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[FLINK-15670][connector] Adds the producer for KafkaShuffle.
KafkaShuffle provides a transparent Kafka source and sink pair, through which the network traffic of a shuffle step is persisted and redirected.
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...n/java/org/apache/flink/streaming/connectors/kafka/shuffle/FlinkKafkaShuffleProducer.java
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| /* | ||
| * 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 | ||
| * | ||
| * https://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. | ||
| */ | ||
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| package org.apache.flink.streaming.connectors.kafka.shuffle; | ||
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| import org.apache.flink.annotation.Internal; | ||
| import org.apache.flink.api.common.typeutils.TypeSerializer; | ||
| import org.apache.flink.api.java.functions.KeySelector; | ||
| import org.apache.flink.core.memory.DataOutputSerializer; | ||
| import org.apache.flink.runtime.state.KeyGroupRangeAssignment; | ||
| import org.apache.flink.streaming.api.watermark.Watermark; | ||
| import org.apache.flink.streaming.connectors.kafka.FlinkKafkaException; | ||
| import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer; | ||
| import org.apache.flink.util.Preconditions; | ||
| import org.apache.flink.util.PropertiesUtil; | ||
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| import org.apache.kafka.clients.producer.ProducerRecord; | ||
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| import java.io.IOException; | ||
| import java.io.Serializable; | ||
| import java.util.Properties; | ||
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| import static org.apache.flink.streaming.connectors.kafka.shuffle.FlinkKafkaShuffle.PARTITION_NUMBER; | ||
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| /** | ||
| * Flink Kafka Shuffle Producer Function. | ||
| * It is different from {@link FlinkKafkaProducer} in the way handling elements and watermarks | ||
| */ | ||
| @Internal | ||
| public class FlinkKafkaShuffleProducer<IN, KEY> extends FlinkKafkaProducer<IN> { | ||
| private final KafkaSerializer<IN> kafkaSerializer; | ||
| private final KeySelector<IN, KEY> keySelector; | ||
| private final int numberOfPartitions; | ||
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| FlinkKafkaShuffleProducer( | ||
| String defaultTopicId, | ||
| TypeSerializer<IN> typeSerializer, | ||
| Properties props, | ||
| KeySelector<IN, KEY> keySelector, | ||
| Semantic semantic, | ||
| int kafkaProducersPoolSize) { | ||
| super(defaultTopicId, (element, timestamp) -> null, props, semantic, kafkaProducersPoolSize); | ||
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| this.kafkaSerializer = new KafkaSerializer<>(typeSerializer); | ||
| this.keySelector = keySelector; | ||
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| Preconditions.checkArgument( | ||
| props.getProperty(PARTITION_NUMBER) != null, | ||
| "Missing partition number for Kafka Shuffle"); | ||
| numberOfPartitions = PropertiesUtil.getInt(props, PARTITION_NUMBER, Integer.MIN_VALUE); | ||
| } | ||
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| /** | ||
| * This is the function invoked to handle each element. | ||
| * | ||
| * @param transaction Transaction state; | ||
| * elements are written to Kafka in transactions to guarantee different level of data consistency | ||
| * @param next Element to handle | ||
| * @param context Context needed to handle the element | ||
| * @throws FlinkKafkaException for kafka error | ||
| */ | ||
| @Override | ||
| public void invoke(KafkaTransactionState transaction, IN next, Context context) throws FlinkKafkaException { | ||
| checkErroneous(); | ||
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| // write timestamp to Kafka if timestamp is available | ||
| Long timestamp = context.timestamp(); | ||
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| int[] partitions = getPartitions(transaction); | ||
| int partitionIndex; | ||
| try { | ||
| partitionIndex = KeyGroupRangeAssignment | ||
| .assignKeyToParallelOperator(keySelector.getKey(next), partitions.length, partitions.length); | ||
| } catch (Exception e) { | ||
| throw new RuntimeException("Fail to assign a partition number to record", e); | ||
| } | ||
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| ProducerRecord<byte[], byte[]> record = new ProducerRecord<>( | ||
| defaultTopicId, | ||
| partitionIndex, | ||
| timestamp, | ||
| null, | ||
