一、背景
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最近项目中使用Flink消费kafka消息，并将消费的消息存储到MySQL中，看似一个很简单的需求，在网上也有很多flink消费kafka的例子，但看了一圈也没看到能解决重复消费的问题的文章，于是在flink官网中搜索此类场景的处理方式，发现官网也没有实现flink到mysql的Exactly-Once例子，但是官网却有类似的例子来解决端到端的仅一次消费问题。这个现成的例子就是FlinkKafkaProducer011这个类，它保证了通过FlinkKafkaProducer011发送到kafka的消息是Exactly-Once的，主要的实现方式就是继承了TwoPhaseCommitSinkFunction这个类，关于TwoPhaseCommitSinkFunction这个类的作用可以先看上一篇文章https://blog.51cto.com/simplelife/2401411。
二、实现思想
这里简单说下这个类的作用就是实现这个类的方法：beginTransaction、preCommit、commit、abort，达到事件（preCommit）预提交的逻辑（当事件进行自己的逻辑处理后进行预提交，如果预提交成功之后才进行真正的（commit）提交，如果预提交失败则调用abort方法进行事件的回滚操作），结合flink的checkpoint机制，来保存topic中partition的offset。
达到的效果我举个例子来说明下：比如checkpoint每10s进行一次，此时用FlinkKafkaConsumer011实时消费kafka中的消息，消费并处理完消息后，进行一次预提交数据库的操作，如果预提交没有问题，10s后进行真正的插入数据库操作，如果插入成功，进行一次checkpoint，flink会自动记录消费的offset，可以将checkpoint保存的数据放到hdfs中，如果预提交出错，比如在5s的时候出错了，此时Flink程序就会进入不断的重启中，重启的策略可以在配置中设置，当然下一次的checkpoint也不会做了，checkpoint记录的还是上一次成功消费的offset，本次消费的数据因为在checkpoint期间，消费成功，但是预提交过程中失败了，注意此时数据并没有真正的执行插入操作，因为预提交（preCommit）失败，提交（commit）过程也不会发生了。等你将异常数据处理完成之后，再重新启动这个Flink程序，它会自动从上一次成功的checkpoint中继续消费数据，以此来达到Kafka到Mysql的Exactly-Once。
三、具体实现代码三个类
1、StreamDemoKafka2Mysql.java
package com.fwmagic.flink.streaming;
import com.fwmagic.flink.sink.MySqlTwoPhaseCommitSink;
import org.apache.flink.runtime.state.filesystem.FsStateBackend;
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.node.ObjectNode;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase;
import org.apache.flink.streaming.util.serialization.JSONKeyValueDeserializationSchema;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import java.util.Properties;
/**
* 消费kafka消息，sink(自定义)到mysql中，保证kafka to mysql的Exactly-Once
*/
@SuppressWarnings("all")
public class StreamDemoKafka2Mysql {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//设置并行度，为了方便测试，查看消息的顺序，这里设置为1，可以更改为多并行度
env.setParallelism(1);
//checkpoint设置
//每隔10s进行启动一个检查点【设置checkpoint的周期】
env.enableCheckpointing(10000);
//设置模式为：exactly_one，仅一次语义
env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
//确保检查点之间有1s的时间间隔【checkpoint最小间隔】
env.getCheckpointConfig().setMinPauseBetweenCheckpoints(1000);
//检查点必须在10s之内完成，或者被丢弃【checkpoint超时时间】
env.getCheckpointConfig().setCheckpointTimeout(10000);
//同一时间只允许进行一次检查点
env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);
//表示一旦Flink程序被cancel后，会保留checkpoint数据，以便根据实际需要恢复到指定的checkpoint
//env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
//设置statebackend,将检查点保存在hdfs上面，默认保存在内存中。这里先保存到本地
env.setStateBackend(new FsStateBackend("file:///Users/temp/cp/"));
//设置kafka消费参数
Properties props = new Properties();
props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "hd1:9092,hd2:9092,hd3:9092");
props.put(ConsumerConfig.GROUP_ID_CONFIG, "flink-consumer-group1");
//kafka分区自动发现周期
props.put(FlinkKafkaConsumerBase.KEY_PARTITION_DISCOVERY_INTERVAL_MILLIS, "3000");
/*SimpleStringSchema可以获取到kafka消息，JSONKeyValueDeserializationSchema可以获取都消息的key,value，metadata:topic,partition，offset等信息*/
// FlinkKafkaConsumer011 kafkaConsumer011 = new FlinkKafkaConsumer011<>(topic, new SimpleStringSchema(), props);
FlinkKafkaConsumer011 kafkaConsumer011 = new FlinkKafkaConsumer011<>("demo123", new JSONKeyValueDeserializationSchema(true), props);
//加入kafka数据源
DataStreamSource streamSource = env.addSource(kafkaConsumer011);
//数据传输到下游
streamSource.addSink(new MySqlTwoPhaseCommitSink()).name("MySqlTwoPhaseCommitSink");
//触发执行
env.execute(StreamDemoKafka2Mysql.class.getName());
}
}2、MySqlTwoPhaseCommitSink.java
package com.fwmagic.flink.sink;
import com.fwmagic.flink.util.DBConnectUtil;
import org.apache.flink.api.common.ExecutionConfig;
import org.apache.flink.api.common.typeutils.base.VoidSerializer;
import org.apache.flink.api.java.typeutils.runtime.kryo.KryoSerializer;
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.node.ObjectNode;
import org.apache.flink.streaming.api.functions.sink.TwoPhaseCommitSinkFunction;
import java.sql.Connection;
