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https://issues.apache.org/jira/browse/SOLR-12688?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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STANISLAV LIVOTOV updated SOLR-12688:
-------------------------------------
Description:
This ticket is related to 2 performance and 1 functional/performance issue that
I had found during integrating LTR in our e-commerce search engine :
# FieldValueFeature doesn't support pure DocValues fields (Stored false).
Please also note that for fields which are both stored and DocValues it is
working not optimal because it is extracting just one field from the stored
document. DocValues are obviously faster for such circumstances. Below are
screenshots of JFR profiles without and with new support of DocValues for the
case when it can be read from DocValues.
!LTRwithoutDVOptimisation.png!
!LTRwithDVOptimisation.png!
# SolrFeature was not optimally implemented for the case when no fq parameter
was passed. I'm not absolutely sure what was the intention to introduce fq
parameter for SolrFeature at all, so I decided not to change behavior but just
optimize described case !LTRSolrFeatureBefore.png! !LTRSolrFeatureAfter.png!
# LTRScoringModel was a mutable object. It was leading to the calculation of
hashcode on each query, which in turn can consume a lot of time in cases when a
model is big(In our case we were using LambdaMART with 100 trees and leaves
which was consuming 3MB of the disk size). So I decided to make LTRScoringModel
immutable and cache hashCode calculation. Below are the screenshots before and
after. !LTRModelHashCodeBefore.png!!LTRModelHashCodeAfter.png!
In our case, we had a feature.json file with 8 FieldValueFeatures, 5
SolrFeatures and 1 OriginalScoreFeature.
Before introducing the optimizations performance overhead for LTR reranking of
top 48 documents was 300ms. With all the optimizations in it was decreased to
35ms.
was:
This ticket is related to 2 performance and 1 functional/performance issue that
I had found during integrating LTR in our e-commerce search engine :
# FieldValueFeature doesn't support pure DocValues fields (Stored false).
Please also note that for fields which are both stored and DocValues it is
working not optimal because it is extracting just one field from the stored
document. DocValues are obviously faster for such circumstances. Below are
screenshots of JFR profiles without and with new support of DocValues for the
case when it can be read from DocValues.
!LTRwithoutDVOptimisation.png!
!LTRwithDVOptimisation.png!
# SolrFeature was not optimally implemented for the case when no fq parameter
was passed. I'm not absolutely sure what was the intention to introduce fq
parameter for SolrFeature at all, so I decided not to change behavior but just
optimize described case !LTRSolrFeatureBefore.png! !LTRSolrFeatureAfter.png!
# LTRScoringModel was a mutable object. It was leading to the calculation of
hashcode on each query, which in turn can consume a lot of time in cases when a
model is big(In our case we were using LambdaMART with 100 trees and leaves
which was consuming 3MB of the disk size). So I decided to make LTRScoringModel
immutable and cache hashCode calculation. Below are the screenshots before and
after. !LTRModelHashCodeBefore.png!!LTRModelHashCodeAfter.png!
In our case, we had a feature.json file with 8 FieldValueFeatures, 5
SolrFeatures and 1 OriginalScoreFeature.
Before introducing optimizations performance overhead for LTR reranking of top
48 documents was 300ms. With all the optimizations in it was decreased to 35ms.
> LTR Multiple performance fixes + pure DocValues support for FieldValueFeature
> -----------------------------------------------------------------------------
>
> Key: SOLR-12688
> URL: https://issues.apache.org/jira/browse/SOLR-12688
> Project: Solr
> Issue Type: Bug
> Security Level: Public(Default Security Level. Issues are Public)
> Reporter: STANISLAV LIVOTOV
> Priority: Major
> Attachments: LTRModelHashCodeAfter.png, LTRModelHashCodeBefore.png,
> LTRSolrFeatureAfter.png, LTRSolrFeatureBefore.png, LTRwithDVOptimisation.png,
> LTRwithoutDVOptimisation.png, MultiplePerformanceFixes.patch
>
>
> This ticket is related to 2 performance and 1 functional/performance issue
> that I had found during integrating LTR in our e-commerce search engine :
> # FieldValueFeature doesn't support pure DocValues fields (Stored false).
> Please also note that for fields which are both stored and DocValues it is
> working not optimal because it is extracting just one field from the stored
> document. DocValues are obviously faster for such circumstances. Below are
> screenshots of JFR profiles without and with new support of DocValues for the
> case when it can be read from DocValues.
> !LTRwithoutDVOptimisation.png!
> !LTRwithDVOptimisation.png!
> # SolrFeature was not optimally implemented for the case when no fq
> parameter was passed. I'm not absolutely sure what was the intention to
> introduce fq parameter for SolrFeature at all, so I decided not to change
> behavior but just optimize described case !LTRSolrFeatureBefore.png!
> !LTRSolrFeatureAfter.png!
> # LTRScoringModel was a mutable object. It was leading to the calculation of
> hashcode on each query, which in turn can consume a lot of time in cases when
> a model is big(In our case we were using LambdaMART with 100 trees and leaves
> which was consuming 3MB of the disk size). So I decided to make
> LTRScoringModel immutable and cache hashCode calculation. Below are the
> screenshots before and after.
> !LTRModelHashCodeBefore.png!!LTRModelHashCodeAfter.png!
> In our case, we had a feature.json file with 8 FieldValueFeatures, 5
> SolrFeatures and 1 OriginalScoreFeature.
> Before introducing the optimizations performance overhead for LTR reranking
> of top 48 documents was 300ms. With all the optimizations in it was decreased
> to 35ms.
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