This section was removed to hide this ability to new users.
This change restores the section and adds a warning regarding the expected performance.
Closes#27336
extract all clauses from a conjunction query.
When clauses from a conjunction are extracted the number of clauses is
also stored in an internal doc values field (minimum_should_match field).
This field is used by the CoveringQuery and allows the percolator to
reduce the number of false positives when selecting candidate matches and
in certain cases be absolutely sure that a conjunction candidate match
will match and then skip MemoryIndex validation. This can greatly improve
performance.
Before this change only a single clause was extracted from a conjunction
query. The percolator tried to extract the clauses that was rarest in order
(based on term length) to attempt less candidate queries to be selected
in the first place. However this still method there is still a very high
chance that candidate query matches are false positives.
This change also removes the influencing query extraction added via #26081
as this is no longer needed because now all conjunction clauses are extracted.
https://www.elastic.co/guide/en/elasticsearch/reference/6.x/percolator.html#_influencing_query_extractionCloses#26307
Numeric fields no longer support the index_options parameter. This changes the parameter
to be rejected in numeric field types after it was deprecated in 6.0.
Closes#21475
The percolator will add a `_percolator_document_slot` field to all percolator
hits to indicate with what document it has matched. This number matches with
the order in which the documents have been specified in the percolate query.
Also improved the support for multiple percolate queries in a search request.
The `index.percolator.map_unmapped_fields_as_text` is a more better name, because unmapped fields are mapped to a text field with default settings
and string is no longer a field type (it is either keyword or text).
* Remove the _all metadata field
This change removes the `_all` metadata field. This field is deprecated in 6
and cannot be activated for indices created in 6 so it can be safely removed in
the next major version (e.g. 7).
The percolator field mapper doesn't need to extract all terms and ranges from a bool query with must or filter clauses.
In order to help to default extraction behavior, boost fields can be configured, so that fields that are known for not being
selective enough can be ignored in favor for other fields or clauses with specific fields can forcefully take precedence over other clauses.
This can help selecting clauses for fields that don't match with a lot of percolator queries over other clauses and thus improving performance of the percolate query.
For example a status like field is something that should configured as an ignore field.
Queries on this field tend to match with more documents and so if clauses for this fields
get selected as best clause then that isn't very helpful for the candidate query that the
percolate query generates to filter out percolator queries that are likely not going to match.
The Writeble representation is less heavy to parse and that will benefit percolate performance and throughput.
The query builder's binary format has now the same bwc guarentees as the xcontent format.
Added a qa test that verifies that percolator queries written in older versions are still readable by the current version.
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.
Indexing a join field on a document requires a value of type "object" and two sub fields "name"
and "parent". The "parent" field is only required on child documents, but the "name" field which
denotes the name of the relation is always needed. Previously, only the short-hand version of the
join field was documented. This adds documentation for the long-hand join field data, and
explicitly points out that just specifying the name of the relation for the field value is a
convenience shortcut.
* Add documentation for the new parent-join field
This commit adds the docs for the new parent-join field.
It explains how to define, index and query this new field.
Relates #20257
This commit adds back "id" as the key within a script to specify a
stored script (which with file scripts now gone is no longer ambiguous).
It also adds "source" as a replacement for "code". This is in an attempt
to normalize how scripts are specified across both put stored scripts and script usages, including search template requests. This also deprecates the old inline/stored keys.
This change removes the `postings` highlighter. This highlighter has been removed from Lucene master (7.x) because it behaves
exactly like the `unified` highlighter when index_options is set to `offsets`:
https://issues.apache.org/jira/browse/LUCENE-7815
It also makes the `unified` highlighter the default choice for highlighting a field (if `type` is not provided).
The strategy used internally by this highlighter remain the same as before, it checks `term_vectors` first, then `postings` and ultimately it re-analyzes the text.
Ultimately it rewrites the docs so that the options that the `unified` highlighter cannot handle are clearly marked as such.
There are few features that the `unified` highlighter is not able to handle which is why the other highlighters (`plain` and `fvh`) are still available.
