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https://issues.apache.org/jira/browse/CASSANDRA-14660?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16589314#comment-16589314
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Benedict commented on CASSANDRA-14660:
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bq. cache invalidation is protected by the write lock
I assume the bottleneck you are encountering is inside the
{{cachedOnlyTokenMap}} method, and if this were updated by the writer
performing the cache invalidation, this method would never touch either the
read or write lock, so the readers would be completely non-blocking.
This could be updated without the writer holding the write lock, in exactly the
same manner as it is now; we would only have to ensure the calls to
{{invalidateCachedRings}} were made outside of the write lock.
Thinking on it just a little, though, I think the proper solution is to replace
these collections with immutable data structures (i.e. that re-use their
unmodified contents on update). We actually have such a data structure
in-tree, just not currently exposed in a user-friendly way for this scenario
right now (our memtable BTree fits the bill, but we would need to write a
MultiMap wrapper). Perhaps for 4.x we can rustle up that patch, as it will
have zero cost clone, and approximately the same complexity for updates. Or
perhaps there's an existing CoW Multimap we could pull in for this patch.
> Improve TokenMetaData cache populating performance for large cluster
> --------------------------------------------------------------------
>
> Key: CASSANDRA-14660
> URL: https://issues.apache.org/jira/browse/CASSANDRA-14660
> Project: Cassandra
> Issue Type: Improvement
> Components: Coordination
> Environment: Benchmark is on MacOSX 10.13.5, 2017 MBP
> Reporter: Pengchao Wang
> Priority: Critical
> Labels: Performance
> Fix For: 3.0.x, 3.11.x, 4.x
>
> Attachments: 14660-trunk.txt, TokenMetaDataBenchmark.java
>
>
> TokenMetaData#cachedOnlyTokenMap is a method C* used to get a consistent
> token and topology view on coordinations without paying read lock cost. Upon
> first read the method acquire a synchronize lock and generate a copy of major
> token meta data structures and cached it, and upon every token meta data
> changes(due to gossip changes), the cache get cleared and next read will
> taking care of cache population.
> For small to medium size clusters this strategy works pretty well. But large
> clusters can actually be suffered from the locking since cache populating is
> much slower. On one of our largest cluster (~1000 nodes, 125k tokens, C*
> 3.0.15) each cache population take about 500~700ms, and during that there
> are no requests can go through since synchronize lock was acquired. This
> caused waves of timeouts errors when there are large amount gossip messages
> propagating cross the cluster, such as in the case of cluster restarting.
> Base on profiling we found that the cost mostly comes from copying
> tokenToEndpointMap. It is a SortedBiMultiValueMap made from a forward map use
> TreeMap and a reverse map use guava TreeMultiMap. There is an optimization in
> TreeMap helps reduce copying complexity from O(N*log(N)) to O(N) when copying
> from already ordered data. But guava's TreeMultiMap copying missed that
> optimization and make it ~10 times slower than it actually need to be on our
> size of cluster.
> The patch attached to the issue replace the reverse TreeMultiMap<K, V> to a
> vanilla TreeMap<K, TreeSet<V>> in SortedBiMultiValueMap to make sure we can
> copy it O(N) time.
> I also attached a benchmark script (TokenMetaDataBenchmark.java), which
> simulates a large cluster then measures average latency for TokenMetaData
> cache populating.
> Benchmark result before and after that patch:
> {code:java}
> trunk:
> before 100ms, after 13ms
> 3.0.x:
> before 199ms, after 15ms
> {code}
> (On 3.0.x even the forward TreeMap copying is slow, the O(N*log(N)) to O(N)
> optimization is not applied because the key comparator is dynamically created
> and TreeMap cannot determine the source and dest are in same order)
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