Merge pull request #560 from ash211/patch-3

Typos: cluser -> cluster

docs/streaming-programming-guide.md

@@ -83,7 +83,7 @@ DStreams support many of the transformations available on normal Spark RDD's:
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   <td> <b>groupByKey</b>([<i>numTasks</i>]) </td>
   <td> When called on a DStream of (K, V) pairs, returns a new DStream of (K, Seq[V]) pairs by grouping together all the values of each key in the RDDs of the source DStream. <br />
-  <b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluser) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
+  <b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluster) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.
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@@ -132,7 +132,7 @@ Spark Streaming features windowed computations, which allow you to apply transfo
   <td> <b>groupByKeyAndWindow</b>(<i>windowDuration</i>, <i>slideDuration</i>, [<i>numTasks</i>])
   </td>
   <td> When called on a DStream of (K, V) pairs, returns a new DStream of (K, Seq[V]) pairs by grouping together values of each key over batches in a sliding window. <br />
-<b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluser) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.</td>
+<b>Note:</b> By default, this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluster) to do the grouping. You can pass an optional <code>numTasks</code> argument to set a different number of tasks.</td>
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   <td> <b>reduceByKeyAndWindow</b>(<i>func</i>, <i>windowDuration</i>, <i>slideDuration</i>, [<i>numTasks</i>]) </td>