clean up code

Author: Ken Takagiwa

python/pyspark/streaming/context.py

@@ -22,15 +22,15 @@
 from pyspark.storagelevel import *
 from pyspark.rdd import RDD
 from pyspark.context import SparkContext
+from pyspark.streaming.dstream import DStream
 
 from py4j.java_collections import ListConverter
 
-from pyspark.streaming.dstream import DStream
 
 class StreamingContext(object):
     """
     Main entry point for Spark Streaming functionality. A StreamingContext represents the
-    connection to a Spark cluster, and can be used to create L{RDD}s and
+    connection to a Spark cluster, and can be used to create L{DStream}s and
     broadcast variables on that cluster.
     """
 
@@ -71,34 +71,35 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
     def _initialize_context(self, jspark_context, jduration):
         return self._jvm.JavaStreamingContext(jspark_context, jduration)
 
-    def actorStream(self, props, name, storageLevel, supervisorStrategy):
-        raise NotImplementedError
-
-    def addStreamingListener(self, streamingListener):
-        raise NotImplementedError
+    def start(self):
+        """
+        Start the execution of the streams.
+        """
+        self._jssc.start()
 
     def awaitTermination(self, timeout=None):
+        """
+        Wait for the execution to stop.
+        """
         if timeout:
             self._jssc.awaitTermination(timeout)
         else:
             self._jssc.awaitTermination()
 
     # start from simple one. storageLevel is not passed for now.
     def socketTextStream(self, hostname, port):
+        """
+        Create an input from TCP source hostname:port. Data is received using
+        a TCP socket and receive byte is interpreted as UTF8 encoded '\n' delimited
+        lines.
+        """
         return DStream(self._jssc.socketTextStream(hostname, port), self, UTF8Deserializer())
 
-    def start(self):
-        self._jssc.start()
-
-    def stop(self, stopSparkContext=True):
-        raise NotImplementedError
-
     def textFileStream(self, directory):
+        """
+        Create an input stream that monitors a Hadoop-compatible file system
+        for new files and reads them as text files. Files must be wrriten to the
+        monitored directory by "moving" them from another location within the same
+        file system. FIle names starting with . are ignored.
+        """
         return DStream(self._jssc.textFileStream(directory), self, UTF8Deserializer())
-
-    def transform(self, seq):
-        raise NotImplementedError
-
-    def union(self, seq):
-        raise NotImplementedError
-

python/pyspark/streaming/dstream.py

@@ -2,8 +2,6 @@
 from itertools import chain, ifilter, imap
 import operator
 
-import logging
-
 from pyspark.serializers import NoOpSerializer,\
     BatchedSerializer, CloudPickleSerializer, pack_long
 from pyspark.rdd import _JavaStackTrace
@@ -25,64 +23,86 @@ def count(self):
 
         """
         #TODO make sure count implementation, thiis different from what pyspark does
-        return self.mapPartitions(lambda i: [sum(1 for _ in i)]).map(lambda x: (None, 1))
+        return self._mapPartitions(lambda i: [sum(1 for _ in i)]).map(lambda x: (None, 1))
 
     def _sum(self):
         """
         """
-        return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
+        return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
     def print_(self):
         """
+        Since print is reserved name for python, we cannot make a print method function.
+        This function prints serialized data in RDD in DStream because Scala and Java cannot
+        deserialized pickled python object. Please use DStream.pyprint() instead to print result.
+
+        Call DStream.print().
         """
-        # print is a reserved name of Python. We cannot give print to function name
+        #hack to call print function in DStream
         getattr(self._jdstream, "print")()
 
     def pyprint(self):
         """
+        Print the first ten elements of each RDD generated in this DStream. This is an output
+        operator, so this DStream will be registered as an output stream and there materialized.
+
         """
         self._jdstream.pyprint()
 
     def filter(self, f):
         """
+        Return DStream containing only the elements that satisfy predicate.
         """
         def func(iterator): return ifilter(f, iterator)
-        return self.mapPartitions(func)
+        return self._mapPartitions(func)
 
     def flatMap(self, f, preservesPartitioning=False):
         """
+        Pass each value in the key-value pair DStream through flatMap function
+        without changing the keys: this also retains the original RDD's partition.
         """
         def func(s, iterator): return chain.from_iterable(imap(f, iterator))
-        return self.mapPartitionsWithIndex(func, preservesPartitioning)
+        return self._mapPartitionsWithIndex(func, preservesPartitioning)
 
-    def map(self, f, preservesPartitioning=False):
+    def map(self, f):
         """
+        Return DStream by applying a function to each element of DStream.
         """
         def func(iterator): return imap(f, iterator)
-        return self.mapPartitions(func)
-        #return PipelinedDStream(self, func, preservesPartitioning)
+        return self._mapPartitions(func)
 
-    def mapPartitions(self, f):
+    def _mapPartitions(self, f):
         """
+        Return a new DStream by applying a function to each partition of this DStream.
         """
         def func(s, iterator): return f(iterator)
-        return self.mapPartitionsWithIndex(func)
+        return self._mapPartitionsWithIndex(func)
 
-    def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
+    def _mapPartitionsWithIndex(self, f, preservesPartitioning=False):
         """
-
+        Return a new DStream by applying a function to each partition of this DStream,
+        While tracking the index of the original partition.
         """
         return PipelinedDStream(self, f, preservesPartitioning)
 
-    def reduce(self, func, numPartitions=None):
+
+    def reduceByKey(self, func, numPartitions=None):
         """
+        Merge the value for each key using an associative reduce function.
+
+        This will also perform the merging locally on each mapper before
+        sending resuls to reducer, similarly to a "combiner" in MapReduce.
 
