大数据-业务数据采集[通俗易懂]

CDC

CDC 是 Change Data Capture(变更数据获取)的简称。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他服务进行订阅及消费。

CDC 的种类

CDC 主要分为基于查询和基于 Binlog 两种方式，我们主要了解一下这两种之间的区别：

FlinkCDC

Flink 社区开发了 flink-cdc-connectors 组件，这是一个可以直接从 MySQL、PostgreSQL 等数据库直接读取【全量数据】和【增量变更数据】的 source 组件。而不需要使用类似 Kafka 之类的中间件中转数据
目前也已开源，开源地址：https://github.com/ververica/flink-cdc-connectors

DataStream:

• 优点： 多库多表
• 缺点： 需要自定义反序列化器（但灵活）
  FlinkSQL:
• 优点： 不需要自定义反序列化器
• 缺点： 单表

Demo

注意开启 binlog_format=ROW
my.ini

log-bin=mysql-bin
#binlog_format="STATEMENT"
binlog_format="ROW"
#binlog_format="MIXED"
#service-id=1

POM

  <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>

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

基于 DataStream

CustomerDeserialization.java

package com.vipsoft;

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

import java.util.List;

public class CustomerDeserialization implements DebeziumDeserializationSchema<String> {

    /**
     * 封装的数据格式
     * {
     * "database":"",
     * "tableName":"",
     * "before":{"id":"","tm_name":""....},
     * "after":{"id":"","tm_name":""....},
     * "type":"c u d",
     * //"ts":156456135615
     * }
     */
    @Override
    public void deserialize(SourceRecord sourceRecord, Collector<String> collector) throws Exception {

        //1.创建JSON对象用于存储最终数据
        JSONObject result = new JSONObject();

        //2.获取库名&表名
        String topic = sourceRecord.topic();
        String[] fields = topic.split("\.");
        String database = fields[1];
        String tableName = fields[2];

        Struct value = (Struct) sourceRecord.value();
        //3.获取"before"数据
        Struct before = value.getStruct("before");
        JSONObject beforeJson = new JSONObject();
        if (before != null) {
            Schema beforeSchema = before.schema();
            List<Field> beforeFields = beforeSchema.fields();
            for (Field field : beforeFields) {
                Object beforeValue = before.get(field);
                beforeJson.put(field.name(), beforeValue);
            }
        }

        //4.获取"after"数据
        Struct after = value.getStruct("after");
        JSONObject afterJson = new JSONObject();
        if (after != null) {
            Schema afterSchema = after.schema();
            List<Field> afterFields = afterSchema.fields();
            for (Field field : afterFields) {
                Object afterValue = after.get(field);
                afterJson.put(field.name(), afterValue);
            }
        }

        //5.获取操作类型  CREATE UPDATE DELETE
        Envelope.Operation operation = Envelope.operationFor(sourceRecord);
        String type = operation.toString().toLowerCase();
        if ("create".equals(type)) {
            type = "insert";
        }

        //6.将字段写入JSON对象
        result.put("database", database);
        result.put("tableName", tableName);
        result.put("before", beforeJson);
        result.put("after", afterJson);
        result.put("type", type);

        //7.输出数据
        collector.collect(result.toJSONString());

    }

    @Override
    public TypeInformation<String> getProducedType() {
        return BasicTypeInfo.STRING_TYPE_INFO;
    }
}

FlinkCDC.java

package com.vipsoft;

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.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);

        //2.通过FlinkCDC构建SourceFunction并读取数据
        DebeziumSourceFunction<String> sourceFunction = MySQLSource.<String>builder()
                .hostname("localhost")
                .serverTimeZone("GMT+8")  //时区报错增加这个设置
                .port(3306)
                .username("root")
                .password("110")
                .databaseList("springboot")
                .tableList("springboot.sys_user")   //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())
                .deserializer(new CustomerDeserialization()) //使用自定义反序列化器
                .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");

    }
}

运行效果

• 默认 StringDebeziumDeserializationSchema
  
• 自定义反序列化器
  

FlinkSQL

package com.vipsoft;

import org.apache.flink.api.java.tuple.Tuple2;
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 FlinkCDCWithSQL {

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

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

        //2.DDL方式建表
        tableEnv.executeSql("CREATE TABLE mysql_binlog ( " +
                " id STRING NOT NULL, " +
                " username STRING, " +
                " nick_name STRING " +
                ") WITH ( " +
                " "connector" = "mysql-cdc", " +
                " "hostname" = "localhost", " +
                " "port" = "3306", " +
                " "username" = "root", " +
                " "password" = "110", " +
                " "database-name" = "springboot", " +
                " "table-name" = "sys_user" " +
                ")");

        //3.查询数据
        Table table = tableEnv.sqlQuery("select * from mysql_binlog");

        //4.将动态表转换为流
        DataStream<Tuple2<Boolean, Row>> retractStream = tableEnv.toRetractStream(table, Row.class);
        retractStream.print();

        //5.启动任务
        env.execute("FlinkCDCWithSQL");
    }
}

运行效果

对比

通过对比，FlinkCDC 最舒服

插入对比

插入两条数据

INSER INTO z_user_info VALUES(30,"zhang3","13800000000"),(31,"li4","13999999999")

FlinkCDC 每条变化都会产生一条 json

Maxwell 每条变化都会产生一条 json

Canal 一次性执行的SQL，会产生一条JSON（两条数据组合在一起）【不方便，需要炸开解析】

更新对比

UPDATE z_user_info SET user_name="wang5" WHERE id IN(30,31)

FlinkCDC 包括了修改前的 before 数据

Maxwell 不包括修改前的数据

Canal 仍然是一条json

删除对比

DELETE FROM z_user_info WHERE id IN(30,31)

FlinkCDC 两条删除的 json 数据

Maxwell

Canal

【尚硅谷】Flink数据仓库视频教程