The `top_metrics` agg is kind of like `top_hits` but it only works on
doc values so it *should* be faster.
At this point it is fairly limited in that it only supports a single,
numeric sort and a single, numeric metric. And it only fetches the "very
topest" document worth of metric. We plan to support returning a
configurable number of top metrics, requesting more than one metric and
more than one sort. And, eventually, non-numeric sorts and metrics. The
trick is doing those things fairly efficiently.
Co-Authored by: Zachary Tong <zach@elastic.co>
Backport of #48849. Update `.editorconfig` to make the Java settings the
default for all files, and then apply a 2-space indent to all `*.gradle`
files. Then reformat all the files.
This PR performs the following changes:
* Split `ScoreScriptUtilsTests` into `DenseVectorFunctionTests` and
`SparseVectorFunctionTests`. This will make it easier to delete all sparse
vector function tests once we remove support on 8.x.
* As much as possible, break up the large test methods into individual tests
for each vector function (`cosineSimilarity`, `l2norm`, etc.).
Previously the functions accepted a doc values reference, whereas they now
accept the name of the vector field. Here's an example of how a vector function
was called before and after the change.
```
Before: cosineSimilarity(params.query_vector, doc['field'])
After: cosineSimilarity(params.query_vector, 'field')
```
This seems more intuitive, since we don't allow direct access to vector doc
values and the the meaning of `doc['field']` is unclear.
The PR makes the following changes (broken into distinct commits):
* Add new function signatures of the form `function(params.query_vector,
'field')` and deprecates the old ones. Because Painless doesn't allow two
methods with the same name and number of arguments, we allow a generic `Object`
to be passed in to the function and decide on the behavior through an
`instanceof` check.
* Refactor the class bindings so that the document field is passed to the
constructor instead of the instance method. This allows us to avoid retrieving
the vector doc values on every function invocation, which gives a tiny speed-up
in benchmarks.
Note that this PR adds new signatures for the sparse vector functions too, even
though sparse vectors are deprecated. It seemed simplest to understand (for both
us and users) to keep everything symmetric between dense and sparse vectors.
We have not seen much adoption of this experimental field type, and don't see a
clear use case as it's currently designed. This PR deprecates the field type in
7.x. It will be removed from 8.0 in a follow-up PR.
XPackPlugin holds data in statics and can only be initialized once. This
caused tests to fail primarily when running with a low max-workers.
Replaced usages with the LocalStateCompositeXPackPlugin, which handles
this properly for testing.
This PR merges the `vectors-optimize-brute-force` feature branch, which makes
the following changes to how vector functions are computed:
* Precompute the L2 norm of each vector at indexing time. (#45390)
* Switch to ByteBuffer for vector encoding. (#45936)
* Decode vectors and while computing the vector function. (#46103)
* Use an array instead of a List for the query vector. (#46155)
* Precompute the normalized query vector when using cosine similarity. (#46190)
Co-authored-by: Mayya Sharipova <mayya.sharipova@elastic.co>
Currently, when using script_score functions like cosineSimilarity, the query
vector is treated as an array of doubles. Since the stored document vectors use
floats, it seems like the least surprising behavior for the query vectors to
also be float arrays.
In addition to improving consistency, this change may help with some
optimizations we have been considering around vector dot product.
Currently when a document misses a vector value, vector function
returns 0 as a score for this document. We think this is incorrect
behaviour.
With this change, an error will be thrown if vector functions are
used with docs that are missing vector doc values.
Also VectorScriptDocValues is modified to allow size() function,
which can be used to check if a document has a value for the
vector field.
Typically, dense vectors of both documents and queries must have the same
number of dimensions. Different number of dimensions among documents
or query vector indicate an error. This PR enforces that all vectors
for the same field have the same number of dimensions. It also enforces
that query vectors have the same number of dimensions.
Nik Everett
Mayya Sharipova
Rory Hunter
Julie Tibshirani
Henning Andersen