* Removed the docs for `index.compound_format` and `index.compound_on_flush` - these are expert settings which should probably be removed (see https://github.com/elastic/elasticsearch/issues/10778)
* Removed the docs for `index.index_concurrency` - another expert setting
* Labelled the segments verbose output as experimental
* Marked the `compression`, `precision_threshold` and `rehash` options as experimental in the cardinality and percentile aggs
* Improved the experimental text on `significant_terms`, `execution_hint` in the terms agg, and `terminate_after` param on count and search
* Removed the experimental flag on the `geobounds` agg
* Marked the settings in the `merge` and `store` modules as experimental, rather than the modules themselves
Closes#10782
This commit adds scripting capability to significant_terms.
Custom heuristics can be implemented with a script that provides
parameters subset_freq, superset_freq,subset_size, superset_size.
closes#7850
We now have a very useful annotation to mark features or parameters as
experimental. Let's use it! This commit replaces some custom text warnings with
this annotation and adds this annotation to some existing features/parameters:
- inner_hits (unreleased yet)
- terminate_after (released in 1.4)
- per-bucket doc count errors in the terms agg (released in 1.4)
I also tagged with this annotation settings which should either be not needed
(like the ability to evict entries from the filter cache based on time) or that
are too deep into the way that Elasticsearch works like the Directory
implementation or merge settings.
Close#9563
This commit adds the infrastructure to allow pluging in different
measures for computing the significance of a term.
Significance measures can be provided externally by overriding
- SignificanceHeuristic
- SignificanceHeuristicBuilder
- SignificanceHeuristicParser
closes#6561
Significant terms internally maintain a priority queue per shard with a size potentially
lower than the number of terms. This queue uses the score as criterion to determine if
a bucket is kept or not. If many terms with low subsetDF score very high
but the `min_doc_count` is set high, this might result in no terms being
returned because the pq is filled with low frequent terms which are all sorted
out in the end.
This can be avoided by increasing the `shard_size` parameter to a higher value.
However, it is not immediately clear to which value this parameter must be set
because we can not know how many terms with low frequency are scored higher that
the high frequent terms that we are actually interested in.
On the other hand, if there is no routing of docs to shards involved, we can maybe
assume that the documents of classes and also the terms therein are distributed evenly
across shards. In that case it might be easier to not add documents to the pq that have
subsetDF <= `shard_min_doc_count` which can be set to something like
`min_doc_count`/number of shards because we would assume that even when summing up
the subsetDF across shards `min_doc_count` will not be reached.
closes#5998closes#6041
Significance is related to the changes in document frequency observed between everyday use in the corpus and
frequency observed in the result set. The asciidocs include extensive details on the applications of this feature.
Closes#5146
Clinton Gormley
Britta Weber
markharwood
Adrien Grand
Boaz Leskes
Veres Lajos
Simon Willnauer