We currently special-case SynonymFilterFactory and SynonymGraphFilterFactory, which need to
know their predecessors in the analysis chain in order to correctly analyze their synonym lists. This
special-casing doesn't work with Referring filter factories, such as the Multiplexer or Conditional
filters. We also have a number of filters (eg the Multiplexer) that will break synonyms when they
appear before them in a chain, because they produce multiple tokens at the same position.
This commit adds two methods to the TokenFilterFactory interface.
* `getChainAwareTokenFilterFactory()` allows a filter factory to rewrite itself against its preceding
filter chain, or to resolve references to other filters. It replaces `ReferringFilterFactory` and
`CustomAnalyzerProvider.checkAndApplySynonymFilter`, and by default returns `this`.
* `getSynonymFilter()` defines whether or not a filter should be applied when building a synonym
list `Analyzer`. By default it returns `true`.
Fixes#33609
This allows users to filter out tokens from a TokenStream using painless scripts,
instead of having to write specialised Java code and packaging it up into a plugin.
The commit also refactors the AnalysisPredicateScript.Token class so that it wraps
and makes read-only an AttributeSource.
The main benefit of the upgrade for users is the search optimization for top scored documents when the total hit count is not needed. However this optimization is not activated in this change, there is another issue opened to discuss how it should be integrated smoothly.
Some comments about the change:
* Tests that can produce negative scores have been adapted but we need to forbid them completely: #33309Closes#32899
This allows tokenfilters to be applied selectively, depending on the status of the current token in the tokenstream. The filter takes a scripted predicate, and only applies its subfilter when the predicate returns true.
Currently the `keep_types` token filter includes all token types specified using
its `types` parameter. Lucenes TypeTokenFilter also provides a second mode where
instead of keeping the specified tokens (include) they are filtered out
(exclude). This change exposes this option as a new `mode` parameter that can
either take the values `include` (the default, if not specified) or `exclude`.
Closes#29277
* Added lenient flag for synonym-tokenfilter.
Relates to #30968
* added docs for synonym-graph-tokenfilter
-- Also made lenient final
-- changed from !lenient to lenient == false
* Changes after review (1)
-- Renamed to ElasticsearchSynonymParser
-- Added explanation for ElasticsearchSynonymParser::add method
-- Changed ElasticsearchSynonymParser::logger instance to static
* Added lenient option for WordnetSynonymParser
-- also added more documentation
* Added additional documentation
* Improved documentation
The `multiplexer` filter emits multiple tokens at the same position, each
version of the token haivng been passed through a different filter chain.
Identical tokens at the same position are removed.
This allows users to, for example, index lowercase and original-case tokens,
or stemmed and unstemmed versions, in the same field, so that they can search
for a stemmed term within x positions of an unstemmed term.
=== Char Group Tokenizer
The `char_group` tokenizer breaks text into terms whenever it encounters
a
character which is in a defined set. It is mostly useful for cases where
a simple
custom tokenization is desired, and the overhead of use of the
<<analysis-pattern-tokenizer, `pattern` tokenizer>>
is not acceptable.
=== Configuration
The `char_group` tokenizer accepts one parameter:
`tokenize_on_chars`::
A string containing a list of characters to tokenize the string on.
Whenever a character
from this list is encountered, a new token is started. Also supports
escaped values like `\\n` and `\\f`,
and in addition `\\s` to represent whitespace, `\\d` to represent
digits and `\\w` to represent letters.
Defaults to an empty list.
=== Example output
```The 2 QUICK Brown-Foxes jumped over the lazy dog's bone for $2```
When the configuration `\\s-:<>` is used for `tokenize_on_chars`, the
above sentence would produce the following terms:
```[ The, 2, QUICK, Brown, Foxes, jumped, over, the, lazy, dog's, bone,
for, $2 ]```
This commit fixes docs failure on language analyzers when compared to the built in analyzers.
The `elision` filters used by the rebuilt language analyzers should be case insensitive to match
the definition of the prebuilt analyzers.
Closes#30557
This commit changes the default out-of-the-box configuration for the
number of shards from five to one. We think this will help address a
common problem of oversharding. For users with time-based indices that
need a different default, this can be managed with index templates. For
users with non-time-based indices that find they need to re-shard with
the split API in place they no longer need to resort only to
reindexing.
