Flink CDC 与Hudi整合

介绍

之前写过Flink CDC sink 到 Iceberg中,本篇主要实践如何CDC到hudi中.

什么是hudi?

Hudi is a rich platform to build streaming data lakes with incremental data pipelines
on a self-managing database layer, while being optimized for lake engines and regular batch processing.

OmUyj0.md.png

hudi 主要解决什么问题?

  • HDFS的可伸缩性限制
  • 需要在Hadoop中更快地呈现数据
  • 没有直接支持对现有数据的更新和删除
  • 快速的ETL和建模
  • 要检索所有更新的记录，无论这些更新是添加到最近日期分区的新记录还是对旧数据的更新，Hudi都允许用户使用最后一个检查点时间戳。此过程不用执行扫描整个源表的查询

hudi的特性:

  • Upserts, Deletes with fast, pluggable indexing.
  • Incremental queries, Record level change streams
  • Transactions, Rollbacks, Concurrency Control.
  • SQL Read/Writes from Spark, Presto, Trino, Hive & more
  • Automatic file sizing, data clustering, compactions, cleaning.
  • Streaming ingestion, Built-in CDC sources & tools.
  • Built-in metadata tracking for scalable storage access.
  • Backwards compatible schema evolution and enforcement.

版本

Flink: 1.13.1

Hudi: 0.10.1

环境搭建

使用本地环境, hadoop 使用之前虚拟机安装的环境

MySQL Docker 安装个镜像,主要用于模拟数据变更,产生binlog数据

dockerpull mysql:latest
 
docker run -itd--name mysql -p 3306:3306 -e MYSQL_ROOT_PASSWORD=123456 mysql
 
进入容器，可以使用mysql连接验证：
 
dockerexec -it 07e946b1fa9a /bin/bash
 
 
mysql -uroot -p123456

创建MySQL表:

createtable users
(
    id bigint auto_increment primary key,
    name varchar(20) null,
    birthday timestamp defaultCURRENT_TIMESTAMP not null,
    ts timestamp defaultCURRENT_TIMESTAMP not null, 
    sex int
);

整合代码实践

pom.xml:

<?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>com.chaplinthink</groupId>
    <artifactId>flink-hudi</artifactId>
    <version>1.0-SNAPSHOT</version>
 
    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
    </properties>
 
    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.2.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>3.2.1</version>
            <exclusions>
                <exclusion>
                    <groupId>javax.servlet</groupId>
                    <artifactId>servlet-api</artifactId>
                </exclusion>
            </exclusions>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>3.2.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-core</artifactId>
            <version>1.13.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>1.13.1</version>
        </dependency> <!-- <dependency>--> <!-- <groupId>org.apache.flink</groupId>--> <!-- <artifactId>flink-jdbc_2.12</artifactId>--> <!-- <version>1.10.3</version>--> <!-- </dependency>-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-jdbc_2.11</artifactId>
            <version>1.13.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.13.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>1.13.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java-bridge_2.11</artifactId>
            <version>1.13.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-common</artifactId>
            <version>1.13.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner_2.11</artifactId>
            <version>1.13.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-blink_2.11</artifactId>
            <version>1.13.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-blink_2.11</artifactId>
            <version>1.13.1</version>
            <type>test-jar</type>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-runtime-web_2.11</artifactId>
            <version>1.13.1</version>
        </dependency>
        <dependency>
            <groupId>com.ververica</groupId>
            <!-- add the dependency matching your database -->
            <artifactId>flink-sql-connector-mysql-cdc</artifactId>
            <!-- The dependency is available only for stable releases, SNAPSHOT dependency need build by yourself. -->
            <version>2.2.0</version>
        </dependency>
<!--        <dependency>-->
<!--            <groupId>com.alibaba.ververica</groupId>-->
<!--            <artifactId>flink-connector-mysql-cdc</artifactId>-->
<!--            <version>1.2.0</version>-->
<!--        </dependency>-->
        <dependency>
            <groupId>org.apache.hudi</groupId>
            <artifactId>hudi-flink-bundle_2.11</artifactId>
            <version>0.10.1</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.49</version>
        </dependency>
    </dependencies>
</project>

使用FlinkSQL 创建MySQL数据源表、Hudi目标表,通过
INSERT INTO hudi_users2 SELECT *, DATE_FORMAT(birthday, 'yyyyMMdd') FROM mysql_users 将数据写入hudi

核心代码:

        final EnvironmentSettings fsSettings = EnvironmentSettings.newInstance()
                .useBlinkPlanner()
                .inStreamingMode()
                .build();
        final StreamExecutionEnvironment environment = StreamExecutionEnvironment.getExecutionEnvironment();
        environment.setParallelism(1);
        environment.enableCheckpointing(3000);
 
        final StreamTableEnvironment tableEnvironment = StreamTableEnvironment.create(environment, fsSettings);
        tableEnvironment.getConfig().setSqlDialect(SqlDialect.DEFAULT);
 
