diff --git a/docs/rdd-programming-guide.md b/docs/rdd-programming-guide.md
index c0215c8fb62f..26025984da64 100644
--- a/docs/rdd-programming-guide.md
+++ b/docs/rdd-programming-guide.md
@@ -978,40 +978,40 @@ for details.
| Return a new RDD that contains the intersection of elements in the source dataset and the argument. |
- | distinct([numTasks])) |
+ distinct([numPartitions])) |
Return a new dataset that contains the distinct elements of the source dataset. |
- | groupByKey([numTasks]) |
+ groupByKey([numPartitions]) |
When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable<V>) pairs.
Note: If you are grouping in order to perform an aggregation (such as a sum or
average) over each key, using reduceByKey or aggregateByKey will yield much better
performance.
Note: By default, the level of parallelism in the output depends on the number of partitions of the parent RDD.
- You can pass an optional numTasks argument to set a different number of tasks.
+ You can pass an optional numPartitions argument to set a different number of tasks.
|
- | reduceByKey(func, [numTasks]) |
+ reduceByKey(func, [numPartitions]) |
When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function func, which must be of type (V,V) => V. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument. |
- | aggregateByKey(zeroValue)(seqOp, combOp, [numTasks]) |
+ aggregateByKey(zeroValue)(seqOp, combOp, [numPartitions]) |
When called on a dataset of (K, V) pairs, returns a dataset of (K, U) pairs where the values for each key are aggregated using the given combine functions and a neutral "zero" value. Allows an aggregated value type that is different than the input value type, while avoiding unnecessary allocations. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument. |
- | sortByKey([ascending], [numTasks]) |
+ sortByKey([ascending], [numPartitions]) |
When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean ascending argument. |
- | join(otherDataset, [numTasks]) |
+ join(otherDataset, [numPartitions]) |
When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key.
Outer joins are supported through leftOuterJoin, rightOuterJoin, and fullOuterJoin.
|
- | cogroup(otherDataset, [numTasks]) |
+ cogroup(otherDataset, [numPartitions]) |
When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (Iterable<V>, Iterable<W>)) tuples. This operation is also called groupWith. |