import java.sql.PreparedStatement;
import java.sql.Timestamp;
import java.text.SimpleDateFormat;
import java.util.Date;
/**
* 自定义kafka to mysql，继承TwoPhaseCommitSinkFunction,实现两阶段提交。
* 功能：保证kafak to mysql 的Exactly-Once
*/
public class MySqlTwoPhaseCommitSink extends TwoPhaseCommitSinkFunction {
public MySqlTwoPhaseCommitSink() {
super(new KryoSerializer<>(Connection.class, new ExecutionConfig()), VoidSerializer.INSTANCE);
}
/**
* 执行数据入库操作
* @param connection
* @param objectNode
* @param context
* @throws Exception
*/
@Override
protected void invoke(Connection connection, ObjectNode objectNode, Context context) throws Exception {
System.err.println("start invoke.......");
String date = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(new Date());
System.err.println("===>date:" + date + " " + objectNode);
String value = objectNode.get("value").toString();
String sql = "insert into `t_test` (`value`,`insert_time`) values (?,?)";
PreparedStatement ps = connection.prepareStatement(sql);
ps.setString(1, value);
ps.setTimestamp(2, new Timestamp(System.currentTimeMillis()));
//执行insert语句
ps.execute();
//手动制造异常
if(Integer.parseInt(value) == 15) System.out.println(1/0);
}
/**
* 获取连接，开启手动提交事物（getConnection方法中）
* @return
* @throws Exception
*/
@Override
protected Connection beginTransaction() throws Exception {
String url = "jdbc:mysql://localhost:3306/test?useUnicode=true&characterEncoding=UTF-8&zeroDateTimeBehavior=convertToNull&useSSL=false&autoReconnect=true";
Connection connection = DBConnectUtil.getConnection(url, "root", "123456");
System.err.println("start beginTransaction......."+connection);
return connection;
}
/**
* 预提交，这里预提交的逻辑在invoke方法中
* @param connection
* @throws Exception
*/
@Override
protected void preCommit(Connection connection) throws Exception {
System.err.println("start preCommit......."+connection);
}
/**
* 如果invoke执行正常则提交事物
* @param connection
*/
@Override
protected void commit(Connection connection) {
System.err.println("start commit......."+connection);
DBConnectUtil.commit(connection);
}
@Override
protected void recoverAndCommit(Connection connection) {
System.err.println("start recoverAndCommit......."+connection);
}
@Override
protected void recoverAndAbort(Connection connection) {
System.err.println("start abort recoverAndAbort......."+connection);
}
/**
* 如果invoke执行异常则回滚事物，下一次的checkpoint操作也不会执行
* @param connection
*/
@Override
protected void abort(Connection connection) {
System.err.println("start abort rollback......."+connection);
DBConnectUtil.rollback(connection);
}
}
3、DBConnectUtil.java
package com.fwmagic.flink.util;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.SQLException;
public class DBConnectUtil {
/**
* 获取连接
*
* @param url
* @param user
* @param password
* @return
* @throws SQLException
*/
public static Connection getConnection(String url, String user, String password) throws SQLException {
Connection conn = null;
try {
Class.forName("com.mysql.jdbc.Driver");
} catch (ClassNotFoundException e) {
e.printStackTrace();
}
conn = DriverManager.getConnection(url, user, password);
//设置手动提交
conn.setAutoCommit(false);
return conn;
}
/**
* 提交事物
*/
public static void commit(Connection conn) {
if (conn != null) {
try {
conn.commit();
} catch (SQLException e) {
e.printStackTrace();
} finally {
close(conn);
}
}
}
/**
* 事物回滚
*
* @param conn
*/
public static void rollback(Connection conn) {
if (conn != null) {
try {
conn.rollback();
} catch (SQLException e) {
e.printStackTrace();
} finally {
close(conn);
}
}
}
/**
* 关闭连接
*
* @param conn
*/
public static void close(Connection conn) {
if (conn != null) {
try {
conn.close();
} catch (SQLException e) {
e.printStackTrace();
}
}
}
}四、代码测试
为了方便发送消息，我用一个定时任务每秒发送一个数字，1～16，在发送到数字15之前，应该是做过一次checkpoint了，并且快要到第二次checkpoint的时间，第一次checkpoint的消费数据成功将插入数据库中，在消费到数字15的时候，手动造一个异常，此时数据库中应该只有第一次checkpoint后commit的数据，第二次checkpoint的数据并不会插入到数据库中（因为预提交已经失败，不会进行真正的提交），我实验的日志信息：
start invoke.......
