aloyszhang commented on code in PR #10231: URL: https://github.com/apache/inlong/pull/10231#discussion_r1603126663
########## inlong-sort/sort-flink/sort-flink-v1.15/sort-connectors/hudi/src/main/java/org/apache/inlong/sort/hudi/sink/utils/Pipelines.java: ########## @@ -0,0 +1,468 @@ +/* + * 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.inlong.sort.hudi.sink.utils; + +import org.apache.inlong.sort.base.metric.MetricOption; +import org.apache.inlong.sort.hudi.sink.StreamWriteOperator; +import org.apache.inlong.sort.hudi.sink.append.AppendWriteOperator; +import org.apache.inlong.sort.hudi.sink.bulk.BulkInsertWriteOperator; + +import org.apache.flink.api.common.functions.Partitioner; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.runtime.state.KeyGroupRangeAssignment; +import org.apache.flink.streaming.api.datastream.DataStream; +import org.apache.flink.streaming.api.datastream.DataStreamSink; +import org.apache.flink.streaming.api.functions.sink.SinkFunction; +import org.apache.flink.streaming.api.operators.KeyedProcessOperator; +import org.apache.flink.table.data.RowData; +import org.apache.flink.table.planner.plan.nodes.exec.utils.ExecNodeUtil; +import org.apache.flink.table.runtime.typeutils.InternalTypeInfo; +import org.apache.flink.table.types.logical.RowType; +import org.apache.hudi.common.model.ClusteringOperation; +import org.apache.hudi.common.model.HoodieKey; +import org.apache.hudi.common.model.HoodieRecord; +import org.apache.hudi.configuration.FlinkOptions; +import org.apache.hudi.configuration.OptionsResolver; +import org.apache.hudi.sink.CleanFunction; +import org.apache.hudi.sink.bootstrap.BootstrapOperator; +import org.apache.hudi.sink.bootstrap.batch.BatchBootstrapOperator; +import org.apache.hudi.sink.bucket.BucketBulkInsertWriterHelper; +import org.apache.hudi.sink.bucket.BucketStreamWriteOperator; +import org.apache.hudi.sink.bulk.RowDataKeyGen; +import org.apache.hudi.sink.bulk.sort.SortOperatorGen; +import org.apache.hudi.sink.clustering.ClusteringCommitEvent; +import org.apache.hudi.sink.clustering.ClusteringCommitSink; +import org.apache.hudi.sink.clustering.ClusteringOperator; +import org.apache.hudi.sink.clustering.ClusteringPlanEvent; +import org.apache.hudi.sink.clustering.ClusteringPlanOperator; +import org.apache.hudi.sink.common.WriteOperatorFactory; +import org.apache.hudi.sink.compact.CompactOperator; +import org.apache.hudi.sink.compact.CompactionCommitEvent; +import org.apache.hudi.sink.compact.CompactionCommitSink; +import org.apache.hudi.sink.compact.CompactionPlanEvent; +import org.apache.hudi.sink.compact.CompactionPlanOperator; +import org.apache.hudi.sink.partitioner.BucketAssignFunction; +import org.apache.hudi.sink.partitioner.BucketIndexPartitioner; +import org.apache.hudi.sink.transform.RowDataToHoodieFunctions; +import org.apache.hudi.table.format.FilePathUtils; + +import java.util.HashMap; +import java.util.Map; +import java.util.stream.Collectors; + +/** + * Utilities to generate all kinds of sub-pipelines. + * <p> + * Copy from org.apache.hudi:hudi-flink1.15-bundle:0.12.3 + */ +public class Pipelines { + + /** + * Bulk insert the input dataset at once. + * + * <p>By default, the input dataset would shuffle by the partition path first then + * sort by the partition path before passing around to the write function. + * The whole pipeline looks like the following: + * + * <pre> + * | input1 | ===\ /=== |sorter| === | task1 | (p1, p2) + * shuffle + * | input2 | ===/ \=== |sorter| === | task2 | (p3, p4) + * + * Note: Both input1 and input2's dataset come from partitions: p1, p2, p3, p4 + * </pre> + * + * <p>The write task switches to new file handle each time it receives a record + * from the different partition path, the shuffle and sort would reduce small files. + * + * <p>The bulk insert should be run in batch execution mode. + * + * @param conf The configuration + * @param rowType The input row type + * @param dataStream The input data stream + * @return the bulk insert data stream sink + */ + public static DataStreamSink<Object> bulkInsert(Configuration conf, RowType rowType, DataStream<RowData> dataStream, + MetricOption metricOption) { + WriteOperatorFactory<RowData> operatorFactory = BulkInsertWriteOperator.getFactory(conf, rowType, metricOption); + if (OptionsResolver.isBucketIndexType(conf)) { + String indexKeys = conf.getString(FlinkOptions.INDEX_KEY_FIELD); + int numBuckets = conf.getInteger(FlinkOptions.BUCKET_INDEX_NUM_BUCKETS); + + BucketIndexPartitioner<HoodieKey> partitioner = new