（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：

代码语言：javascript复制

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：

代码语言：javascript复制

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：

应用效果展示：

sql spark大数据 流计算 spark sparksql sparkstreaming

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