Adds conceptual docs for token graphs.
These docs cover:
* How a token graph is constructed from a token stream
* How synonyms and multi-position tokens impact token graphs
* How token graphs are used during search
* Why some token filters produce invalid token graphs
Also makes the following supporting changes:
* Adds anchors to the 'Anatomy of an Analyzer' docs for cross-linking
* Adds several SVGs for token graph diagrams
Updates the SVG for a token graph to make the layout consistent with
other graphs. This means moving the text directly above the edge lines.
Previously, the text was above the edge line.
Makes the following changes to the `word_delimiter_graph` token filter
docs:
* Updates the Lucene experimental admonition.
* Updates description
* Adds analyze snippet
* Adds custom analyzer and custom filter snippets
* Reorganizes and updates parameter list
* Expands and updates section re: differences between `word_delimiter`
and `word_delimiter_graph`
* Refresh snapshots with latest look
Add new snapshots with the connection editor to reflect the latest UI.
* Document the effect of the late added params
Add details about the Cloud ID setting, as well as those on the Misc
tab.
(cherry picked from commit afa67625e847e99a22264f5dd6fa0daa37786c6f)
The just released SQuirrel SQL 4.0.0 provides an Elasticsearch driver
definition out of the box; update the documentation to reflect that.
(cherry picked from commit 3aa417ed74947e69f0ff605b1c210a0678a3cb9f)
Refresh the setup for the new versions of DbVisualizer and SQL
Workbench/J which have Elasticsearch JDBC support out of the box.
(cherry picked from commit 6d257194c1055d060505e0faaaa37b41e21699f5)
This adds a `rare_terms` aggregation. It is an aggregation designed
to identify the long-tail of keywords, e.g. terms that are "rare" or
have low doc counts.
This aggregation is designed to be more memory efficient than the
alternative, which is setting a terms aggregation to size: LONG_MAX
(or worse, ordering a terms agg by count ascending, which has
unbounded error).
This aggregation works by maintaining a map of terms that have
been seen. A counter associated with each value is incremented
when we see the term again. If the counter surpasses a predefined
threshold, the term is removed from the map and inserted into a cuckoo
filter. If a future term is found in the cuckoo filter we assume it
was previously removed from the map and is "common".
The map keys are the "rare" terms after collection is done.
* Add MSI installation to documentation
Move installation documentation for Windows with the .zip archive into the zip and tar installation documentation, and clearly indicate any differences for installing on macOS/Linux and Windows.
* Separate out installation with .zip on Windows
- Using log() to indicate natural log can add some confusion when trying to further adjust/tweak scores. Other parts of the API (field_value_factor on this same page) use 'ln' and 'log', so this change should be more consistent
- Fixes#20027
- I generated the images using http://latex2png.com/ at a resolution of 150 which seemed to be about the same size as before
This aggregation computes unique term counts using the hyperloglog++ algorithm
which uses linear counting to estimate low cardinalities and hyperloglog on
higher cardinalities.
Since this algorithm works on hashes, it is useful for high-cardinality fields
to store the hash of values directly in the index, which is the purpose of
the new `murmur3` field type. This is less necessary on low-cardinality
string fields because the aggregator is smart enough to only compute the hash
once per unique value per segment thanks to ordinals, or on numeric fields
since hashing them is very fast.
Close#5426
James Rodewig
Bogdan Pintea
Costin Leau
Tal Levy
Zachary Tong
Costin Leau
Andrei Stefan
Stuart Cam
Russ Cam
Greg Ichneumon Brown
Adrien Grand
Clinton Gormley
gmarz
Zachary Tong
Colin Goodheart-Smithe
Matias Tealdi
Britta Weber