源文件内容如下（包含 id,name,age）：
1,Ella,36
2,Bob,29
3,Jack,29
请将数据复制保存到 Linux 系统中，命名为 employee.txt，实现从 RDD 转换得到 DataFrame，并按“id:1,name:Ella,age:36”的格式打印出 DataFrame 的所有数据。请写出程序代码。
scala> import org.apache.spark.sql.types._ import org.apache.spark.sql.types._ scala> import org.apache.spark.sql.Row import org.apache.spark.sql.Row scala> val peopleRDD = spark.sparkContext.textFile("file:///home/hadoop/77/employee.txt") peopleRDD: org.apache.spark.rdd.RDD[String] = file:///home/hadoop/77/employee.txt MapPartitionsRDD[1] at textFile at <console>:27 scala> val schemaString = "id name age" schemaString: String = id name age scala> val fields = schemaString.split(" ").map(fieldName => StructField(fieldName, StringType, nullable = true)) fields: Array[org.apache.spark.sql.types.StructField] = Array(StructField(id,StringType,true), StructField(name,StringType,true), StructField(age,StringType,true)) scala> val schema = StructType(fields) schema: org.apache.spark.sql.types.StructType = StructType(StructField(id,StringType,true), StructField(name,StringType,true), StructField(age,StringType,true)) scala> val rowRDD = peopleRDD.map(_.split(",")).map(attributes => Row(attributes(0), attributes(1).trim, attributes(2).trim)) rowRDD: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[3] at map at <console>:29 scala> val peopleDF = spark.createDataFrame(rowRDD, schema) peopleDF: org.apache.spark.sql.DataFrame = [id: string, name: string ... 1 more field] scala> peopleDF.createOrReplaceTempView("people") scala> val results = spark.sql("SELECT id,name,age FROM people") results: org.apache.spark.sql.DataFrame = [id: string, name: string ... 1 more field] scala> results.map(attributes => "id: " + attributes(0)+","+"name:"+attributes(1)+","+"age:"+attributes(2)).show() +--------------------+ | value| +--------------------+ |id: 1,name:Ella,age:36| |id: 2,name:Bob,age:29| |id: 3,name:Jack,age:29| +--------------------+