Start instrumenting Weight#count function in ProfileWeight because we start to use it to compute total hit counts and aggregation counts.
Resolve#85203
Loading of stored fields is currently handled directly in FetchPhase, with
some fairly complex logic examining various bits of the FetchContext to work
out what fields need to be loaded. This is further complicated by synthetic
source, which may have its own stored field requirements.
This commit tries to separate out these concerns a little by adding a new
StoredFieldsSpec record that holds information about which stored fields
need to be loaded. Each FetchSubPhaseProcessor can now report a
StoredFieldsSpec detailing what its requirements are, and these specs can
be merged together, along with requirements from a SourceLoader, to
determine up-front what fields should be loaded by the StoredFieldLoader.
The stored fields themselves are added into the SearchHit by a new
StoredFieldsPhase, which handles alias resolution and value post-
processing. The logic to determine when source should be loaded and
when not, based on the presence of script fields or stored fields, is
moved into FetchContext, which highlights some inconsistencies that
can be fixed in follow-up commits.
Adds the query option to the _semantic_search endpoint for hybrid retrieval.
Scoring is controlled by the boost fields of the knn search and the query.
This commit removes the experimental tag from kNN search docs and makes some
docs improvements:
* Add a prominent warning about memory usage in the kNN search guide
* Link to the performance tuning guide from the main guide
* Clarify the memory requirements section in the tuning guide
This change adds an element_type as an optional mapping parameter for dense vector fields as
described in #89784. This also adds a byte element_type for dense vector fields that supports storing
dense vectors using only 8-bits per dimension. This is only supported when the mapping parameter
index is set to true.
The code follows a similar pattern to our NumberFieldMapper where we have an enum for
ElementType, and it has methods that DenseVectorFieldType and DenseVectorMapper can delegate to
to support each available type (just float and byte for now).
Adds a {index}_semantic_search endpoint which first converts the query text into a dense vector
using a NLP text embedding model then performs a knn search against an index containing
dense vectors created with the same embedding model.
This PR expands the approximate kNN docs to clarify the filter is applied during
the kNN search, not after. It explains the downsides of postfiltering.
This change adds the filter query for a filtered alias to the knn query during the dfs phase on the
shard. This ensures the correct number of k results are returned instead of removing results as a post
filter.
Fixes: #89561
This commit adds a short note to the 'search your data' docs around kNN search
to explain how approximate kNN works with aggregations:
* Make section on 'hybrid retrieval' more general and include aggregations info
* Remove an example response from the previous section on filtering, since this
page was getting long
To assist the user in configuring the visualizations correctly while leveraging TSDB
functionality, information about TSDB configuration should be exposed via the field
caps API per field.
Especially for metrics fields, it must be clear which fields are metrics and if they belong
to only time-series indexes or mixed time-series and non-time-series indexes.
To further distinguish metric fields when they belong to any of the following indices:
- Standard (non-time-series) indexes
- Time series indexes
- Downsampled time series indexes
This PR modifies the field caps API so that the mapping parameters time_series_dimension
and time_series_dimension are presented only when they are set on fields of time-series indexes.
Those parameters are completely ignored when they are set on standard (non-time-series) indexes.
This PR revisits some of the conventions adopted by #78790
This change deprecates the kNN search API in favor of the new 'knn' option
inside the search API. The 'knn' option is now the preferred way of performing
kNN search.
Relates to #87625
This PR adds a new `knn` option to the `_search` API to support ANN search.
It's powered by the same Lucene ANN capabilities as the old `_knn_search`
endpoint. The `knn` option can be combined with other search features like
queries and aggregations.
Addresses #87625
* Revert "Revert "[DOCS] Add TSDS docs (#86905)" (#87702)"
This reverts commit 0c86d7b9b2.
* First fix to tests
* Add data_stream object to index template
* small rewording
* Add enable data stream object in gradle example setup
* Add bullet about data stream must be enabled in template
* [DOCS] Add TSDB docs
* Update docs/build.gradle
Co-authored-by: Adam Locke <adam.locke@elastic.co>
* Address Nik's comments, part 1
* Address Nik's comments, part deux
* Reword write index
* Add feature flags
* Wrap one more section in feature flag
* Small fixes
* set index.routing_path to optional
* Update storage reduction value
* Update create index template code example
Co-authored-by: James Rodewig <40268737+jrodewig@users.noreply.github.com>
Co-authored-by: Adam Locke <adam.locke@elastic.co>
This change tries to make it easier to find kNN with filtering in the docs:
* Mention filtering support in the kNN API description
* In kNN tutorial, link to the kNN search API page more prominently
Back when we introduced the fields parameter to the search API, it could only fetch values from _source, hence
the corresponding sub-fetch phase fails early whenever _source is disabled. Today though runtime fields can
be retrieved from a separate value fetcher that reads from fielddata, and metadata fields can be retrieved
from stored fields. These two scenarios currently throw an unnecessary error whenever _source is disabled.
This commit removes the check for disabled _source, so that runtime fields and metadata fields can be retrieved even when _source is disabled. Fields that need to be loaded from _source are simply skipped whenever _source is disabled, similar to when a field is not found in _source.
Closes#87072
* Soft-deprecation of point/geo_point formats
Since GeoJSON and WKT are now common formats for all three types:
geo_shape, geo_point and point
We decided to soft-deprecate the other point formats by ordering:
* GeoJSON (object with keys `type` and `coordinates`)
* WKT `POINT(x y)`
* Object with keys `lat` and `lon` (or `x` and `y` for point)
* Array [lon,lat]
* String `"lat,lon"` (or `"x,y"` in point)
* String with geohash (only in `geo_point`)
The geohash is last because it is only in one field type.
