关于spark:4SparkSQL中如何定义UDF和使用UDF

Spark SQL中用户自定义函数，用法和Spark SQL中的内置函数相似；是saprk SQL中内置函数无奈满足要求，用户依据业务需要自定义的函数。
首先定义一个UDF函数：

package com.udf;import org.apache.spark.sql.api.java.UDF1;import org.apache.spark.sql.api.java.UDF2;import org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema;import scala.collection.mutable.WrappedArray;/** * Created by lj on 2022-07-25. */public class TestUDF  implements UDF1<String, String> {    @Override    public String call(String s) throws Exception {        return s+"_udf";    }}

应用UDF函数：

package com.examples;import com.pojo.WaterSensor;import com.udf.TestUDF;import org.apache.spark.SparkConf;import org.apache.spark.api.java.JavaRDD;import org.apache.spark.api.java.function.Function;import org.apache.spark.api.java.function.VoidFunction2;import org.apache.spark.sql.Dataset;import org.apache.spark.sql.Row;import org.apache.spark.sql.SparkSession;import org.apache.spark.sql.types.DataTypes;import org.apache.spark.streaming.Durations;import org.apache.spark.streaming.Time;import org.apache.spark.streaming.api.java.JavaDStream;import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;import org.apache.spark.streaming.api.java.JavaStreamingContext;/** * Created by lj on 2022-07-25. */public class SparkSql_Socket_UDF  {    private static String appName = "spark.streaming.demo";    private static String master = "local[*]";    private static String host = "localhost";    private static int port = 9999;    public static void main(String[] args) {        //初始化sparkConf        SparkConf sparkConf = new SparkConf().setMaster(master).setAppName(appName);        //取得JavaStreamingContext        JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.minutes(3));        /**         * 设置日志的级别： 防止日志反复         */        ssc.sparkContext().setLogLevel("ERROR");        //从socket源获取数据        JavaReceiverInputDStream<String> lines = ssc.socketTextStream(host, port);        JavaDStream<WaterSensor> mapDStream = lines.map(new Function<String, WaterSensor>() {            private static final long serialVersionUID = 1L;            public WaterSensor call(String s) throws Exception {                String[] cols = s.split(",");                WaterSensor waterSensor = new WaterSensor(cols[0], Long.parseLong(cols[1]), Integer.parseInt(cols[2]));                return waterSensor;            }        }).window(Durations.minutes(6), Durations.minutes(9));      //指定窗口大小 和 滑动频率 必须是批处理工夫的整数倍        mapDStream.foreachRDD(new VoidFunction2<JavaRDD<WaterSensor>, Time>() {            @Override            public void call(JavaRDD<WaterSensor> waterSensorJavaRDD, Time time) throws Exception {                SparkSession spark = JavaSparkSessionSingleton.getInstance(waterSensorJavaRDD.context().getConf());                spark.udf().register("TestUDF", new TestUDF(), DataTypes.StringType);                Dataset<Row> dataFrame = spark.createDataFrame(waterSensorJavaRDD, WaterSensor.class);                // 创立长期表                dataFrame.createOrReplaceTempView("log");                Dataset<Row> result = spark.sql("select *,TestUDF(id) as udftest from log");                System.out.println("========= " + time + "=========");                //输入前20条数据                result.show();            }        });        //开始作业        ssc.start();        try {            ssc.awaitTermination();        } catch (Exception e) {            e.printStackTrace();        } finally {            ssc.close();        }    }}

代码阐明：

利用成果展现：