SPY-1394: CSD caching policy at block level #189
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ianlcsd
changed the title
ianlcsd
commented
Jul 21, 2017
| * sufficient memory in spark's user memory space to be set apart: | ||
| * (2G + overhead) per thread and per operator/RDD compute. | ||
| */ | ||
| def fetchUntilCsdBlockSizeLimit[T]( |
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This api is challenging as we need to keep all the seen values until reaching 2G size limit. it is a potential OOM thread and hard to test.
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jttp |
davidnavas
reviewed
Jul 25, 2017
| if (!shouldCache) { | ||
| logBlockSizeLimitMessage(blockId, vector.estimateSize()) | ||
| } else { | ||
| // We ran out of space while unrolling the values for this block |
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not sure I understand this comment for this case. Wouldn't this be the case where it's still cacheable?
oic, this is the original comment. Maybe need to put a line before this to separate cache from unroll.
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The problem originated from the fact that spark can not read a cache block from disk store with size over 2G. When data is distributed highly skewed over partitions, we see problems recorded in SPY-1394, with any RDD cached on disk.
The ultimate solution of being to adapt the partitions to skew is a long shot,
but imposing a CSD policy on cache block size is feasible.
This PR is proposing the following policy:
A negative value should disable the block policy.
The default is set to Integer.MAX_VALUE, but we could choose to make it smaller.
By applying the above policy, we ensure all the cache block is less 2G in size either in memory or disk. This braces us against skewed data and other type of abnormal caching pattern.
@davidnavas @markhamstra