BucketIndexPartitioner<>(numBuckets, indexKeys); + RowDataKeyGen keyGen = RowDataKeyGen.instance(conf, rowType); + RowType rowTypeWithFileId = BucketBulkInsertWriterHelper.rowTypeWithFileId(rowType); + InternalTypeInfo<RowData> typeInfo = InternalTypeInfo.of(rowTypeWithFileId); + + Map<String, String> bucketIdToFileId = new HashMap<>(); + dataStream = dataStream.partitionCustom(partitioner, keyGen::getHoodieKey) + .map(record -> BucketBulkInsertWriterHelper.rowWithFileId(bucketIdToFileId, keyGen, record, + indexKeys, numBuckets), typeInfo) + .setParallelism(conf.getInteger(FlinkOptions.WRITE_TASKS)); // same parallelism as write task to + // avoid shuffle + if (conf.getBoolean(FlinkOptions.WRITE_BULK_INSERT_SORT_INPUT)) { + SortOperatorGen sortOperatorGen = BucketBulkInsertWriterHelper.getFileIdSorterGen(rowTypeWithFileId); + dataStream = dataStream.transform("file_sorter", typeInfo, sortOperatorGen.createSortOperator()) + .setParallelism(conf.getInteger(FlinkOptions.WRITE_TASKS)); // same parallelism as write task to + // avoid shuffle + ExecNodeUtil.setManagedMemoryWeight(dataStream.getTransformation(), + conf.getInteger(FlinkOptions.WRITE_SORT_MEMORY) * 1024L * 1024L); + } + return dataStream + .transform(opName("bucket_bulk_insert", conf), TypeInformation.of(Object.class), operatorFactory) + .uid(opUID("bucket_bulk_insert", conf)) + .setParallelism(conf.getInteger(FlinkOptions.WRITE_TASKS)) + .addSink(DummySink.INSTANCE) + .name("dummy"); + } + + final String[] partitionFields = FilePathUtils.extractPartitionKeys(conf); + if (partitionFields.length > 0) { + RowDataKeyGen rowDataKeyGen = RowDataKeyGen.instance(conf, rowType); + if (conf.getBoolean(FlinkOptions.WRITE_BULK_INSERT_SHUFFLE_INPUT)) { + + // shuffle by partition keys + // use #partitionCustom instead of #keyBy to avoid duplicate sort operations, + // see BatchExecutionUtils#applyBatchExecutionSettings for details. + Partitioner<String> partitioner = + (key, channels) -> KeyGroupRangeAssignment.assignKeyToParallelOperator(key, + KeyGroupRangeAssignment.computeDefaultMaxParallelism( + conf.getInteger(FlinkOptions.WRITE_TASKS)), + channels); + dataStream = dataStream.partitionCustom(partitioner, rowDataKeyGen::getPartitionPath); + } + if (conf.getBoolean(FlinkOptions.WRITE_BULK_INSERT_SORT_INPUT)) { + SortOperatorGen sortOperatorGen = new SortOperatorGen(rowType, partitionFields); + // sort by partition keys + dataStream = dataStream + .transform("partition_key_sorter", + InternalTypeInfo.of(rowType), + sortOperatorGen.createSortOperator()) + .setParallelism(conf.getInteger(FlinkOptions.WRITE_TASKS)); + ExecNodeUtil.setManagedMemoryWeight(dataStream.getTransformation(), + conf.getInteger(FlinkOptions.WRITE_SORT_MEMORY) * 1024L * 1024L); + } + } + return dataStream + .transform(opName("hoodie_bulk_insert_write", conf), + TypeInformation.of(Object.class), + operatorFactory) + // follow the parallelism of upstream operators to avoid shuffle + .setParallelism(conf.getInteger(FlinkOptions.WRITE_TASKS)) + .addSink(DummySink.INSTANCE) + .name("dummy"); + } + + /** + * Insert the dataset with append mode(no upsert or deduplication). + * + * <p>The input dataset would be rebalanced among the write tasks: + * + * <pre> + * | input1 | ===\ /=== | task1 | (p1, p2, p3, p4) + * shuffle + * | input2 | ===/ \=== | task2 | (p1, p2, p3, p4) + * + * Note: Both input1 and input2's dataset come from partitions: p1, p2, p3, p4 + * </pre> + * + * <p>The write task switches to new file handle each time it receives a record + * from the different partition path, so there may be many small files. + * + * @param conf The configuration + * @param rowType The input row type + * @param dataStream The input data stream + * @param bounded Whether the input stream is bounded + * @return the appending data stream sink + */ + public static DataStream<Object> append( + Configuration conf, + RowType rowType, + DataStream<RowData> dataStream, + boolean bounded, + MetricOption metricOption) { + if (!bounded) { + // In principle, the config should be immutable, but the boundedness + // is only visible when creating the sink pipeline. + conf.setBoolean(FlinkOptions.WRITE_BULK_INSERT_SORT_INPUT, false); + } + WriteOperatorFactory<RowData> operatorFactory = AppendWriteOperator.getFactory(conf, rowType, metricOption); + + return dataStream + .transform(opName("hoodie_append_write", conf), TypeInformation.of(Object.class), operatorFactory) + .uid(opUID("hoodie_stream_write", conf)) + .setParallelism(conf.getInteger(FlinkOptions.WRITE_TASKS)); + } + + /** + * Constructs