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
This includes:
- All regular numeric types such as int, long, scaled-float, double, etc
- IP addresses
- Dates
- Geopoints and Geoshapes
Relates to #19784
Adds `warnings` syntax to the yaml test that allows you to expect
a `Warning` header that looks like:
```
- do:
warnings:
- '[index] is deprecated'
- quotes are not required because yaml
- but this argument is always a list, never a single string
- no matter how many warnings you expect
get:
index: test
type: test
id: 1
```
These are accessible from the docs with:
```
// TEST[warning:some warning]
```
This should help to force you to update the docs if you deprecate
something. You *must* add the warnings marker to the docs or the build
will fail. While you are there you *should* update the docs to add
deprecation warnings visible in the rendered results.
This is a tentative to revive #15939 motivated by elastic/beats#1941.
Half-floats are a pretty bad option for storing percentages. They would likely
require 2 bytes all the time while they don't need more than one byte.
So this PR exposes a new `scaled_float` type that requires a `scaling_factor`
and internally indexes `value*scaling_factor` in a long field. Compared to the
original PR it exposes a lower-level API so that the trade-offs are clearer and
avoids any reference to fixed precision that might imply that this type is more
accurate (actually it is *less* accurate).
In addition to being more space-efficient for some use-cases that beats is
interested in, this is also faster that `half_float` unless we can improve the
efficiency of decoding half-float bits (which is currently done using software)
or until Java gets first-class support for half-floats.
If there are percolator queries containing `range` queries with ranges based on the current time then this can lead to incorrect results if the `percolate` query gets cached. These ranges are changing each time the `percolate` query gets executed and if this query gets cached then the results will be based on how the range was at the time when the `percolate` query got cached.
The ExtractQueryTermsService has been renamed `QueryAnalyzer` and now only deals with analyzing the query (extracting terms and deciding if the entire query is a verified match) . The `PercolatorFieldMapper` is responsible for adding the right fields based on the analysis the `QueryAnalyzer` has performed, because this is highly dependent on the field mappings. Also the `PercolatorFieldMapper` is responsible for creating the percolate query.
Rename `fields` to `stored_fields` and add `docvalue_fields`
`stored_fields` parameter will no longer try to retrieve fields from the _source but will only return stored fields.
`fields` will throw an exception if the user uses it.
Add `docvalue_fields` as an adjunct to `fielddata_fields` which is deprecated. `docvalue_fields` will try to load the value from the docvalue and fallback to fielddata cache if docvalues are not enabled on that field.
Closes#18943
`stored_fields` parameter will no longer try to retrieve fields from the _source but will only return stored fields.
`fields` will throw an exception if the user uses it.
Add `docvalue_fields` as an adjunct to `fielddata_fields` which is deprecated. `docvalue_fields` will try to load the value from the docvalue and fallback to fielddata cache if docvalues are not enabled on that field.
Closes#18943
They have been implemented in https://issues.apache.org/jira/browse/LUCENE-7289.
Ranges are implemented so that the accuracy loss only occurs at index time,
which means that if you are searching for values between A and B, the query will
match exactly all documents whose value rounded to the closest half-float point
is between A and B.
`doc_values` for _type field are created but any attempt to load them throws an IAE.
This PR re-enables `doc_values` loading for _type, it also enables `fielddata` loading for indices created between 2.0 and 2.1 since doc_values were disabled during that period.
It also restores the old docs that gives example on how to sort or aggregate on _type field.
Remove the arbitrary limit on epoch_millis and epoch_seconds of 13 and 10
characters, respectively. Instead allow any character combination that can
be converted to a Java Long.
Update the docs to reflect this change.
* Docs: First pass at improving analyzer docs
I've rewritten the intro to analyzers plus the docs
for all analyzers to provide working examples.
I've also removed:
* analyzer aliases (see #18244)
* analyzer versions (see #18267)
* snowball analyzer (see #8690)
Next steps will be tokenizers, token filters, char filters
* Fixed two typos
Adds infrastructure so `gradle :docs:check` will extract tests from
snippets in the documentation and execute the tests. This is included
in `gradle check` so it should happen on CI and during a normal build.
By default each `// AUTOSENSE` snippet creates a unique REST test. These
tests are executed in a random order and the cluster is wiped between
each one. If multiple snippets chain together into a test you can annotate
all snippets after the first with `// TEST[continued]` to have the
generated tests for both snippets joined.
Snippets marked as `// TESTRESPONSE` are checked against the response
of the last action.
See docs/README.asciidoc for lots more.
Closes#12583. That issue is about catching bugs in the docs during build.
This catches *some* bugs in the docs during build which is a good start.
* Added an extra `field` parameter to the `percolator` query to indicate what percolator field should be used. This must be an existing field in the mapping of type `percolator`.