The string version is second last because it is the most controversial
being the only version to reverse the coordinate order from all other
formats (for geo_point only, since the coordinates are not reversed
in point).
In addition we replaced many examples in both documentation and tests
to prioritize WKT over the plain string format.
Many remaining examples of array format or object with keys still exist
and could be replaced by, for example, GeoJSON, if we feel the need.
* Incorrect quote position
Support label position in REST vector tiles
There is a need to provide sensibly calculated label positions for polygons and lines in Kibana maps. A very convenient way to satisfy this need is through a runtime field that the rest API can make use of when labels are requested. This has the advantage of providing painless access to the label position as well.
This work adds support for the REST API to provide label positions to MVT queries, both for the HITS layer and the AGGS layer. To enable this feature, set with_labels to true as a query parameter to the vector tile search query.
This attempts to shrink the index by implementing a "synthetic _source" field.
You configure it by in the mapping:
```
{
"mappings": {
"_source": {
"synthetic": true
}
}
}
```
And we just stop storing the `_source` field - kind of. When you go to access
the `_source` we regenerate it on the fly by loading doc values. Doc values
don't preserve the original structure of the source you sent so we have to
make some educated guesses. And we have a rule: the source we generate would
result in the same index if you sent it back to us. That way you can use it
for things like `_reindex`.
Fetching the `_source` from doc values does slow down loading somewhat. See
numbers further down.
## Supported fields
This only works for the following fields:
* `boolean`
* `byte`
* `date`
* `double`
* `float`
* `geo_point` (with precision loss)
* `half_float`
* `integer`
* `ip`
* `keyword`
* `long`
* `scaled_float`
* `short`
* `text` (when there is a `keyword` sub-field that is compatible with this feature)
## Educated guesses
The synthetic source generator makes `_source` fields that are:
* sorted alphabetically
* as "objecty" as possible
* pushes all arrays to the "leaf" fields
* sorts most array values
* removes duplicate text and keyword values
These are mostly artifacts of how doc values are stored.
### sorted alphabetically
```
{
"b": 1,
"c": 2,
"a": 3
}
```
becomes
```
{
"a": 3,
"b": 1,
"c": 2
}
```
### as "objecty" as possible
```
{
"a.b": "foo"
}
```
becomes
```
{
"a": {
"b": "foo"
}
}
```
### pushes all arrays to the "leaf" fields
```
{
"a": [
{
"b": "foo",
"c": "bar"
},
{
"c": "bort"
},
{
"b": "snort"
}
}
```
becomes
```
{
"a" {
"b": ["foo", "snort"],
"c": ["bar", "bort"]
}
}
```
### sorts most array values
```
{
"a": [2, 3, 1]
}
```
becomes
```
{
"a": [1, 2, 3]
}
```
### removes duplicate text and keyword values
```
{
"a": ["bar", "baz", "baz", "baz", "foo", "foo"]
}
```
becomes
```
{
"a": ["bar", "baz", "foo"]
}
```
## `_recovery_source`
Elasticsearch's shard "recovery" process needs `_source` *sometimes*. So does
cross cluster replication. If you disable source or filter it somehow we store
a `_recovery_source` field for as long as the recovery process might need it.
When everything is running smoothly that's generally a few seconds or minutes.
Then the fields is removed on merge. This synthetic source feature continues
to produce `_recovery_source` and relies on it for recovery. It's *possible*
to synthesize `_source` during recovery but we don't do it.
That means that synethic source doesn't speed up writing the index. But in the
future we might be able to turn this on to trade writing less data at index
time for slower recovery and cross cluster replication. That's an area of
future improvement.
## perf numbers
I loaded the entire tsdb data set with this change and the size:
```
standard -> synthetic
store size 31.0 GB -> 7.0 GB (77.5% reduction)
_source 24695.7 MB -> 47.6 MB (99.8% reduction - synthetic is in _recovery_source)
```
A second _forcemerge a few minutes after rally finishes should removes the
remaining 47.6MB of _recovery_source.
With this fetching source for 1,000 documents seems to take about 500ms. I
spot checked a lot of different areas and haven't seen any different hit. I
*expect* this performance impact is based on the number of doc values fields
in the index and how sparse they are.
* adds a test for mixed cluster requests
* fixes a bad stream version check (above test will fail if this isn't included)
* replaces private FieldCapsFilter interface with Predicate
* renames 'allowedTypes' to 'types' to maintain consistency with external API
* adds javadoc to ResponseRewriter
* removes isRuntimeField from FieldTypeLookup
Relates to #83636
This commit adds a new optional parameter on the vector tiles API called `grid_agg` with two
possible values, geotile (default) and geohex. This will allow to build the aggs layer using different
grid aggregations, for example we can have a grid aggregation that is built using hexagons.
This PR integrates support for ANN with filtering added in Lucene 9.1. It adds
a new `filter` section to the `_knn_search` endpoint, which accepts a query (in
the Elasticsearch query DSL). The value can either be a single query or a list
of queries, which matches the syntax we use for defining filter clauses in a
`bool` query.
Closes#81788.
Abdon Pijpelink
David Kyle
Olivier Cavadenti
Luigi Dell'Aquila
István Zoltán Szabó
Alan Woodward
debadair
Lisa Cawley
Julie Tibshirani
Stéphane Campinas
Jack Conradson
Ievgen Degtiarenko
Adam Locke
Anthony McGlone
Christos Soulios
Navanit Dubey
Ignacio Vera
Nhat Nguyen
David Kilfoyle
Luca Cavanna
Craig Taverner
Sohail Mirza
Nik Everett
Yannick Welsch
Ignacio Vera