# RDDs Spark支持兩種方法將存在的RDDs轉換為SchemaRDDs(DataFrame/DataSet)。 * 第一種方法使用反射來推斷包含特定對象類型的RDD的模式(schema)。在你寫spark程序的同時，當你已經知道了模式，這種基于反射的 方法可以使代碼更簡潔并且程序工作得更好。 * 創建SchemaRDDs的第二種方法是通過一個編程接口來實現，這個接口允許你構造一個模式，然后在存在的RDDs上使用它。雖然這種方法更冗長，但是它允許你在運行期之前不知道列以及列的類型的情況下構造SchemaRDDs。 ## 一、利用反射推斷模式 Spark SQL的Scala接口支持將包含樣本類的RDDs自動轉換為SchemaRDD。這個樣本類定義了表的模式。 給樣本類的參數名字通過反射來讀取，然后作為列的名字。樣本類可以嵌套或者包含復雜的類型如序列或者數組。這個RDD可以隱式轉化為一個SchemaRDD，然后注冊為一個表。表可以在后續的 sql語句中使用。 ```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、編程指定模式 當樣本類不能提前確定（例如，記錄的結構是經過編碼的字符串，或者一個文本集合將會被解析，不同的字段投影給不同的用戶），一個SchemaRDD可以通過三步來創建。 - 從原來的RDD創建一個行的RDD - 創建由一個`StructType`表示的模式與第一步創建的RDD的行結構相匹配 - 在行RDD上通過`applySchema`方法應用模式 ```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.定義每個列的列類型 val fields = Array(StructField("deptno", LongType, nullable = true), StructField("dname", StringType, nullable = true), StructField("loc", StringType, nullable = true)) // 2.創建 schema val schema = StructType(fields) // 3.創建 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.將 RDD 轉換為 dataFrame val deptDF = spark.createDataFrame(rowRDD, schema) deptDF.show() ```