Flink1.11.2-pg-source&sink_flink pg sink-优快云博客

本文链接：https://blog.youkuaiyun.com/qq_31316733/article/details/114556318

本文档介绍了如何使用Apache Flink 1.11.2版的CSVSource和PgsqlSink实现从CSV文件到PostgreSQL数据库的实时数据导入，包括数据解析、转换和持久化的过程，以及从数据库读取数据的示例。

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Flink1.11.2-pg-source&sink

[CSVSource_ToPg.java]

package com.flink.source.pg_jdbc;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

import java.util.HashMap;
import java.util.Map;

public class CSVSource_ToPg {
   
    public static void main(String[] args) throws Exception {
   
        StreamExecutionEnvironment env=StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        DataStreamSource<String> dsFile = env.readTextFile("D:\\code\\flink111\\src\\main\\resources\\input\\water_sensor.txt");
        dsFile.map(new MapFunction<String, Map>() {
   
            @Override
            public Map map(String s) throws Exception {
   
                String[] strings = s.split(",");
                HashMap<String, String> map = new HashMap<>();
                map.put("id",strings[0]);
                map.put("ts",strings[1]);
                map.put("vc",strings[2]);