import org.apache.spark.sql.{SparkSession, Row} import org.apache.spark.sql.types.{StringType, IntegerType, StructType, StructField} def inferRefection(spark:SpakSession):Unit={ //得到rdd val infoRdd = spark.sparkContext.textFile(".../info.txt") //导入隐式转换包 import spark.implicits._...
1.1继承org.apache.spark.sql.api.java.UDFxx(1-22); 1.2、实现call方法 @Override public String call(Long v1, String v2, String split) throws Exception { return String.valueOf(v1) + split + v2; } 1. 2. 3. 4. 完整代码实现 package com.chb.shopanalysis.hive.UDF; import org.apache.spa...
fields: Array[org.apache.spark.sql.types.StructField]= Array(StructField(id,StringType,true), StructField(name,StringType,true), StructField(age,StringType,true)) scala> val schema =StructType(fields) schema: org.apache.spark.sql.types.StructType= StructType(StructField(id,StringType,true),Stru...
Spark SQL 用户自定义函数UDF、用户自定义聚合函数UDAF 教程(Java踩坑教学版) spark大数据 在Spark中,也支持Hive中的自定义函数。自定义函数大致可以分为三种: UDF(User-Defined-Function),即最基本的自定义函数,类似to_char,to_date等 UDAF(User- Defined Aggregation Funcation),用户自定义聚合函数,类似在group ...
SparkSQL自定义函数 一:自定义函数分类 在Spark中,也支持Hive中的自定义函数。自定义函数大致可以分为三种: 1.UDF(User-Defined-Function),即最基本的自定义函数,类似to_char,to_date等 2.UDAF(User- Defined Aggregation Funcation),用户自定义聚合函数,类似在group by之后使用的sum,avg等...
为了方便调试Spark SQL源码,我把SQL语句写在了scala代码中。同时,在程序执行的末尾添加了一个阻塞标准输入。这样我们就可以去查看下Spark的WebUI了。 def main(args: Array[String]): Unit = { val conf = new SparkConf conf.set("spark.hive.enable", "true") conf.set("spark.sql.hive.metastore.version...
import org.apache.spark.sql.types.StructField; import org.apache.spark.sql.types.StructType; import java.util.ArrayList; import java.util.List; /** * Created by xinghailong on 2017/2/23. */ public class test3 { public static void main(String[] args) { ...
file_index))valtt = partitionData.mapPartitionsWithIndex((index: Int, it: Iterator[(String,Int)]) =>it.toList.map(x => (index,x)).toIterator)println("map partitions with index:")tt.collect().foreach(println(_)) //likethis: (,(421.txt,4))//firstCharInFileName , firstCharInFile...
spark.sql("select name from people where age >= 20")optimizedPlan:Project[name#3]+-Filter(age#4L>=20)+-SerializeFromObject[staticinvoke(classorg.apache.spark.unsafe.types.UTF8String,StringType,fromString,assertnotnull(input[0,TestSpark$Person,true]).name,true,false)ASname#3,assertnotnull(...
// 代码1val spark=SparkSession.builder.appName("SparkSQL Test").master("local[4]").getOrCreate()spark.sql("select * from table").show(false)---// 代码2defsql(sqlText:String):DataFrame={Dataset.ofRows(self,sessionState.sqlParser.parsePlan(sqlText))}---// 代码3override defparsePlan(...