使用Apache Flink实现MySQL数据读取和写入的完整指南

1. 导言：

Apache Flink是一款功能强大的流式处理引擎，可用于实时处理大规模数据。本文将介绍如何使用Flink与MySQL数据库进行交互，以清洗股票数据为例。

2. 环境准备：

首先，确保已安装Apache Flink并配置好MySQL数据库。导入相关依赖包，并创建必要的Table。同时需要提前创建好mysql表，一行source表，一张sink表。

java 复制代码

CREATE TABLE `re_stock_code_price` (
  `id` bigint NOT NULL AUTO_INCREMENT,
  `code` varchar(64) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '股票代码',
  `name` varchar(64) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '股票名称',
  `close` double DEFAULT NULL COMMENT '最新价',
  `change_percent` double DEFAULT NULL COMMENT '涨跌幅',
  `change` double DEFAULT NULL COMMENT '涨跌额',
  `volume` double DEFAULT NULL COMMENT '成交量（手）',
  `amount` double DEFAULT NULL COMMENT '成交额',
  `amplitude` double DEFAULT NULL COMMENT '振幅',
  `turnover_rate` double DEFAULT NULL COMMENT '换手率',
  `peration` double DEFAULT NULL COMMENT '市盈率',
  `volume_rate` double DEFAULT NULL COMMENT '量比',
  `hign` double DEFAULT NULL COMMENT '最高',
  `low` double DEFAULT NULL COMMENT '最低',
  `open` double DEFAULT NULL COMMENT '今开',
  `previous_close` double DEFAULT NULL COMMENT '昨收',
  `pb` double DEFAULT NULL COMMENT '市净率',
  `create_time` varchar(64) NOT NULL COMMENT '写入时间',
  `rise` int NOT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=11207 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci 

CREATE TABLE `t_stock_code_price` (
  `id` bigint NOT NULL AUTO_INCREMENT,
  `code` varchar(64) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '股票代码',
  `name` varchar(64) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '股票名称',
  `close` double DEFAULT NULL COMMENT '最新价',
  `change_percent` double DEFAULT NULL COMMENT '涨跌幅',
  `change` double DEFAULT NULL COMMENT '涨跌额',
  `volume` double DEFAULT NULL COMMENT '成交量（手）',
  `amount` double DEFAULT NULL COMMENT '成交额',
  `amplitude` double DEFAULT NULL COMMENT '振幅',
  `turnover_rate` double DEFAULT NULL COMMENT '换手率',
  `peration` double DEFAULT NULL COMMENT '市盈率',
  `volume_rate` double DEFAULT NULL COMMENT '量比',
  `hign` double DEFAULT NULL COMMENT '最高',
  `low` double DEFAULT NULL COMMENT '最低',
  `open` double DEFAULT NULL COMMENT '今开',
  `previous_close` double DEFAULT NULL COMMENT '昨收',
  `pb` double DEFAULT NULL COMMENT '市净率',
  `create_time` varchar(64) NOT NULL COMMENT '写入时间',
  PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=11207 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci

scala 复制代码

package org.east;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment;

object TableETL {
    def main(args: Array[String]): Unit = {
        val senv = StreamExecutionEnvironment.getExecutionEnvironment
            .setRuntimeMode(RuntimeExecutionMode.STREAMING)
        val tEnv = StreamTableEnvironment.create(senv)

        // 定义源表
        val source_table =
            """
            CREATE TEMPORARY TABLE t_stock_code_price (
                id BIGINT NOT NULL,
                code STRING NOT NULL,
                -- 其他字段...
                create_time STRING NOT NULL,
                PRIMARY KEY (id) NOT ENFORCED
            ) WITH (
                'connector' = 'jdbc',
                'url' = 'jdbc:mysql://localhost:3306/mydb',
                'driver' = 'com.mysql.cj.jdbc.Driver',
                'table-name' = 't_stock_code_price',
                'username' = 'root',
                'password' = '12345678'
            )
            """.stripMargin

