added basic operation test cases

examples/src/main/python/streaming/test_oprations.py

@@ -9,22 +9,23 @@
     conf = SparkConf()
     conf.setAppName("PythonStreamingNetworkWordCount")
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
-
-    test_input = ssc._testInputStream([1,2,3])
-    class buff:
+    class Buff:
+        result = list()
         pass
+    Buff.result = list()
+
+    test_input = ssc._testInputStream([range(1,4), range(4,7), range(7,10)])
 
     fm_test = test_input.map(lambda x: (x, 1))
-    fm_test.test_output(buff)
+    fm_test.pyprint()
+    fm_test._test_output(Buff.result)
 
     ssc.start()
     while True:
         ssc.awaitTermination(50)
-        try:
-            buff.result
+        if len(Buff.result) == 3:
             break
-        except AttributeError:
-            pass
 
     ssc.stop()
-    print buff.result
+    print Buff.result
+

python/pyspark/streaming/context.py

@@ -150,6 +150,7 @@ def _testInputStream(self, test_inputs, numSlices=None):
         This implementation is inspired by QueStream implementation.
         Give list of RDD to generate DStream which contains the RDD.
         """
+<<<<<<< HEAD
         test_rdds = list()
         test_rdd_deserializers = list()
         for test_input in test_inputs:
@@ -161,3 +162,38 @@ def _testInputStream(self, test_inputs, numSlices=None):
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
         return DStream(jinput_stream, self, test_rdd_deserializers[0])
+=======
+        self._jssc.checkpoint(directory)
+
+    def _testInputStream(self, test_inputs, numSlices=None):
+        """
+        Generate multiple files to make "stream" in Scala side for test.
+        Scala chooses one of the files and generates RDD using PythonRDD.readRDDFromFile.
+        """
+        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):
+                c = 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)
+            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, PickleSerializer())
+>>>>>>> added basic operation test cases

python/pyspark/streaming/dstream.py

@@ -236,7 +236,6 @@ def pyprint(self):
         operator, so this DStream will be registered as an output stream and there materialized.
         """
         def takeAndPrint(rdd, time):
-            print "take and print ==================="
             taken = rdd.take(11)
             print "-------------------------------------------"
             print "Time: %s" % (str(time))

python/pyspark/streaming_tests.py

@@ -449,12 +449,11 @@ def tearDownClass(cls):
             current_time = time.time()
             # check time out
             if (current_time - start_time) > self.timeout:
-                self.ssc.stop()
                 break
             self.ssc.awaitTermination(50)
-            if buff.result is not None:
+            if len(expected_output) == len(StreamOutput.result):
                 break
-        return buff.result
+        return StreamOutput.result
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -55,8 +55,6 @@ class PythonDStream[T: ClassTag](
   override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
     parent.getOrCompute(validTime) match{
       case Some(rdd) =>
-        logInfo("RDD ID in python DStream     ===========")
-        logInfo("RDD id " + rdd.id)
         val pythonRDD = new PythonRDD(rdd, command, envVars, pythonIncludes, preservePartitoning, pythonExec, broadcastVars, accumulator)
         Some(pythonRDD.asJavaRDD.rdd)
       case None => None