I'll open separate issues for these features and we'll deprecate the `fvh` and `plain` highlighters when full support for these features have been added to the `unified`.
Currently global ordinals are documented under `fielddata`. It moves them to
their own file since they also work with doc values and fielddata is on the way
out.
Closes#23101
* SignificantText aggregation - like significant_terms but doesn’t require fielddata=true, recommended used with `sampler` agg to limit expense of tokenizing docs and takes optional `filter_duplicate_text`:true setting to avoid stats skew from repeated sections of text in search results.
Closes#23674
Now that indices have a single type by default, we can move to the next step
and identify documents using their `_id` rather than the `_uid`.
One notable change in this commit is that I made deletions implicitly create
types. This helps with the live version map in the case that documents are
deleted before the first type is introduced. Otherwise there would be no way
to differenciate `DELETE index/foo/1` followed by `PUT index/foo/1` from
`DELETE index/bar/1` followed by `PUT index/foo/1`, even though those are
different if versioning is involved.
This commit adds support for indexing and searching a new ip_range field type. Both IPv4 and IPv6 formats are supported. Tests are updated and docs are added.
Adds CONSOLE to cross-cluster-search docs but skips them for testing
because we don't have a second cluster set up. This gets us the
`VIEW IN CONSOLE` and `COPY AS CURL` links and makes sure that they
are valid yaml (not json, technically) but doesn't get testing.
Which is better than we had before.
Adds CONSOLE to the dynamic templates docs and ingest-node docs.
The ingest-node docs contain a *ton* of non-console snippets. We
might want to convert them to full examples later, but that can be
a separate thing.
Relates to #18160
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
Add option "enable_position_increments" with default value true.
If option is set to false, indexed value is the number of tokens
(not position increments count)
Before now ranges where forbidden, because the percolator query itself could get cached and then the percolator queries with now ranges that should no longer match, incorrectly will continue to match.
By disabling caching when the `percolator` is being used, the percolator can now correctly support range queries with now based ranges.
I think this is the right tradeoff. The percolator query is likely to not be the same between search requests and disabling range queries with now ranges really disabled people using the percolator for their use cases.
Also fixed an issue that existed in the percolator fieldmapper, it was unable to find forbidden queries inside `dismax` queries.
Closes#23859
Fielddata can no longer be configured to be loaded eagerly (it only accepts
`true` and `false`), so this line is a little misleading because it talks about
a procedure we can no longer do.
Turns the top example in each of the geo aggregation docs into a working
example that can be opened in CONSOLE. Subsequent examples can all also
be opened in console and will work after you've run the first example.
All examples are tested as part of the build.
This commit adds the boolean similarity scoring from Lucene to
Elasticsearch. The boolean similarity provides a means to specify that
a field should not be scored with typical full-text ranking algorithms,
but rather just whether the query terms match the document or not.
Boolean similarity scores a query term equal to its query boost only.
Boolean similarity is available as a default similarity option and thus
a field can be specified to have boolean similarity by declaring in its
mapping:
"similarity": "boolean"
Closes#6731
Since `_all` is now deprecated and cannot be set for new indices, we should also
disallow any field that has the `include_in_all` parameter set.
Resolves#22923
This PR removes all leniency in the conversion of Strings to booleans: "true"
is converted to the boolean value `true`, "false" is converted to the boolean
value `false`. Everything else raises an error.
This change makes it possible for custom routing values to go to a subset of shards rather than
just a single shard. This enables the ability to utilize the spatial locality that custom routing can
provide while mitigating the likelihood of ending up with an imbalanced cluster or suffering
from a hot shard.
This is ideal for large multi-tenant indices with custom routing that suffer from one or both of
the following:
- The big tenants cannot fit into a single shard or there is so many of them that they will likely
end up on the same shard
- Tenants often have a surge in write traffic and a single shard cannot process it fast enough
Beyond that, this should also be useful for use cases where most queries are done under the context
of a specific field (e.g. a category) since it gives a hint at how the data can be stored to minimize
the number of shards to check per query. While a similar solution can be achieved with multiple
concrete indices or aliases per value today, those approaches breakdown for high cardinality fields.
