edited the comment to add more precise description

Author: giwa

python/pyspark/streaming/context.py

@@ -146,7 +146,6 @@ 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:
@@ -158,38 +157,3 @@ 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