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https://issues.apache.org/jira/browse/LUCENE-10315?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17520523#comment-17520523
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Ignacio Vera commented on LUCENE-10315:
---------------------------------------
> I'm seeing readInts24ForUtil runs 3 times faster than readInts24Legacy. This
> speed is attractive to me.
In theory we should call this methods several times less frequently than the
visitor method so we should not try to optimise for that. For example, in 1D
this method should only be called at most two times, but the visitor one can be
called several times.
> Speed up BKD leaf block ids codec by a 512 ints ForUtil
> -------------------------------------------------------
>
> Key: LUCENE-10315
> URL: https://issues.apache.org/jira/browse/LUCENE-10315
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Feng Guo
> Assignee: Feng Guo
> Priority: Major
> Attachments: addall.svg, cpu_profile_baseline.html,
> cpu_profile_path.html
>
> Time Spent: 6.5h
> Remaining Estimate: 0h
>
> Elasticsearch (which based on lucene) can automatically infers types for
> users with its dynamic mapping feature. When users index some low cardinality
> fields, such as gender / age / status... they often use some numbers to
> represent the values, while ES will infer these fields as {{{}long{}}}, and
> ES uses BKD as the index of {{long}} fields. When the data volume grows,
> building the result set of low-cardinality fields will make the CPU usage and
> load very high.
> This is a flame graph we obtained from the production environment:
> [^addall.svg]
> It can be seen that almost all CPU is used in addAll. When we reindex
> {{long}} to {{{}keyword{}}}, the cluster load and search latency are greatly
> reduced ( We spent weeks of time to reindex all indices... ). I know that ES
> recommended to use {{keyword}} for term/terms query and {{long}} for range
> query in the document, but there are always some users who didn't realize
> this and keep their habit of using sql database, or dynamic mapping
> automatically selects the type for them. All in all, users won't realize that
> there would be such a big difference in performance between {{long}} and
> {{keyword}} fields in low cardinality fields. So from my point of view it
> will make sense if we can make BKD works better for the low/medium
> cardinality fields.
> As far as i can see, for low cardinality fields, there are two advantages of
> {{keyword}} over {{{}long{}}}:
> 1. {{ForUtil}} used in {{keyword}} postings is much more efficient than BKD's
> delta VInt, because its batch reading (readLongs) and SIMD decode.
> 2. When the query term count is less than 16, {{TermsInSetQuery}} can lazily
> materialize of its result set, and when another small result clause
> intersects with this low cardinality condition, the low cardinality field can
> avoid reading all docIds into memory.
> This ISSUE is targeting to solve the first point. The basic idea is trying to
> use a 512 ints {{ForUtil}} for BKD ids codec. I benchmarked this optimization
> by mocking some random {{LongPoint}} and querying them with
> {{PointInSetQuery}}.
> *Benchmark Result*
> |doc count|field cardinality|query point|baseline QPS|candidate QPS|diff
> percentage|
> |100000000|32|1|51.44|148.26|188.22%|
> |100000000|32|2|26.8|101.88|280.15%|
> |100000000|32|4|14.04|53.52|281.20%|
> |100000000|32|8|7.04|28.54|305.40%|
> |100000000|32|16|3.54|14.61|312.71%|
> |100000000|128|1|110.56|350.26|216.81%|
> |100000000|128|8|16.6|89.81|441.02%|
> |100000000|128|16|8.45|48.07|468.88%|
> |100000000|128|32|4.2|25.35|503.57%|
> |100000000|128|64|2.13|13.02|511.27%|
> |100000000|1024|1|536.19|843.88|57.38%|
> |100000000|1024|8|109.71|251.89|129.60%|
> |100000000|1024|32|33.24|104.11|213.21%|
> |100000000|1024|128|8.87|30.47|243.52%|
> |100000000|1024|512|2.24|8.3|270.54%|
> |100000000|8192|1|3333.33|5000|50.00%|
> |100000000|8192|32|139.47|214.59|53.86%|
> |100000000|8192|128|54.59|109.23|100.09%|
> |100000000|8192|512|15.61|36.15|131.58%|
> |100000000|8192|2048|4.11|11.14|171.05%|
> |100000000|1048576|1|2597.4|3030.3|16.67%|
> |100000000|1048576|32|314.96|371.75|18.03%|
> |100000000|1048576|128|99.7|116.28|16.63%|
> |100000000|1048576|512|30.5|37.15|21.80%|
> |100000000|1048576|2048|10.38|12.3|18.50%|
> |100000000|8388608|1|2564.1|3174.6|23.81%|
> |100000000|8388608|32|196.27|238.95|21.75%|
> |100000000|8388608|128|55.36|68.03|22.89%|
> |100000000|8388608|512|15.58|19.24|23.49%|
> |100000000|8388608|2048|4.56|5.71|25.22%|
> The indices size is reduced for low cardinality fields and flat for high
> cardinality fields.
> {code:java}
> 113M index_100000000_doc_32_cardinality_baseline
> 114M index_100000000_doc_32_cardinality_candidate
> 140M index_100000000_doc_128_cardinality_baseline
> 133M index_100000000_doc_128_cardinality_candidate
> 193M index_100000000_doc_1024_cardinality_baseline
> 174M index_100000000_doc_1024_cardinality_candidate
> 241M index_100000000_doc_8192_cardinality_baseline
> 233M index_100000000_doc_8192_cardinality_candidate
> 314M index_100000000_doc_1048576_cardinality_baseline
> 315M index_100000000_doc_1048576_cardinality_candidate
> 392M index_100000000_doc_8388608_cardinality_baseline
> 391M index_100000000_doc_8388608_cardinality_candidate
> {code}
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