FlinkCDC 实时监控 MySQL

通过 FlinkCDC 实现 MySQL 数据库、表的实时变化监控，这里只把变化打印了出来，后面会实现如何再写入其他 MySQL 库中；

1、开启 MySQL 的 binlog

在 my.cnf 中开启 binlog，我这里指定了 test 库，然后重启 MySQL

server.id=1
log-bin=mysql-bin
binlog-do-db=test

2、在 MySQL 中创建测试库和表

mysql> create database test;
mysql> create table user_info(id int unsigned not null auto_increment primary key, username varchar(60), sex tinyint(1), nickname varchar(60), addr varchar(255))ENGINE=InnoDB default charset=utf8mb4;

3、Flink 代码

在 IDEA 中新建工程 flinkcdc

pom.xml

    4.0.0

    com.zsoft.flinkcdc
    flinkcdc
    1.0-SNAPSHOT

    
        8
        8
        1.13.1
    

    
        
        
            org.apache.flink
            flink-java
            ${flink.version}
        

        
            org.apache.flink
            flink-streaming-java_2.12
            ${flink.version}
        

        
            org.apache.flink
            flink-clients_2.12
            ${flink.version}
        

        
            org.apache.hadoop
            hadoop-client
            3.1.3
        

        
            mysql
            mysql-connector-java
            8.0.22
        

        
            com.alibaba.ververica
            flink-connector-mysql-cdc
            1.4.0
        

        
            com.alibaba
            fastjson
            1.2.75
        
    

    
        
            
                org.apache.maven.plugins
                maven-assembly-plugin
                3.0.0
                
                    
                        jar-with-dependencies
                    
                
                
                    
                        make-assembly
                        package
                        
                            single
                        
                    
                
            
        
    

resources/log4j.properties

log4j.rootLogger=warn,stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.target=System.out
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n

反序列化类：

com/zsoft/flinkcdc/MyDeserializationSchema.java

package com.zsoft.flinkcdc;

import com.alibaba.fastjson.JSONObject;
import com.alibaba.ververica.cdc.debezium.DebeziumDeserializationSchema;
import io.debezium.data.Envelope;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.util.Collector;
import org.apache.kafka.connect.data.Field;
import org.apache.kafka.connect.data.Struct;
import org.apache.kafka.connect.source.SourceRecord;

public class MyDeserializationSchema implements DebeziumDeserializationSchema {
    @Override
    public void deserialize(SourceRecord sourceRecord, Collector collector) throws Exception {
        Struct valueStruct = (Struct) sourceRecord.value();
        Struct sourceStruct = valueStruct.getStruct("source");

        // 获取数据库的名称
        String database = sourceStruct.getString("db");
        // 获取表名
        String table = sourceStruct.getString("table");

        // 获取类型( c -> insert, u -> update)
        String type = Envelope.operationFor(sourceRecord).toString().toLowerCase();
        if(type.equals("create")){
            type = "insert";
        }
        JSONObject jsonObj = new JSONObject();
        jsonObj.put("database",database);
        jsonObj.put("table", table);
        jsonObj.put("type", type);

        // 获取数据 data
        Struct afterStruct = valueStruct.getStruct("after");
        JSONObject dataJsonObj = new JSONObject();
        if(afterStruct != null) {
            for(Field field : afterStruct.schema().fields()) {
                String fieldName = field.name();
                Object fieldValue = afterStruct.get(field);
                dataJsonObj.put(fieldName, fieldValue);
            }
        }
        jsonObj.put("data", dataJsonObj);

        collector.collect(jsonObj.toJSONString());
    }

    @Override
    public TypeInformation getProducedType() {
        return TypeInformation.of(String.class);
    }
}

主类：

com/zsoft/flinkcdc/FlinkCdcDataStream.java

package com.zsoft.flinkcdc;

import com.alibaba.ververica.cdc.connectors.mysql.MySQLSource;
import com.alibaba.ververica.cdc.connectors.mysql.table.StartupOptions;
import com.alibaba.ververica.cdc.debezium.StringDebeziumDeserializationSchema;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
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.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;

import java.util.Properties;

public class FlinkCdcDataStream {
    public static void main(String[] args) throws Exception {
        // TODO 1. 准备流处理环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        // TODO 2. 开启检查点
        // 2.1 开启 Checkpoint
        env.enableCheckpointing(5000L, CheckpointingMode.EXACTLY_ONCE);
        // 2.2 设置超时时间
        env.getCheckpointConfig().setCheckpointTimeout(60000);
        // 2.3 指定从 CK 自动重启策略
        env.setRestartStrategy(RestartStrategies.fixedDelayRestart(1, 6000L));
        // 2.4 设置任务关闭时候保留最后一次 CK 数据
        env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        // 2.5 设置状态后端
        env.setStateBackend(new FsStateBackend("hdfs://s1:8020/flinkCDC_DS"));
        // 2.6 设置访问 HDFS 的用户名
        System.setProperty("HADOOP_USER_NAME", "hadoop");

        // TODO 3. 创建 Flink-MySQL-CDC 的 Source

        Properties props = new Properties();
        props.setProperty("scan.startup.mode", "initial");
        SourceFunction sourceFunction = MySQLSource.builder()
                .hostname("s1")
                .port(3306)
                .username("root")
                .password("123456")
                .databaseList("test")
                .tableList("test.user_info")
                .startupOptions(StartupOptions.earliest())
                .debeziumProperties(props)
                .deserializer(new MyDeserializationSchema())
                .build();


        // TODO 4. 使用 CDC Source 从 MySQL 读取数据
        DataStreamSource mysqlDS = env.addSource(sourceFunction).setParallelism(1);

        // TODO 5. 打印输出
        mysqlDS.print();

        // TODO 6. 执行任务
        env.execute();
    }
}

4、打包运行

在 IDEA 中打包项目 package

将生成的 flinkcdc-1.0-SNAPSHOT-jar-with-dependencies.jar 通过 Flink 的 webUI 上传

在 Flink 的 WebUI 中上传 jar 包

Submit New Job 页面点击 + Add New 按钮

上传后的 jar 包下填入：

• Entry Class：com.zsoft.flinkcdc.FlinkCdcDataStream
• Parallelism：1
• Program Arguments：
• Savepoint Path：

点击 ”Submit“ 提交应用

5、测试

此时在 MySQL 中插入如下数据：

mysql> insert into user_info values(null, 'zhangsan', 1, 'zhs','beijing');

mysql> insert into user_info values(null, 'lisi', 1, 'ls','shanghai');

mysql> insert into user_info values(null, 'wangwu', 1, 'ww','wangwu');

在 Flink 的 webUI 中 Task Managers 中点击项目，在 Stdout 中有输出日志：

{"database":"test","data":{"sex":1,"nickname":"zhs","id":1,"addr":"beijing","username":"zhangsan"},"type":"insert","table":"user_info"}
{"database":"test","data":{"sex":1,"nickname":"ls","id":2,"addr":"shanghai","username":"lisi"},"type":"insert","table":"user_info"}
{"database":"test","data":{"sex":1,"nickname":"ww","id":3,"addr":"wangwu","username":"wangwu"},"type":"insert","table":"user_info"}