shuffle data to disk . So the
only diffrence with caching or no-caching version is :
.map { case (x, (n, i)) = (x, n)}
-
Thanks,
Nieyuan
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Because map-reduce tasks like join will save shuffle data to disk . So the
only diffrence with caching or no-caching version is :
.map { case (x, (n, i)) = (x, n)}
-
Thanks,
Nieyuan
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Advantage-of-using
Hi,
thank you for your response. I removed issues you mentioned. Now I read
RDDs from files, whole rdd is cached, I don't use random and rdd1 and rdd2
are identical.
RDDs that are joined contains 100k entries and result contains 10m entries.
rdd1 and rdd2 after join also contains 10m entries. Here
Hi,
I tried to write small program which shows that using cache() can speed up
execution but results with and without cache were similar. Could help me
with this issue? I tried to compute rdd and use it later in two places and
I thought in second usage this rdd is recomputed but it doesn't:
Your rdd2 and rdd3 differ in two ways so it's hard to track the exact
effect of caching. In rdd3, in addition to the fact that rdd will be
cached, you are also doing a bunch of extra random number generation. So it
will be hard to isolate the effect of caching.
On Wed, Aug 20, 2014 at 7:48 AM,