+        Output will be hash-partitioned with C{numPartitions} partitions, or
+        the default parallelism level if C{numPartitions} is not specified.
         """
         return self.combineByKey(lambda x:x, func, func, numPartitions)
 
     def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
                       numPartitions = None):
         """
+        Count the number of elements for each key, and return the result to the
+        master as a dictionary
         """
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
@@ -148,30 +168,27 @@ def add_shuffle_key(split, iterator):
         dstream._partitionFunc = partitionFunc
         return dstream
 
-    def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
-        """
-
-        """
-        return PipelinedDStream(self, f, preservesPartitioning)
-
     def _defaultReducePartitions(self):
         """
+        Returns the default number of partitions to use during reduce tasks (e.g., groupBy).
+        If spark.default.parallelism is set, then we'll use the value from SparkContext
+        defaultParallelism, otherwise we'll use the number of partitions in this RDD.
 
+        This mirrors the behavior of the Scala Partitioner#defaultPartitioner, intended to reduce
+        the likelihood of OOMs. Once PySpark adopts Partitioner-based APIs, this behavior will
+        be inherent.
         """
-        # hard code to avoid the error
         if self.ctx._conf.contains("spark.default.parallelism"):
             return self.ctx.defaultParallelism
         else:
             return self.getNumPartitions()
 
     def getNumPartitions(self):
-      """
-      Returns the number of partitions in RDD
-      >>> rdd = sc.parallelize([1, 2, 3, 4], 2)
-      >>> rdd.getNumPartitions()
-      2
-      """
-      return self._jdstream.partitions().size()
+        """
+        Return the number of partitions in RDD
+        """
+        # TODO: remove hardcoding. RDD has NumPartitions but DStream does not have.
+        return 2
 
 
 class PipelinedDStream(DStream):

python/pyspark/streaming/duration.py

@@ -17,6 +17,7 @@
 
 from pyspark.streaming import utils
 
+
 class Duration(object):
     """
     Duration for Spark Streaming application. Used to set duration

python/pyspark/streaming/pyprint.py

@@ -21,16 +21,22 @@
 
 from pyspark.serializers import PickleSerializer
 
+
 def collect(binary_file_path):
+    """
+    Read pickled file written by SparkStreaming
+    """
     dse = PickleSerializer()
     with open(binary_file_path, 'rb') as tempFile:
         for item in dse.load_stream(tempFile):
             yield item
+
+
 def main():
     try:
         binary_file_path = sys.argv[1]
     except:
-        print "Missed FilePath in argement"
+        print "Missed FilePath in argements"
 
     if not binary_file_path:
         return 
@@ -43,5 +49,6 @@ def main():
             print "..."
             break
 
+
 if __name__ =="__main__":
     exit(main())

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

@@ -59,7 +59,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    * operator, so this PythonDStream will be registered as an output stream and there materialized.
    * This function is for PythonAPI.
    */
-
+  //TODO move this function to PythonDStream
   def pyprint() = dstream.pyprint()
 
   /**

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

@@ -71,7 +71,9 @@ DStream[Array[Byte]](prev.ssc){
       case Some(rdd)=>Some(rdd)
         val pairwiseRDD = new PairwiseRDD(rdd)
         /*
-         * This is equivalent to following python code
+         * Since python operation is executed by Scala after StreamingContext.start.
+         * What PairwiseDStream does is equivalent to following python code in pySpark.
+         *
          * with _JavaStackTrace(self.context) as st:
          *    pairRDD = self.ctx._jvm.PairwiseRDD(keyed._jrdd.rdd()).asJavaPairRDD()
          *    partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,

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

@@ -623,37 +623,36 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-//TODO move pyprint to PythonDStream
+//TODO move pyprint to PythonDStream and executed by py4j call back function
   /**
    * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
    * operator, so this PythonDStream will be registered as an output stream and there materialized.
    * Since serialized Python object is readable by Python, pyprint writes out binary data to
    * temporary file and run python script to deserialized and print the first ten elements
+   *
+   * Currently call python script directly. We should avoid this
    */
   private[streaming] def pyprint() {
     def foreachFunc = (rdd: RDD[T], time: Time) => {
       val iter = rdd.take(11).iterator
 
-      // make a temporary file
+      // Generate a temporary file
       val prefix = "spark"
       val suffix = ".tmp"
       val tempFile = File.createTempFile(prefix, suffix)
       val tempFileStream = new DataOutputStream(new FileOutputStream(tempFile.getAbsolutePath))
-      //write out serialized python object
+      // Write out serialized python object to temporary file
       PythonRDD.writeIteratorToStream(iter, tempFileStream)
       tempFileStream.close()
 
-      // This value has to be passed from python
-      // Python currently does not do cluster deployment. But what happened
+      // pythonExec should be passed from python. Move pyprint to PythonDStream
       val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
       val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
-      //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???
-      //absolute path to the python script is needed to change because we do not use pysparkstreaming
+      // Call python script to deserialize and print result in stdout
       val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath)
       val workerEnv = pb.environment()
 
-      //envVars also need to be pass
-      //workerEnv.putAll(envVars)
+      // envVars also should be pass from python
       val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
       workerEnv.put("PYTHONPATH", pythonPath)
       val worker = pb.start()
@@ -665,7 +664,7 @@ abstract class DStream[T: ClassTag] (
       println ("Time: " + time)
       println ("-------------------------------------------")
 
-      //print value from python std out
+      // Print values which is from python std out
       var line = ""
       breakable {
         while (true) {
@@ -674,7 +673,7 @@ abstract class DStream[T: ClassTag] (
           println(line)
         }
       }
-      //delete temporary file
+      // Delete temporary file
       tempFile.delete()
       println()