularity.SEGMENT);
runIndexTask(null, true);
final Builder builder = new Builder(DATA_SOURCE, getSegmentCacheManagerFactory(), RETRY_POLICY_FACTORY);
final CompactionTask compactionTask = builder.inputSpec(new CompactionIntervalSpec(INTERVAL_TO_INDEX, null)).tuningConfig(newTuningConfig(new DimensionRangePartitionsSpec(7, null, Arrays.asList("dim1", "dim2"), false), 1, true)).build();
final Set<DataSegment> compactedSegments = runTask(compactionTask);
for (DataSegment segment : compactedSegments) {
// Expect compaction state to exist as store compaction state by default
Map<String, String> expectedLongSumMetric = new HashMap<>();
expectedLongSumMetric.put("type", "longSum");
expectedLongSumMetric.put("name", "val");
expectedLongSumMetric.put("fieldName", "val");
expectedLongSumMetric.put("expression", null);
Assert.assertSame(DimensionRangeShardSpec.class, segment.getShardSpec().getClass());
CompactionState expectedState = new CompactionState(new DimensionRangePartitionsSpec(7, null, Arrays.asList("dim1", "dim2"), false), new DimensionsSpec(DimensionsSpec.getDefaultSchemas(ImmutableList.of("ts", "dim"))), ImmutableList.of(expectedLongSumMetric), null, compactionTask.getTuningConfig().getIndexSpec().asMap(getObjectMapper()), getObjectMapper().readValue(getObjectMapper().writeValueAsString(new UniformGranularitySpec(Granularities.HOUR, Granularities.MINUTE, true, ImmutableList.of(segment.getInterval()))), Map.class));
Assert.assertEquals(expectedState, segment.getLastCompactionState());
}
}
use of org.apache.druid.timeline.CompactionState in project druid by druid-io.
the class CompactionTaskParallelRunTest method testRunParallelWithRangePartitioningWithSingleTask.
@Test
public void testRunParallelWithRangePartitioningWithSingleTask() throws Exception {
// Range partitioning is not supported with segment lock yet
Assume.assumeFalse(lockGranularity == LockGranularity.SEGMENT);
runIndexTask(null, true);
final Builder builder = new Builder(DATA_SOURCE, getSegmentCacheManagerFactory(), RETRY_POLICY_FACTORY);
final CompactionTask compactionTask = builder.inputSpec(new CompactionIntervalSpec(INTERVAL_TO_INDEX, null)).tuningConfig(newTuningConfig(new SingleDimensionPartitionsSpec(7, null, "dim", false), 1, true)).build();
final Set<DataSegment> compactedSegments = runTask(compactionTask);
for (DataSegment segment : compactedSegments) {
// Expect compaction state to exist as store compaction state by default
Map<String, String> expectedLongSumMetric = new HashMap<>();
expectedLongSumMetric.put("type", "longSum");
expectedLongSumMetric.put("name", "val");
expectedLongSumMetric.put("fieldName", "val");
expectedLongSumMetric.put("expression", null);
Assert.assertSame(SingleDimensionShardSpec.class, segment.getShardSpec().getClass());
CompactionState expectedState = new CompactionState(new SingleDimensionPartitionsSpec(7, null, "dim", false), new DimensionsSpec(DimensionsSpec.getDefaultSchemas(ImmutableList.of("ts", "dim"))), ImmutableList.of(expectedLongSumMetric), null, compactionTask.getTuningConfig().getIndexSpec().asMap(getObjectMapper()), getObjectMapper().readValue(getObjectMapper().writeValueAsString(new UniformGranularitySpec(Granularities.HOUR, Granularities.MINUTE, true, ImmutableList.of(segment.getInterval()))), Map.class));
Assert.assertEquals(expectedState, segment.getLastCompactionState());
}
}
use of org.apache.druid.timeline.CompactionState in project druid by druid-io.
the class CompactionTaskParallelRunTest method testRunParallelWithDynamicPartitioningMatchCompactionState.
@Test
public void testRunParallelWithDynamicPartitioningMatchCompactionState() throws Exception {
runIndexTask(null, true);
final Builder builder = new Builder(DATA_SOURCE, getSegmentCacheManagerFactory(), RETRY_POLICY_FACTORY);
final CompactionTask compactionTask = builder.inputSpec(new CompactionIntervalSpec(INTERVAL_TO_INDEX, null)).tuningConfig(AbstractParallelIndexSupervisorTaskTest.DEFAULT_TUNING_CONFIG_FOR_PARALLEL_INDEXING).build();
final Set<DataSegment> compactedSegments = runTask(compactionTask);
for (DataSegment segment : compactedSegments) {
Assert.assertSame(lockGranularity == LockGranularity.TIME_CHUNK ? NumberedShardSpec.class : NumberedOverwriteShardSpec.class, segment.getShardSpec().getClass());
// Expect compaction state to exist as store compaction state by default
Map<String, String> expectedLongSumMetric = new HashMap<>();
expectedLongSumMetric.put("type", "longSum");
expectedLongSumMetric.put("name", "val");
expectedLongSumMetric.put("fieldName", "val");
expectedLongSumMetric.put("expression", null);
CompactionState expectedState = new CompactionState(new DynamicPartitionsSpec(null, Long.MAX_VALUE), new DimensionsSpec(DimensionsSpec.getDefaultSchemas(ImmutableList.of("ts", "dim"))), ImmutableList.of(expectedLongSumMetric), null, compactionTask.getTuningConfig().getIndexSpec().asMap(getObjectMapper()), getObjectMapper().readValue(getObjectMapper().writeValueAsString(new UniformGranularitySpec(Granularities.HOUR, Granularities.MINUTE, true, ImmutableList.of(segment.getInterval()))), Map.class));
Assert.assertEquals(expectedState, segment.getLastCompactionState());
}
}
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