[SPARK-3325][Streaming] Add a parameter to the method print in class DStream

project/MimaExcludes.scala

@@ -54,6 +54,9 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.mllib.linalg.Matrices.rand")
           ) ++ Seq(
+            // SPARK-3325
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.streaming.api.java.JavaDStreamLike.print"),
             // SPARK-2757
             ProblemFilters.exclude[IncompatibleResultTypeProblem](
               "org.apache.spark.streaming.flume.sink.SparkAvroCallbackHandler." +

python/pyspark/streaming/dstream.py

@@ -157,18 +157,20 @@ def foreachRDD(self, func):
         api = self._ssc._jvm.PythonDStream
         api.callForeachRDD(self._jdstream, jfunc)
 
-    def pprint(self):
+    def pprint(self, num=10):
         """
-        Print the first ten elements of each RDD generated in this DStream.
+        Print the first num elements of each RDD generated in this DStream.
+
+        @param num: the number of elements from the first will be printed.
         """
         def takeAndPrint(time, rdd):
-            taken = rdd.take(11)
+            taken = rdd.take(num + 1)
             print "-------------------------------------------"
             print "Time: %s" % time
             print "-------------------------------------------"
-            for record in taken[:10]:
+            for record in taken[:num]:
                 print record
-            if len(taken) > 10:
+            if len(taken) > num:
                 print "..."
             print
 

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

@@ -51,7 +51,15 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    * operator, so this DStream will be registered as an output stream and there materialized.
    */
   def print(): Unit = {
-    dstream.print()
+    print(10)
+  }
+
+  /**
+   * Print the first num 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.
+   */
+  def print(num: Int): Unit = {
+    dstream.print(num)
   }
 
   /**

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

@@ -605,13 +605,21 @@ abstract class DStream[T: ClassTag] (
    * operator, so this DStream will be registered as an output stream and there materialized.
    */
   def print() {
+    print(10)
+  }
+
+  /**
+   * Print the first num 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.
+   */
+  def print(num: Int) {
     def foreachFunc = (rdd: RDD[T], time: Time) => {
-      val first11 = rdd.take(11)
+      val firstNum = rdd.take(num + 1)
       println ("-------------------------------------------")
       println ("Time: " + time)
       println ("-------------------------------------------")
-      first11.take(10).foreach(println)
-      if (first11.size > 10) println("...")
+      firstNum.take(num).foreach(println)
+      if (firstNum.size > num) println("...")
       println()
     }
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()