Flink流处理引擎零基础速通之数据的抽取篇

2024-04-02 19:04:59 877人浏览薄情痞子

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摘要

目录一、CDC二、常见CDC的比较三、flink CDC四、Flink CDC支持的数据库五、阿里实现的FlinkCDC使用示例依赖引入基于table基于sql总结一、CDC CDC

一、CDC

CDC （Change Data Capture），在广义的概念上，只要能捕获数据变更的技术，都可以称为 CDC 。但通常我们说的CDC 技术主要面向数据库（包括常见的Mysql,oracle, mongoDB等）的变更，是一种用于捕获数据库中数据变更的技术。

二、常见CDC的比较

常见的主要包括Flink CDC,DataX,Canal,Sqoop,Kettle,Oracle Goldengate,Debezium等。

DataX，Sqoop和kettle的CDC实现技术主要是基于查询的方式实现的，通过离线调度查询作业，实现批处理请求。这种作业方式无法保证数据的一致性，实时性也较差。
Flink CDC，Canal，Debezium和Oracle Goldengate是基于日志的CDC技术。这种技术，利用流处理的方式，实时处理日志数据，保证了数据的一致性，为其他服务提供了实时数据。

三、Flink CDC

2020年 Flink cdc 首次在 Flink forward 大会上官宣，由 jark Wu & Qingsheng Ren 两位大佬提出。

Flink CDC connector 可以捕获在一个或多个表中发生的所有变更。该模式通常有一个前记录和一个后记录。Flink CDC connector 可以直接在Flink中以非约束模式（流）使用，而不需要使用类似 kafka 之类的中间件中转数据。

四、Flink CDC支持的数据库

PS：

Flink CDC 2.2才新增OceanBase，PolarDB-X，SqlServer，TiDB 四种数据源接入，均支持全量和增量一体化同步。

截止到目前FlinkCDC已经支持12+数据源。

五、阿里实现的FlinkCDC使用示例

依赖引入

    <!-- flink table支持 -->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-table-api-java</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-table-api-java-bridge_${Scala.binary.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-table-planner-blink_${scala.binary.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <!-- 阿里实现的flink mysql CDC -->
    <dependency>
      <groupId>com.alibaba.ververica</groupId>
      <artifactId>flink-connector-mysql-cdc</artifactId>
      <version>1.4.0</version>
    </dependency>
    <dependency>
      <groupId>mysql</groupId>
      <artifactId>mysql-connector-java</artifactId>
      <version>8.0.28</version>
    </dependency>
    <dependency>
      <groupId>com.alibaba</groupId>
      <artifactId>fastJSON</artifactId>
      <version>1.2.80</version>
    </dependency>
    <!-- jackson报错解决 -->
    <dependency>
      <groupId>com.fasterxml.jackson.core</groupId>
      <artifactId>jackson-core</artifactId>
      <version>${jackson.version}</version>
    </dependency>
    <dependency>
      <groupId>com.fasterxml.jackson.core</groupId>
      <artifactId>jackson-databind</artifactId>
      <version>${jackson.version}</version>
    </dependency>
    <dependency>
      <groupId>com.fasterxml.jackson.module</groupId>
      <artifactId>jackson-module-parameter-names</artifactId>
      <version>${jackson.version}</version>
    </dependency>

基于table

package spendreport.cdc;
import com.alibaba.fastjson.JSONObject;
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 io.debezium.data.Envelope;
import java.util.List;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
import org.apache.flink.api.common.typeinfo.TypeInfORMation;
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;
;

public class TestMySqlFlinkCDC {
  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));
    DebeziumSourceFunction<String> sourceFunction = MySQLSource.<String>builder()
        .hostname("127.0.0.1")
        .serverTimeZone("GMT+8")  //时区报错增加这个设置
        .port(3306)
        .username("root")
        .password("123456")
        .databaseList("wz")
        .tableList("wz.user_info")  //注意表一定要写库名.表名这种，多个，隔开
        .startupOptions(StartupOptions.initial())
        //自定义转json格式化
        .deserializer(new MyJsonDebeziumDeserializationSchema())
        //自带string格式序列化
        //.deserializer(new StringDebeziumDeserializationSchema())
        .build();
    DataStreamSource<String> streamSource = env.addSource(sourceFunction);
    //TODO 可以keyBy，比如根据table或type，然后开窗处理
    //3.打印数据
    streamSource.print();
    //streamSource.addSink(); 输出
    //4.执行任务
    env.execute("flinkTableCDC");
  }
  private static class MyJsonDebeziumDeserializationSchema implements
      com.alibaba.ververica.cdc.debezium.DebeziumDeserializationSchema<String> {
    @Override
    public void deserialize(SourceRecord sourceRecord, Collector<String> collector)
        throws Exception {
      Struct value = (Struct) sourceRecord.value();
      Struct source = value.getStruct("source");
      //获取数据库名称
      String db = source.getString("db");
      String table = source.getString("table");
      //获取数据类型
      String type = Envelope.operationFor(sourceRecord).toString().toLowerCase();
      if (type.equals("create")) {
        type = "insert";
      }
      JSONObject jsonObject = new JSONObject();
      jsonObject.put("database", db);
      jsonObject.put("table", table);
      jsonObject.put("type", type);
      //获取数据data
      Struct after = value.getStruct("after");
      JSONObject dataJson = new JSONObject();
      List<Field> fields = after.schema().fields();
      for (Field field : fields) {
        String field_name = field.name();
        Object fieldValue = after.get(field);
        dataJson.put(field_name, fieldValue);
      }
      jsonObject.put("data", dataJson);
      collector.collect(JSONObject.toJSONString(jsonObject));
    }
    @Override
    public TypeInformation<String> getProducedType() {
      return BasicTypeInfo.STRING_TYPE_INFO;
    }
  }
}

运行效果

PS：

操作数据库的增删改就会立马触发
这里是自定义的序列化转json格式字符串，自带的字符串序列化也是可以的（可以自己试试打印的内容）

基于sql

package spendreport.cdc;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

public class TestMySqlFlinkCDC2 {
  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
    String connectorName = "mysql-cdc";
    String dbHostName = "127.0.0.1";
    String dbPort = "3306";
    String dbUsername = "root";
    String dbPassWord = "123456";
    String dbDatabaseName = "wz";
    String dbTableName = "user_info";
    String tableSql = "CREATE TABLE t_user_info ("
        + "id int,mobile varchar(20),"
        + "user_name varchar(30),"
        + "real_name varchar(60),"
        + "id_card varchar(20),"
        + "org_name varchar(100),"
        + "user_stars int,"
        + "create_by int,"
        // + "create_time datetime,"
        + "update_by int,"
        // + "update_time datetime,"
        + "is_deleted int) "
        + " WITH ("
        + " 'connector' = '" + connectorName + "',"
        + " 'hostname' = '" + dbHostName + "',"
        + " 'port' = '" + dbPort + "',"
        + " 'username' = '" + dbUsername + "',"
        + " 'password' = '" + dbPassword + "',"
        + " 'database-name' = '" + dbDatabaseName + "',"
        + " 'table-name' = '" + dbTableName + "'"
        + ")";
    tableEnv.executeSql(tableSql);
    tableEnv.executeSql("select * from t_user_info").print();
    env.execute();
  }
}

运行效果：