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一文解开主流开源变更数据捕获技术之Flink CDC的入门使用_flink-cdc-connectors

作者：从前慢现在也慢 | 2024-04-23 08:30:45

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flink-cdc-connectors

文章目录

概述

定义

flink-cdc-connectors 官网 https://github.com/ververica/flink-cdc-connectors 源码release最新版本2.4.0

flink-cdc-connectors 文档地址 https://ververica.github.io/flink-cdc-connectors/master/

flink-cdc-connectors 源码地址 https://github.com/ververica/flink-cdc-connectors

CDC Connectors for Apache Flink 是Apache Flink的一组源连接器，使用更改数据捕获(CDC)从不同的数据库摄取更改，其集成了Debezium作为捕获数据变化的引擎，因此它可以充分利用Debezium的能力。

Flink CDC是由Flink社区开发的flink-cdc-connectors 的source组件，基于数据库日志的 Change Data Caputre 技术，实现了从 MySQL、PostgreSQL 等数据库全量和增量的一体化读取能力，并借助 Flink 优秀的管道能力和丰富的上下游生态，支持捕获多种数据库的变更，并将这些变更实时同步到下游存储。

什么是CDC?

这里也简单说明下，CDC为三个英文Change Data Capture（变更数据捕获）的缩写，核心思想是监测并捕获数据库的变动（包括数据或数据表的插入、更新以及删除等），将这些变更按发生的顺序完整记录下来，写入到消息中间件中以供其它服务进行订阅及消费。

CDC的分类

CDC主要分为基于查询的CDC和基于binlog的CDC，两者之间区别主要如下：

image-20230626155037373

特性

  • 支持读取数据库快照，即使发生故障，也只进行一次处理，继续读取事务日志。
  • 数据流API的CDC连接器，用户可以在单个作业中消费多个数据库和表上的更改，而无需部署Debezium和Kafka。
  • 用于表/SQL API的CDC连接器，用户可以使用SQL DDL创建CDC源来监视单个表上的更改。

应用场景

  • 数据分发，将一个数据源分发给多个下游，常用于业务解耦、微服务。
  • 数据集成，将分散异构的数据源集成到数据仓库中，消除数据孤岛，便于后续的分析。
  • 数据迁移，常用于数据库备份、容灾等。

支持数据源

CDC Connectors for Apache Flink支持从多种数据库到Flink摄取快照数据和实时更改，然后转换和下沉到各种下游系统

image-20230626154109523

支撑数据源包括如下：

image-20230626160745676

实战

Flink DataStream方式代码示例

这里以MySQL作为数据源为例，通过flink-connector-mysql-cdc实现数据变更获取，先准备MySQL环境，这里复用前面<<实时采集MySQL数据之轻量工具Maxwell实操>>的文章环境，数据库有两个my_maxwell_01，my_maxwell_02，每个数据库都有相同account和product表。pom文件引入依赖

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>cn.itxs.flink</groupId>
    <artifactId>flink-cdc-demo</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <flink.version>1.17.1</flink.version>
        <flink.cdc.version>2.4.0</flink.cdc.version>
        <mysql.client.version>8.0.29</mysql.client.version>
        <fastjson.version>1.2.83</fastjson.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-base</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java-bridge</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-loader</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-runtime</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>${mysql.client.version}</version>
        </dependency>
        <dependency>
            <groupId>com.ververica</groupId>
            <artifactId>flink-connector-mysql-cdc</artifactId>
            <version>${flink.cdc.version}</version>
        </dependency>
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>${fastjson.version}</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>3.2.4</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <artifactSet>
                                <excludes>
                                    <exclude>com.google.code.findbugs:jsr305</exclude>
                                    <exclude>org.slf4j:*</exclude>
                                    <exclude>log4j:*</exclude>
                                </excludes>
                            </artifactSet>
                            <filters>
                                <filter>
                                    <!-- Do not copy the signatures in the META-INF folder.
                                    Otherwise, this might cause SecurityExceptions when using the JAR. -->
                                    <artifact>*:*</artifact>
                                    <excludes>
                                        <exclude>META-INF/*.SF</exclude>
                                        <exclude>META-INF/*.DSA</exclude>
                                        <exclude>META-INF/*.RSA</exclude>
                                    </excludes>
                                </filter>
                            </filters>
                            <transformers combine.children="append">
                                <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer">
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

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创建DataStreamDemo.java，

package cn.itxs.cdc;

import com.ververica.cdc.connectors.mysql.source.MySqlSource;
import com.ververica.cdc.debezium.JsonDebeziumDeserializationSchema;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

public class DataStreamDemo {
    public static void main(String[] args) throws Exception {
        MySqlSource<String> mySqlSource = MySqlSource.<String>builder()
                .hostname("mysqlserver")
                .port(3306)
                .databaseList("my_maxwell_01,my_maxwell_02")
                .tableList("my_maxwell_01.*,my_maxwell_02.product")
                .username("root")
                .password("123456")
                .startupOptions(StartupOptions.initial())
                .deserializer(new JsonDebeziumDeserializationSchema()) // 将SourceRecord转换为JSON字符串
                .build();

