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  1. Lucene - Core
  2. LUCENE-8681

Prorated early termination



    • Improvement
    • Status: Resolved
    • Major
    • Resolution: Won't Fix
    • None
    • None
    • core/search
    • None
    • New


      In this issue we'll exploit the distribution of top K documents among segments to extract performance gains when using early termination. The basic idea is we do not need to collect K documents from every segment and then merge. Rather we can collect a number of documents that is proportional to the segment's size plus an error bound derived from the combinatorics seen as a (multinomial) probability distribution.

      https://github.com/apache/lucene-solr/pull/564 has the proposed change.

      rcmuir pointed out on the mailing list that this patch confounds two settings: (1) whether to collect all hits, ensuring correct hit counts, and (2) whether to guarantee that the top K hits are precisely the top K.

      The current patch treats this as the same thing. It takes the position that if the user says it's OK to have approximate counts, then it's also OK to introduce some small chance of ranking error; occasionally some of the top K we return may draw from the top K + epsilon.

      Instead we could provide some additional knobs to the user. Currently the public API is TopFieldCOllector.create(Sort, int, FieldDoc, int threshold). The threshold parameter controls when to apply early termination; it allows the collector to terminate once the given number of documents have been collected.

      Instead of using the same threshold to control leaf-level early termination, we could provide an additional leaf-level parameter. For example, this could be a scale factor on the error bound, eg a number of standard deviations to apply. The patch uses 3, but a much more conservative bound would be 4 or even 5. With these values, some speedup would still result, but with a much lower level of ranking errors. A value of MAX_INT would ensure no leaf-level termination would ever occur.

      We could also hide the precise numerical bound and offer users a three-way enum (EXACT, APPROXIMATE_COUNT, APPROXIMATE_RANK) that controls whether to apply this optimization, using some predetermined error bound.

      I posted the patch without any user-level tuning since I think the user has already indicated a preference for speed over precision by specifying a finite (global) threshold, but if we want to provide finer control, these two options seem to make the most sense to me. Providing access to the number of standard deviation to allow from the expected distribution gives the user the finest control, but it could be hard to explain its proper use.


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              Unassigned Unassigned
              sokolov Michael Sokolov
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