Update experimental labels in the docs (#25727)
Relates https://github.com/elastic/elasticsearch/issues/19798 Removed experimental label from: * Painless * Diversified Sampler Agg * Sampler Agg * Significant Terms Agg * Terms Agg document count error and execution_hint * Cardinality Agg precision_threshold * Pipeline Aggregations * index.shard.check_on_startup * index.store.type (added warning) * Preloading data into the file system cache * foreach ingest processor * Field caps API * Profile API Added experimental label to: * Moving Average Agg Prediction Changed experimental to beta for: * Adjacency matrix agg * Normalizers * Tasks API * Index sorting Labelled experimental in Lucene: * ICU plugin custom rules file * Flatten graph token filter * Synonym graph token filter * Word delimiter graph token filter * Simple pattern tokenizer * Simple pattern split tokenizer Replaced experimental label with warning that details may change in the future: * Analysis explain output format * Segments verbose output format * Percentile Agg compression and HDR Histogram * Percentile Rank Agg HDR Histogram
This commit is contained in:
Clinton Gormley
GitHub
parent
0d8b753325
commit
ff4a2519f2
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@ -1,8 +1,6 @@
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[[painless-debugging]]
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=== Painless Debugging
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experimental[The Painless scripting language is new and is still marked as experimental. The syntax or API may be changed in the future in non-backwards compatible ways if required.]
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==== Debug.Explain
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Painless doesn't have a
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@ -1,8 +1,6 @@
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[[painless-getting-started]]
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== Getting Started with Painless
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experimental[The Painless scripting language is new and is still marked as experimental. The syntax or API may be changed in the future in non-backwards compatible ways if required.]
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include::painless-description.asciidoc[]
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[[painless-examples]]
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@ -1,8 +1,6 @@
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[[painless-syntax]]
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=== Painless Syntax
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experimental[The Painless scripting language is new and is still marked as experimental. The syntax or API may be changed in the future in non-backwards compatible ways if required.]
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[float]
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[[control-flow]]
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==== Control flow
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@ -113,7 +113,7 @@ PUT icu_sample
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===== Rules customization
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experimental[]
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experimental[This functionality is marked as experimental in Lucene]
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You can customize the `icu-tokenizer` behavior by specifying per-script rule files, see the
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http://userguide.icu-project.org/boundaryanalysis#TOC-RBBI-Rules[RBBI rules syntax reference]
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@ -6,7 +6,7 @@ The request provides a collection of named filter expressions, similar to the `f
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request.
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Each bucket in the response represents a non-empty cell in the matrix of intersecting filters.
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experimental[The `adjacency_matrix` aggregation is a new feature and we may evolve its design as we get feedback on its use. As a result, the API for this feature may change in non-backwards compatible ways]
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beta[The `adjacency_matrix` aggregation is a new feature and we may evolve its design as we get feedback on its use. As a result, the API for this feature may change in non-backwards compatible ways]
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Given filters named `A`, `B` and `C` the response would return buckets with the following names:
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@ -1,8 +1,6 @@
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[[search-aggregations-bucket-diversified-sampler-aggregation]]
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=== Diversified Sampler Aggregation
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experimental[]
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Like the `sampler` aggregation this is a filtering aggregation used to limit any sub aggregations' processing to a sample of the top-scoring documents.
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The `diversified_sampler` aggregation adds the ability to limit the number of matches that share a common value such as an "author".
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[[search-aggregations-bucket-sampler-aggregation]]
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=== Sampler Aggregation
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experimental[]
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A filtering aggregation used to limit any sub aggregations' processing to a sample of the top-scoring documents.
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.Example use cases:
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@ -3,8 +3,6 @@
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An aggregation that returns interesting or unusual occurrences of terms in a set.
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experimental[The `significant_terms` aggregation can be very heavy when run on large indices. Work is in progress to provide more lightweight sampling techniques. As a result, the API for this feature may change in non-backwards compatible ways]
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.Example use cases:
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* Suggesting "H5N1" when users search for "bird flu" in text
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* Identifying the merchant that is the "common point of compromise" from the transaction history of credit card owners reporting loss
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@ -197,8 +197,6 @@ could have the 4th highest document count.
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==== Per bucket document count error
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experimental[]
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The second error value can be enabled by setting the `show_term_doc_count_error` parameter to true. This shows an error value
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for each term returned by the aggregation which represents the 'worst case' error in the document count and can be useful when
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deciding on a value for the `shard_size` parameter. This is calculated by summing the document counts for the last term returned
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@ -728,8 +726,6 @@ collection mode need to replay the query on the second pass but only for the doc
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[[search-aggregations-bucket-terms-aggregation-execution-hint]]
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==== Execution hint
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experimental[The automated execution optimization is experimental, so this parameter is provided temporarily as a way to override the default behaviour]
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There are different mechanisms by which terms aggregations can be executed:
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- by using field values directly in order to aggregate data per-bucket (`map`)
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@ -767,7 +763,7 @@ in inner aggregations.