| kafkaSerializer.serializeRecord(next, timestamp)); | ||
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| pendingRecords.incrementAndGet(); | ||
| transaction.getProducer().send(record, callback); | ||
| } | ||
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| /** | ||
| * This is the function invoked to handle each watermark. | ||
| * | ||
| * @param watermark Watermark to handle | ||
| * @throws FlinkKafkaException For kafka error | ||
| */ | ||
| public void invoke(Watermark watermark) throws FlinkKafkaException { | ||
| checkErroneous(); | ||
| KafkaTransactionState transaction = currentTransaction(); | ||
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| int[] partitions = getPartitions(transaction); | ||
| int subtask = getRuntimeContext().getIndexOfThisSubtask(); | ||
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| // broadcast watermark | ||
| long timestamp = watermark.getTimestamp(); | ||
| for (int partition : partitions) { | ||
| ProducerRecord<byte[], byte[]> record = new ProducerRecord<>( | ||
| defaultTopicId, | ||
| partition, | ||
| timestamp, | ||
| null, | ||
| kafkaSerializer.serializeWatermark(watermark, subtask)); | ||
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| pendingRecords.incrementAndGet(); | ||
| transaction.getProducer().send(record, callback); | ||
| } | ||
| } | ||
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| private int[] getPartitions(KafkaTransactionState transaction) { | ||
| int[] partitions = topicPartitionsMap.get(defaultTopicId); | ||
| if (partitions == null) { | ||
| partitions = getPartitionsByTopic(defaultTopicId, transaction.getProducer()); | ||
| topicPartitionsMap.put(defaultTopicId, partitions); | ||
| } | ||
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| Preconditions.checkArgument(partitions.length == numberOfPartitions); | ||
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| return partitions; | ||
| } | ||
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| /** | ||
| * Flink Kafka Shuffle Serializer. | ||
| */ | ||
| public static final class KafkaSerializer<IN> implements Serializable { | ||
| public static final int TAG_REC_WITH_TIMESTAMP = 0; | ||
| public static final int TAG_REC_WITHOUT_TIMESTAMP = 1; | ||
| public static final int TAG_WATERMARK = 2; | ||
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| private static final long serialVersionUID = 2000002L; | ||
| // easy for updating SerDe format later | ||
| private static final int KAFKA_SHUFFLE_VERSION = 0; | ||
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| private final TypeSerializer<IN> serializer; | ||
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| private transient DataOutputSerializer dos; | ||
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| KafkaSerializer(TypeSerializer<IN> serializer) { | ||
| this.serializer = serializer; | ||
| } | ||
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| /** | ||
| * Format: Version(byte), TAG(byte), [timestamp(long)], record. | ||
| */ | ||
| byte[] serializeRecord(IN record, Long timestamp) { | ||
| if (dos == null) { | ||
| dos = new DataOutputSerializer(16); | ||
| } | ||
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| try { | ||
| dos.write(KAFKA_SHUFFLE_VERSION); | ||
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| if (timestamp == null) { | ||
| dos.write(TAG_REC_WITHOUT_TIMESTAMP); | ||
| } else { | ||
| dos.write(TAG_REC_WITH_TIMESTAMP); | ||
| dos.writeLong(timestamp); | ||
| } | ||
| serializer.serialize(record, dos); | ||
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| } catch (IOException e) { | ||
| throw new RuntimeException("Unable to serialize record", e); | ||
| } | ||
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| byte[] ret = dos.getCopyOfBuffer(); | ||
| dos.clear(); | ||
| return ret; | ||
| } | ||
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| /** | ||
| * Format: Version(byte), TAG(byte), subtask(int), timestamp(long). | ||
| */ | ||
| byte[] serializeWatermark(Watermark watermark, int subtask) { | ||
| if (dos == null) { | ||
| dos = new DataOutputSerializer(16); | ||
| } | ||
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| try { | ||
| dos.write(KAFKA_SHUFFLE_VERSION); | ||
| dos.write(TAG_WATERMARK); | ||
| dos.writeInt(subtask); | ||
| dos.writeLong(watermark.getTimestamp()); | ||
| } catch (IOException e) { | ||
| throw new RuntimeException("Unable to serialize watermark", e); | ||
| } | ||
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| byte[] ret = dos.getCopyOfBuffer(); | ||
| dos.clear(); | ||
| return ret; | ||
| } | ||
| } | ||
| } |