[SPARK-2024] code formatting

xiliu82 · Jul 30, 2014 · c01e3ef · c01e3ef
1 parent 6591e37
commit c01e3ef
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Showing 3 changed files with 19 additions and 19 deletions.
diff --git a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
@@ -93,26 +93,25 @@ private[python] object SerDeUtil extends Logging {
     }
     pyRDD.mapPartitions { iter =>
       val unpickle = new Unpickler
-      val unpickled = if (batchSerialized) {
-        iter.flatMap { batch =>
-          unpickle.loads(batch) match {
-            case objs: java.util.List[_] => collectionAsScalaIterable(objs)
-            case other => throw new SparkException(
-              s"Unexpected type ${other.getClass.getName} for batch serialized Python RDD")
+      val unpickled =
+        if (batchSerialized) {
+          iter.flatMap { batch =>
+            unpickle.loads(batch) match {
+              case objs: java.util.List[_] => collectionAsScalaIterable(objs)
+              case other => throw new SparkException(
+                s"Unexpected type ${other.getClass.getName} for batch serialized Python RDD")
+            }
           }
+        } else {
+          iter.map(unpickle.loads(_))
         }
-      } else {
-        iter.map(unpickle.loads(_))
-      }
       unpickled.map {
-        // we only accept pickled (K, V)
         case obj if isPair(obj) =>
+          // we only accept (K, V)
           val arr = obj.asInstanceOf[Array[_]]
-          // arr has only 2 elements K and V
           (arr.head.asInstanceOf[K], arr.last.asInstanceOf[V])
-        case other =>
-          throw new SparkException(
-            s"RDD element of type ${other.getClass.getName} cannot be used")
+        case other => throw new SparkException(
+          s"RDD element of type ${other.getClass.getName} cannot be used")
       }
     }
   }

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
@@ -1079,7 +1079,7 @@ def saveAsNewAPIHadoopFile(self, path, outputFormatClass, keyClass=None, valueCl
         pickledRDD = self._toPickleSerialization()
         batched = isinstance(pickledRDD._jrdd_deserializer, BatchedSerializer)
         self.ctx._jvm.PythonRDD.saveAsNewAPIHadoopFile(pickledRDD._jrdd, batched, path,
-                    outputFormatClass, keyClass, valueClass, keyConverter, valueConverter, jconf)
+            outputFormatClass, keyClass, valueClass, keyConverter, valueConverter, jconf)
 
     def saveAsHadoopDataset(self, conf, keyConverter=None, valueConverter=None):
         """
@@ -1099,7 +1099,8 @@ def saveAsHadoopDataset(self, conf, keyConverter=None, valueConverter=None):
                                                     keyConverter, valueConverter, False)
 
     def saveAsHadoopFile(self, path, outputFormatClass, keyClass=None, valueClass=None,
-                keyConverter=None, valueConverter=None, conf=None, compressionCodecClass=None):
+                         keyConverter=None, valueConverter=None, conf=None,
+                         compressionCodecClass=None):
         """
         Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
         system, using the old Hadoop OutputFormat API (mapred package). Key and value types
@@ -1123,8 +1124,8 @@ def saveAsHadoopFile(self, path, outputFormatClass, keyClass=None, valueClass=No
         jconf = self.ctx._dictToJavaMap(conf)
         pickledRDD = self._toPickleSerialization()
         batched = isinstance(pickledRDD._jrdd_deserializer, BatchedSerializer)
-        self.ctx._jvm.PythonRDD.saveAsHadoopFile(pickledRDD._jrdd, batched,
-            path, outputFormatClass, keyClass, valueClass, keyConverter, valueConverter,
+        self.ctx._jvm.PythonRDD.saveAsHadoopFile(pickledRDD._jrdd, batched, path,
+            outputFormatClass, keyClass, valueClass, keyConverter, valueConverter,
             jconf, compressionCodecClass)
 
     def saveAsSequenceFile(self, path, compressionCodecClass=None):

diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
@@ -704,7 +704,7 @@ def test_unbatched_save_and_read(self):
 
     def test_malformed_RDD(self):
         basepath = self.tempdir.name
-        # non-batch-serialized RDD of type RDD[[(K, V)]] should be rejected
+        # non-batch-serialized RDD[[(K, V)]] should be rejected
         data = [[(1, "a")], [(2, "aa")], [(3, "aaa")]]
         rdd = self.sc.parallelize(data, numSlices=len(data))
         self.assertRaises(Exception, lambda: rdd.saveAsSequenceFile(