The use of the avg aggregation for sorting the terms aggregation is not encouraged since it has unbounded error. This changes the examples to use the max aggregation which does not suffer the same issues
and be much more stingy about what we consider a console candidate.
* Add `// CONSOLE` to check-running
* Fix version in some snippets
* Mark groovy snippets as groovy
* Fix versions in plugins
* Fix language marker errors
* Fix language parsing in snippets
This adds support for snippets who's language is written like
`[source, txt]` and `["source","js",subs="attributes,callouts"]`.
This also makes language required for snippets which is nice because
then we can be sure we can grep for snippets in a particular language.
This change adds a special field named _none_ that allows to disable the retrieval of the stored fields in a search request or in a TopHitsAggregation.
To completely disable stored fields retrieval (including disabling metadata fields retrieval such as _id or _type) use _none_ like this:
````
POST _search
{
"stored_fields": "_none_"
}
````
Most of the examples in the pipeline aggregation docs use a small
"sales" test data set and I converted all of the examples that use
it to `// CONSOLE`. There are still a bunch of snippets in the pipeline
aggregation docs that aren't `// CONSOLE` so they aren't tested. Most
of them are "this is the most basic form of this aggregation" so they
are more immune to errors and bit rot then the examples that I converted.
I'd like to do something with them as well but I'm not sure what.
Also, the moving average docs and serial diff docs didn't get a lot of
love from this pass because they don't use the test data set or follow
the same general layout.
Relates to #18160
Currently both aggregations really share the same implementation. This commit
splits the implementations so that regular histograms can support decimal
intervals/offsets and compute correct buckets for negative decimal values.
However the response API is still the same. So for intance both regular
histograms and date histograms will produce an
`org.elasticsearch.search.aggregations.bucket.histogram.Histogram`
aggregation.
The optimization to compute an identifier of the rounded value and the
rounded value itself has been removed since it was only used by regular
histograms, which now do the rounding themselves instead of relying on the
Rounding abstraction.
Closes#8082Closes#4847
The current heuristic to compute a default shard size is pretty aggressive,
it returns `max(10, number_of_shards * size)` as a value for the shard size.
I think making it less aggressive has the benefit that it would reduce the
likelyness of running into OOME when there are many shards (yearly
aggregations with time-based indices can make numbers of shards in the
thousands) and make the use of breadth-first more likely/efficient.
This commit replaces the heuristic with `size * 1.5 + 10`, which is enough
to have good accuracy on zipfian distributions.
This change adds a new special path to the buckets_path syntax
`_bucket_count`. This new option will return the number of buckets for a
multi-bucket aggregation, which can then be used in pipeline
aggregations.
Closes#19553
Today the default precision for the cardinality aggregation depends on how many
parent bucket aggregations it had. The reasoning was that the more parent bucket
aggregations, the more buckets the cardinality had to be computed on. And this
number could be huge depending on what the parent aggregations actually are.
However now that we run terms aggregations in breadth-first mode by default when
there are sub aggregations, it is less likely that we have to run the cardinality
aggregation on kagilions of buckets. So we could use a static default, which will
be less confusing to users.
Rename `fields` to `stored_fields` and add `docvalue_fields`
`stored_fields` parameter will no longer try to retrieve fields from the _source but will only return stored fields.
`fields` will throw an exception if the user uses it.
Add `docvalue_fields` as an adjunct to `fielddata_fields` which is deprecated. `docvalue_fields` will try to load the value from the docvalue and fallback to fielddata cache if docvalues are not enabled on that field.
Closes#18943
`stored_fields` parameter will no longer try to retrieve fields from the _source but will only return stored fields.
`fields` will throw an exception if the user uses it.
Add `docvalue_fields` as an adjunct to `fielddata_fields` which is deprecated. `docvalue_fields` will try to load the value from the docvalue and fallback to fielddata cache if docvalues are not enabled on that field.
Closes#18943
This commit adds a new metric aggregator for computing the geo_centroid over a set of geo_point fields. This can be combined with other aggregators (e.g., geohash_grid, significant_terms) for computing the geospatial centroid based on the document sets from other aggregation results.
This pipeline will calculate percentiles over a set of sibling buckets. This is an exact
implementation, meaning it needs to cache a copy of the series in memory and sort it to determine
the percentiles.
This comes with a few limitations: to prevent serializing data around, only the requested percentiles
are calculated (unlike the TDigest version, which allows the java API to ask for any percentile).
It also needs to store the data in-memory, resulting in some overhead if the requested series is
very large.
