Flink提供了自定义函数的基础能力，在需要满足特殊业务场景需求时，根据自身需要按需定制自己的UDF 下面将简单演示一个UDF的定义和UDF的使用过程：
（1）定义一个UDF
代码语言：javascript复制package com.udf;
import org.apache.flink.table.functions.ScalarFunction;
/**
* Created by lj on 2022-07-25.
*/
public class TestUDF extends ScalarFunction {
public String eval(String value) {
return value "_udf";
}
}（2）使用UDF
代码语言：javascript复制 public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
DataStreamSource<String> streamSource = env.socketTextStream("127.0.0.1", 9999,"n");
SingleOutputStreamOperator<WaterSensor> waterDS = streamSource.map(new MapFunction<String, WaterSensor>() {
@Override
public WaterSensor map(String s) throws Exception {
String[] split = s.split(",");
return new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
}
});
// 将流转化为表
Table table = tableEnv.fromDataStream(waterDS,
$("id"),
$("ts"),
$("vc"),
$("pt").proctime());
tableEnv.createTemporaryView("EventTable", table);
/*
// 1. 直接调用自定义udf 函数
// table.select(call(myFunction.class,$("id"))).execute().print();
// 2. 先注册在使用
tableEnv.createTemporarySystemFunction("MyLength",myFunction.class);
//2.1 在使用注册的自定义函数 名称为MyLength
// table.select(call("MyLength",$("id"))).execute().print();
// 2.2 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();
* */
tableEnv.createTemporarySystemFunction("MyLength",TestUDF.class);
Table result = tableEnv.sqlQuery(
"SELECT "
"id as componentname, " //window_start, window_end,
"COUNT(ts) as componentcount ,SUM(ts) as componentsum, "
"MyLength(cast(COUNT(ts) as string)) as testudf "
"FROM TABLE( "
"TUMBLE( TABLE EventTable , "
"DESCRIPTOR(pt), "
"INTERVAL '10' SECOND)) "
"GROUP BY id , window_start, window_end"
);
tableEnv.toRetractStream(result, Row.class).print("toRetractStream"); //缩进模式
env.execute();
}（3）应用效果
