Github user Ishiihara commented on a diff in the pull request:
https://github.com/apache/spark/pull/2490#discussion_r17959346
--- Diff: docs/programming-guide.md ---
@@ -1121,6 +1121,11 @@ than shipping a copy of it with tasks. They can be
used, for example, to give ev
large input dataset in an efficient manner. Spark also attempts to
distribute broadcast variables
using efficient broadcast algorithms to reduce communication cost.
+Spark automatically broadcasts the common data needed by tasks within each
stage. The data
+broadcasted this way is cached in serialized form and deserialized before
running each task. This
+means that explicitly creating broadcast variables is only useful when
tasks across multiple stages
--- End diff --
The concept of stage is mentioned only in the two added paragraphs. Users
new to Spark may not know the internals and the execution mmechanism. It would
be nice to if some background is introduced here.
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