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https://issues.apache.org/jira/browse/LUCENE-10297?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Feng Guo updated LUCENE-10297:
------------------------------
Description:
We already have a bitset optimization for low cardinality fields, but the
optimization only works on extremly low cardinality fields (doc count > 1/16
total doc), for medium cardinality case like 32/128 can rarely get this
optimization.
In [https://github.com/apache/lucene-solr/pull/1538], we made some effort to
use readLELongs to speed up BKD id blocks, but did not get a obvious gain on
this approach. Maybe this is because we are trying to optimize the unsorted
situation, which typically happens for high cardinality fields, and the
bottleneck of queries on high cardinality fields is usually {{visitDocValues}}
but not {{readDocIds}} ?
Maybe medium cardinality fields are tempted for this optimization, The basic
idea is that compute the delta of the sorted ids and encode/decode them like
what we do in {{StoredFieldsInts}}. I benchmarked the optimization by mocking
some random longPoint and querying them with {{PointInSetQuery}}. As expected,
the medium cardinality fields got spped up and high cardinality fields get even
results.
*Benchmark Result*
|doc count|field cardinality|field term count|baseline(ms)|candidate(ms)|diff
percentage|
|100000000|32|1|19|16|-15.79%|
|100000000|32|2|34|14|-58.82%|
|100000000|32|4|76|22|-71.05%|
|100000000|32|8|139|42|-69.78%|
|100000000|32|16|279|82|-70.61%|
|100000000|128|1|17|11|-35.29%|
|100000000|128|8|75|23|-69.33%|
|100000000|128|16|126|25|-80.16%|
|100000000|128|32|245|50|-79.59%|
|100000000|128|64|528|97|-81.63%|
|100000000|1024|1|3|2|-33.33%|
|100000000|1024|8|13|8|-38.46%|
|100000000|1024|32|31|19|-38.71%|
|100000000|1024|128|120|67|-44.17%|
|100000000|1024|512|480|133|-72.29%|
|100000000|8192|1|3|3|0.00%|
|100000000|8192|16|18|15|-16.67%|
|100000000|8192|64|19|14|-26.32%|
|100000000|8192|512|69|43|-37.68%|
|100000000|8192|2048|236|134|-43.22%|
|100000000|1048576|1|3|2|-33.33%|
|100000000|1048576|16|18|19|5.56%|
|100000000|1048576|64|17|17|0.00%|
|100000000|1048576|512|34|32|-5.88%|
|100000000|1048576|2048|89|93|4.49%|
was:
We already have a bitset optimization for low cardinality fields, but the
optimization only works on extremly low cardinality fields (doc count > 1/16
total doc), for medium cardinality case like 32/128 can rarely get this
optimization.
In [https://github.com/apache/lucene-solr/pull/1538], we made some effort to
use readLELongs to speed up BKD id blocks, but did not get a obvious gain on
this approach. I think this is because we are trying to optimize the unsorted
situation, which typically happens for high cardinality fields, and the
bottleneck of queries on high cardinality fields is usually {{visitDocValues}}
but not {{readDocIds}}.
Maybe medium cardinality fields are tempted for this optimization, The basic
idea is that compute the delta of the sorted ids and encode/decode them like
what we do in {{StoredFieldsInts}}. I benchmarked the optimization by mocking
some random longPoint and querying them with {{PointInSetQuery}}. As expected,
the medium cardinality fields got spped up and high cardinality fields get even
results.
*Benchmark Result*
|doc count|field cardinality|field term count|baseline(ms)|candidate(ms)|diff
percentage|
|100000000|32|1|19|16|-15.79%|
|100000000|32|2|34|14|-58.82%|
|100000000|32|4|76|22|-71.05%|
|100000000|32|8|139|42|-69.78%|
|100000000|32|16|279|82|-70.61%|
|100000000|128|1|17|11|-35.29%|
|100000000|128|8|75|23|-69.33%|
|100000000|128|16|126|25|-80.16%|
|100000000|128|32|245|50|-79.59%|
|100000000|128|64|528|97|-81.63%|
|100000000|1024|1|3|2|-33.33%|
|100000000|1024|8|13|8|-38.46%|
|100000000|1024|32|31|19|-38.71%|
|100000000|1024|128|120|67|-44.17%|
|100000000|1024|512|480|133|-72.29%|
|100000000|8192|1|3|3|0.00%|
|100000000|8192|16|18|15|-16.67%|
|100000000|8192|64|19|14|-26.32%|
|100000000|8192|512|69|43|-37.68%|
|100000000|8192|2048|236|134|-43.22%|
|100000000|1048576|1|3|2|-33.33%|
|100000000|1048576|16|18|19|5.56%|
|100000000|1048576|64|17|17|0.00%|
|100000000|1048576|512|34|32|-5.88%|
|100000000|1048576|2048|89|93|4.49%|
> Speed up medium cardinality fields with readLELongs and SIMD
> ------------------------------------------------------------
>
> Key: LUCENE-10297
> URL: https://issues.apache.org/jira/browse/LUCENE-10297
> Project: Lucene - Core
> Issue Type: Improvement
> Components: core/codecs
> Reporter: Feng Guo
> Priority: Major
> Time Spent: 10m
> Remaining Estimate: 0h
>
> We already have a bitset optimization for low cardinality fields, but the
> optimization only works on extremly low cardinality fields (doc count > 1/16
> total doc), for medium cardinality case like 32/128 can rarely get this
> optimization.
> In [https://github.com/apache/lucene-solr/pull/1538], we made some effort to
> use readLELongs to speed up BKD id blocks, but did not get a obvious gain on
> this approach. Maybe this is because we are trying to optimize the unsorted
> situation, which typically happens for high cardinality fields, and the
> bottleneck of queries on high cardinality fields is usually
> {{visitDocValues}} but not {{readDocIds}} ?
> Maybe medium cardinality fields are tempted for this optimization, The basic
> idea is that compute the delta of the sorted ids and encode/decode them like
> what we do in {{StoredFieldsInts}}. I benchmarked the optimization by mocking
> some random longPoint and querying them with {{PointInSetQuery}}. As
> expected, the medium cardinality fields got spped up and high cardinality
> fields get even results.
> *Benchmark Result*
> |doc count|field cardinality|field term count|baseline(ms)|candidate(ms)|diff
> percentage|
> |100000000|32|1|19|16|-15.79%|
> |100000000|32|2|34|14|-58.82%|
> |100000000|32|4|76|22|-71.05%|
> |100000000|32|8|139|42|-69.78%|
> |100000000|32|16|279|82|-70.61%|
> |100000000|128|1|17|11|-35.29%|
> |100000000|128|8|75|23|-69.33%|
> |100000000|128|16|126|25|-80.16%|
> |100000000|128|32|245|50|-79.59%|
> |100000000|128|64|528|97|-81.63%|
> |100000000|1024|1|3|2|-33.33%|
> |100000000|1024|8|13|8|-38.46%|
> |100000000|1024|32|31|19|-38.71%|
> |100000000|1024|128|120|67|-44.17%|
> |100000000|1024|512|480|133|-72.29%|
> |100000000|8192|1|3|3|0.00%|
> |100000000|8192|16|18|15|-16.67%|
> |100000000|8192|64|19|14|-26.32%|
> |100000000|8192|512|69|43|-37.68%|
> |100000000|8192|2048|236|134|-43.22%|
> |100000000|1048576|1|3|2|-33.33%|
> |100000000|1048576|16|18|19|5.56%|
> |100000000|1048576|64|17|17|0.00%|
> |100000000|1048576|512|34|32|-5.88%|
> |100000000|1048576|2048|89|93|4.49%|
>
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