bootstrap pipeline as streaming. + * The bootstrap operator loads the existing data index (primary key to file id mapping), + * then sends the indexing data set to subsequent operator(usually the bucket assign operator). + */ + public static DataStream<HoodieRecord> bootstrap( + Configuration conf, + RowType rowType, + DataStream<RowData> dataStream) { + return bootstrap(conf, rowType, dataStream, false, false); + } + + /** + * Constructs bootstrap pipeline. + * The bootstrap operator loads the existing data index (primary key to file id mapping), + * then send the indexing data set to subsequent operator(usually the bucket assign operator). + * + * @param conf The configuration + * @param rowType The row type + * @param dataStream The data stream + * @param bounded Whether the source is bounded + * @param overwrite Whether it is insert overwrite + */ + public static DataStream<HoodieRecord> bootstrap( + Configuration conf, + RowType rowType, + DataStream<RowData> dataStream, + boolean bounded, + boolean overwrite) { + final boolean globalIndex = conf.getBoolean(FlinkOptions.INDEX_GLOBAL_ENABLED); + if (overwrite || OptionsResolver.isBucketIndexType(conf)) { + return rowDataToHoodieRecord(conf, rowType, dataStream); + } else if (bounded && !globalIndex && OptionsResolver.isPartitionedTable(conf)) { + return boundedBootstrap(conf, rowType, dataStream); + } else { + return streamBootstrap(conf, rowType, dataStream, bounded); + } + } + + private static DataStream<HoodieRecord> streamBootstrap( + Configuration conf, + RowType rowType, + DataStream<RowData> dataStream, + boolean bounded) { + DataStream<HoodieRecord> dataStream1 = rowDataToHoodieRecord(conf, rowType, dataStream); + + if (conf.getBoolean(FlinkOptions.INDEX_BOOTSTRAP_ENABLED) || bounded) { + dataStream1 = dataStream1 + .transform( + "index_bootstrap", + TypeInformation.of(HoodieRecord.class), + new BootstrapOperator<>(conf)) + .setParallelism( + conf.getOptional(FlinkOptions.INDEX_BOOTSTRAP_TASKS).orElse(dataStream1.getParallelism())) + .uid(opUID("index_bootstrap", conf)); + } + + return dataStream1; + } + + /** + * Constructs bootstrap pipeline for batch execution mode. + * The indexing data set is loaded before the actual data write + * in order to support batch UPSERT. + */ + private static DataStream<HoodieRecord> boundedBootstrap( + Configuration conf, + RowType rowType, + DataStream<RowData> dataStream) { + final RowDataKeyGen rowDataKeyGen = RowDataKeyGen.instance(conf, rowType); + // shuffle by partition keys + dataStream = dataStream + .keyBy(rowDataKeyGen::getPartitionPath); + + return rowDataToHoodieRecord(conf, rowType, dataStream) + .transform( + "batch_index_bootstrap", + TypeInformation.of(HoodieRecord.class), + new BatchBootstrapOperator<>(conf)) + .setParallelism( + conf.getOptional(FlinkOptions.INDEX_BOOTSTRAP_TASKS).orElse(dataStream.getParallelism())) + .uid(opUID("batch_index_bootstrap", conf)); + } + + /** + * Transforms the row data to hoodie records. + */ + public static DataStream<HoodieRecord> rowDataToHoodieRecord(Configuration conf, RowType rowType, + DataStream<RowData> dataStream) { + return dataStream.map(RowDataToHoodieFunctions.create(rowType, conf), TypeInformation.of(HoodieRecord.class)) + .setParallelism(dataStream.getParallelism()).name("row_data_to_hoodie_record"); + } + + /** + * The streaming write pipeline. + * + * <p>The input dataset shuffles by the primary key first then + * shuffles by the file group ID before passing around to the write function. + * The whole pipeline looks like the following: + * + * <pre> + * | input1 | ===\ /=== | bucket assigner | ===\ /=== | task1 | + * shuffle(by PK) shuffle(by bucket ID) + * | input2 | ===/ \=== | bucket assigner | ===/ \=== | task2 | + * + * Note: a file group must be handled by one write task to avoid write conflict. + * </pre> + * + * <p>The bucket assigner assigns the inputs to suitable file groups, the write task caches + * and flushes the data set to disk. + * + * @param conf The configuration + * @param dataStream The input data stream + * @return the stream write data stream pipeline + */ + public static DataStream<Object> hoodieStreamWrite(Configuration conf, DataStream<HoodieRecord> dataStream, + MetricOption metricOption) { + if (OptionsResolver.isBucketIndexType(conf)) { + WriteOperatorFactory<HoodieRecord> operatorFactory = BucketStreamWriteOperator.getFactory(conf); Review Comment: Miss metricOption here -- This is an automated message from the Apache Git Service. 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