Spark DataFrame基本操作

java/scala  ==> jvm
python ==> python runtime

java/scala/python ==> Logic Plan

import org.apache.spark.sql.SparkSession

/**
  * DataFrame API基本操作
  */
object DataFrameApp {
  def main(args: Array[String]): Unit = {

    val spark = SparkSession
      .builder()
      .appName("DataFrameApp")
      .master("local[2]")
      .getOrCreate();

    // 将json文件加载成一个dataframe
    val peopleDF = spark.read.json("C:\\Users\\Administrator\\IdeaProjects\\SparkSQLProject\\spark-warehouse\\people.json");
    // Prints the schema to the console in a nice tree format.
    peopleDF.printSchema();

    // 输出数据集的前20条记录
    peopleDF.show();

    //查询某列所有的数据： select name from table
    peopleDF.select("name").show();

    // 查询某几列所有的数据，并对列进行计算： select name, age+10 as age2 from table
    peopleDF.select(peopleDF.col("name"), (peopleDF.col("age") + 10).as("age2")).show();

    //根据某一列的值进行过滤： select * from table where age>19
    peopleDF.filter(peopleDF.col("age") > 19).show();

    //根据某一列进行分组，然后再进行聚合操作： select age,count(1) from table group by age
    peopleDF.groupBy("age").count().show();

 spark.stop();
  }
}