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https://issues.apache.org/jira/browse/LUCENE-10315?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17460463#comment-17460463
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Feng Guo commented on LUCENE-10315:
-----------------------------------
I noticed that benchmark in LuceneUtil is mainly for geo scenes (BKD can
support multi-dimension points is really a powerful feature! ), but the main
direction of this optimization is the low/medium cardinality 1D point scenario
(high cardinality of 1D field has also been improved by nearly 20%), so here
I'd like to describe some background of optimizing medium/low cardinality
fields in BKD:
Elasticsearch (which based on lucene) often 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 its
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 is such a big
difference in performance between {{long}} and {{keyword}} fields in low
cardinality fields. So from my potint 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. When the result set is really built, {{keyword}} ForUtil is much more
efficient than BKD delta vint because of its batch reading(readLongs) and SIMD
decode.
2. When the query term 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 id into memory.
This ISSUE is targeting to solve the first point. I hope these words can
explain a bit the motivation of this ISSUE :)
> 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
> Priority: Major
> Attachments: addall.svg
>
> Time Spent: 10m
> Remaining Estimate: 0h
>
> This issue tried 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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