4.3.3 Flink-流处理框架-Flink CDC数据实时数据同步-Flink CDC实操-DataStream方式1.写在前面2.相关依赖3.代码实现4.运行效果

60 阅读 0 评论 40 点赞

我是靠谱客的博主踏实花卷，最近开发中收集的这篇文章主要介绍4.3.3 Flink-流处理框架-Flink CDC数据实时数据同步-Flink CDC实操-DataStream方式1.写在前面2.相关依赖3.代码实现4.运行效果，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

1.写在前面

2.相关依赖

3.代码实现

4.运行效果

1.写在前面

Flink CDC有两种实现方式，一种是DataStream方式，一种是FlinkSQL方式。

2.相关依赖

<dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.12.7</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.12</artifactId>
            <version>1.12.7</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>1.12.7</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.7</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.49</version>
        </dependency>

        <dependency>
            <groupId>com.alibaba.ververica</groupId>
            <artifactId>flink-connector-mysql-cdc</artifactId>
            <version>1.2.0</version>
        </dependency>

        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.75</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-blink_2.12</artifactId>
            <version>1.12.7</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-assembly-plugin</artifactId>
                <version>3.0.0</version>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <configuration>
                    <source>8</source>
                    <target>8</target>
                </configuration>
            </plugin>
        </plugins>
    </build>

3.代码实现

import com.alibaba.ververica.cdc.connectors.mysql.MySQLSource;
import com.alibaba.ververica.cdc.connectors.mysql.table.StartupOptions;
import com.alibaba.ververica.cdc.debezium.DebeziumSourceFunction;
import com.alibaba.ververica.cdc.debezium.StringDebeziumDeserializationSchema;
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.StreamExecutionEnvironment;

public class FlinkCDC {

    public static void main(String[] args) throws Exception {

        //1.获取执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //1.1 开启CK并指定状态后端为FS    memory  fs  rocksdb
        env.setStateBackend(new FsStateBackend("hdfs://192.168.0.111:9000/gmall-flink-cdc/ck"));
        env.enableCheckpointing(5000L);
        env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
        env.getCheckpointConfig().setCheckpointTimeout(10000L);
        env.getCheckpointConfig().setMaxConcurrentCheckpoints(2);
        env.getCheckpointConfig().setMinPauseBetweenCheckpoints(3000);

//        env.setRestartStrategy(RestartStrategies.fixedDelayRestart());

        //2.通过FlinkCDC构建SourceFunction并读取数据
        DebeziumSourceFunction<String> sourceFunction = MySQLSource.<String>builder()
                .hostname("192.168.0.111")
                .port(3306)
                .username("root")
                .password("123456")
                .databaseList("gmall2021")
                .tableList("gmall2021.user_info")   //如果不添加该参数,则消费指定数据库中所有表的数据.如果指定,指定方式为db.table
                .deserializer(new StringDebeziumDeserializationSchema())
                .startupOptions(StartupOptions.initial())
                .build();
        DataStreamSource<String> streamSource = env.addSource(sourceFunction);
        //3.打印数据
        streamSource.print();
        //4.启动任务
        env.execute("FlinkCDC");
    }
}