ccaominh commented on a change in pull request #8925: Parallel indexing single dim partitions URL: https://github.com/apache/incubator-druid/pull/8925#discussion_r352982361
########## File path: indexing-service/src/main/java/org/apache/druid/indexing/common/task/batch/parallel/PartialDimensionDistributionTask.java ########## @@ -0,0 +1,448 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.druid.indexing.common.task.batch.parallel; + +import com.fasterxml.jackson.annotation.JacksonInject; +import com.fasterxml.jackson.annotation.JsonCreator; +import com.fasterxml.jackson.annotation.JsonProperty; +import com.google.common.annotations.VisibleForTesting; +import com.google.common.base.Preconditions; +import com.google.common.hash.BloomFilter; +import org.apache.druid.client.indexing.IndexingServiceClient; +import org.apache.druid.data.input.HandlingInputRowIterator; +import org.apache.druid.data.input.InputFormat; +import org.apache.druid.data.input.InputRow; +import org.apache.druid.data.input.InputRowSchema; +import org.apache.druid.data.input.InputSource; +import org.apache.druid.data.input.InputSourceReader; +import org.apache.druid.indexer.TaskStatus; +import org.apache.druid.indexer.partitions.SingleDimensionPartitionsSpec; +import org.apache.druid.indexing.common.TaskToolbox; +import org.apache.druid.indexing.common.actions.TaskActionClient; +import org.apache.druid.indexing.common.task.ClientBasedTaskInfoProvider; +import org.apache.druid.indexing.common.task.IndexTaskClientFactory; +import org.apache.druid.indexing.common.task.TaskResource; +import org.apache.druid.indexing.common.task.batch.parallel.distribution.StringDistribution; +import org.apache.druid.indexing.common.task.batch.parallel.distribution.StringSketch; +import org.apache.druid.indexing.common.task.batch.parallel.distribution.TimeDimTuple; +import org.apache.druid.indexing.common.task.batch.parallel.distribution.TimeDimTupleFactory; +import org.apache.druid.indexing.common.task.batch.parallel.distribution.TimeDimTupleFunnel; +import org.apache.druid.indexing.common.task.batch.parallel.iterator.IndexTaskInputRowIteratorBuilder; +import org.apache.druid.indexing.common.task.batch.parallel.iterator.RangePartitionIndexTaskInputRowIteratorBuilder; +import org.apache.druid.java.util.common.granularity.Granularity; +import org.apache.druid.java.util.common.logger.Logger; +import org.apache.druid.java.util.common.parsers.CloseableIterator; +import org.apache.druid.java.util.common.parsers.ParseException; +import org.apache.druid.query.aggregation.AggregatorFactory; +import org.apache.druid.segment.indexing.DataSchema; +import org.apache.druid.segment.indexing.granularity.GranularitySpec; +import org.joda.time.DateTime; +import org.joda.time.Interval; + +import javax.annotation.Nullable; +import java.util.Arrays; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.function.Supplier; +import java.util.stream.Collectors; + +/** + * The worker task of {@link PartialDimensionDistributionParallelIndexTaskRunner}. This task + * determines the distribution of dimension values of input data. + */ + +public class PartialDimensionDistributionTask extends PerfectRollupWorkerTask +{ + public static final String TYPE = "partial_dimension_distribution"; + private static final Logger LOG = new Logger(PartialDimensionDistributionTask.class); + + private final int numAttempts; + private final ParallelIndexIngestionSpec ingestionSchema; + private final String supervisorTaskId; + private final IndexingServiceClient indexingServiceClient; + private final IndexTaskClientFactory<ParallelIndexSupervisorTaskClient> taskClientFactory; + + // For testing + private final Supplier<UngroupedRowDimensionValueFilter> ungroupedRowDimValueFilterSupplier; + + @JsonCreator + PartialDimensionDistributionTask( + // id shouldn't be null except when this task is created by ParallelIndexSupervisorTask + @JsonProperty("id") @Nullable String id, + @JsonProperty("groupId") final String groupId, + @JsonProperty("resource") final TaskResource taskResource, + @JsonProperty("supervisorTaskId") final String supervisorTaskId, + @JsonProperty("numAttempts") final int numAttempts, // zero-based counting + @JsonProperty("spec") final ParallelIndexIngestionSpec ingestionSchema, + @JsonProperty("context") final Map<String, Object> context, + @JacksonInject IndexingServiceClient indexingServiceClient, + @JacksonInject IndexTaskClientFactory<ParallelIndexSupervisorTaskClient> taskClientFactory + ) + { + this( + id, + groupId, + taskResource, + supervisorTaskId, + numAttempts, + ingestionSchema, + context, + indexingServiceClient, + taskClientFactory, + () -> new UngroupedRowDimensionValueFilter( + ingestionSchema.getDataSchema().getGranularitySpec().getQueryGranularity() + ) + ); + } + + @VisibleForTesting // Only for testing + PartialDimensionDistributionTask( + @Nullable String id, + final String groupId, + final TaskResource taskResource, + final String supervisorTaskId, + final int numAttempts, + final ParallelIndexIngestionSpec ingestionSchema, + final Map<String, Object> context, + IndexingServiceClient indexingServiceClient, + IndexTaskClientFactory<ParallelIndexSupervisorTaskClient> taskClientFactory, + Supplier<UngroupedRowDimensionValueFilter> ungroupedRowDimValueFilterSupplier + ) + { + super( + getOrMakeId(id, TYPE, ingestionSchema.getDataSchema().getDataSource()), + groupId, + taskResource, + ingestionSchema.getDataSchema(), + ingestionSchema.getTuningConfig(), + context + ); + + Preconditions.checkArgument( + ingestionSchema.getTuningConfig().getPartitionsSpec() instanceof SingleDimensionPartitionsSpec, + "%s partitionsSpec required", + SingleDimensionPartitionsSpec.NAME + ); + + this.numAttempts = numAttempts; + this.ingestionSchema = ingestionSchema; + this.supervisorTaskId = supervisorTaskId; + this.indexingServiceClient = indexingServiceClient; + this.taskClientFactory = taskClientFactory; + this.ungroupedRowDimValueFilterSupplier = ungroupedRowDimValueFilterSupplier; + } + + @JsonProperty + private int getNumAttempts() + { + return numAttempts; + } + + @JsonProperty("spec") + private ParallelIndexIngestionSpec getIngestionSchema() + { + return ingestionSchema; + } + + @JsonProperty + private String getSupervisorTaskId() + { + return supervisorTaskId; + } + + @Override + public String getType() + { + return TYPE; + } + + @Override + public boolean isReady(TaskActionClient taskActionClient) throws Exception + { + return tryTimeChunkLock( + taskActionClient, + getIngestionSchema().getDataSchema().getGranularitySpec().inputIntervals() + ); + } + + @Override + public TaskStatus runTask(TaskToolbox toolbox) throws Exception + { + DataSchema dataSchema = ingestionSchema.getDataSchema(); + GranularitySpec granularitySpec = dataSchema.getGranularitySpec(); + ParallelIndexTuningConfig tuningConfig = ingestionSchema.getTuningConfig(); + + SingleDimensionPartitionsSpec partitionsSpec = (SingleDimensionPartitionsSpec) tuningConfig.getPartitionsSpec(); + Preconditions.checkNotNull(partitionsSpec); + String partitionDimension = partitionsSpec.getPartitionDimension(); + Preconditions.checkNotNull(partitionDimension, "partitionDimension required"); + boolean isAssumeGrouped = partitionsSpec.isAssumeGrouped(); + + InputSource inputSource = ingestionSchema.getIOConfig().getNonNullInputSource( + ingestionSchema.getDataSchema().getParser() + ); + List<String> metricsNames = Arrays.stream(dataSchema.getAggregators()) + .map(AggregatorFactory::getName) + .collect(Collectors.toList()); + InputFormat inputFormat = ParallelIndexSupervisorTask.getInputFormat(ingestionSchema); + InputSourceReader inputSourceReader = dataSchema.getTransformSpec().decorate( + inputSource.reader( + new InputRowSchema( + dataSchema.getTimestampSpec(), + dataSchema.getDimensionsSpec(), + metricsNames + ), + inputFormat, + null + ) + ); + + try ( + CloseableIterator<InputRow> inputRowIterator = inputSourceReader.read(); + HandlingInputRowIterator iterator = new RangePartitionIndexTaskInputRowIteratorBuilder(partitionDimension) + .delegate(inputRowIterator) + .granularitySpec(granularitySpec) + .nullRowRunnable(IndexTaskInputRowIteratorBuilder.NOOP_RUNNABLE) + .absentBucketIntervalConsumer(IndexTaskInputRowIteratorBuilder.NOOP_CONSUMER) + .build() + ) { + Map<Interval, StringDistribution> distribution = determineDistribution( + iterator, + granularitySpec, + partitionDimension, + isAssumeGrouped, + tuningConfig.isLogParseExceptions(), + tuningConfig.getMaxParseExceptions() + ); + sendReport(new DimensionDistributionReport(getId(), distribution)); + } + + return TaskStatus.success(getId()); + } + + private Map<Interval, StringDistribution> determineDistribution( + HandlingInputRowIterator inputRowIterator, + GranularitySpec granularitySpec, + String partitionDimension, + boolean isAssumeGrouped, + boolean isLogParseExceptions, + long maxParseExceptions + ) + { + Map<Interval, StringDistribution> intervalToDistribution = new HashMap<>(); + DimensionValueFilter dimValueFilter = + isAssumeGrouped + ? new GroupedRowDimensionValueFilter() + : ungroupedRowDimValueFilterSupplier.get(); + + long numParseExceptions = 0; + + while (inputRowIterator.hasNext()) { + try { + InputRow inputRow = inputRowIterator.next(); + if (inputRow == null) { + continue; + } + + DateTime timestamp = inputRow.getTimestamp(); + + //noinspection OptionalGetWithoutIsPresent (InputRowIterator returns rows with present intervals) + Interval interval = granularitySpec.bucketInterval(timestamp).get(); + StringDistribution stringDistribution = + intervalToDistribution.computeIfAbsent(interval, k -> new StringSketch()); + + String dimensionValue = dimValueFilter.accept( + interval, + timestamp, + inputRow.getDimension(partitionDimension).get(0) + ); + + if (dimensionValue != null) { + stringDistribution.put(dimensionValue); + } + } + catch (ParseException e) { + if (isLogParseExceptions) { + LOG.error(e, "Encountered parse exception:"); + } + + numParseExceptions++; + if (numParseExceptions > maxParseExceptions) { + throw new RuntimeException("Max parse exceptions exceeded, terminating task..."); + } + } + } + + // UngroupedDimValueFilter may not accept the min/max dimensionValue. If needed, add the min/max + // values to the distributions so they have an accurate min/max. + dimValueFilter.getIntervalToMinDimensionValue() + .forEach((interval, min) -> intervalToDistribution.get(interval).putIfNewMin(min)); + dimValueFilter.getIntervalToMaxDimensionValue() + .forEach((interval, max) -> intervalToDistribution.get(interval).putIfNewMax(max)); + + return intervalToDistribution; + } + + private void sendReport(DimensionDistributionReport report) + { + final ParallelIndexSupervisorTaskClient taskClient = taskClientFactory.build( + new ClientBasedTaskInfoProvider(indexingServiceClient), + getId(), + 1, // always use a single http thread + ingestionSchema.getTuningConfig().getChatHandlerTimeout(), + ingestionSchema.getTuningConfig().getChatHandlerNumRetries() + ); + taskClient.report(supervisorTaskId, report); + } + + private interface DimensionValueFilter + { + /** + * @return Dimension value if it should be accepted, else null + */ + @Nullable + String accept(Interval