WIP added test case

core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala

@@ -350,8 +350,6 @@ private[spark] object PythonRDD extends Logging {
     } catch {
       case eof: EOFException => {}
     }
-    println("RDDDD ==================")
-    println(objs)
     JavaRDD.fromRDD(sc.sc.parallelize(objs, parallelism))
   }
 

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

@@ -9,15 +9,22 @@
     conf = SparkConf()
     conf.setAppName("PythonStreamingNetworkWordCount")
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
-    ssc.checkpoint("/tmp/spark_ckp")
 
-    test_input = ssc._testInputStream([[1],[1],[1]])
-#    ssc.checkpoint("/tmp/spark_ckp")
-    fm_test = test_input.flatMap(lambda x: x.split(" "))
-    mapped_test = fm_test.map(lambda x: (x, 1))
+    test_input = ssc._testInputStream([1,2,3])
+    class buff:
+        pass
+
+    fm_test = test_input.map(lambda x: (x, 1))
+    fm_test.test_output(buff)
 
-
-    mapped_test.print_()
     ssc.start()
-#    ssc.awaitTermination()
-#    ssc.stop()
+    while True:
+        ssc.awaitTermination(50)
+        try:
+            buff.result
+            break
+        except AttributeError:
+            pass
+
+    ssc.stop()
+    print buff.result

python/pyspark/streaming/context.py

@@ -100,10 +100,10 @@ def awaitTermination(self, timeout=None):
         """
         Wait for the execution to stop.
         """
-        if timeout:
-            self._jssc.awaitTermination(timeout)
-        else:
+        if timeout is None:
             self._jssc.awaitTermination()
+        else:
+            self._jssc.awaitTermination(timeout)
 
     # start from simple one. storageLevel is not passed for now.
     def socketTextStream(self, hostname, port):
@@ -137,6 +137,7 @@ def stop(self, stopSparkContext=True):
 
     def checkpoint(self, directory):
         """
+        Not tested
         """
         self._jssc.checkpoint(directory)
 
@@ -147,8 +148,7 @@ def _testInputStream(self, test_input, numSlices=None):
         # because it sends O(n) Py4J commands.  As an alternative, serialized
         # objects are written to a file and loaded through textFile().
 
-        #tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
-        tempFile = open("/tmp/spark_rdd", "wb")
+        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):
@@ -160,10 +160,8 @@ def _testInputStream(self, test_input, numSlices=None):
         else:
             serializer = self._sc._unbatched_serializer
         serializer.dump_stream(test_input, tempFile)
-        tempFile.flush()
-        tempFile.close()
-        print tempFile.name
+
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc,
                                                         tempFile.name,
                                                         numSlices).asJavaDStream()
-        return DStream(jinput_stream, self, UTF8Deserializer())
+        return DStream(jinput_stream, self, PickleSerializer())

python/pyspark/streaming/dstream.py

@@ -47,7 +47,7 @@ def _sum(self):
         """
         return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
-    def print_(self):
+    def print_(self, label=None):
         """
         Since print is reserved name for python, we cannot define a print method function.
         This function prints serialized data in RDD in DStream because Scala and Java cannot
@@ -56,7 +56,7 @@ def print_(self):
         Call DStream.print().
         """
         # a hack to call print function in DStream
-        getattr(self._jdstream, "print")()
+        getattr(self._jdstream, "print")(label)
 
     def filter(self, f):
         """
@@ -230,6 +230,7 @@ def pyprint(self):
 
         """
         def takeAndPrint(rdd, time):
+            print "take and print ==================="
             taken = rdd.take(11)
             print "-------------------------------------------"
             print "Time: %s" % (str(time))
@@ -242,11 +243,24 @@ def takeAndPrint(rdd, time):
 
         self.foreachRDD(takeAndPrint)
 
-    #def transform(self, func):
+    #def transform(self, func): - TD
     #    from utils import RDDFunction
     #    wrapped_func = RDDFunction(self.ctx, self._jrdd_deserializer, func)
     #    jdstream = self.ctx._jvm.PythonTransformedDStream(self._jdstream.dstream(), wrapped_func).toJavaDStream
-    #    return DStream(jdstream, self._ssc, ...)  ## DO NOT KNOW HOW 
+    #    return DStream(jdstream, self._ssc, ...)  ## DO NOT KNOW HOW
+
+    def _test_output(self, buff):
+        """
+        This function is only for testcase.
+        Store data in dstream to buffer to valify the result in tesecase
+        """
+        def get_output(rdd, time):
+            taken = rdd.take(11)
+            buff.result = taken
+        self.foreachRDD(get_output)
+
+    def output(self):
+        self._jdstream.outputToFile()
 
 
 class PipelinedDStream(DStream):

python/pyspark/streaming_tests.py

@@ -19,12 +19,13 @@
 Unit tests for PySpark; additional tests are implemented as doctests in
 individual modules.
 