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}
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--------------------------------------------------
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<1> experimental[] the possible values are `map`, `global_ordinals`, `global_ordinals_hash` and `global_ordinals_low_cardinality`
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<1> The possible values are `map`, `global_ordinals`, `global_ordinals_hash` and `global_ordinals_low_cardinality`
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Please note that Elasticsearch will ignore this execution hint if it is not applicable and that there is no backward compatibility guarantee on these hints.
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@ -43,8 +43,6 @@ Response:
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This aggregation also supports the `precision_threshold` option:
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experimental[The `precision_threshold` option is specific to the current internal implementation of the `cardinality` agg, which may change in the future]
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[source,js]
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--------------------------------------------------
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POST /sales/_search?size=0
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@ -247,8 +247,6 @@ it. It would not be the case on more skewed distributions.
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[[search-aggregations-metrics-percentile-aggregation-compression]]
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==== Compression
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experimental[The `compression` parameter is specific to the current internal implementation of percentiles, and may change in the future]
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Approximate algorithms must balance memory utilization with estimation accuracy.
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This balance can be controlled using a `compression` parameter:
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@ -287,7 +285,7 @@ the TDigest will use less memory.
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==== HDR Histogram
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experimental[]
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NOTE: This setting exposes the internal implementation of HDR Histogram and the syntax may change in the future.
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https://github.com/HdrHistogram/HdrHistogram[HDR Histogram] (High Dynamic Range Histogram) is an alternative implementation
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that can be useful when calculating percentiles for latency measurements as it can be faster than the t-digest implementation
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@ -159,7 +159,7 @@ This will interpret the `script` parameter as an `inline` script with the `painl
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==== HDR Histogram
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experimental[]
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NOTE: This setting exposes the internal implementation of HDR Histogram and the syntax may change in the future.
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https://github.com/HdrHistogram/HdrHistogram[HDR Histogram] (High Dynamic Range Histogram) is an alternative implementation
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that can be useful when calculating percentile ranks for latency measurements as it can be faster than the t-digest implementation
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@ -2,8 +2,6 @@
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== Pipeline Aggregations
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experimental[]
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Pipeline aggregations work on the outputs produced from other aggregations rather than from document sets, adding
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information to the output tree. There are many different types of pipeline aggregation, each computing different information from
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other aggregations, but these types can be broken down into two families:
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[[search-aggregations-pipeline-avg-bucket-aggregation]]
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=== Avg Bucket Aggregation
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experimental[]
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A sibling pipeline aggregation which calculates the (mean) average value of a specified metric in a sibling aggregation.
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The specified metric must be numeric and the sibling aggregation must be a multi-bucket aggregation.
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[[search-aggregations-pipeline-bucket-script-aggregation]]
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=== Bucket Script Aggregation
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experimental[]
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A parent pipeline aggregation which executes a script which can perform per bucket computations on specified metrics
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in the parent multi-bucket aggregation. The specified metric must be numeric and the script must return a numeric value.
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[[search-aggregations-pipeline-bucket-selector-aggregation]]
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=== Bucket Selector Aggregation
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experimental[]
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A parent pipeline aggregation which executes a script which determines whether the current bucket will be retained
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in the parent multi-bucket aggregation. The specified metric must be numeric and the script must return a boolean value.
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If the script language is `expression` then a numeric return value is permitted. In this case 0.0 will be evaluated as `false`
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[[search-aggregations-pipeline-cumulative-sum-aggregation]]
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=== Cumulative Sum Aggregation
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experimental[]
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A parent pipeline aggregation which calculates the cumulative sum of a specified metric in a parent histogram (or date_histogram)
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aggregation. The specified metric must be numeric and the enclosing histogram must have `min_doc_count` set to `0` (default
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for `histogram` aggregations).
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[[search-aggregations-pipeline-derivative-aggregation]]
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=== Derivative Aggregation
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experimental[]
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A parent pipeline aggregation which calculates the derivative of a specified metric in a parent histogram (or date_histogram)
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aggregation. The specified metric must be numeric and the enclosing histogram must have `min_doc_count` set to `0` (default
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for `histogram` aggregations).
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[[search-aggregations-pipeline-extended-stats-bucket-aggregation]]
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=== Extended Stats Bucket Aggregation
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experimental[]
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A sibling pipeline aggregation which calculates a variety of stats across all bucket of a specified metric in a sibling aggregation.
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The specified metric must be numeric and the sibling aggregation must be a multi-bucket aggregation.
