Merge branch 'master' of https://github.com/apache/spark into allocateExecutors

witgo · witgo · commit 056b8c7f0fbb · 2014-06-09T23:13:08.000+08:00
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -1024,7 +1024,7 @@ private[spark] class BlockManager(
     if (blockId.isShuffle) {
       // Reducer may need to read many local shuffle blocks and will wrap them into Iterators
       // at the beginning. The wrapping will cost some memory (compression instance
-      // initialization, etc.). Reducer read shuffle blocks one by one so we could do the
+      // initialization, etc.). Reducer reads shuffle blocks one by one so we could do the
       // wrapping lazily to save memory.
       class LazyProxyIterator(f: => Iterator[Any]) extends Iterator[Any] {
         lazy val proxy = f
diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -168,7 +168,7 @@ private[spark] object UIUtils extends Logging {
       <body>
         <div class="navbar navbar-static-top">
           <div class="navbar-inner">
-            <a href="/" class="brand">
+            <a href={prependBaseUri("/")} class="brand">
               <img src={prependBaseUri("/static/spark-logo-77x50px-hd.png")} />
             </a>
             <ul class="nav">{header}</ul>
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
@@ -250,7 +250,7 @@ def getCheckpointFile(self):
     def map(self, f, preservesPartitioning=False):
         """
         Return a new RDD by applying a function to each element of this RDD.
-        
+
         >>> rdd = sc.parallelize(["b", "a", "c"])
         >>> sorted(rdd.map(lambda x: (x, 1)).collect())
         [('a', 1), ('b', 1), ('c', 1)]
@@ -312,6 +312,15 @@ def mapPartitionsWithSplit(self, f, preservesPartitioning=False):
             "use mapPartitionsWithIndex instead", DeprecationWarning, stacklevel=2)
         return self.mapPartitionsWithIndex(f, preservesPartitioning)
 
+    def getNumPartitions(self):
+      """
+      Returns the number of partitions in RDD
+      >>> rdd = sc.parallelize([1, 2, 3, 4], 2)
+      >>> rdd.getNumPartitions()
+      2
+      """
+      return self._jrdd.splits().size()
+
     def filter(self, f):
         """
         Return a new RDD containing only the elements that satisfy a predicate.
@@ -413,9 +422,9 @@ def union(self, other):
 
     def intersection(self, other):
         """
-        Return the intersection of this RDD and another one. The output will not 
+        Return the intersection of this RDD and another one. The output will not
         contain any duplicate elements, even if the input RDDs did.
-        
+
         Note that this method performs a shuffle internally.
 
         >>> rdd1 = sc.parallelize([1, 10, 2, 3, 4, 5])
@@ -571,14 +580,14 @@ def foreachPartition(self, f):
         """
         Applies a function to each partition of this RDD.
 
-        >>> def f(iterator): 
-        ...      for x in iterator: 
-        ...           print x 
+        >>> def f(iterator):
+        ...      for x in iterator:
+        ...           print x
         ...      yield None
         >>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f)
         """
         self.mapPartitions(f).collect()  # Force evaluation
-        
+
     def collect(self):
         """
         Return a list that contains all of the elements in this RDD.
@@ -673,7 +682,7 @@ def func(iterator):
             yield acc
 
         return self.mapPartitions(func).fold(zeroValue, combOp)
-        
+
 
     def max(self):
         """
@@ -692,7 +701,7 @@ def min(self):
         1.0
         """
         return self.reduce(min)
-    
+
     def sum(self):
         """
         Add up the elements in this RDD.
@@ -786,7 +795,7 @@ def mergeMaps(m1, m2):
                 m1[k] += v
             return m1
         return self.mapPartitions(countPartition).reduce(mergeMaps)
-    
+
     def top(self, num):
         """
         Get the top N elements from a RDD.
@@ -814,7 +823,7 @@ def merge(a, b):
     def takeOrdered(self, num, key=None):
         """
         Get the N elements from a RDD ordered in ascending order or as specified
-        by the optional key function. 
+        by the optional key function.
 
         >>> sc.parallelize([10, 1, 2, 9, 3, 4, 5, 6, 7]).takeOrdered(6)
         [1, 2, 3, 4, 5, 6]
@@ -834,7 +843,7 @@ def unKey(x, key_=None):
             if key_ != None:
                 x = [i[1] for i in x]
             return x
-        
+
         def merge(a, b):
             return next(topNKeyedElems(a + b))
         result = self.mapPartitions(lambda i: topNKeyedElems(i, key)).reduce(merge)
@@ -1169,21 +1178,21 @@ def _mergeCombiners(iterator):
                     combiners[k] = mergeCombiners(combiners[k], v)
             return combiners.iteritems()
         return shuffled.mapPartitions(_mergeCombiners)
-    
+
     def foldByKey(self, zeroValue, func, numPartitions=None):
         """
         Merge the values for each key using an associative function "func" and a neutral "zeroValue"
-        which may be added to the result an arbitrary number of times, and must not change 
-        the result (e.g., 0 for addition, or 1 for multiplication.).                
+        which may be added to the result an arbitrary number of times, and must not change
+        the result (e.g., 0 for addition, or 1 for multiplication.).
 
         >>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
         >>> from operator import add
         >>> rdd.foldByKey(0, add).collect()
         [('a', 2), ('b', 1)]
         """
         return self.combineByKey(lambda v: func(zeroValue, v), func, func, numPartitions)
-    
-    
+
+
     # TODO: support variant with custom partitioner
     def groupByKey(self, numPartitions=None):
         """
@@ -1302,7 +1311,7 @@ def keyBy(self, f):
     def repartition(self, numPartitions):
         """
          Return a new RDD that has exactly numPartitions partitions.
-          
+
          Can increase or decrease the level of parallelism in this RDD. Internally, this uses
          a shuffle to redistribute data.
          If you are decreasing the number of partitions in this RDD, consider using `coalesce`,
