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

  1. // sc is an existing SparkContext.
  2. val sqlContext = new org.apache.spark.sql.SQLContext(sc)
  4. // A JSON dataset is pointed to by path.
  5. // The path can be either a single text file or a directory storing text files.
  6. val path = "examples/src/main/resources/people.json"
  7. // Create a SchemaRDD from the file(s) pointed to by path
  8. val people = sqlContext.jsonFile(path)
  10. // The inferred schema can be visualized using the printSchema() method.
  11. people.printSchema()
  12. // root
  13. //  |-- age: integer (nullable = true)
  14. //  |-- name: string (nullable = true)
  16. // Register this SchemaRDD as a table.
  17. people.registerTempTable("people")
  19. // SQL statements can be run by using the sql methods provided by sqlContext.
  20. val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
  22. // Alternatively, a SchemaRDD can be created for a JSON dataset represented by
  23. // an RDD[String] storing one JSON object per string.
  24. val anotherPeopleRDD = sc.parallelize(
  25.   """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
  26. val anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD)