all tests are passed if numSlice is 2 and the numver of each input is…

… over 4

python/pyspark/streaming/context.py

@@ -142,49 +142,9 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
 
     def _testInputStream(self, test_inputs, numSlices=None):
         """
-<<<<<<< HEAD
         This function is only for test.
         This implementation is inspired by QueStream implementation.
         Give list of RDD to generate DStream which contains the RDD.
-=======
-        Generate multiple files to make "stream" in Scala side for test.
-        Scala chooses one of the files and generates RDD using PythonRDD.readRDDFromFile.
-
-        QueStream maybe good way to implement this function
-        """
-        numSlices = numSlices or self._sc.defaultParallelism
-        # Calling the Java parallelize() method with an ArrayList is too slow,
-        # because it sends O(n) Py4J commands.  As an alternative, serialized
-        # objects are written to a file and loaded through textFile().
-
-        tempFiles = list()
-        for test_input in test_inputs:
-            tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
-
-            # Make sure we distribute data evenly if it's smaller than self.batchSize
-            if "__len__" not in dir(test_input):
-                test_input = list(test_input)    # Make it a list so we can compute its length
-            batchSize = min(len(test_input) // numSlices, self._sc._batchSize)
-            if batchSize > 1:
-                serializer = BatchedSerializer(self._sc._unbatched_serializer,
-                                               batchSize)
-            else:
-                serializer = self._sc._unbatched_serializer
-            serializer.dump_stream(test_input, tempFile)
-            tempFile.close()
-            tempFiles.append(tempFile.name)
-
-        jtempFiles = ListConverter().convert(tempFiles, SparkContext._gateway._gateway_client)
-        jinput_stream = self._jvm.PythonTestInputStream(self._jssc,
-                                                        jtempFiles,
-                                                        numSlices).asJavaDStream()
-        return DStream(jinput_stream, self, BatchedSerializer(PickleSerializer()))
-
-    def _testInputStream2(self, test_inputs, numSlices=None):
-        """
-        This is inpired by QueStream implementation. Give list of RDD and generate DStream
-        which contain the RDD.
->>>>>>> broke something
         """
         test_rdds = list()
         test_rdd_deserializers = list()
@@ -196,10 +156,4 @@ def _testInputStream2(self, test_inputs, numSlices=None):
         jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
-<<<<<<< HEAD
         return DStream(jinput_stream, self, test_rdd_deserializers[0])
-=======
-        dstream = DStream(jinput_stream, self, test_rdd_deserializers[0])
-        dstream._test_switch_dserializer(test_rdd_deserializers)
-        return dstream
->>>>>>> broke something

streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala

@@ -280,4 +280,21 @@ class PythonTestInputStream2(ssc_ : JavaStreamingContext, inputRDDs: JArrayList[
 
   val asJavaDStream  = JavaDStream.fromDStream(this)
 }
->>>>>>> broke something
+
+
+class PythonTestInputStream3(ssc_ : JavaStreamingContext)
+  extends InputDStream[Any](JavaStreamingContext.toStreamingContext(ssc_)) {
+
+  def start() {}
+
+  def stop() {}
+
+  def compute(validTime: Time): Option[RDD[Any]] = {
+    val index = ((validTime - zeroTime) / slideDuration - 1).toInt
+    val selectedInput = ArrayBuffer(1, 2, 3).toSeq
+    val rdd :RDD[Any] = ssc.sc.makeRDD(selectedInput, 2)
+    Some(rdd)
+  }
+
+  val asJavaDStream = JavaDStream.fromDStream(this)
+}>>>>>>> broke something