用flink datastream 写一个程序，分析一个mysql表 写入另一个MySQL表

好的，以下是一个使用 Flink DataStream API 分析 MySQL 表并将结果写入另一个 MySQL 表的示例程序：

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple;
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.streaming.api.functions.sink.SinkFunction;
import org.apache.flink.streaming.connectors.jdbc.JdbcConnectionOptions;
import org.apache.flink.streaming.connectors.jdbc.JdbcSink;
import org.apache.flink.streaming.connectors.jdbc.JdbcStatementBuilder;
import org.apache.flink.streaming.connectors.jdbc.JdbcTableSink;
import org.apache.flink.streaming.connectors.jdbc.JdbcWriter;
import org.apache.flink.streaming.connectors.jdbc.JdbcWriterBuilder;
import org.apache.flink.streaming.connectors.jdbc.JdbcUpsertTableSink;
import org.apache.flink.streaming.connectors.jdbc.JdbcUpsertTableSinkBuilder;
import org.apache.flink.streaming.connectors.jdbc.JdbcUtils;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.descriptors.ConnectorDescriptor;
import org.apache.flink.table.descriptors.Jdbc;
import org.apache.flink.table.descriptors.Schema;
import org.apache.flink.types.Row;

import java.sql.PreparedStatement;
import java.sql.SQLException;

public class MySQLTableAnalysis {

    public static void main(String[] args) throws Exception {
        // 设置执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

        // 定义 MySQL 连接信息
        String url = "jdbc:mysql://localhost:3306/test";
        String username = "root";
        String password = "root";

        // 定义输入表的连接器描述符
        Jdbc jdbc = new Jdbc()
                .url(url)
                .username(username)
                .password(password)
                .table("input_table")
                .lookupOptions(JdbcOptions.builder()
                        .setFetchSize(1000)
                        .build());

        // 定义输出表的连接器描述符
        Jdbc outputJdbc = new Jdbc()
                .url(url)
                .username(username)
                .password(password)
                .table("output_table");

        // 注册输入表
        tableEnv
                .connect(jdbc)
                .withFormat(new Csv())
                .withSchema(new Schema()
                        .field("id", DataTypes.INT())
                        .field("name", DataTypes.STRING())
                        .field("age", DataTypes.INT()))
                .createTemporaryTable("input_table");

        // 读取输入表并进行分析
        Table inputTable = tableEnv.from("input_table");
        Table resultTable = inputTable
                .groupBy("name")
                .select("name, count(id) as cnt");

        // 将结果转换为 DataStream<Tuple2<String, Integer>>
        DataStream<Tuple2<String, Integer>> resultStream = tableEnv.toRetractStream(resultTable, Row.class)
                .map(new MapFunction<Tuple2<Boolean, Row>, Tuple2<String, Integer>>() {
                    @Override
                    public Tuple2<String, Integer> map(Tuple2<Boolean, Row> value) throws Exception {
                        String name = value.f1.getField(0).toString();
                        Integer cnt = Integer.parseInt(value.f1.getField(1).toString());
                        return Tuple2.of(name, cnt);
                    }
                });

        // 定义 KeySelector 和 SinkFunction
        KeySelector<Tuple2<String, Integer>, String> keySelector = new KeySelector<Tuple2<String, Integer>, String>() {
            @Override
            public String getKey(Tuple2<String, Integer> value) throws Exception {
                return value.f0;
            }
        };

        SinkFunction<Tuple2<String, Integer>> sinkFunction = new SinkFunction<Tuple2<String, Integer>>() {
            @Override
            public void invoke(Tuple2<String, Integer> value, Context context) throws Exception {
                String sql = "INSERT INTO output_table (name, cnt) VALUES (?, ?) ON DUPLICATE KEY UPDATE cnt = ?";
                JdbcUtils.write(
                        value,
                        PreparedStatement::setString,
                        PreparedStatement::setInt,
                        new int[]{3},
                        sql,
                        JdbcConnectionOptions.builder()
                                .withUrl(url)
                                .withUsername(username)
                                .withPassword(password)
                                .build());
            }
        };

        // 将结果写入输出表
        JdbcUpsertTableSink<Tuple2<String, Integer>> jdbcSink = JdbcUpsertTableSink.builder()
                .setOptions(outputJdbc)
                .setFlushIntervalMills(5000)
                .setKeyFields(new String[]{"name"})
                .build();

        resultStream
                .addSink(jdbcSink)
                .name("MySQL Upsert Sink");

        // 执行程序
        env.execute("MySQL Table Analysis");
    }
}

在这个示例程序中，我们首先定义了输入表和输出表的连接信息，并使用 Flink Table API 定义了输入表的结构。然后，我们使用 Flink Table API 计算了输入表的分析结果，并将结果转换为 DataStream。最后，我们使用 Flink JDBC Upsert Table Sink 将结果写入输出表。

请注意，这个示例程序假设输入表和输出表都已经在 MySQL 中存在，并且输出表具有一个名为“name”的唯一索引。如果您需要创建表，请使用 SQL DDL 语句或其他适当的工具创建表。