 
        // 数据源表
        String sourceDDL = "CREATE TABLE mysql_users (\n" +
                "                             id BIGINT PRIMARY KEY NOT ENFORCED ,\n" +
                "                             name STRING,\n" +
                "                             birthday TIMESTAMP(3),\n" +
 
                "                             ts TIMESTAMP(3)\n" +
                ") WITH (\n" +
                "      'connector' = 'mysql-cdc',\n" +
                "      'hostname' = '192.168.70.3',\n" +
                "      'port' = '3306',  " +
                "      'username' = 'aa',\n" +
                "      'password' = 'aa',  " +
                "      'server-time-zone' = 'Asia/Shanghai'," +
                "      'database-name' = 'test',\n" +
                "      'table-name' = 'users'\n" +
                "      )";
 
 
        /**
         *  触发器策略是在完成五次提交后执行压缩
         */
        // 输出目标表
        String sinkDDL = "CREATE TABLE hudi_users2\n" +
                "(\n" +
                "    id BIGINT PRIMARY KEY NOT ENFORCED,\n" +
                "    name STRING,\n" +
                "    birthday TIMESTAMP(3),\n" +
                "    ts TIMESTAMP(3),\n" +
                "    `partition` VARCHAR(20)\n" +
                ") PARTITIONED BY (`partition`) WITH (\n" +
                "    'connector' = 'hudi',\n" +
                "    'table.type' = 'MERGE_ON_READ',\n" +
                "    'path' = 'hdfs://ip:8020/hudi/hudi_users2'\n " +
                ")";
 
 
        String transformSQL = "INSERT INTO hudi_users2 SELECT *, DATE_FORMAT(birthday, 'yyyyMMdd') FROM mysql_users\n";
 
 
        tableEnvironment.executeSql(sourceDDL);
        tableEnvironment.executeSql(sinkDDL);
        tableEnvironment.executeSql(transformSQL);
        
        environment.execute("mysql-to-hudi");

本地启动Flink程序

然后进行MySQL DML 操作

insertinto users (name) values ('hello');
insertinto users (name) values ('world');
insertinto users (name) values ('iceberg');
insertinto users (name) values ('hudi');
 
update users set name = 'hello spark' where id = 4;
delete from users where id = 5;

查看HDFS上hudi数据路径:

Om0e8H.md.png

Hudi 默认情况下，MERGE_ON_READ表的压缩是启用的, 触发器策略是在完成五次提交后执行压缩. 在MySQL执行insert、update、delete等操作后，就可以用hive/spark-sql/presto进行查询。
如果没有生成parquet文件，我们建的parquet表是查询不出数据的。

五次提交后可以看到数据文件:

OmBwSH.md.png

关掉Flink CDC程序，  单独写个FlinkSQL程序读取HDFS 上hudi数据:

public static void main(String[] args) throwsException {
        final EnvironmentSettings fsSettings =EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
        final StreamExecutionEnvironmentenvironment = StreamExecutionEnvironment.getExecutionEnvironment();
        environment.setParallelism(1);
        final StreamTableEnvironmenttableEnvironment = StreamTableEnvironment.create(environment, fsSettings);
       tableEnvironment.getConfig().setSqlDialect(SqlDialect.DEFAULT);
 
        String sourceDDL = "CREATE TABLEhudi_users2\n" +
                "(\n" +
                "    id BIGINT PRIMARY KEY NOT ENFORCED,\n"+
                "    name STRING,\n" +
                "    birthday TIMESTAMP(3),\n" +
                "    ts TIMESTAMP(3),\n" +
                "    `partition` VARCHAR(20)\n" +
                ") PARTITIONED BY(`partition`) WITH (\n" +
                "    'connector' = 'hudi',\n" +
                "    'table.type' = 'MERGE_ON_READ',\n" +
                "    'path' ='hdfs://ip:8020/hudi/hudi_users2',\n" +
                "    'read.streaming.enabled' = 'true',\n"+
                "    'read.streaming.check-interval' = '1'\n" +
                ")";
        tableEnvironment.executeSql(sourceDDL);
        TableResult result2 =tableEnvironment.executeSql("select * from hudi_users2");
        result2.print();
 
       environment.execute("read_hudi");
    }

FlinkSQL读取到打印的数据：

OmDJjs.md.png

与MySQL 数据库表数据比对可以看到数据是一致的:

OmDogH.md.png

至此flink + hudi 湖仓一体化方案的原型就构建完成了.

总结

本篇主要讲解Flink CDC与hudi整合实践, 探索新的湖仓一体架构, 业内37手游的湖仓一体架构也可供参考如下:

OmsT0I.md.png

对频繁增加表字段的痛点需求,同步下游系统的时候希望能够自动加入这个字段，目前还没有完美的解决方案，Flink CDC社区后续看是否提供 Schema Evolution 的支持.

目前MySQL新增字段,是需要修改Flink程序,然后重启.

参考:

  1. https://hudi.apache.org/cn/
  2. https://cloud.tencent.com/developer/article/1884134
  3. https://developer.aliyun.com/article/791526
posted @   chaplinthink  阅读(1264)  评论(0)  编辑  收藏  举报
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