# JSON数据集 Spark SQL能够自动推断JSON数据集的模式，加载它为一个SchemaRDD。这种转换可以通过下面两种方法来实现 - jsonFile ：从一个包含JSON文件的目录中加载。文件中的每一行是一个JSON对象 - jsonRDD ：从存在的RDD加载数据，这些RDD的每个元素是一个包含JSON对象的字符串 注意，作为jsonFile的文件不是一个典型的JSON文件，每行必须是独立的并且包含一个有效的JSON对象。结果是，一个多行的JSON文件经常会失败 ~~~ // 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) ~~~