===>date:2019-05-28 18:36:50 {"value":1,"metadata":{"offset":892,"topic":"gaga","partition":0}}
start invoke.......
===>date:2019-05-28 18:36:51 {"value":2,"metadata":{"offset":887,"topic":"gaga","partition":2}}
start invoke.......
===>date:2019-05-28 18:36:52 {"value":3,"metadata":{"offset":889,"topic":"gaga","partition":1}}
start invoke.......
===>date:2019-05-28 18:36:53 {"value":4,"metadata":{"offset":893,"topic":"gaga","partition":0}}
start invoke.......
===>date:2019-05-28 18:36:54 {"value":5,"metadata":{"offset":888,"topic":"gaga","partition":2}}
start invoke.......
===>date:2019-05-28 18:36:55 {"value":6,"metadata":{"offset":890,"topic":"gaga","partition":1}}
start invoke.......
===>date:2019-05-28 18:36:56 {"value":7,"metadata":{"offset":894,"topic":"gaga","partition":0}}
start invoke.......
===>date:2019-05-28 18:36:57 {"value":8,"metadata":{"offset":889,"topic":"gaga","partition":2}}
start preCommit.......
start beginTransaction.......
start commit.......com.mysql.jdbc.JDBC4Connection@3c5ad420
start invoke.......
===>date:2019-05-28 18:36:58 {"value":9,"metadata":{"offset":891,"topic":"gaga","partition":1}}
start invoke.......
===>date:2019-05-28 18:36:59 {"value":10,"metadata":{"offset":895,"topic":"gaga","partition":0}}
start invoke.......
===>date:2019-05-28 18:37:00 {"value":11,"metadata":{"offset":890,"topic":"gaga","partition":2}}
start invoke.......
===>date:2019-05-28 18:37:01 {"value":12,"metadata":{"offset":892,"topic":"gaga","partition":1}}
start invoke.......
===>date:2019-05-28 18:37:02 {"value":13,"metadata":{"offset":896,"topic":"gaga","partition":0}}
start invoke.......
===>date:2019-05-28 18:37:03 {"value":14,"metadata":{"offset":891,"topic":"gaga","partition":2}}
start invoke.......
===>date:2019-05-28 18:37:04 {"value":15,"metadata":{"offset":893,"topic":"gaga","partition":1}}
start abort rollback.......com.mysql.jdbc.JDBC4Connection@5f2afc1b
start commit.......com.mysql.jdbc.JDBC4Connection@71ed09a
java.lang.ArithmeticException: / by zero
at com.fwmagic.flink.sink.MySqlTwoPhaseCommitSink.invoke(MySqlTwoPhaseCommitSink.java:36)
at com.fwmagic.flink.sink.MySqlTwoPhaseCommitSink.invoke(MySqlTwoPhaseCommitSink.java:16)
at org.apache.flink.streaming.api.functions.sink.TwoPhaseCommitSinkFunction.invoke(TwoPhaseCommitSinkFunction.java:228)
at org.apache.flink.streaming.api.operators.StreamSink.processElement(StreamSink.java:56)
at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator(OperatorChain.java:579)
at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:554)
at org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect(OperatorChain.java:534)
at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:718)
at org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect(AbstractStreamOperator.java:696)
at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collect(StreamSourceContexts.java:104)
at org.apache.flink.streaming.api.operators.StreamSourceContexts$NonTimestampContext.collectWithTimestamp(StreamSourceContexts.java:111)
at org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher.emitRecordWithTimestamp(AbstractFetcher.java:398)
at org.apache.flink.streaming.connectors.kafka.internal.Kafka010Fetcher.emitRecord(Kafka010Fetcher.java:91)
at org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop(Kafka09Fetcher.java:156)
at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run(FlinkKafkaConsumerBase.java:711)
at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:93)
at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:57)
at org.apache.flink.streaming.runtime.tasks.SourceStreamTask.run(SourceStreamTask.java:97)
at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:300)
at org.apache.flink.runtime.taskmanager.Task.run(Task.java:711)
at java.lang.Thread.run(Thread.java:748)从日志中可以看到第一次commit时间在2019-05-28 18:36:57，成功入库到数据为1-8，第二次消费到数字15的时候，提交失败，日志最后一行发生了回滚，关闭了连接，然后进行conmit的时候也失败了，消费的数据9-15不会插入到数据库中，此时checkpoint也不会做了，checkpoint保存的还是上一次成功消费后的offset数据。
数据库表：t_test
CREATE TABLE `t_test` (
`id` bigint(20) NOT NULL AUTO_INCREMENT,
`value` varchar(255) DEFAULT NULL,
`insert_time` datetime DEFAULT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
表中的数据：
五、完整代码地址：https://gitee.com/fang_wei/fwmagic-flink
分享名称：Flink实现Kafka到Mysql的Exactly-Once
分享地址：
http://www.cdkjz.cn/article/ihooid.html
