Prevent humongous allocations when calculating scalar quantiles #13090
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lucene/core/src/java/org/apache/lucene/util/ScalarQuantizer.java
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mayya-sharipova
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Feb 8, 2024
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@benwtrent Thanks, the Lucene implementation LGTM as long as we are ok with the math and decreased recall that it brings.
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The initial release of scalar quantization would periodically create a humongous allocation, which can put unwarranted pressure on the GC & on the heap usage as a whole. This commit adjusts this by only allocating a float array of 20*dimensions and averaging the discovered quantiles from there. Why does this work? - Quantiles based on confidence intervals are (generally) unbiased and doing an average gives statistically good results - The selector algorithm scales linearly, so the cost is just about the same - We need to do more than `1` vector at a time to prevent extreme confidence intervals interacting strangely with edge cases
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The initial release of scalar quantization would periodically create a humongous allocation, which can put unwarranted pressure on the GC & on the heap usage as a whole.
This commit adjusts this by only allocating a float array of 20*dimensions and averaging the discovered quantiles from there.
Why does this work?
1vector at a time to prevent extreme confidence intervals interacting strangely with edge casesI benchmarked this over 500k vectors.
candidate
baseline
100k vectors
candidate
baseline
There does seem to be a slight increase in merge time (these are single threaded numbers) and a slight change in recall.
But to me, these seem acceptable given we are no longer allocating a ginormous array.