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[[search-aggregations-pipeline-max-bucket-aggregation]]
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=== Max Bucket Aggregation
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experimental[]
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A sibling pipeline aggregation which identifies the bucket(s) with the maximum value of a specified metric in a sibling aggregation
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and outputs both the value and the key(s) of the bucket(s). The specified metric must be numeric and the sibling aggregation must
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be a multi-bucket aggregation.
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[[search-aggregations-pipeline-min-bucket-aggregation]]
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=== Min Bucket Aggregation
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experimental[]
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A sibling pipeline aggregation which identifies the bucket(s) with the minimum value of a specified metric in a sibling aggregation
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and outputs both the value and the key(s) of the bucket(s). The specified metric must be numeric and the sibling aggregation must
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be a multi-bucket aggregation.
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[[search-aggregations-pipeline-movavg-aggregation]]
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=== Moving Average Aggregation
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experimental[]
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Given an ordered series of data, the Moving Average aggregation will slide a window across the data and emit the average
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value of that window. For example, given the data `[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]`, we can calculate a simple moving
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average with windows size of `5` as follows:
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@ -513,6 +511,8 @@ POST /_search
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==== Prediction
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experimental[]
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All the moving average model support a "prediction" mode, which will attempt to extrapolate into the future given the
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current smoothed, moving average. Depending on the model and parameter, these predictions may or may not be accurate.
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[[search-aggregations-pipeline-percentiles-bucket-aggregation]]
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=== Percentiles Bucket Aggregation
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experimental[]
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A sibling pipeline aggregation which calculates percentiles across all bucket of a specified metric in a sibling aggregation.
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The specified metric must be numeric and the sibling aggregation must be a multi-bucket aggregation.
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[[search-aggregations-pipeline-serialdiff-aggregation]]
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=== Serial Differencing Aggregation
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experimental[]
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Serial differencing is a technique where values in a time series are subtracted from itself at
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different time lags or periods. For example, the datapoint f(x) = f(x~t~) - f(x~t-n~), where n is the period being used.
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[[search-aggregations-pipeline-stats-bucket-aggregation]]
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=== Stats Bucket Aggregation
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experimental[]
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A sibling pipeline aggregation which calculates a variety of stats across all bucket of a specified metric in a sibling aggregation.
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The specified metric must be numeric and the sibling aggregation must be a multi-bucket aggregation.
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[[search-aggregations-pipeline-sum-bucket-aggregation]]
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=== Sum Bucket Aggregation
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experimental[]
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A sibling pipeline aggregation which calculates the sum across all bucket of a specified metric in a sibling aggregation.
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The specified metric must be numeric and the sibling aggregation must be a multi-bucket aggregation.
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[[analysis-normalizers]]
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== Normalizers
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experimental[]
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beta[]
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Normalizers are similar to analyzers except that they may only emit a single
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token. As a consequence, they do not have a tokenizer and only accept a subset
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[[analysis-flatten-graph-tokenfilter]]
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=== Flatten Graph Token Filter
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experimental[]
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experimental[This functionality is marked as experimental in Lucene]
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The `flatten_graph` token filter accepts an arbitrary graph token
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stream, such as that produced by
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[[analysis-synonym-graph-tokenfilter]]
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=== Synonym Graph Token Filter
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experimental[]
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experimental[This functionality is marked as experimental in Lucene]
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The `synonym_graph` token filter allows to easily handle synonyms,
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including multi-word synonyms correctly during the analysis process.
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[[analysis-word-delimiter-graph-tokenfilter]]
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=== Word Delimiter Graph Token Filter
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experimental[]
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experimental[This functionality is marked as experimental in Lucene]
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Named `word_delimiter_graph`, it splits words into subwords and performs
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optional transformations on subword groups. Words are split into
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[[analysis-simplepattern-tokenizer]]
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=== Simple Pattern Tokenizer
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experimental[]
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experimental[This functionality is marked as experimental in Lucene]
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The `simple_pattern` tokenizer uses a regular expression to capture matching
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text as terms. The set of regular expression features it supports is more
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[[analysis-simplepatternsplit-tokenizer]]
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=== Simple Pattern Split Tokenizer
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experimental[]
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experimental[This functionality is marked as experimental in Lucene]
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The `simple_pattern_split` tokenizer uses a regular expression to split the
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input into terms at pattern matches. The set of regular expression features it
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[[tasks]]
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== Task Management API
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experimental[The Task Management API is new and should still be considered experimental. The API may change in ways that are not backwards compatible]
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beta[The Task Management API is new and should still be considered a beta feature. The API may change in ways that are not backwards compatible]
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[float]
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=== Current Tasks Information
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@ -47,7 +47,7 @@ specific index module:
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`index.shard.check_on_startup`::
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+
|
||||
--
|
||||
experimental[] Whether or not shards should be checked for corruption before opening. When
|
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Whether or not shards should be checked for corruption before opening. When
|
||||
corruption is detected, it will prevent the shard from being opened. Accepts:
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`false`::
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@ -69,7 +69,7 @@ corruption is detected, it will prevent the shard from being opened. Accepts:
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as corrupted will be automatically removed. This option *may result in data loss*.
|
||||
Use with extreme caution!
