* Add limits for ngram and shingle settings (#27211)
Create index-level settings:
max_ngram_diff - maximum allowed difference between max_gram and min_gram in
NGramTokenFilter/NGramTokenizer. Default is 1.
max_shingle_diff - maximum allowed difference between max_shingle_size and
min_shingle_size in ShingleTokenFilter. Default is 3.
Throw an IllegalArgumentException when
trying to create NGramTokenFilter, NGramTokenizer, ShingleTokenFilter
where difference between max_size and min_size exceeds the settings value.
Closes#25887
Other tokenizers like the standard tokenizer allow overriding the default
maximum token length of 255 using the `"max_token_length` parameter. This change
enables using this parameter also with the whitespace tokenizer. The range that
is currently allowed is from 0 to StandardTokenizer.MAX_TOKEN_LENGTH_LIMIT,
which is 1024 * 1024 = 1048576 characters.
Closes#26643
Today if we search across a large amount of shards we hit every shard. Yet, it's quite
common to search across an index pattern for time based indices but filtering will exclude
all results outside a certain time range ie. `now-3d`. While the search can potentially hit
hundreds of shards the majority of the shards might yield 0 results since there is not document
that is within this date range. Kibana for instance does this regularly but used `_field_stats`
to optimize the indexes they need to query. Now with the deprecation of `_field_stats` and it's upcoming removal a single dashboard in kibana can potentially turn into searches hitting hundreds or thousands of shards and that can easily cause search rejections even though the most of the requests are very likely super cheap and only need a query rewriting to early terminate with 0 results.
This change adds a pre-filter phase for searches that can, if the number of shards are higher than a the `pre_filter_shard_size` threshold (defaults to 128 shards), fan out to the shards
and check if the query can potentially match any documents at all. While false positives are possible, a negative response means that no matches are possible. These requests are not subject to rejection and can greatly reduce the number of shards a request needs to hit. The approach here is preferable to the kibana approach with field stats since it correctly handles aliases and uses the correct threadpools to execute these requests. Further it's completely transparent to the user and improves scalability of elasticsearch in general on large clusters.
This snapshot has faster range queries on range fields (LUCENE-7828), more
accurate norms (LUCENE-7730) and the ability to use fake term frequencies
(LUCENE-7854).
Expose the experimental simplepattern and
simplepatternsplit tokenizers in the common
analysis plugin. They provide tokenization based
on regular expressions, using Lucene's
deterministic regex implementation that is usually
faster than Java's and has protections against
creating too-deep stacks during matching.
Both have a not-very-useful default pattern of the
empty string because all tokenizer factories must
be able to be instantiated at index creation time.
They should always be configured by the user
in practice.
* Docs: Improved tokenizer docs
Added descriptions and runnable examples
* Addressed Nik's comments
* Added TESTRESPONSEs for all tokenizer examples
* Added TESTRESPONSEs for all analyzer examples too
* Added docs, examples, and TESTRESPONSES for character filters
* Skipping two tests:
One interprets "$1" as a stack variable - same problem exists with the REST tests
The other because the "took" value is always different
* Fixed tests with "took"
* Fixed failing tests and removed preserve_original from fingerprint analyzer
Currently regexes in Pattern Tokenizer docs are escaped (it seems according to Java rules). I think it is better not to escape them because JSON escaping should be automatic in client libraries, and string escaping depends on a client language used. The default pattern is `\W+`, not `\\W+`.
Closes#6615
Add `irish` analyzer
Add `sorani` analyzer (Kurdish)
Add `classic` tokenizer: specific to english text and tries to recognize hostnames, companies, acronyms, etc.
Add `thai` tokenizer: segments thai text into words.
Add `classic` tokenfilter: cleans up acronyms and possessives from classic tokenizer
Add `apostrophe` tokenfilter: removes text after apostrophe and the apostrophe itself
Add `german_normalization` tokenfilter: umlaut/sharp S normalization
Add `hindi_normalization` tokenfilter: accounts for hindi spelling differences
Add `indic_normalization` tokenfilter: accounts for different unicode representations in Indian languages
Add `sorani_normalization` tokenfilter: normalizes kurdish text
Add `scandinavian_normalization` tokenfilter: normalizes Norwegian, Danish, Swedish text
Add `scandinavian_folding` tokenfilter: much more aggressive form of `scandinavian_normalization`
Add additional languages to stemmer tokenfilter: `galician`, `minimal_galician`, `irish`, `sorani`, `light_nynorsk`, `minimal_nynorsk`
Add support access to default Thai stopword set "_thai_"
Fix some bugs and broken links in documentation.
Closes#5935
Mayya Sharipova
Christoph Büscher
Clinton Gormley
Neil Rickards
Simon Willnauer
Andy Bristol
Adrien Grand
Shubham Aggarwal
Allen Torres
Pascal Borreli
Nik Everett
Jim Ferenczi
Mikhail Korobov
Robert Muir
Richard Boulton
Yousef