在idea項目的pom.xml中添加依賴。

<!--spark sql依賴，注意版本號--><dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.12</artifactId> <version>3.0.0</version></dependency>2.案例代碼

package com.zf.bigdata.spark.sqlimport org.apache.spark.SparkConfimport org.apache.spark.rdd.RDDimport org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}object Spark01_SparkSql_Basic { def main(args: Array[String]): Unit = {//創建上下文環境配置對象val sparkConf = new SparkConf().setMaster('local[*]').setAppName('sparkSql')//創建 SparkSession 對象val spark = SparkSession.builder().config(sparkConf).getOrCreate()// DataFrameval df: DataFrame = spark.read.json('datas/user.json')//df.show()// DataFrame => Sql//df.createOrReplaceTempView('user')//spark.sql('select * from user').show()//spark.sql('select age from user').show()//spark.sql('select avg(age) from user').show()//DataFrame => Dsl//如果涉及到轉換操作，轉換需要引入隱式轉換規則，否則無法轉換，比如使用$提取數據的值//spark 不是包名，是上下文環境對象名import spark.implicits._//df.select('age','username').show()//df.select($'age'+1).show()//df.select(’age+1).show()// DataSet//val seq = Seq(1,2,3,4)//val ds: Dataset[Int] = seq.toDS()// ds.show()// RDD <=> DataFrameval rdd = spark.sparkContext.makeRDD(List((1,'張三',10),(2,'李四',20)))val df1: DataFrame = rdd.toDF('id', 'name', 'age')val rdd1: RDD[Row] = df1.rdd// DataFrame <=> DataSetval ds: Dataset[User] = df1.as[User]val df2: DataFrame = ds.toDF()// RDD <=> DataSetval ds1: Dataset[User] = rdd.map { case (id, name, age) => {User(id, name = name, age = age) }}.toDS()val rdd2: RDD[User] = ds1.rddspark.stop() } case class User(id:Int,name:String,age:Int)}

PS：下面看下在IDEA中開發Spark SQL程序

IDEA 中程序的打包和運行方式都和 SparkCore 類似，Maven 依賴中需要添加新的依賴項：

<dependency><groupId>org.apache.spark</groupId><artifactId>spark-sql_2.11</artifactId><version>2.1.1</version></dependency>一、指定Schema格式

import org.apache.spark.sql.SparkSessionimport org.apache.spark.sql.types.StructTypeimport org.apache.spark.sql.types.StructFieldimport org.apache.spark.sql.types.IntegerTypeimport org.apache.spark.sql.types.StringTypeimport org.apache.spark.sql.Rowobject Demo1 { def main(args: Array[String]): Unit = { //使用Spark Session 創建表 val spark = SparkSession.builder().master('local').appName('UnderstandSparkSession').getOrCreate() //從指定地址創建RDD val personRDD = spark.sparkContext.textFile('D:tmp_filesstudent.txt').map(_.split('t')) //通過StructType聲明Schema val schema = StructType( List(StructField('id', IntegerType),StructField('name', StringType),StructField('age', IntegerType))) //把RDD映射到rowRDD val rowRDD = personRDD.map(p=>Row(p(0).toInt,p(1),p(2).toInt)) val personDF = spark.createDataFrame(rowRDD, schema) //注冊表 personDF.createOrReplaceTempView('t_person') //執行SQL val df = spark.sql('select * from t_person order by age desc limit 4') df.show() spark.stop() }}二、使用case class

import org.apache.spark.sql.SparkSession//使用case classobject Demo2 { def main(args: Array[String]): Unit = { //創建SparkSession val spark = SparkSession.builder().master('local').appName('CaseClassDemo').getOrCreate() //從指定的文件中讀取數據，生成對應的RDD val lineRDD = spark.sparkContext.textFile('D:tmp_filesstudent.txt').map(_.split('t')) //將RDD和case class 關聯 val studentRDD = lineRDD.map( x => Student(x(0).toInt,x(1),x(2).toInt)) //生成 DataFrame，通過RDD 生成DF,導入隱式轉換 import spark.sqlContext.implicits._ val studentDF = studentRDD.toDF //注冊表 視圖 studentDF.createOrReplaceTempView('student') //執行SQL spark.sql('select * from student').show() spark.stop() }}//case class 一定放在外面case class Student(stuID:Int,stuName:String,stuAge:Int)三、把數據保存到數據庫

import org.apache.spark.sql.types.IntegerTypeimport org.apache.spark.sql.types.StringTypeimport org.apache.spark.sql.SparkSessionimport org.apache.spark.sql.types.StructTypeimport org.apache.spark.sql.types.StructFieldimport org.apache.spark.sql.Rowimport java.util.Propertiesobject Demo3 { def main(args: Array[String]): Unit = { //使用Spark Session 創建表 val spark = SparkSession.builder().master('local').appName('UnderstandSparkSession').getOrCreate() //從指定地址創建RDD val personRDD = spark.sparkContext.textFile('D:tmp_filesstudent.txt').map(_.split('t')) //通過StructType聲明Schema val schema = StructType( List(StructField('id', IntegerType),StructField('name', StringType),StructField('age', IntegerType))) //把RDD映射到rowRDD val rowRDD = personRDD.map(p => Row(p(0).toInt, p(1), p(2).toInt)) val personDF = spark.createDataFrame(rowRDD, schema) //注冊表 personDF.createOrReplaceTempView('person') //執行SQL val df = spark.sql('select * from person ') //查看SqL內容 //df.show() //將結果保存到mysql中 val props = new Properties() props.setProperty('user', 'root') props.setProperty('password', '123456') props.setProperty('driver', 'com.mysql.jdbc.Driver') df.write.mode('overwrite').jdbc('jdbc:mysql://localhost:3306/company?serverTimezone=UTC&characterEncoding=utf-8', 'student', props) spark.close() }}

以上內容轉自：https://blog.csdn.net/weixin_43520450/article/details/106093582作者：故明所以

到此這篇關于IDEA 開發配置SparkSQL及簡單使用案例代碼的文章就介紹到這了,更多相關IDEA 開發 SparkSQL內容請搜索好吧啦網以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持好吧啦網！