        // 定义目标表
        val sink_table =
            """
            CREATE TEMPORARY TABLE re_stock_code_price (
                id BIGINT NOT NULL,
                code STRING NOT NULL,
                -- 其他字段...
                create_time STRING NOT NULL,
                rise INT,
                PRIMARY KEY (id) NOT ENFORCED
            ) WITH (
                'connector' = 'jdbc',
                'url' = 'jdbc:mysql://localhost:3306/mydb',
                'driver' = 'com.mysql.cj.jdbc.Driver',
                'table-name' = 're_stock_code_price',
                'username' = 'root',
                'password' = '12345678'
            )
            """.stripMargin

        tEnv.executeSql(source_table)
        tEnv.executeSql(sink_table)

在这段代码中，我们首先创建了Flink的流式执行环境和StreamTableEnvironment。然后，我们定义了两个临时表，用于存储原始股票数据和清洗后的数据。

3. 数据清洗：

接下来，我们执行数据清洗操作，并将结果写入目标表。

scala 复制代码

        // 执行清洗操作，并将结果写入目标表
        tEnv.executeSql("INSERT INTO re_stock_code_price " +
            "SELECT *, CASE WHEN change_percent > 0 THEN 1 ELSE 0 END AS rise FROM t_stock_code_price")

在这里，我们计算了股票涨跌情况，并将结果写入到目标表中。在这个例子中，我们假设change_percent字段表示股票价格的变化百分比，rise字段为1表示股票上涨，为0表示股票下跌。

4. 结果展示：

最后，我们查询目标表并打印结果。

5. 完整代码：

下面是完整的代码：

scala 复制代码

package org.east;
import org.apache.flink.api.common.RuntimeExecutionMode
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment

object TableETL {

  def main(args: Array[String]): Unit = {

    val senv = StreamExecutionEnvironment.getExecutionEnvironment
      .setRuntimeMode(RuntimeExecutionMode.STREAMING)
    val tEnv = StreamTableEnvironment.create(senv)

    val source_table =
      """
        |CREATE TEMPORARY TABLE t_stock_code_price (
        |  id BIGINT NOT NULL,
        |  code STRING NOT NULL,
        |  name STRING NOT NULL,
        |  `close` DOUBLE,
        |  change_percent DOUBLE,
        |  change DOUBLE,
        |  volume DOUBLE,
        |  amount DOUBLE,
        |  amplitude DOUBLE,
        |  turnover_rate DOUBLE,
        |  peration DOUBLE,
        |  volume_rate DOUBLE,
        |  hign DOUBLE,
        |  low DOUBLE,
        |  `open` DOUBLE,
        |  previous_close DOUBLE,
        |  pb DOUBLE,
        |  create_time STRING NOT NULL,
        |  PRIMARY KEY (id) NOT ENFORCED
        |) WITH (
        |   'connector' = 'jdbc',
        |   'url' = 'jdbc:mysql://localhost:3306/mydb',
        |   'driver' = 'com.mysql.cj.jdbc.Driver',
        |   'table-name' = 't_stock_code_price',
        |   'username' = 'root',
        |   'password' = '12345678'
        |)
        |""".stripMargin


    val sink_table =
      """
        |CREATE TEMPORARY TABLE re_stock_code_price (
        |  id BIGINT NOT NULL,
        |  code STRING NOT NULL,
        |  name STRING NOT NULL,
        |  `close` DOUBLE,
        |  change_percent DOUBLE,
        |  change DOUBLE,
        |  volume DOUBLE,
        |  amount DOUBLE,
        |  amplitude DOUBLE,
        |  turnover_rate DOUBLE,
        |  peration DOUBLE,
        |  volume_rate DOUBLE,
        |  hign DOUBLE,
        |  low DOUBLE,
        |  `open` DOUBLE,
        |  previous_close DOUBLE,
        |  pb DOUBLE,
        |  create_time STRING NOT NULL,
        |  rise int,
        |  PRIMARY KEY (id) NOT ENFORCED
        |) WITH (
        |   'connector' = 'jdbc',
        |   'url' = 'jdbc:mysql://localhost:3306/mydb',
        |   'driver' = 'com.mysql.cj.jdbc.Driver',
        |   'table-name' = 're_stock_code_price',
        |   'username' = 'root',
        |   'password' = '12345678'
        |)
        |""".stripMargin


    tEnv.executeSql(source_table)
    tEnv.executeSql(sink_table)
    tEnv.executeSql("insert into re_stock_code_price select *,case when change_percent>0 then 1 else 0 end as rise from t_stock_code_price")

    val user_DS = tEnv.executeSql("select * from re_stock_code_price")

    user_DS.print()
  }
}

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