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Diffstat (limited to 'docs/cluster-overview.md')
| -rw-r--r-- | docs/cluster-overview.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md index e16703292c..a555a7b502 100644 --- a/docs/cluster-overview.md +++ b/docs/cluster-overview.md @@ -13,7 +13,7 @@ object in your main program (called the _driver program_). Specifically, to run on a cluster, the SparkContext can connect to several types of _cluster managers_ (either Spark's own standalone cluster manager or Mesos/YARN), which allocate resources across applications. Once connected, Spark acquires *executors* on nodes in the cluster, which are -worker processes that run computations and store data for your application. +processes that run computations and store data for your application. Next, it sends your application code (defined by JAR or Python files passed to SparkContext) to the executors. Finally, SparkContext sends *tasks* for the executors to run. |