记录kafka-flink-kafka的end-to-end的exactly-once语义

步骤
代码

步骤

开启checkpoint、stateBackend的设置和checkpoint配置
设置kafka source的配置
读取kafka source message
随意的transformation；并打印结果
kafka sink端的配置
输出到kafka sink端
执行

代码

package com.javaye.demo.exactly;import org.apache.commons.lang3.SystemUtils;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.connector.base.DeliveryGuarantee;
import org.apache.flink.connector.kafka.sink.KafkaRecordSerializationSchema;
import org.apache.flink.connector.kafka.sink.KafkaSink;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer;
import org.apache.flink.runtime.state.filesystem.FsStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;import java.util.Properties;
import java.util.Random;
import java.util.concurrent.TimeUnit;/*** @Author: Java页大数据* @Date: 2024-04-11:17:59* @Describe:*  kafka - flink - kafka 验证end-to-end的exactly once*/
public class ExactlyOnce {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//        1.1. 开启checkpoint，间隔为1000L msenv.enableCheckpointing(1000L);//        1.2. stateBackend:checkpoint持久化目录if (SystemUtils.IS_OS_WINDOWS) {env.setStateBackend(new FsStateBackend("file:///D:/ckp"));} else {env.setStateBackend(new FsStateBackend("hdfs://only:9870/flink-checkpoints"));}CheckpointConfig config = env.getCheckpointConfig();
//        1.3. ckp的配置
//        1.3.1. 前后两次checkpoint的最小间隔:防止前后两次的checkpoint重叠config.setMinPauseBetweenCheckpoints(500L);
//        1.3.2. 容忍5次checkpoint失败config.setTolerableCheckpointFailureNumber(5);
//        1.3.3. job被取消时，保留外部的checkpointconfig.setExternalizedCheckpointCleanup(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
//        1.3.4. 设置checkpoint的语义为 exactly-onceconfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
//        1.3.5. 设置checkpoint的超时时间，若checkpoint超过该超时时间则说明该次checkpoint失败，丢弃该checkpointconfig.setCheckpointTimeout(60 * 1000);
//        1.3.6. 设置同一时刻允许多少个checkpoint同时执行config.setMaxConcurrentCheckpoints(1);//        1.4. 设置重启策略env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, Time.of(10, TimeUnit.SECONDS)));//        2. 设置kafka source的配置String kafkaServer = "only:9092";String sourceTopic = "flink_kafka_source";String groupId = "flink_kafka_source_exactly_once";String clientIdPrefix = "flink_exactly_once";Properties kafkaSourceProp = new Properties();KafkaSource<String> kafkaSource = KafkaSource.<String>builder().setBootstrapServers(kafkaServer).setTopics(sourceTopic).setGroupId(groupId).setClientIdPrefix(clientIdPrefix).setStartingOffsets(OffsetsInitializer.latest()) // Start from latest offset.setProperty("partition.discovery.interval.ms", "50000") // discover new partitions per 50 seconds.setProperty("auto.offset.reset", "latest").setValueOnlyDeserializer(new SimpleStringSchema())
//                执行checkpoint时提交offset到checkpoint，flink内部使用，并且提交一份到默认主题__consumer_offsets
//                .setCommitOffsetsOnCheckpoints(true) // checkpoint开启默认为true，否则为false；不支持该方法.setProperties(kafkaSourceProp).build();//        3. 读取kafka source messageDataStreamSource<String> kafkaDS = env.fromSource(kafkaSource, WatermarkStrategy.noWatermarks(), "flink_kafka_exactly_once", TypeInformation.of(String.class));//        4. 随意的transformationSingleOutputStreamOperator<String> flatMapDS = kafkaDS.flatMap(new FlatMapFunction<String, String>() {@Overridepublic void flatMap(String value, Collector<String> out) throws Exception {String[] words = value.split(",");for (String word : words) {Random random = new Random();int i = random.nextInt(5);if (i > 3) {System.out.println("模拟出现bug...");throw new RuntimeException("模拟出现bug...");}System.out.println(word + "===" + i);out.collect(word + "===" + i);}}});//        4.1. 打印结果容易观察flatMapDS.print();//        5. kafka sink端的配置Properties kafkaSinkProp = new Properties();kafkaSinkProp.setProperty("transaction.timeout.ms", 1000 * 5 + ""); //设置事务超时时间，也可在kafka配置中设置KafkaSink<String> kafkaSink = KafkaSink.<String>builder().setBootstrapServers(kafkaServer).setRecordSerializer(KafkaRecordSerializationSchema.builder().setTopic("flink_kafka_sink").setValueSerializationSchema(new SimpleStringSchema()).build()).setDeliverGuarantee(DeliveryGuarantee.EXACTLY_ONCE).setKafkaProducerConfig(kafkaSinkProp).build();//        6. 输出到kafka sink端flatMapDS.sinkTo(kafkaSink);//        7. 执行env.execute(ExactlyOnce.class.getName());}
}