kaivalnp commented on PR #15979:
URL: https://github.com/apache/lucene/pull/15979#issuecomment-4310658702

   `luceneutil` KNN benchmarks on Cohere v3 vectors, 1024d, `DOT_PRODUCT` 
similarity, 1M vectors, 10K queries, no quantization.
   
   Important: `filterStrategy = index-time-filter` is used here (added in 
https://github.com/mikemccand/luceneutil/pull/468), which simply creates a new 
vector field with randomly selected `filterSelectivity` proportion of docs, and 
uses the smaller field for search.
   
   `main`
   
   ```
   recall  latency(ms)  netCPU  visited  index(s)  index_docs/s  force_merge(s) 
 index_size(MB)  filterSelectivity
    0.988        2.987   2.982     8122    239.73       4171.41          418.68 
        6077.99               0.50
    0.991        2.398   2.397     7415    211.53       4727.37          296.96 
        4862.33               0.20
   ```
   
   This PR
   
   ```
   recall  latency(ms)  netCPU  visited  index(s)  index_docs/s  force_merge(s) 
 index_size(MB)  filterSelectivity
    0.988        2.724   2.720     8129    248.46       4024.79          466.43 
        4130.17               0.50
    0.991        2.173   2.170     7414    214.40       4664.16          396.85 
        4085.20               0.20
   ```
   
   As we can imagine, if the same vector is indexed in a second field for 50% 
of docs, the index size increases by ~50% on `main` (roughly 4GB -> 6GB). 
However with the de-duplicating vector format, the same vectors are re-used and 
there is (almost) no increase in index size!
   
   Indexing is slightly slower (<5%) + merges are non-trivially slower (up to 
33%, currently looking into speeding this up).


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