This change fixes a bug in `MultiNormsLeafSimScorer` that assumes that each
field should have a norm for every term/document.
As part of the fix, it adds validation that the fields have consistent norms
settings.
The previous equals and hashCode methods only compared query terms. This meant
that queries on different fields, or with different field weights, were
considered the same
During boolean query rewrites, duplicate clauses are removed. So because equals/
hashCode was incorrect, rewrites could accidentally drop CombinedFieldQuery
clauses.
The previous svn-based link no longer works. Instead point at the
license file in github: it is for icu4c, but see the repo: user is
explicitly directed at this license file for both icu4c and icu4j.
Good case to have a correct link, as the ICU license is complicated. It
even has "if (version > X)" conditionals in the legalese!!!
Re-enable the randomized testing here, but with a separate test for each
mode rather than all in one method. It gives better testing and also easier-to-debug
testing.
Normalization-inert characters need not be required as boundaries
for incremental processing. It is sufficient to check `hasBoundaryAfter`
and `hasBoundaryBefore`, substantially improving worst-case performance.
It's possible to create a DisjunctionMaxQuery with no clauses. This is now
rewritten to MatchNoDocsQuery, matching the approach we take for BooleanQuery.
DocComparator should not skip docs with the same docID on multiple
sorts with search after.
Because of the optimization introduced in LUCENE-9449, currently when
searching with sort on [_doc, other fields] with search after,
DocComparator can efficiently skip all docs before and including
the provided [search after docID]. This is a desirable behaviour
in a single index search. But in a distributed search, where multiple
indices have docs with the same docID, and when searching on
[_doc, other fields], the sort optimization should NOT skip
documents with the same docIDs.
This PR fixes this.
Relates to LUCENE-9449
* Added a explicit Flush Task to flush data at Thread level once it completes the processing
* Included explicit flush per Thread level
* Done changes for parallel processing
* Removed extra brace
* Removed unused variable
* Removed unused variable initialization
* Did the required formating
* Refactored the code and added required comments & checks
This helps simplify the code, and also adds some optimizations to ordinals like
better compression for long runs of equal values or fields that are used in
index sorts.
Iterators over subSources of DisjunctionIntervalsSource may
return elements in indeterminate order, requiring special handling
to make toString() output stable across equivalent instances
With this change, doc-value terms dictionaries use a shared `ByteBlockPool`
across all fields, and points, binary doc values and doc-value ordinals use
slightly smaller page sizes.
DWPTPool currently always returns the last DWPT that was added to the
pool. By returning the largest DWPT instead, we could try to do larger
flushes by finishing DWPTs that are close to being full instead of the
last one that was added to the pool, which might be close to being
empty.
When indexing wikimediumall, this change did not seem to improve the
indexing rate significantly, but it didn't slow things down either and
the number of flushes went from 224-226 to 216, about 4% less.
My expectation is that our nightly benchmarks are a best-case scenario
for DWPTPool as the same number of threads is dedicated to indexing over
time, but in the case when you have e.g. a single fixed threadpool that
is responsible for indexing into several indices, the number of indexing
threads that contribute to a given index might greatly vary over time.
The newly added assertion in the bulk-merge logic doesn't always hold
because we do not create a new instance of
Lucene90CompressingTermVectorsReader for merges and that reader can be
accessed in tests (as long as it happens on the same thread).
This change clones a new term vectors reader for merges.
This change enables bulk-merge for term vectors with index sort. The
algorithm used here is similar to the one that is used to merge stored
fields.
Relates #134
This change enables bulk-merge for term vectors with index sort. The
algorithm used here is similar to the one that is used to merge stored
fields.
Relates #134
This change adds new IndexSearcher and Collector implementations to profile
search execution and break down the timings. The breakdown includes the total
time spent in each of the following categories along with the number of times
visited: create weight, build scorer, next doc, advance, score, match.
Co-authored-by: Julie Tibshirani <julietibs@gmail.com>
Tomoko Uchida
Michael Wechner
Julie Tibshirani
Jim Ferenczi
Robert Muir
Michael Gibney
Patrick Zhai
zacharymorn
Uwe Schindler
Mayya Sharipova
Christine Poerschke
Gautam Worah
Geoffrey Lawson
Mike McCandless
Greg Miller
balmukundblr
Adrien Grand
Karl Wright
kkewwei
Nhat Nguyen
Dawid Weiss
Jack Conradson
Michael Sokolov