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https://issues.apache.org/jira/browse/SOLR-10205?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yonik Seeley updated SOLR-10205:
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Attachment: cache_performance_test.txt
Here's the results of testing with different numbers of reserved blocks (up to
4) and different number of calls to cleanUp when the map size exceeds the
number of blocks - reserved.
The speedups compared to trunk range from 11% to 68% for these artificial
random tests.
Based on the results, I think the right balance is going with reserved blocks =
4 and a single call to cleanUp in the outer loop of
BlockCache.findEmptyLocation()
> Evaluate and reduce BlockCache store failures
> ---------------------------------------------
>
> Key: SOLR-10205
> URL: https://issues.apache.org/jira/browse/SOLR-10205
> Project: Solr
> Issue Type: Improvement
> Security Level: Public(Default Security Level. Issues are Public)
> Reporter: Yonik Seeley
> Assignee: Yonik Seeley
> Attachments: cache_performance_test.txt, SOLR-10205.patch,
> SOLR-10205.patch
>
>
> The BlockCache is written such that requests to cache a block
> (BlockCache.store call) can fail, making caching less effective. We should
> evaluate the impact of this storage failure and potentially reduce the number
> of storage failures.
> The implementation reserves a single block of memory. In store, a block of
> memory is allocated, and then a pointer is inserted into the underling map.
> A block is only freed when the underlying map evicts the map entry.
> This means that when two store() operations are called concurrently (even
> under low load), one can fail. This is made worse by the fact that
> concurrent maps typically tend to amortize the cost of eviction over many
> keys (i.e. the actual size of the map can grow beyond the configured maximum
> number of entries... both the older ConcurrentLinkedHashMap and newer
> Caffeine do this). When this is the case, store() won't be able to find a
> free block of memory, even if there aren't any other concurrently operating
> stores.
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