Spark框架——SparkSql入门(基本信息的调用)

SparkSql入门(基本信息的调用)

package sql

import org.apache.avro.ipc.specific.Person
import org.apache.spark
import org.apache.spark.rdd.RDD
import org.apache.spark.sql
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.{
    
    DataFrame, Dataset, Row, SparkSession}
import org.junit.Test

class Intro {
    
    
  @Test
  def dsIntro(): Unit ={
    
    
    val spark: SparkSession = new sql.SparkSession.Builder()
      .appName("ds intro")
      .master("local[6]")
      .getOrCreate()

    //导入隐算是shi转换
    import spark.implicits._

    val sourceRDD: RDD[Person] =spark.sparkContext.parallelize(Seq(Person("张三",10),Person("李四",15)))
    val personDS: Dataset[Person] =sourceRDD.toDS();
//personDS.printSchema()打印出错信息

    val resultDS: Dataset[Person] =personDS.where('age>10)
      .select('name,'age)
      .as[Person]
    resultDS.show()

  }
  @Test
  def dfIntro(): Unit ={
    
    
    val spark: SparkSession =new SparkSession.Builder()
      .appName("ds intro")
      .master("local")
      .getOrCreate()

    import spark.implicits._
    val sourceRDD: RDD[Person] = spark.sparkContext.parallelize(Seq(Person("张三",10),Person("李四",15)))
    val df: DataFrame = sourceRDD.toDF()//隐shi转换

    df.createOrReplaceTempView("person")//创建表
    val resultDF: DataFrame =spark.sql("select name from person where age>=10 and age<=20")
    resultDF.show()

  }
  @Test
  def database1(): Unit ={
    
    
    //1.创建sparkSession
    val spark: SparkSession =new SparkSession.Builder()
      .appName("database1")
      .master("local[6]")
      .getOrCreate()
      //2.导入引入shi子转换
    import spark.implicits._

    //3.演示
    val sourceRDD: RDD[Person] =spark.sparkContext.parallelize(Seq(Person("张三",10),Person("李四",15)))
    val dataset: Dataset[Person] =sourceRDD.toDS()

    //Dataset 支持强类型的API
    dataset.filter(item => item.age >10).show()
    //Dataset 支持若弱类型的API
    dataset.filter('age>10).show()
    //Dataset 可以直接编写SQL表达式
    dataset.filter("age>10").show()
  }