Implement Setting Deduplication via String Interning (#80493)#80590
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original-brownbear merged 1 commit intoelastic:8.0from Nov 10, 2021
original-brownbear:80493-8.0
Merged
Implement Setting Deduplication via String Interning (#80493)#80590original-brownbear merged 1 commit intoelastic:8.0from original-brownbear:80493-8.0
original-brownbear merged 1 commit intoelastic:8.0from
original-brownbear:80493-8.0
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This is a somewhat crude solution to #78892 that addresses 95%+ of duplicate setting entry memory consumption in large clusters. The remaining duplicate structures (lists of all the same strings) are comparatively cheap in their heap consumption. In heavy benchmarking for #77466 no runtime impact of adding this extra step to setting creation has been found despite pushing setting creation harder than is expected in real-world usage (part of the low relative impact here is the fact that populating a tree-map is quite expensive to begin with so adding the string interning which is fast via the CHM cache doesn't add much overhead). On the other hand, the heap use impact for use-cases that come with a large number of duplicate settings (many similar indices) is significant. As an example, 10k AuditBeat indices consume about 500M of heap for duplicate settings data structures without this change. This cahnge brings the heap consumption from duplicate settings down to O(1M) on every node in the cluster. Relates and addresses most of #78892 Relates #77466
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This is a somewhat crude solution to #78892 that addresses
95%+ of duplicate setting entry memory consumption in large clusters.
The remaining duplicate structures (lists of all the same strings) are
comparatively cheap in their heap consumption.
In heavy benchmarking for #77466 no runtime impact of adding this extra step
to setting creation has been found despite pushing setting creation harder
than is expected in real-world usage (part of the low relative impact here is
the fact that populating a tree-map is quite expensive to begin with so adding
the string interning which is fast via the CHM cache doesn't add much overhead).
On the other hand, the heap use impact for use-cases that come with a large number
of duplicate settings (many similar indices) is significant. As an example,
10k AuditBeat indices consume about 500M of heap for duplicate settings data structures
without this change. This cahnge brings the heap consumption from duplicate settings down to
O(1M) on every node in the cluster.
Relates and addresses most of #78892
Relates #77466
backport of #80493