Hi there,
Just playing around in the Spark shell, I am now a bit confused by the
performance I observe when the dataset does not fit into memory :
- i load a dataset with roughly 500 million rows
- i do a count, it takes about 20 seconds
- now if I cache the RDD and do a count again (which will try cache the data
again), it takes roughly 90 seconds (the fraction cached is only 25%).
=> is this expected? to be roughly 5 times slower when caching and not
enough RAM is available?
- the subsequent calls to count are also really slow : about 90 seconds as well.
=> I can see that the first 25% tasks are fast (the ones dealing with
data in memory), but then it gets really slow…
Am I missing something?
I thought performance would decrease kind of linearly with the amour of data
fit into memory…
Thanks for your help!
Cheers
Pierre Borckmans
RealImpact Analytics | Brussels Office
www.realimpactanalytics.com | [email protected]
FR +32 485 91 87 31 | Skype pierre.borckmans
