Spark SQL中 RDD 转换到 DataFrame (方法二）

Posted 2020-10-10 soyosuyang

1.people.txt:
soyo8, 35
小周, 30
小华, 19
soyo,88

/**
  * Created by soyo on 17-10-10.
  * 使用编程方式定义RDD模式
  */
import org.apache.spark.sql.types._
import org.apache.spark.sql.{Row, SparkSession}
object RDD_To_DataFrame2 {
  def main(args: Array[String]): Unit = {
    val spark=SparkSession.builder().getOrCreate()

    val peopleRDD=spark.sparkContext.textFile("file:///home/soyo/桌面/spark编程测试数据/people.txt")
    val schema_S="name age"
    val fields=schema_S.split(" ").map(x=>StructField(x,StringType,nullable = true))
    //生成模式
    val schema=StructType(fields)
    val rowRDD=peopleRDD.map(_.split(",")).map(x=>Row(x(0),x(1).trim))
    val peopleDF=spark.createDataFrame(rowRDD,schema)
    peopleDF.createOrReplaceTempView("people2")
    val results=spark.sql("select * from people2")
    results.show()
    results.groupBy("age").count().show()
  }


}

结果：
+-----+---+
| name|age|
+-----+---+
|soyo8| 35|
|   小周| 30|
|   小华| 19|
| soyo| 88|
+-----+---+

+---+-----+
|age|count|
+---+-----+
| 30|    1|
| 35|    1|
| 19|    1|
| 88|    1|
+---+-----+