Implemented DStream.foreachRDD in the Python API using Py4J callback …

…server.

python/pyspark/java_gateway.py

@@ -102,7 +102,7 @@ def run(self):
         EchoOutputThread(proc.stdout).start()
 
     # Connect to the gateway
-    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False)
+    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=True)
 
     # Import the classes used by PySpark
     java_import(gateway.jvm, "org.apache.spark.SparkConf")

python/pyspark/streaming/dstream.py

@@ -445,7 +445,6 @@ def pipeline_func(split, iterator):
             self._prev_jdstream = prev._prev_jdstream  # maintain the pipeline
             self._prev_jrdd_deserializer = prev._prev_jrdd_deserializer
         self.is_cached = False
-        self.is_checkpointed = False
         self._ssc = prev._ssc
         self.ctx = prev.ctx
         self.prev = prev
@@ -482,4 +481,5 @@ def _jdstream(self):
         return self._jdstream_val
 
     def _is_pipelinable(self):
-        return not (self.is_cached or self.is_checkpointed)
+        return not (self.is_cached)
+

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

@@ -54,14 +54,6 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
     dstream.print()
   }
 
-  /**
-   * Print the first ten elements of each PythonRDD generated in the PythonDStream. This is an output
-   * 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()
-
   /**
    * 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

@@ -170,6 +170,7 @@ class PythonTestInputStream3(ssc_ : JavaStreamingContext)
 
   val asJavaDStream = JavaDStream.fromDStream(this)
 }
+
 class PythonForeachDStream(
     prev: DStream[Array[Byte]],
     foreachFunction: PythonRDDFunction

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

@@ -620,66 +620,6 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-//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
-
-      // 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 to temporary file
-      PythonRDD.writeIteratorToStream(iter, tempFileStream)
-      tempFileStream.close()
-
-      // 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")
-      // 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 should be pass from python
-      val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
-      workerEnv.put("PYTHONPATH", pythonPath)
-      val worker = pb.start()
-      val is = worker.getInputStream()
-      val isr = new InputStreamReader(is)
-      val br = new BufferedReader(isr)
-
-      println ("-------------------------------------------")
-      println ("Time: " + time)
-      println ("-------------------------------------------")
-
-      // Print values which is from python std out
-      var line = ""
-      breakable {
-        while (true) {
-          line = br.readLine()
-          if (line == null) break()
-          println(line)
-        }
-      }
-      // Delete temporary file
-      tempFile.delete()
-      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