Since this has the impact of changing the default number of shards used
in REST tests, we want to ensure that we still have coverage for issues
that could arise from multiple shards. As such, we randomize (rarely)
the default number of shards in REST tests to two. This is managed via a
global index template. However, some tests check the templates that are
in the cluster state during the test. Since this template is randomly
there, we need a way for tests to skip adding the template used to set
the number of shards to two. For this we add the default_shards feature
skip. To avoid having to write our docs in a complicated way because
sometimes they might be behind one shard, and sometimes they might be
behind two shards we apply the default_shards feature skip to all docs
tests. That is, these tests will always run with the default number of
shards (one).
We have a pile of documentation describing how to rebuild the built in
language analyzers and, previously, our documentation testing framework
made sure that the examples successfully built *an* analyzer but they
didn't assert that the analyzer built by the documentation matches the
built in anlayzer. Unsuprisingly, some of the examples aren't quite
right.
This adds a mechanism that tests that the analyzers built by the docs.
The mechanism is fairly simple and brutal but it seems to be working:
build a hundred random unicode sequences and send them through the
`_analyze` API with the rebuilt analyzer and then again through the
built in analyzer. Then make sure both APIs return the same results.
Each of these calls to `_anlayze` takes about 20ms on my laptop which
seems fine.
We do want to keep this functionality in the future and we provide support for it.
This change is a first step towards replacing the `synonym` token filter with `synonym_graph`.
Allowing `_doc` as a type will enable users to make the transition to 7.0
smoother since the index APIs will be `PUT index/_doc/id` and `POST index/_doc`.
This also moves most of the documentation to `_doc` as a type name.
Closes#27750Closes#27751
The `delimited_payload_filter` is renamed to `delimited_payload`, the old name is
deprecated and should be replaced by `delimited_payload`.
Closes#21978
* 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
There was some confusion about the fact that tokens emitted from a Pattern
Capture Token Filter are treated as synonyms when used to analyze a search
query. This commit adds an explanation to the note in the docs to emphasize this
behaviour.
Closes#25746
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.
* [Analysis] Parse synonyms with the same analysis chain
Synonym Token Filter / Synonym Graph Filter tokenize synonyms with whatever tokenizer and token filters appear before it in the chain.
Close#7199
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.
This adds the `index.mapping.single_type` setting, which enforces that indices
have at most one type when it is true. The default value is true for 6.0+ indices
and false for old indices.
Relates #15613
Converts the analysis docs to that were marked as json into `CONSOLE`
format. A few of them were in yaml but marked as json for historical
reasons. I added more complete examples for a few of the less obvious
sounding ones.
Relates to #18160
The pattern-analyzer docs contained a snippet that was an expanded
regex that was marked as `[source,js]`. This changes it to
`[source,regex]`.
The htmlstrip-charfilter and pattern-replace-charfilter docs had
examples that were actually a list of tokens but marked `[source,js]`.
This marks them as `[source,text]` so they don't count as unconverted
CONSOLE snippets.
The pattern-replace-charfilter also had a doc who's test was
skipped because of funny interaction with the test framework. This
fixes the test.
Three more down, eighty-two to go.
Relates to #18160
CONSOLEifies the lang-analyzer docs and replaces the (invalid)
empty `keyword_marker` setups that were on the page with one
that contains the word "example" translated into the appropriate
language.
Relates to #18160
This commit adds support for the pattern keyword marker filter in
Lucene. Previously, the keyword marker filter in Elasticsearch
supported specifying a keywords set or a path to a set of keywords.
This commit exposes the regular expression pattern based keyword marker
filter also available in Lucene, so that any token matching the pattern
specified by the `keywords_pattern` setting is excluded from being
stemmed by any stemming filters.
Closes#4877
Christoph Büscher
Jim Ferenczi
Nikolay Vasiliev
Alan Woodward
Matthias Sieber
Sohaib Iftikhar
Itamar Syn-Hershko
Nik Everett
Jason Tedor
Mayya Sharipova
deepybee
Adrien Grand
Martijn van Groningen
Christoph Büscher
kel
Md. Abdulla-Al-Sun
markwalkom
olcbean
Christoph Büscher
Tahmim Ahmed Shibli
Clinton Gormley
Neil Rickards
Simon Willnauer
Jun Ohtani
Andy Bristol
debadair
Jim Ferenczi
Abdon Pijpelink
Ali Beyad