This move the `murmur3` field to the `mapper-murmur3` plugin and fixes its
defaults so that values will not be indexed by default, as the only purpose
of this field is to speed up `cardinality` aggregations on high-cardinality
string fields, which only requires doc values.
I also removed the `rehash` option from the `cardinality` aggregation as it
doesn't bring much value (rehashing is cheap) and allowed to remove the
coupling between the `cardinality` aggregation and the `murmur3` field.
Close#12874
HDRHistogram has been added as an option in the percentiles and percentile_ranks aggregation. It has one option `number_significant_digits` which controls the accuracy and memory size for the algorithm
Closes#8324
This pipeline aggregation runs a script on each bucket in the parent aggregation to determine whether the bucket is kept in the final aggregation tree. If the script returns true the bucket is retained, if it returns false the bucket is dropped
The filters aggregation now has an option to add an 'other' bucket which will, when turned on, contain all documents which do not match any of the defined filters. There is also an option to change the name of the 'other' bucket from the default of '_other_'
Closes#11289
In order to be more consistent with what they do, the query cache has been
renamed to request cache and the filter cache has been renamed to query
cache.
A known issue is that package/logger names do no longer match settings names,
please speak up if you think this is an issue.
Here are the settings for which I kept backward compatibility. Note that they
are a bit different from what was discussed on #11569 but putting `cache` before
the name of what is cached has the benefit of making these settings consistent
with the fielddata cache whose size is configured by
`indices.fielddata.cache.size`:
* index.cache.query.enable -> index.requests.cache.enable
* indices.cache.query.size -> indices.requests.cache.size
* indices.cache.filter.size -> indices.queries.cache.size
Close#11569
This adds a new pipeline aggregation, the cumulative sum aggregation. This is a parent aggregation which must be specified as a sub-aggregation to a histogram or date_histogram aggregation. It will add a new aggregation to each bucket containing the sum of a specified metrics over this and all previous buckets.
Replace the previous example which leveraged a range filter, which causes unnecessary confusion about when to use a range filter to create a single bucket or a range aggregation with exactly one member in ranges.
Closes#11704
This change unifies the way scripts and templates are specified for all instances in the codebase. It builds on the Script class added previously and adds request building and parsing support as well as the ability to transfer script objects between nodes. It also adds a Template class which aims to provide the same functionality for template APIs
Closes#11091
Most aggregations (terms, histogram, stats, percentiles, geohash-grid) now
support a new `missing` option which defines the value to consider when a
field does not have a value. This can be handy if you eg. want a terms
aggregation to handle the same way documents that have "N/A" or no value
for a `tag` field.
This works in a very similar way to the `missing` option on the `sort`
element.
One known issue is that this option sometimes cannot make the right decision
in the unmapped case: it needs to replace all values with the `missing` value
but might not know what kind of values source should be produced (numerics,
strings, geo points?). For this reason, we might want to add an `unmapped_type`
option in the future like we did for sorting.
Related to #5324
This commit makes queries and filters parsed the same way using the
QueryParser abstraction. This allowed to remove duplicate code that we had
for similar queries/filters such as `range`, `prefix` or `term`.
Previously, we were using the "statistical", technically accurate name. Instead, we
should probably use the name that people are familiar with, e.g. "Holt Winters" instead
of "triple exponential". To that end:
- `single_exp` becomes `ewma` (exponentially weighted moving average)
- `double_exp` becomes `holt`
When the `triple_exp` is added, it will be called `holt_winters`.
Johannes Kanavin
Florian Hopf
Adrien Grand
Colin Goodheart-Smithe
Adrin Jalali
Carney Wu
markharwood
Chris Fritz
Christoph Büscher
Nik Everett
Sumit Gupta
Clinton Gormley
Robin Clarke
Pascal Borreli
Jim Ferenczi
Jack Conradson
Thomas Decaux
Deb Adair
Ryan Biesemeyer
Jared McQueen
Leon Weidauer
Jason Tedor
Robert Muir
Robert Muir
Jerry Liu
Glen Smith
Nicholas Knize
Sergii Golubev
ericamick
Martijn van Groningen
Dmitrii Izgurskii
evanfreed
Robert
Dongjoon Hyun
Kevin Adams
Eugene Pirogov
omiend
KangYongKyun
Alex
Taehee Kim
Zachary Tong
Lee Hinman
Shane Connelly
Murilo Pereira
Ian Truslove
Asimov4
Sylvain Zimmer
Ryan Ernst
William Li
caldwecr
Colin Goodheart-Smithe