1.添加依赖
在idea项目的pom.xml中添加依赖。
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<!--spark sql依赖，注意版本号--><dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.12</artifactId> <version>3.0.0</version></dependency> |
2.案例代码
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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 = { //创建上下文环境配置对象 val sparkConf = new SparkConf().setMaster("local[*]").setAppName("sparkSql") //创建 SparkSession 对象 val spark = SparkSession.builder().config(sparkConf).getOrCreate() // DataFrame val 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 //如果涉及到转换操作，转换需要引入隐式转换规则，否则无法转换，比如使用$提取数据的值 //spark 不是包名，是上下文环境对象名 import spark.implicits._ //df.select("age","username").show() //df.select($"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 <=> DataFrame val rdd = spark.sparkContext.makeRDD(List((1,"张三",10),(2,"李四",20))) val df1: DataFrame = rdd.toDF("id", "name", "age") val rdd1: RDD[Row] = df1.rdd // DataFrame <=> DataSet val ds: Dataset[User] = df1.as[User] val df2: DataFrame = ds.toDF() // RDD <=> DataSet val ds1: Dataset[User] = rdd.map { case (id, name, age) => { User(id, name = name, age = age) } }.toDS() val rdd2: RDD[User] = ds1.rdd spark.stop() } case class User(id:Int,name:String,age:Int)} |
PS：下面看下在IDEA中开发Spark SQL程序
IDEA 中程序的打包和运行方式都和 SparkCore 类似，Maven 依赖中需要添加新的依赖项：
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<dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.11</artifactId> <version>2.1.1</version></dependency> |
一、指定Schema格式
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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 创建表 val spark = SparkSession.builder().master("local").appName("UnderstandSparkSession").getOrCreate() //从指定地址创建RDD val personRDD = spark.sparkContext.textFile("D:\\tmp_files\\student.txt").map(_.split("\t")) //通过StructType声明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) //注册表 personDF.createOrReplaceTempView("t_person") //执行SQL val df = spark.sql("select * from t_person order by age desc limit 4") df.show() spark.stop() }} |
二、使用case class
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import org.apache.spark.sql.SparkSession//使用case classobject Demo2 { def main(args: Array[String]): Unit = { //创建SparkSession val spark = SparkSession.builder().master("local").appName("CaseClassDemo").getOrCreate() //从指定的文件中读取数据，生成对应的RDD val lineRDD = spark.sparkContext.textFile("D:\\tmp_files\\student.txt").map(_.split("\t")) //将RDD和case class 关联 val studentRDD = lineRDD.map( x => Student(x(0).toInt,x(1),x(2).toInt)) //生成 DataFrame，通过RDD 生成DF,导入隐式转换 import spark.sqlContext.implicits._ val studentDF = studentRDD.toDF //注册表 视图 studentDF.createOrReplaceTempView("student") //执行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、把数据保存到数据库
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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 创建表 val spark = SparkSession.builder().master("local").appName("UnderstandSparkSession").getOrCreate() //从指定地址创建RDD val personRDD = spark.sparkContext.textFile("D:\\tmp_files\\student.txt").map(_.split("\t")) //通过StructType声明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) //注册表 personDF.createOrReplaceTempView("person") //执行SQL val df = spark.sql("select * from person ") //查看SqL内容 //df.show() //将结果保存到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() }} |
以上内容转自：
https://blog.csdn.net/weixin_43520450/article/details/106093582
作者：故明所以
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