* The `.percolator` type is now forbidden. (just like any type that starts with a `.`)
This only applies for new indices created on 5.0 and later. Indices created on previous versions the .percolator type is still allowed to exist.
The new `percolator` field type isn't active in such indices and the `PercolatorQueryCache` knows how to load queries from these legacy indices.
The `PercolatorQueryBuilder` will not enforce that the `field` parameter is of type `percolator`.
This makes all numeric fields including `date`, `ip` and `token_count` use
points instead of the inverted index as a lookup structure. This is expected
to perform worse for exact queries, but faster for range queries. It also
requires less storage.
Notes about how the change works:
- Numeric mappers have been split into a legacy version that is essentially
the current mapper, and a new version that uses points, eg.
LegacyDateFieldMapper and DateFieldMapper.
- Since new and old fields have the same names, the decision about which one
to use is made based on the index creation version.
- If you try to force using a legacy field on a new index or a field that uses
points on an old index, you will get an exception.
- IP addresses now support IPv6 via Lucene's InetAddressPoint and store them
in SORTED_SET doc values using the same encoding (fixed length of 16 bytes
and sortable).
- The internal MappedFieldType that is stored by the new mappers does not have
any of the points-related properties set. Instead, it keeps setting the index
options when parsing the `index` property of mappings and does
`if (fieldType.indexOptions() != IndexOptions.NONE) { // add point field }`
when parsing documents.
Known issues that won't fix:
- You can't use numeric fields in significant terms aggregations anymore since
this requires document frequencies, which points do not record.
- Term queries on numeric fields will now return constant scores instead of
giving better scores to the rare values.
Known issues that we could work around (in follow-up PRs, this one is too large
already):
- Range queries on `ip` addresses only work if both the lower and upper bounds
are inclusive (exclusive bounds are not exposed in Lucene). We could either
decide to implement it, or drop range support entirely and tell users to
query subnets using the CIDR notation instead.
- Since IP addresses now use a different representation for doc values,
aggregations will fail when running a terms aggregation on an ip field on a
list of indices that contains both pre-5.0 and 5.0 indices.
- The ip range aggregation does not work on the new ip field. We need to either
implement range aggs for SORTED_SET doc values or drop support for ip ranges
and tell users to use filters instead. #17700Closes#16751Closes#17007Closes#11513
The doc mentions match_path in one place but the correct syntax is path_match which is mentioned everywhere else. Using the wrong string leads to errors because the mapping becomes too greedy, and matches things it shouldn't.
This is to prevent mapping explosion when dynamic keys such as UUID are used as field names. index.mapping.total_fields.limit specifies the total number of fields an index can have. An exception will be thrown when the limit is reached. The default limit is 1000. Value 0 means no limit. This setting is runtime adjustable
Closes#11443
This commit updates the documentation for GeoPointField by removing all references to the coerce and doc_values parameters. DocValues are enabled in lucene GeoPointField by default (required for boundary filtering). The QueryBuilders are updated to automatically normalize points (ignoring the coerce parameter) for any index created onOrAfter version 2.2.
Warmers are now barely useful and will be removed in 3.0. Note that this only
removes the warmer API and query-based warmers. We still have warmers internally
for eg. global ordinals.
Close#15607
This commit adds the following:
* SpatialStrategy documentation to the geo-shape reference docs.
* Updates relation documentation to geo-shape-query reference docs.
* Updates GeoShapeFiledMapper to set points_only to true if TERM strategy is used (to be consistent with documentation)
Some users may already be familiar with column stores, so saying more explicitly
that doc values are a columnar representation of the data may help them better
and/or more quickly understand what doc values are about.
Jim Ferenczi
Andrey Groshev
Adrien Grand
markharwood
Nicholas Knize
Nik Everett
Danilo Akamine
Fabien Baligand
Loek van Gool
Martijn van Groningen
Lee Hinman
Ali Beyad
AlexNodex
Clinton Gormley
Yannick Welsch
Daniel Mitterdorfer
Scott Somerville
Alex
Jason Veatch
Lee Hinman
Alexander Lin
LakumiNarayanan
Jun Ohtani
Colin Goodheart-Smithe
Pascal Borreli
Anatolii Stepaniuk
David Pilato
Munish Goyal
Simon Willnauer
Britta Weber
Robert Muir
Robert Muir
G. Richard Bellamy
Robin Joseph
Shane Connelly
Christoph Büscher
LeonardGC
Ibrahim Awwal
Sergii Golubev
Yanjun Huang
Dongjoon Hyun
Rachit Gupta
Yannick Welsch
Felipe Forbeck
Imran Azad
umeku
Andrew Williams
Ben Tse
Jason Tedor
xuzha