A partitioned index enforces that mappings have routing required, that the partition size does not
change when shrinking an index (the partitions will shrink proportionally), and rejects mappings
that have parent/child relationships.
Closes#21585
* Grammatical correction
* Add note for legacy string mapping type
* Update truncate token filter to not mention the keyword tokenizer
The advice predates the existence of the keyword field
Closes#22650
This change disables the _all meta field by default.
Now that we have the "all-fields" method of query execution, we can save both
indexing time and disk space by disabling it.
_all can no longer be configured for indices created after 6.0.
Relates to #20925 and #21341Resolves#19784
This adds a new `normalizer` property to `keyword` fields that pre-processes the
field value prior to indexing, but without altering the `_source`. Note that
only the normalization components that work on a per-character basis are
applied, so for instance stemming filters will be ignored while lowercasing or
ascii folding will be applied.
Closes#18064
Our `float`/`double` fields generally assume that `-0` compares less than `+0`,
except when bounds are exclusive: an exclusive lower bound on `-0` excludes
`+0` and an exclusive upper bound on `+0` excludes `-0`.
Closes#22167
Lucene 6.2 added index and query support for numeric ranges. This commit adds a new RangeFieldMapper for indexing numeric (int, long, float, double) and date ranges and creating appropriate range and term queries. The design is similar to NumericFieldMapper in that it uses a RangeType enumerator for implementing the logic specific to each type. The following range types are supported by this field mapper: int_range, float_range, long_range, double_range, date_range.
Lucene does not provide a DocValue field specific to RangeField types so the RangeFieldMapper implements a CustomRangeDocValuesField for handling doc value support.
When executing a Range query over a Range field, the RangeQueryBuilder has been enhanced to accept a new relation parameter for defining the type of query as one of: WITHIN, CONTAINS, INTERSECTS. This provides support for finding all ranges that are related to a specific range in a desired way. As with other spatial queries, DISJOINT can be achieved as a MUST_NOT of an INTERSECTS query.
This changes only the query parsing behavior to be strict when searching on
boolean values. We continue to accept the variety of values during index time,
but searches will only be parsed using `"true"` or `"false"`.
Resolves#21545
* Allows for an array of index template patterns to be provided to an
index template, and rename the field from 'template' to 'index_pattern'.
Closes#20690
With the cut over to LatLonPoint the geohash, geohash_precision, lat_lon, and geohash_prefix parameters have been removed. This commit fixes the doc build by removing the remaining dangling references to these removed parameters.
This includes:
- All regular numeric types such as int, long, scaled-float, double, etc
- IP addresses
- Dates
- Geopoints and Geoshapes
Relates to #19784
This changes Elasticsearch to automatically downgrade `text` and
`keyword` fields into appropriate `string` fields when changing the
mapping of indexes imported from 2.x. This allows users to use the
modern, documented syntax against 2.x indexes. It also makes it clear
that reindexing in order to recreate the index in 5.0 is required for
any long lived indexes. This change is useful for the times when you
can't (cluster is just starting, not stable enough for reindex) or
shouldn't (index will only live 90 days or something).
Fix field examples to make documents actually visible
This commit adds refresh calls to field examples an removes not working
`_routing` and `_field_names` script access.
Closes#20118
Jim Ferenczi
Martijn van Groningen
Nicholas Knize
Holger Bartnick
markwalkom
David Turner
Adrien Grand
Clinton Gormley
Christoph Büscher
Michael Basnight
Bernd
Daniel A. Ochoa
Colin Goodheart-Smithe
Simon Willnauer
James Baiera
Nathan Taylor
Russ Cam
Ryan Ernst
Andrey Groshev
markharwood
Nik Everett
Danilo Akamine
Fabien Baligand
Loek van Gool
Lee Hinman
Ali Beyad
AlexNodex
Yannick Welsch
Daniel Mitterdorfer
Scott Somerville
Alex
Jason Veatch
Lee Hinman
Alexander Lin
LakumiNarayanan
Jun Ohtani
Pascal Borreli
Anatolii Stepaniuk
David Pilato
Munish Goyal
Simon Willnauer