add test cases for saveAsHadoopDataSet (new&old API)

Author: CodingCat

core/src/test/scala/org/apache/spark/FileSuite.scala

@@ -24,11 +24,12 @@ import scala.io.Source
 import com.google.common.io.Files
 import org.apache.hadoop.io._
 import org.apache.hadoop.io.compress.DefaultCodec
-import org.apache.hadoop.mapred.FileAlreadyExistsException
+import org.apache.hadoop.mapred.{JobConf, FileAlreadyExistsException, TextOutputFormat}
+import org.apache.hadoop.mapreduce.lib.output.{TextOutputFormat => NewTextOutputFormat}
+import org.apache.hadoop.mapreduce.Job
 import org.scalatest.FunSuite
 
 import org.apache.spark.SparkContext._
-import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat, TextOutputFormat}
 
 class FileSuite extends FunSuite with LocalSparkContext {
 
@@ -236,18 +237,44 @@ class FileSuite extends FunSuite with LocalSparkContext {
     val tempdir = Files.createTempDir()
     val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1)
     intercept[FileAlreadyExistsException] {
-      randomRDD.saveAsNewAPIHadoopFile[TextOutputFormat[String, String]](tempdir.getPath)
+      randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempdir.getPath)
     }
   }
 
   test ("prevent user from overwriting the non-empty directory (new Hadoop API)") {
     sc = new SparkContext("local", "test")
     val tempdir = Files.createTempDir()
     val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1)
-    randomRDD.saveAsNewAPIHadoopFile[TextOutputFormat[String, String]](tempdir.getPath + "/output")
+    randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempdir.getPath + "/output")
     assert(new File(tempdir.getPath + "/output/part-r-00000").exists() === true)
     intercept[FileAlreadyExistsException] {
-      randomRDD.saveAsNewAPIHadoopFile[TextOutputFormat[String, String]](tempdir.getPath)
+      randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempdir.getPath)
     }
   }
+
+  test ("save Hadoop Dataset through old Hadoop API") {
+    sc = new SparkContext("local", "test")
+    val tempdir = Files.createTempDir()
+    val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1)
+    val job = new JobConf()
+    job.setOutputKeyClass(classOf[String])
+    job.setOutputValueClass(classOf[String])
+    job.set("mapred.output.format.class", classOf[TextOutputFormat[String, String]].getName)
+    job.set("mapred.output.dir", tempdir.getPath + "/outputDataset_old")
+    randomRDD.saveAsHadoopDataset(job)
+    assert(new File(tempdir.getPath + "/outputDataset_old/part-00000").exists() === true)
+  }
+
+  test ("save Hadoop Dataset through new Hadoop API") {
+    sc = new SparkContext("local", "test")
+    val tempdir = Files.createTempDir()
+    val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1)
+    val job = new Job(sc.hadoopConfiguration)
+    job.setOutputKeyClass(classOf[String])
+    job.setOutputValueClass(classOf[String])
+    job.setOutputFormatClass(classOf[NewTextOutputFormat[String, String]])
+    job.getConfiguration.set("mapred.output.dir", tempdir.getPath + "/outputDataset_new")
+    randomRDD.saveAsNewAPIHadoopDataset(job.getConfiguration)
+    assert(new File(tempdir.getPath + "/outputDataset_new/part-r-00000").exists() === true)
+  }
 }