-This file will merged to tests.py. But for now, this file is separated to
-focus to streaming test case
+This file will merged to tests.py. But for now, this file is separated due
+to focusing to streaming test case
 
 """
 from fileinput import input
 from glob import glob
+from itertools import chain
 import os
 import re
 import shutil
@@ -41,18 +42,69 @@
 
 SPARK_HOME = os.environ["SPARK_HOME"]
 
+class buff:
+    """
+    Buffer for store the output from stream
+    """
+    result = None
 
 class PySparkStreamingTestCase(unittest.TestCase):
-
     def setUp(self):
-        self._old_sys_path = list(sys.path)
+        print "set up"
         class_name = self.__class__.__name__
         self.ssc = StreamingContext(appName=class_name, duration=Seconds(1))
 
     def tearDown(self):
+        print "tear donw"
         self.ssc.stop()
-        sys.path = self._old_sys_path
+        time.sleep(10)
+
+class TestBasicOperationsSuite(PySparkStreamingTestCase):
+    def setUp(self):
+        PySparkStreamingTestCase.setUp(self)
+        buff.result = None
+        self.timeout = 10 # seconds
+
+    def tearDown(self):
+        PySparkStreamingTestCase.tearDown(self)
+
+    def test_map(self):
+        test_input = [range(1,5), range(5,9), range(9, 13)]
+        def test_func(dstream):
+            return dstream.map(lambda x: str(x))
+        expected = map(str, test_input)
+        output = self.run_stream(test_input, test_func)
+        self.assertEqual(output, expected)
+
+    def test_flatMap(self):
+        test_input = [range(1,5), range(5,9), range(9, 13)]
+        def test_func(dstream):
+            return dstream.flatMap(lambda x: (x, x * 2))
+        # Maybe there be good way to create flatmap
+        excepted = map(lambda x: list(chain.from_iterable((map(lambda y:[y, y*2], x)))), 
+                       test_input)
+        output = self.run_stream(test_input, test_func)
+
+    def run_stream(self, test_input, test_func):
+        # Generate input stream with user-defined input
+        test_input_stream = self.ssc._testInputStream(test_input)
+        # Applyed test function to stream
+        test_stream = test_func(test_input_stream)
+        # Add job to get outpuf from stream
+        test_stream._test_output(buff)
+        self.ssc.start()
 
+        start_time = time.time()
+        while True:
+            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:
+                break
+        return buff.result
 
 if __name__ == "__main__":
     unittest.main()

streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala

@@ -54,6 +54,15 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
     dstream.print()
   }
 
+  def print(label: String = null): Unit = {
+    dstream.print(label)
+  }
+
+  def outputToFile(): Unit = {
+    dstream.outputToFile()
+  }
+
+
   /**
    * Return a new DStream in which each RDD has a single element generated by counting each RDD
    * of this DStream.

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

@@ -17,9 +17,14 @@
 
 package org.apache.spark.streaming.api.python
 
+import java.io._
+import java.io.{ObjectInputStream, IOException}
 import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections}
 
+import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
+import scala.collection.JavaConversions._
+
 
 import org.apache.spark._
 import org.apache.spark.rdd.RDD
@@ -51,6 +56,8 @@ 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
@@ -152,7 +159,7 @@ DStream[Array[Byte]](prev.ssc){
         val pairwiseRDD = new PairwiseRDD(rdd)
         /*
          * Since python operation is executed by Scala after StreamingContext.start.
-         * What PairwiseDStream does is equivalent to following python code in pySpark.
+         * What PythonPairwiseDStream does is equivalent to python code in pySpark.
          *
          * with _JavaStackTrace(self.context) as st:
          *    pairRDD = self.ctx._jvm.PairwiseRDD(keyed._jrdd.rdd()).asJavaPairRDD()

streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala

@@ -623,6 +623,23 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
+
+  def print(label: String = null) {
+    def foreachFunc = (rdd: RDD[T], time: Time) => {
+      val first11 = rdd.take(11)
+      println ("-------------------------------------------")
+      println ("Time: " + time)
+      println ("-------------------------------------------")
+      if(label != null){
+        println (label)
+      }
+      first11.take(10).foreach(println)
+      if (first11.size > 10) println("...")
+      println()
+    }
+    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
+  }
+
   /**
    * Return a new DStream in which each RDD contains all the elements in seen in a
    * sliding window of time over this DStream. The new DStream generates RDDs with