Hi Andrew,
If you use Spark UI then all your questions are already answered there let
me know if you need any help to browse the UI to look at the contents that
are cached.
Regards,
Gourav
On Tue, 11 Dec 2018, 17:13 Andrew Melo <andrew.m...@gmail.com wrote:
> Greetings, Spark Aficionados-
>
> I'm working on a project to (ab-)use PySpark to do particle physics
> analysis, which involves iterating with a lot of transformations (to
> apply weights and select candidate events) and reductions (to produce
> histograms of relevant physics objects). We have a basic version
> working, but I'm looking to exploit some of Spark's caching behavior
> to speed up the interactive computation portion of the analysis,
> probably by writing a thin convenience wrapper. I have a couple
> questions I've been unable to find definitive answers to, which would
> help me design this wrapper an efficient way:
>
> 1) When cache()-ing a dataframe where only a subset of the columns are
> used, is the entire dataframe placed into the cache, or only the used
> columns. E.G. does "df2" end up caching only "a", or all three
> columns?
>
> df1 = sc.read.load('test.parquet') # Has columns a, b, c
> df2 = df1.cache()
> df2.select('a').collect()
>
> 2) Are caches reference-based, or is there some sort of de-duplication
> based on the logical/physical plans. So, for instance, does spark take
> advantage of the fact that these two dataframes should have the same
> content:
>
> df1 = sc.read.load('test.parquet').cache()
> df2 = sc.read.load('test.parquet').cache()
>
> ...or are df1 and df2 totally independent WRT caching behavior?
>
> 2a) If the cache is reference-based, is it sufficient to hold a
> weakref to the python object to keep the cache in-scope?
>
> 3) Finally, the spark.externalBlockStore.blockManager is intriguing in
> our environment where we have multiple users concurrently analyzing
> mostly the same input datasets. We have enough RAM in our clusters to
> cache a high percentage of the very common datasets, but only if users
> could somehow share their caches (which, conveniently, are the larger
> datasets), We also have very large edge SSD cache servers we use to
> cache trans-oceanic I/O we could throw at this as well.
>
> It looks, however, like that API was removed in 2.0.0 and there wasn't
> a replacement. There are products like Alluxio, but they aren't
> transparent, requiring the user to manually cache their dataframes by
> doing save/loads to external files using "alluxio://" URIs. Is there
> no way around this behavior now?
>
> Sorry for the long email, and thanks!
> Andrew
>
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