# JSON數據集 Spark SQL能夠自動推斷JSON數據集的模式，加載它為一個SchemaRDD。這種轉換可以通過下面兩種方法來實現 - jsonFile ：從一個包含JSON文件的目錄中加載。文件中的每一行是一個JSON對象 - jsonRDD ：從存在的RDD加載數據，這些RDD的每個元素是一個包含JSON對象的字符串 注意，作為jsonFile的文件不是一個典型的JSON文件，每行必須是獨立的并且包含一個有效的JSON對象。結果是，一個多行的JSON文件經常會失敗 ```scala // sc is an existing SparkContext. val sqlContext = new org.apache.spark.sql.SQLContext(sc) // A JSON dataset is pointed to by path. // The path can be either a single text file or a directory storing text files. val path = "examples/src/main/resources/people.json" // Create a SchemaRDD from the file(s) pointed to by path val people = sqlContext.jsonFile(path) // The inferred schema can be visualized using the printSchema() method. people.printSchema() // root // |-- age: integer (nullable = true) // |-- name: string (nullable = true) // Register this SchemaRDD as a table. people.registerTempTable("people") // SQL statements can be run by using the sql methods provided by sqlContext. val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") // Alternatively, a SchemaRDD can be created for a JSON dataset represented by // an RDD[String] storing one JSON object per string. val anotherPeopleRDD = sc.parallelize( """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil) val anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD) ```