Pranshu-S commented on PR #16731:
URL: https://github.com/apache/lucene/pull/16731#issuecomment-5979760229
Interestingly, from my local runs it seems that there is a tip-off point
where this new Reader/Writer pays off: When the number of unqiue vectors within
a field are less - that is same vector is being repeated in the same field.
Logically it can happen - such as when different records reference the same
underlying item embedding.
Here is what my results came out as:
Total Unique = 100
```
==========================================================================
COMPARISON (candidate vs baseline; delta/pct = candidate - baseline)
float32 vectors, filtered KNN search
metrics are mean±stddev over iterations (baseline n=5, candidate n=5)
==========================================================================
metric baseline(n=5) candidate(n=5) delta pct
----------------- ----------------- ----------------- --------- ------
recall 0.809±0.008 0.982±0.000 +0.173 +21.4%
latency(ms) 0.158±0.013 0.212±0.010 +0.055 +34.8%
netCPU 0.152±0.013 0.206±0.010 +0.055 +36.0%
avgCpuCount 0.962±0.003 0.971±0.003 +0.009 +0.9%
nDoc 10000 10000
searchType KNN KNN
topK 10 10
fanout 100 100
resultSimilarity N/A N/A
decay N/A N/A
resultCount 10.000 10.000
maxConn 32 32
beamWidth 200 200
quantized 7 bits 7 bits
indexEncoding float32 float32
visited 230.200±7.155 100.000±0.000 -130.200 -56.6%
index(s) 1.568±0.038 0.890±0.019 -0.678 -43.2%
index_docs/s 6379.994±161.224 11232.210±231.098 +4852.216 +76.1%
merge(s) 0.000±0.000 0.000±0.000 +0.000
force_merge(s) 2.180±0.055 1.060±0.045 -1.120 -51.4%
num_segments 1.000±0.000 1.000±0.000 +0.000 +0.0%
index_size(MB) 0.658±0.004 0.620±0.000 -0.038 -5.8%
filterStrategy index-time-filter index-time-filter
filterSelectivity 0.50 0.50
overSample 1.000 1.000
vec_disk(MB) 48.981±0.000 48.981±0.000 +0.000 +0.0%
vec_RAM(MB) 9.918±0.000 9.918±0.000 +0.000 +0.0%
bp-reorder false false
indexType HNSW HNSW
rerank no no
```
Total Unique = 1000
```
==========================================================================
COMPARISON (candidate vs baseline; delta/pct = candidate - baseline)
float32 vectors, filtered KNN search
metrics are mean±stddev over iterations (baseline n=5, candidate n=5)
==========================================================================
metric baseline(n=5) candidate(n=5) delta pct
----------------- ----------------- ----------------- --------- ------
recall 0.964±0.002 0.965±0.002 +0.001 +0.1%
latency(ms) 0.225±0.004 0.224±0.028 -0.001 -0.3%
netCPU 0.217±0.003 0.217±0.028 +0.000 +0.2%
avgCpuCount 0.966±0.006 0.970±0.007 +0.004 +0.4%
nDoc 10000 10000
searchType KNN KNN
topK 10 10
fanout 100 100
resultSimilarity N/A N/A
decay N/A N/A
resultCount 10.000 10.000
maxConn 32 32
beamWidth 200 200
quantized 7 bits 7 bits
indexEncoding float32 float32
visited 496.000±8.746 422.400±2.302 -73.600 -14.8%
index(s) 2.406±0.065 1.920±0.166 -0.486 -20.2%
index_docs/s 4159.980±109.441 5241.906±499.526 +1081.926 +26.0%
merge(s) 0.000±0.000 0.000±0.000 +0.000
force_merge(s) 3.450±0.071 1.666±0.047 -1.784 -51.7%
num_segments 1.000±0.000 1.000±0.000 +0.000 +0.0%
index_size(MB) 5.140±0.000 5.100±0.000 -0.040 -0.8%
filterStrategy index-time-filter index-time-filter
filterSelectivity 0.50 0.50
overSample 1.000 1.000
vec_disk(MB) 48.981±0.000 48.981±0.000 +0.000 +0.0%
vec_RAM(MB) 9.918±0.000 9.918±0.000 +0.000 +0.0%
bp-reorder false false
indexType HNSW HNSW
rerank no no
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