# RDDs Spark支持两种方法将存在的RDDs转换为SchemaRDDs(DataFrame/DataSet)。 * 第一种方法使用反射来推断包含特定对象类型的RDD的模式(schema)。在你写spark程序的同时，当你已经知道了模式，这种基于反射的 方法可以使代码更简洁并且程序工作得更好。 * 创建SchemaRDDs的第二种方法是通过一个编程接口来实现，这个接口允许你构造一个模式，然后在存在的RDDs上使用它。虽然这种方法更冗长，但是它允许你在运行期之前不知道列以及列的类型的情况下构造SchemaRDDs。 ## 一、利用反射推断模式 Spark SQL的Scala接口支持将包含样本类的RDDs自动转换为SchemaRDD。这个样本类定义了表的模式。 给样本类的参数名字通过反射来读取，然后作为列的名字。样本类可以嵌套或者包含复杂的类型如序列或者数组。这个RDD可以隐式转化为一个SchemaRDD，然后注册为一个表。表可以在后续的 sql语句中使用。 ```scala // sc is an existing SparkContext. val sqlContext = new org.apache.spark.sql.SQLContext(sc) // createSchemaRDD is used to implicitly convert an RDD to a SchemaRDD. import sqlContext.createSchemaRDD // Define the schema using a case class. // Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit, // you can use custom classes that implement the Product interface. case class Person(name: String, age: Int) // Create an RDD of Person objects and register it as a table. val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Person(p(0), p(1).trim.toInt)) 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") // The results of SQL queries are SchemaRDDs and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. teenagers.map(t => "Name: " + t(0)).collect().foreach(println) ``` ## 二、编程指定模式 当样本类不能提前确定（例如，记录的结构是经过编码的字符串，或者一个文本集合将会被解析，不同的字段投影给不同的用户），一个SchemaRDD可以通过三步来创建。 - 从原来的RDD创建一个行的RDD - 创建由一个`StructType`表示的模式与第一步创建的RDD的行结构相匹配 - 在行RDD上通过`applySchema`方法应用模式 ```scala // sc is an existing SparkContext. val sqlContext = new org.apache.spark.sql.SQLContext(sc) // Create an RDD val people = sc.textFile("examples/src/main/resources/people.txt") // The schema is encoded in a string val schemaString = "name age" // Import Spark SQL data types and Row. import org.apache.spark.sql._ // Generate the schema based on the string of schema val schema = StructType( schemaString.split(" ").map(fieldName => StructField(fieldName, StringType, true))) // Convert records of the RDD (people) to Rows. val rowRDD = people.map(_.split(",")).map(p => Row(p(0), p(1).trim)) // Apply the schema to the RDD. val peopleSchemaRDD = sqlContext.applySchema(rowRDD, schema) // Register the SchemaRDD as a table. peopleSchemaRDD.registerTempTable("people") // SQL statements can be run by using the sql methods provided by sqlContext. val results = sqlContext.sql("SELECT name FROM people") // The results of SQL queries are SchemaRDDs and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. results.map(t => "Name: " + t(0)).collect().foreach(println) ``` ``` import org.apache.spark.sql.Row import org.apache.spark.sql.types._ // 1.定义每个列的列类型 val fields = Array(StructField("deptno", LongType, nullable = true), StructField("dname", StringType, nullable = true), StructField("loc", StringType, nullable = true)) // 2.创建 schema val schema = StructType(fields) // 3.创建 RDD val deptRDD = spark.sparkContext.textFile("/usr/file/dept.txt") val rowRDD = deptRDD.map(_.split("\t")).map(line => Row(line(0).toLong, line(1), line(2))) // 4.将 RDD 转换为 dataFrame val deptDF = spark.createDataFrame(rowRDD, schema) deptDF.show() ```