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 开启checkpoint
        env.enableCheckpointing(3000);

        env
                .fromSource(mySqlSource, WatermarkStrategy.noWatermarks(), "MySQL Source")
                // 设置平行度为4
                .setParallelism(4)
                .print().setParallelism(1); // 对sink打印使用并行性1来保持消息顺序

        env.execute("Print MySQL Snapshot + Binlog");
    }
}

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mySqlSource的startupOptions（MySQL CDC 消费者可选的启动模式，SQL对应为scan.startup.mode选项）有几种可选启动模式，包括"initial"，“earliest-offset”，“latest-offset”，“specific-offset” 和 “timestamp”，默认（也即是不设置）为StartupOptions.initial。

由于上面flink的依赖配置provided，因此在IDEA中启动的话需要勾选下面标红的选项

image-20230627165925042

启动程序，查看日志可以看到从mysql读取目前全量的数据，my_maxwell_02也只读取product表数据

image-20230627170148732

修改两个库的表后可以看到相应修改信息，其中也确认my_maxwell_02的account没有读取变更数据。

image-20230627170435984

{"before":{"id":7,"name":"李丹","age":44},"after":{"id":7,"name":"李丹","age":48},"source":{"version":"1.9.7.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1687856595000,"snapshot":"false","db":"my_maxwell_01","sequence":null,"table":"account","server_id":1,"gtid":null,"file":"binlog.000025","pos":2798,"row":0,"thread":330184,"query":null},"op":"u","ts_ms":1687856598620,"transaction":null}
{"before":{"id":1,"name":"iphone13","type":1},"after":{"id":1,"name":"iphone14","type":1},"source":{"version":"1.9.7.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1687856605000,"snapshot":"false","db":"my_maxwell_01","sequence":null,"table":"product","server_id":1,"gtid":null,"file":"binlog.000025","pos":3140,"row":0,"thread":330184,"query":null},"op":"u","ts_ms":1687856608748,"transaction":null}
{"before":{"id":1,"name":"iphone13","type":1},"after":{"id":1,"name":"iphone14","type":1},"source":{"version":"1.9.7.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1687856628000,"snapshot":"false","db":"my_maxwell_02","sequence":null,"table":"product","server_id":1,"gtid":null,"file":"binlog.000025","pos":3486,"row":0,"thread":330184,"query":null},"op":"u","ts_ms":1687856631643,"transaction":null}

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如果需要断点续传可以使用状态后端存储来实现,在集群运行可增加checkpoint的相关配置实现持久化

        env.enableCheckpointing(TimeUnit.SECONDS.toMillis(5), CheckpointingMode.EXACTLY_ONCE);
        CheckpointConfig checkpointConfig = env.getCheckpointConfig();
        checkpointConfig.setCheckpointStorage("hdfs://hadoop2:9000/checkpoints/flink/cdc");
        checkpointConfig.setMinPauseBetweenCheckpoints(TimeUnit.SECONDS.toMillis(2));
        checkpointConfig.setTolerableCheckpointFailureNumber(5);
        checkpointConfig.setCheckpointTimeout(TimeUnit.MINUTES.toMillis(1));
        checkpointConfig.setExternalizedCheckpointCleanup(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);

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增加打包后放到集群上，执行

./bin/flink run -m hadoop1:8081 -c cn.itxs.cdc.DataStreamDemo ./lib/flink-cdc-demo-1.0-SNAPSHOT.jar 

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image-20230627185543185

可以看到的日志也成功输出表的全量的日志和刚才修改增量数据

image-20230627185508643

我这里由于中间停止过重新运行，job编号已变化。因此用新的job，设置保存点

./bin/flink savepoint e4ef292c211975c3128edfe86fbff231 hdfs://hadoop2:9000/savepoints/flink

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image-20230628101036682

在保存点后执行完后修改MySQL的数据；在上面hdfs上目录找到对应创建保存点文件夹，并停止上面运行中的job，从保存点重启启动job

./bin/flink run -m hadoop1:8081 -s hdfs://hadoop2:9000/savepoints/flink/savepoint-7e6b99-99d88b51d00c -c cn.itxs.cdc.DataStreamDemo ./lib/flink-cdc-demo-1.0-SNAPSHOT.jar 

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启动后也修改MySQL的数据，可以看到启动后只读取了后面修改两条数据已中断继续读取功能

image-20230628102252771

FlinkSQL方式代码示例

创建SqlDemo.java文件

package cn.itxs.cdc;

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

public class SqlDemo {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
        tableEnv.executeSql("CREATE TABLE account (\n" +
                " id INT NOT NULL,\n" +
                " name STRING,\n" +
                " age INT,\n" +
                " PRIMARY KEY(id) NOT ENFORCED\n" +
                ") WITH (\n" +
                " 'connector' = 'mysql-cdc',\n" +
                " 'hostname' = 'mysqlserver',\n" +
                " 'port' = '3306',\n" +
                " 'username' = 'root',\n" +
                " 'password' = '123456',\n" +
                " 'database-name' = 'my_maxwell_01',\n" +
                " 'table-name' = 'account'\n" +
                ");");

        Table table = tableEnv.sqlQuery("select * from account");
        DataStream<Row> rowDataStream = tableEnv.toChangelogStream(table);
        rowDataStream.print("account_binlog====");
        env.execute();
    }
}

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启动程序，查看日志可以看到从mysql读取my_maxwell_01库account表的全量的数据，修改表数据也确认读取变更数据。

image-20230627182647771

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