新特性！可以替代 Canal 的数据同步方案——Flink-CDC

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<dependencies>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-java</artifactId>
        <version>1.12.0</version>
    </dependency>


    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-java_2.12</artifactId>
        <version>1.12.0</version>
    </dependency>


    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-clients_2.12</artifactId>
        <version>1.12.0</version>
    </dependency>


    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>3.1.3</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>
</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>
    </plugins>
</build>

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import com.alibaba.ververica.cdc.connectors.mysql.MySQLSource;

import com.alibaba.ververica.cdc.debezium.DebeziumSourceFunction;

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 java.util.Properties;

public class FlinkCDC {

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

        //1.创建执行环境

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        env.setParallelism(1);

        //2.Flink-CDC将读取binlog的位置信息以状态的方式保存在CK,如果想要做到断点续传,需要从Checkpoint或者Savepoint启动程序

        //2.1 开启Checkpoint,每隔5秒钟做一次CK

        env.enableCheckpointing(5000L);

        //2.2 指定CK的一致性语义

env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);

        //2.3 设置任务关闭的时候保留最后一次CK数据

env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);

        //2.4 指定从CK自动重启策略

        env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, 2000L));

        //2.5 设置状态后端

        env.setStateBackend(new FsStateBackend("hdfs://hadoop102:8020/flinkCDC"));

        //2.6 设置访问HDFS的用户名

        System.setProperty("HADOOP_USER_NAME", "atguigu");

        //3.创建Flink-MySQL-CDC的Source

        //initial (default): Performs an initial snapshot on the monitored database tables upon first startup, and continue to read the latest binlog.

        //latest-offset: Never to perform snapshot on the monitored database tables upon first startup, just read from the end of the binlog which means only have the changes since the connector was started.

        //timestamp: Never to perform snapshot on the monitored database tables upon first startup, and directly read binlog from the specified timestamp. The consumer will traverse the binlog from the beginning and ignore change events whose timestamp is smaller than the specified timestamp.

        //specific-offset: Never to perform snapshot on the monitored database tables upon first startup, and directly read binlog from the specified offset.

        DebeziumSourceFunction<String> mysqlSource = MySQLSource.<String>builder()

                .hostname("hadoop102")

                .port(3306)

                .username("root")

                .password("000000")

                .databaseList("gmall-flink")

                .tableList("gmall-flink.z_user_info") //可选配置项,如果不指定该参数,则会读取上一个配置下的所有表的数据，注意：指定的时候需要使用"db.table"的方式

                .debeziumProperties(properties)

                .startupOptions(StartupOptions.initial())

                .build();

        //4.使用CDC Source从MySQL读取数据

        DataStreamSource<String> mysqlDS = env.addSource(mysqlSource);

        //5.打印数据

        mysqlDS.print();

        //6.执行任务

        env.execute();

    }

}

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[atguigu@hadoop102 flink-standalone]$ bin/start-cluster.sh

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[atguigu@hadoop102 flink-standalone]$ start-dfs.sh

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[atguigu@hadoop102 flink-standalone]$ bin/flink run -c com.atguigu.FlinkCDCflink-1.0-SNAPSHOT-jar-with-dependencies.jar

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[atguigu@hadoop102 flink-standalone]$ bin/flink savepoint JobId hdfs://hadoop102:8020/flink/save

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[atguigu@hadoop102 flink-standalone]$ bin/flink run -s hdfs://hadoop102:8020/flink/save/...-c com.atguigu.FlinkCDC flink-1.0-SNAPSHOT-jar-with-dependencies.jar

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<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-table-planner-blink_2.12</artifactId>
    <version>1.12.0</version>
</dependency>

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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.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

public class FlinkSQL_CDC {

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

        //1.创建执行环境
        StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        StreamTableEnvironment tableEnv =StreamTableEnvironment.create(env);
        //2.创建Flink-MySQL-CDC的Source
        tableEnv.executeSql("CREATE TABLE user_info (" +
                "  idINT," +
                " name STRING," +
                " phone_num STRING" +
                ") WITH (" +
                " 'connector' = 'mysql-cdc'," +
                " 'hostname' = 'hadoop102'," +
                " 'port' = '3306'," +
                " 'username' = 'root'," +
                " 'password' = '000000'," +
                " 'database-name' = 'gmall-flink'," +
                " 'table-name' = 'z_user_info'" +
                ")");

        tableEnv.executeSql("select * from user_info").print();

        env.execute();

    }

}

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

import java.util.Properties;

public class Flink_CDCWithCustomerSchema {

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

        //1.创建执行环境
        StreamExecutionEnvironmentenv = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //2.创建Flink-MySQL-CDC的Source
        Properties properties= new Properties();

        //initial (default):Performs an initial snapshot on the monitored database tables upon firststartup, and continue to read the latest binlog.
        //latest-offset: Never to performsnapshot on the monitored database tables upon first startup, just read fromthe end of the binlog which means only have the changes since the connector wasstarted.
        //timestamp: Never to performsnapshot on the monitored database tables upon first startup, and directly readbinlog from the specified timestamp. The consumer will traverse the binlog fromthe beginning and ignore change events whose timestamp is smaller than thespecified timestamp.
        //specific-offset: Never toperform snapshot on the monitored database tables upon first startup, anddirectly read binlog from the specified offset.
       DebeziumSourceFunction<String> mysqlSource =MySQLSource.<String>builder()
                .hostname("hadoop102")
                .port(3306)
                .username("root")
                .password("000000")
                .databaseList("gmall-flink")
                .tableList("gmall-flink.z_user_info")         //可选配置项,如果不指定该参数,则会读取上一个配置下的所有表的数据,注意：指定的时候需要使用"db.table"的方式
                .debeziumProperties(properties)
.startupOptions(StartupOptions.initial())
                .deserializer(new DebeziumDeserializationSchema<String>(){  //自定义数据解析器
                    @Override
                    public void deserialize(SourceRecord sourceRecord,Collector<String> collector) throws Exception {

                        //获取主题信息,包含着数据库和表名 mysql_binlog_source.gmall-flink.z_user_info
                        String topic = sourceRecord.topic();
                        String[] arr =topic.split("\\.");
                        String db = arr[1];
                        String tableName= arr[2];

                        //获取操作类型 READ DELETE UPDATE CREATE
                        Envelope.Operation operation = Envelope.operationFor(sourceRecord);

                        //获取值信息并转换为Struct类型
                        Struct value = (Struct) sourceRecord.value();

                        //获取变化后的数据
                        Struct after = value.getStruct("after");

                        //创建JSON对象用于存储数据信息
                        JSONObject data = new JSONObject();
                        for (Field field : after.schema().fields()) {
                            Object o =after.get(field);
                            data.put(field.name(), o);
                        }

                        //创建JSON对象用于封装最终返回值数据信息
                        JSONObject result = new JSONObject();
                        result.put("operation", operation.toString().toLowerCase());
                        result.put("data", data);
                        result.put("database", db);
                        result.put("table", tableName);

                        //发送数据至下游
                        collector.collect(result.toJSONString());
                    }

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

       //3.使用CDC Source从MySQL读取数据
        DataStreamSource<String> mysqlDS =env.addSource(mysqlSource);

        //4.打印数据
        mysqlDS.print();

        //5.执行任务
        env.execute();
    }
}