Flink-sql自定义udf 函数

1. 用自定义的函数在Flink Sql 中使用

1.1 官网也说的很详细

https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/table/functions/udfs.html

1.1.1 官网上面的例子：

import org.apache.flink.table.api.*;
import org.apache.flink.table.functions.ScalarFunction;
import static org.apache.flink.table.api.Expressions.*;

// define function logic
public static class SubstringFunction extends ScalarFunction {
    
    
  public String eval(String s, Integer begin, Integer end) {
    
    
    return s.substring(begin, end);
  }
}

TableEnvironment env = TableEnvironment.create(...);

// call function "inline" without registration in Table API
env.from("MyTable").select(call(SubstringFunction.class, $("myField"), 5, 12));

// register function
env.createTemporarySystemFunction("SubstringFunction", SubstringFunction.class);

// call registered function in Table API
env.from("MyTable").select(call("SubstringFunction", $("myField"), 5, 12));

// call registered function in SQL
env.sqlQuery("SELECT SubstringFunction(myField, 5, 12) FROM MyTable");

2. 自己实现

需要注意的是： 自己实现注册全局自定义函数

package com.wudl.flink.sql;

import com.google.inject.internal.cglib.proxy.$Callback;
import com.wudl.flink.bean.WaterSensor;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
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.table.functions.ScalarFunction;

import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.call;

/**
 * @ClassName : Flink_Sql_Function_UDF
 * @Description : Flink自定义udf 函数
 * @Author :wudl
 * @Date: 2021-08-11 22:55
 */

public class Flink_Sql_Function_UDF {
    
    
    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. 读取端口中的数据并且转化为javaBean
        SingleOutputStreamOperator<WaterSensor> waterSensorDs = env.socketTextStream("192.168.1.180", 9999)
                .map(line -> {
    
    
                    String[] split = line.split(",");
                    return new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
                });

        // 3. 讲流 转化为动态表
        Table table = tableEnv.fromDataStream(waterSensorDs);
        // 4. 直接调用自定义udf 函数
//        table.select(call(myFunction.class,$("id"))).execute().print();
        // 5. 先注册在使用
        tableEnv.createTemporarySystemFunction("MyLength",myFunction.class);
        //5.1 在使用注册的自定义函数 名称为MyLength
//        table.select(call("MyLength",$("id"))).execute().print();
        // 5.2 采用sql 的方式进行使用自定义函数
            tableEnv.sqlQuery("select id, MyLength(id) from "+table).execute().print();
        //5. 执行任务
        env.execute();
    }

    // 自定义函数类
    public static class  myFunction extends ScalarFunction{
    
    
        public int eval(String value) {
    
    
            return value.length();
        }
    }

}