interval, DateTime timestamp, String dimesionValue); + + /** + * @return Minimum dimension value for each interval processed so far. + */ + Map<Interval, String> getIntervalToMinDimensionValue(); + + /** + * @return Maximum dimension value for each interval processed so far. + */ + Map<Interval, String> getIntervalToMaxDimensionValue(); + } + + @VisibleForTesting + static class UngroupedRowDimensionValueFilter implements DimensionValueFilter + { + // A bloom filter is used to approximately group rows by query granularity. These values assume + // time chunks have fewer than BLOOM_FILTER_EXPECTED_INSERTIONS rows. With the below values, the + // Bloom filter will use about 170MB of memory. + // + // For more details on the Bloom filter memory consumption: + // https://github.com/google/guava/issues/2520#issuecomment-231233736 + private static final int BLOOM_FILTER_EXPECTED_INSERTIONS = 100_000_000; + private static final double BLOOM_FILTER_EXPECTED_FALSE_POSITIVE_PROBABILTY = 0.001; + + private final GroupedRowDimensionValueFilter delegate; + private final TimeDimTupleFactory timeDimTupleFactory; + private final BloomFilter<TimeDimTuple> timeDimTupleBloomFilter; + + UngroupedRowDimensionValueFilter(Granularity queryGranularity) + { + this(queryGranularity, BLOOM_FILTER_EXPECTED_INSERTIONS, BLOOM_FILTER_EXPECTED_FALSE_POSITIVE_PROBABILTY); + } + + @VisibleForTesting // to allow controlling false positive rate of bloom filter + UngroupedRowDimensionValueFilter( + Granularity queryGranularity, + int bloomFilterExpectedInsertions, + double bloomFilterFalsePositiveProbability + ) + { + delegate = new GroupedRowDimensionValueFilter(); + timeDimTupleFactory = new TimeDimTupleFactory(queryGranularity); + timeDimTupleBloomFilter = BloomFilter.create( + TimeDimTupleFunnel.INSTANCE, + bloomFilterExpectedInsertions, + bloomFilterFalsePositiveProbability + ); + } + + @Nullable + @Override + public String accept(Interval interval, DateTime timestamp, String dimensionValue) + { + delegate.accept(interval, timestamp, dimensionValue); + + TimeDimTuple timeDimTuple = timeDimTupleFactory.createWithBucketedTimestamp(timestamp, dimensionValue); + if (timeDimTupleBloomFilter.mightContain(timeDimTuple)) { + return null; + } else { + timeDimTupleBloomFilter.put(timeDimTuple); + return dimensionValue; + } + } + + @Override + public Map<Interval, String> getIntervalToMinDimensionValue() + { + return delegate.getIntervalToMinDimensionValue(); + } + + @Override + public Map<Interval, String> getIntervalToMaxDimensionValue() + { + return delegate.getIntervalToMaxDimensionValue(); + } + } + + private static class GroupedRowDimensionValueFilter implements DimensionValueFilter + { + private final Map<Interval, String> intervalToMinDimensionValue; + private final Map<Interval, String> intervalToMaxDimensionValue; + + GroupedRowDimensionValueFilter() + { + this.intervalToMinDimensionValue = new HashMap<>(); + this.intervalToMaxDimensionValue = new HashMap<>(); + } + + @Override + @Nullable + public String accept(Interval interval, DateTime timestamp, String dimensionValue) + { + updateMinDimensionValue(interval, dimensionValue); + updateMaxDimensionValue(interval, dimensionValue); + return dimensionValue; + } + + private void updateMinDimensionValue(Interval interval, String dimensionValue) + { + String minDimensionValue = intervalToMinDimensionValue.get(interval); + if (minDimensionValue == null || dimensionValue.compareTo(minDimensionValue) < 0) { + intervalToMinDimensionValue.put(interval, dimensionValue); + } Review comment: Changed this method and `updateMaxDimensionValue` to use `Map.compute()` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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