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| -rw-r--r-- | docs/programming-guide.md | 2 | ||||
| -rw-r--r-- | python/pyspark/rdd.py | 4 |
2 files changed, 3 insertions, 3 deletions
diff --git a/docs/programming-guide.md b/docs/programming-guide.md index 2443fc29b4..6486614e71 100644 --- a/docs/programming-guide.md +++ b/docs/programming-guide.md @@ -886,7 +886,7 @@ for details. <td> <b>groupByKey</b>([<i>numTasks</i>]) </td> <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable<V>) pairs. <br /> <b>Note:</b> If you are grouping in order to perform an aggregation (such as a sum or - average) over each key, using <code>reduceByKey</code> or <code>combineByKey</code> will yield much better + average) over each key, using <code>reduceByKey</code> or <code>aggregateByKey</code> will yield much better performance. <br /> <b>Note:</b> By default, the level of parallelism in the output depends on the number of partitions of the parent RDD. diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index f4cfe4845d..efd2f35912 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -1634,8 +1634,8 @@ class RDD(object): Hash-partitions the resulting RDD with into numPartitions partitions. Note: If you are grouping in order to perform an aggregation (such as a - sum or average) over each key, using reduceByKey will provide much - better performance. + sum or average) over each key, using reduceByKey or aggregateByKey will + provide much better performance. >>> x = sc.parallelize([("a", 1), ("b", 1), ("a", 1)]) >>> map((lambda (x,y): (x, list(y))), sorted(x.groupByKey().collect())) |