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||||
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||||
Checking shards may take a lot of time on large indices.
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||||
WARNING: Expert only. Checking shards may take a lot of time on large indices.
|
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--
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[[index-codec]] `index.codec`::
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@ -1,7 +1,7 @@
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[[index-modules-index-sorting]]
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== Index Sorting
|
||||
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experimental[]
|
||||
beta[]
|
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||||
When creating a new index in elasticsearch it is possible to configure how the Segments
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inside each Shard will be sorted. By default Lucene does not apply any sort.
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@ -32,7 +32,7 @@ PUT /my_index
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|||
}
|
||||
---------------------------------
|
||||
|
||||
experimental[This is an expert-only setting and may be removed in the future]
|
||||
WARNING: This is an expert-only setting and may be removed in the future.
|
||||
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||||
The following sections lists all the different storage types supported.
|
||||
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||||
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@ -73,7 +73,7 @@ compatibility.
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|||
|
||||
=== Pre-loading data into the file system cache
|
||||
|
||||
experimental[This is an expert-only setting and may be removed in the future]
|
||||
NOTE: This is an expert setting, the details of which may change in the future.
|
||||
|
||||
By default, elasticsearch completely relies on the operating system file system
|
||||
cache for caching I/O operations. It is possible to set `index.store.preload`
|
||||
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@ -144,7 +144,7 @@ GET _analyze
|
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If you want to get more advanced details, set `explain` to `true` (defaults to `false`). It will output all token attributes for each token.
|
||||
You can filter token attributes you want to output by setting `attributes` option.
|
||||
|
||||
experimental[The format of the additional detail information is experimental and can change at any time]
|
||||
NOTE: The format of the additional detail information is labelled as experimental in Lucene and it may change in the future.
|
||||
|
||||
[source,js]
|
||||
--------------------------------------------------
|
||||
|
|
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|||
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|
@ -79,7 +79,7 @@ compound:: Whether the segment is stored in a compound file. When true, this
|
|||
|
||||
To add additional information that can be used for debugging, use the `verbose` flag.
|
||||
|
||||
experimental[The format of the additional verbose information is experimental and can change at any time]
|
||||
NOTE: The format of the additional detail information is labelled as experimental in Lucene and it may change in the future.
|
||||
|
||||
[source,js]
|
||||
--------------------------------------------------
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||||
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|
|
@ -1010,11 +1010,6 @@ to the requester.
|
|||
[[foreach-processor]]
|
||||
=== Foreach Processor
|
||||
|
||||
experimental[This processor may change or be replaced by something else that provides similar functionality. This
|
||||
processor executes in its own context, which makes it different compared to all other processors and for features like
|
||||
verbose simulation the subprocessor isn't visible. The reason we still expose this processor, is that it is the only
|
||||
processor that can operate on an array]
|
||||
|
||||
Processes elements in an array of unknown length.
|
||||
|
||||
All processors can operate on elements inside an array, but if all elements of an array need to
|
||||
|
|
|
|||
|
|
@ -98,7 +98,6 @@ The following parameters are accepted by `keyword` fields:
|
|||
|
||||
<<normalizer,`normalizer`>>::
|
||||
|
||||
experimental[]
|
||||
How to pre-process the keyword prior to indexing. Defaults to `null`,
|
||||
meaning the keyword is kept as-is.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,8 +1,6 @@
|
|||
[[modules-scripting-painless]]
|
||||
=== Painless Scripting Language
|
||||
|
||||
experimental[The Painless scripting language is new and is still marked as experimental. The syntax or API may be changed in the future in non-backwards compatible ways if required.]
|
||||
|
||||
include::../../../painless/painless-description.asciidoc[]
|
||||
|
||||
Ready to start scripting with Painless? See {painless}/painless-getting-started.html[Getting Started with Painless] in the guide to the
|
||||
|
|
|
|||
|
|
@ -1,8 +1,6 @@
|
|||
[[search-field-caps]]
|
||||
== Field Capabilities API
|
||||
|
||||
experimental[]
|
||||
|
||||
The field capabilities API allows to retrieve the capabilities of fields among multiple indices.
|
||||
|
||||
The field capabilities api by default executes on all indices:
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
[[search-profile]]
|
||||
== Profile API
|
||||
|
||||
experimental[]
|
||||
WARNING: The Profile API is a debugging tool and adds signficant overhead to search execution.
|
||||
|
||||
The Profile API provides detailed timing information about the execution of individual components
|
||||
in a search request. It gives the user insight into how search requests are executed at a low level so that
|
||||
|
|
|
|||
Loading…
Reference in New Issue