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https://issues.apache.org/jira/browse/LUCENE-10572?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17538598#comment-17538598
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Uwe Schindler commented on LUCENE-10572:
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bq. The stopwords are going to skew everything. If someone is removing them,
the distribution of tokens will look much different.
If wikipedia has so many stopwords, this would explain what Mike is seeing.
Every stop word produces a hash that's already known. So the Arrays.equals()
code runs on each stopword every time it is seen over and over.
Maybe let's just change the analyzer that Mike uses to remove those stopwords?
Or are there many stopwords we do not know about?
Nevertheless, this is a valid use case: Text without stopwords and text with
stopwords (especially because we recommend to user not to remove stopwords
anymore).
> Can we optimize BytesRefHash?
> -----------------------------
>
> Key: LUCENE-10572
> URL: https://issues.apache.org/jira/browse/LUCENE-10572
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Michael McCandless
> Priority: Major
> Attachments: Screen Shot 2022-05-16 at 10.28.22 AM.png
>
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> I was poking around in our nightly benchmarks
> ([https://home.apache.org/~mikemccand/lucenebench]) and noticed in the JFR
> profiling that the hottest method is this:
> {noformat}
> PERCENT CPU SAMPLES STACK
> 9.28% 53848 org.apache.lucene.util.BytesRefHash#equals()
> at
> org.apache.lucene.util.BytesRefHash#findHash()
> at org.apache.lucene.util.BytesRefHash#add()
> at
> org.apache.lucene.index.TermsHashPerField#add()
> at
> org.apache.lucene.index.IndexingChain$PerField#invert()
> at
> org.apache.lucene.index.IndexingChain#processField()
> at
> org.apache.lucene.index.IndexingChain#processDocument()
> at
> org.apache.lucene.index.DocumentsWriterPerThread#updateDocuments() {noformat}
> This is kinda crazy – comparing if the term to be inserted into the inverted
> index hash equals the term already added to {{BytesRefHash}} is the hottest
> method during nightly benchmarks.
> Discussing offline with [~rcmuir] and [~jpountz] they noticed a few
> questionable things about our current implementation:
> * Why are we using a 1 or 2 byte {{vInt}} to encode the length of the
> inserted term into the hash? Let's just use two bytes always, since IW
> limits term length to 32 K (< 64K that an unsigned short can cover)
> * Why are we doing byte swapping in this deep hotspot using {{VarHandles}}
> (BitUtil.VH_BE_SHORT.get)
> * Is it possible our growth strategy for {{BytesRefHash}} (on rehash) is not
> aggressive enough? Or the initial sizing of the hash is too small?
> * Maybe {{MurmurHash}} is not great (causing too many conflicts, and too
> many {{equals}} calls as a result?) – {{Fnv}} and {{xxhash}} are possible
> "upgrades"?
> * If we stick with {{{}MurmurHash{}}}, why are we using the 32 bit version
> ({{{}murmurhash3_x86_32{}}})?
> * Are we using the JVM's intrinsics to compare multiple bytes in a single
> SIMD instruction ([~rcmuir] is quite sure we are indeed)?
> * [~jpountz] suggested maybe the hash insert is simply memory bound
> * {{TermsHashPerField.writeByte}} is also depressingly slow (~5% of total
> CPU cost)
> I pulled these observations from a recent (5/6/22) profiler output:
> [https://home.apache.org/~mikemccand/lucenebench/2022.05.06.06.33.00.html]
> Maybe we can improve our performance on this crazy hotspot?
> Or maybe this is a "healthy" hotspot and we should leave it be!
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