IDEA 开发配置SparkSQL及简单使用案例代码

时间:2022-09-06 07:37:56

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.sql
 
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import 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.SparkSession
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.Row
 
object 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 class
object 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.IntegerType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.Row
import java.util.Properties
 
object 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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