IDEA 开发配置SparkSQL及简单使用案例代码

<!--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>

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)

}

<dependency>
	<groupId>org.apache.spark</groupId>
	<artifactId>spark-sql_2.11</artifactId>
	<version>2.1.1</version>
</dependency>

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()

  }
}

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)

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()

  }
}