(4)SparkSQL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中的内置函数类似;是saprk SQL中内置函数无法满足要求,用户根据业务需求自定义的函数。

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";
}
}

使用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);

//获得JavaStreamingContext
JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.minutes(3));

/**
* 设置日志的级别: 避免日志重复
*/
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 必须是批处理时间的整数倍

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);
// 创建临时表
dataFrame.createOrReplaceTempView("log");
Dataset<Row> result = spark.sql("select *,TestUDF(id) as udftest from log");
System.out.println("========= " + time + "=========");
//输出前20条数据
result.show();
}
});


//开始作业
ssc.start();
try {
ssc.awaitTermination();
} catch (Exception e) {
e.printStackTrace();
} finally {
ssc.close();
}
}
}

代码说明:

(4)SparkSQL中如何定义UDF和使用UDF

应用效果展示:

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