def main(args: Array[String]): Unit = {

    val conf: SparkConf = new SparkConf().setMaster("local").setAppName("test")
    val session: SparkSession = SparkSession
      .builder()
      .config(conf)
      .getOrCreate()
    val sc: SparkContext = session.sparkContext
    sc.setLogLevel("ERROR")

    // dataFrame = 数据 + 元数据
    //Spark 的Dataset  既可以按collection，类似于rdd的方法操作，也可以按SQL领域语言定义的方式操作数据
    val dataLists: RDD[String] = sc.textFile("data/person.txt")  //这里的RDD类型影响下面数据的类型，也可以返回DataSet
    val rddRow = dataLists
      .map(_.split(" ")).map(arr => Row.apply(arr(0), arr(1).toInt))
    
    val fields = Array(
      StructField.apply("name", DataTypes.StringType, true),
      StructField.apply("age", DataTypes.IntegerType, true)
    
    val schema = StructType.apply(fields)
    val dataFrame = session.createDataFrame(rddRow, schema)

    dataFrame.show()

+--------+---+
| name|age|
+--------+---+
|zhangsan| 18|
| lisi| 22|
| wangwu| 99|
| xiaoke| 22|
+--------+---+

    dataFrame.printSchema()

root
|-- name: string (nullable = true)
|-- age: integer (nullable = true)

    dataFrame.createTempView("person") //设置表名

    session.sql("select * from person where name = ‘xiaoke‘").show()

+------+---+
| name|age|
+------+---+
|xiaoke| 22|
+------+---+