Opening for more pointed discussion. See latest discussion here: #13281
I was hoping to have a full answer for folks who use byte models by Lucene 10, but I just don't have that.
I still want to remove the internal cosine optimized methods. We can do this if we store magnitudes along side the raw vectors. This way we can remove all the internal optimized cosine code as its complicated.
I noticed that single-term readers are an edge case but not that
uncommon in Elasticsearch heap dumps. It seems quite common to have a
constant value for some field across a complete segment (e.g. a version
value that is repeated endlessly in logs).
Seems simple enough to deduplicate here to save a couple MB of heap.
Looking at how these instances are serialized to disk it appears
that the empty output in the FST metadata is always the same as the
rootCode bytes.
Without changing the serialization we could at least deduplicate here,
saving hundreds of MB in some high-segment count use cases I observed in
ES.
Make it so rejected tasks are execute right away on the caller thread.
Users of the API shouldn't have to worry about rejections when we don't
expose any upper limit to the task count that we put on the executor that
would help in sizing a queue for the executor.
This updates the postings format in order to inline skip data into postings. This format is generally similar to the current `Lucene99PostingsFormat`, e.g. it shares the same block encoding logic, but it has a few differences:
- Skip data is inlined into postings to make the access pattern more sequential.
- There are only 2 levels of skip data: on every block (128 docs) and every 32 blocks (4,096 docs).
In general, I found that the fact that skip data is inlined may slow down a bit queries that don't need skip data at all (e.g. `CountOrXXX` tasks that never advance of consult impacts) and speed up a bit queries that advance by small intervals. The fact that the greatest level only allows skipping 4096 docs at once means that we're slower at advancing by large intervals, but data suggests that it doesn't significantly hurt performance.
This adds a Directory wrapper that counts how many times we wait for I/O to
complete before doing something else, and adds tests that the default codec is
able to parallelize I/O for stored fields retrieval and term lookups.
There's a couple of places in the codebase where we extend `IndexSearcher` to customize
per leaf behaviour, and in order to do that, we need to override the entire search method
that loops through the leaves. A good example is `ScorerIndexSearcher`.
Adding a `searchLeaf` method that provides the per leaf behaviour makes those cases a little
easier to deal with.
It's been pointed multiple times that a difference between Tantivy and Lucene
is the fact that Tantivy uses windows of 4,096 docs when Lucene has a 2x
smaller window size of 2,048 docs and that this might explain part of the
performance difference. luceneutil suggests that bumping the window size to
4,096 does indeed improve performance for counting queries, but not for top-k
queries. I'm still suggesting to bump the window size across the board to keep
our disjunction scorer consistent.
Something I found in a heap dump. For large numbers of `FieldReader`
where the minimum term is an empty string, we allocate MBs worth of
empty `byte[]` in ES. Worth adding the conditional here I think.
This iterates on #13546 to further reduce the overhead of search concurrency by
caching whether the hit count threshold has been reached: once the threshold
has been reached, it cannot get "un-reached" again, so we don't need to pay the
cost of `LongAdder#longValue`.
* lazily write the FST padding byte
* Also write the pad byte when there is emptyOutput
* add comment
* Make Lucene90BlockTreeTermsWriter to write FST off-heap
* Add change log
* Tidy code & Add comments
* use temp IndexOutput for FST writing
* Use IOUtils to delete files
* Update CHANGES.txt
* Update CHANGES.txt
I analyzed a heap dump of Elasticsearch where FixedBitSet uses more than
1GB of memory. Most of these FixedBitSets are used by soft-deletes
reader wrappers, even though these segments have no deletes at all. I
believe these segments previously had soft-deletes, but these deletes
were pruned by merges. The reason we wrap soft-deletes is that the
soft-deletes field exists. Since these segments had soft-deletes
previously, we carried the field-infos into the new segment. Ideally, we
should have ways to check whether the returned docValues iterator is
empty or not so that we can avoid allocating FixedBitSet completely, or
we should prune fields without values after merges.
Currently `MaxScoreBulkScorer` requires its "outer" window to be at least
`WINDOW_SIZE`. The intuition there was that we should make sure we should use
the whole range of the bit set that we are using to collect matches. The
downside is that it may force us to use an upper level in the skip list that
has worse upper bounds for the scores.
This commit uses IOContext.READONCE in more places where the index input is clearly being read once by the thread opening it. We can then enforce that segment files are only opened with READONCE, in the test specific Mock directory wrapper.
Much of the changes in this PR update individual test usage, but there is one non-test change to Directory::copyFrom.
We already have convenient functions for contructing IntervalsSource
for wildcard and fuzzy functions. This adds functions for
regexp and range as well.
This introduces `TermsEnum#prepareSeekExact`, which essentially calls
`IndexInput#prefetch` at the right offset for the given term. Then it takes
advantage of the fact that `BooleanQuery` already calls `Weight#scorerSupplier`
on all clauses, before later calling `ScorerSupplier#get` on all clauses. So
`TermQuery` now calls `TermsEnum#prepareSeekExact` on `Weight#scorerSupplier`
(if scores are not needed), which in-turn means that the I/O all terms
dictionary lookups get parallelized across all term queries of a
`BooleanQuery` on a given segment (intra-segment parallelism).
zhouhui
Jakub Slowinski
Zhang Chao
Greg Miller
Greg Miller
Benjamin Trent
Michael Sokolov
Adrien Grand
Michael Sokolov
Egor Potemkin
Armin Braun
Luca Cavanna
Peter Gromov
Chris Hegarty
Dzung Bui
Michael McCandless
Ignacio Vera
Nhat Nguyen
Carlos Delgado
Mayya Sharipova
Christine Poerschke
Chris Hostetter