This aggregation will perform normalizations of metrics
for a given series of data in the form of bucket values.
The aggregations supports the following normalizations
- rescale 0-1
- rescale 0-100
- percentage of sum
- mean normalization
- z-score normalization
- softmax normalization
To specify which normalization is to be used, it can be specified
in the normalize agg's `normalizer` field.
For example:
```
{
"normalize": {
"buckets_path": <>,
"normalizer": "percent"
}
}
```
Similar to what the moving function aggregation does, except merging windows of percentiles
sketches together instead of cumulatively merging final metrics
This adds a pipeline aggregation that calculates the cumulative
cardinality of a field. It does this by iteratively merging in the
HLL sketch from consecutive buckets and emitting the cardinality up
to that point.
This is useful for things like finding the total "new" users that have
visited a website (as opposed to "repeat" visitors).
This is a Basic+ aggregation and adds a new Data Science plugin
to house it and future advanced analytics/data science aggregations.
Introduce shift field to MovingFunction aggregation.
By default, shift = 0. Behavior, in this case, is the same as before.
Increasing shift by 1 moves starting window position by 1 to the right.
To simply include current bucket to the window, use shift = 1
For center alignment (n/2 values before and after the current bucket), use shift = window / 2
For right alignment (n values after the current bucket), use shift = window.
Introduce shift field to MovingFunction aggregation.
By default, shift = 0. Behavior, in this case, is the same as before.
Increasing shift by 1 moves starting window position by 1 to the right.
To simply include current bucket to the window, use shift = 1
For center alignment (n/2 values before and after the current bucket), use shift = window / 2
For right alignment (n values after the current bucket), use shift = window.
Several `ifdef::asciidoctor` conditionals were added so that AsciiDoc
and Asciidoctor doc builds rendered consistently.
With https://github.com/elastic/docs/pull/827, Elasticsearch Reference
documentation migrated completely to Asciidoctor. We no longer need to
support AsciiDoc so we can remove these conditionals.
Resolves#41722
The date_histogram accepts an interval which can be either a calendar
interval (DST-aware, leap seconds, arbitrary length of months, etc) or
fixed interval (strict multiples of SI units). Unfortunately this is inferred
by first trying to parse as a calendar interval, then falling back to fixed
if that fails.
This leads to confusing arrangement where `1d` == calendar, but
`2d` == fixed. And if you want a day of fixed time, you have to
specify `24h` (e.g. the next smallest unit). This arrangement is very
error-prone for users.
This PR adds `calendar_interval` and `fixed_interval` parameters to any
code that uses intervals (date_histogram, rollup, composite, datafeed, etc).
Calendar only accepts calendar intervals, fixed accepts any combination of
units (meaning `1d` can be used to specify `24h` in fixed time), and both
are mutually exclusive.
The old interval behavior is deprecated and will throw a deprecation warning.
It is also mutually exclusive with the two new parameters. In the future the
old dual-purpose interval will be removed.
The change applies to both REST and java clients.
This pipeline aggregation gives the user the ability to script functions that "move" across a window
of data, instead of single data points. It is the scripted version of MovingAvg pipeline agg.
Through custom script contexts, we expose a number of convenience methods:
- MovingFunctions.max()
- MovingFunctions.min()
- MovingFunctions.sum()
- MovingFunctions.unweightedAvg()
- MovingFunctions.linearWeightedAvg()
- MovingFunctions.ewma()
- MovingFunctions.holt()
- MovingFunctions.holtWinters()
- MovingFunctions.stdDev()
The user can also define any arbitrary logic via their own scripting, or combine with the above methods.
This commit adds a parent pipeline aggregation that allows
sorting the buckets of a parent multi-bucket aggregation.
The aggregation also offers [from] and [size] parameters
in order to truncate the result as desired.
Closes#14928
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
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.
Tal Levy
Ignacio Vera
Florian Kelbert
James Rodewig
Zachary Tong
Nikita Glashenko
Josh Soref
João Barbosa
Russ Cam
lipsill
Zachary Tong
Craig van Tonder
David Shimon
Andrew Banchich
Dimitris Athanasiou
Clinton Gormley
Alexander Kazakov
Suhas Karanth
msancho
Randall Britten
Florian Hopf
Adrien Grand
Jack Conradson
Nik Everett
Thomas Decaux
Deb Adair
Sergii Golubev
Dmitrii Izgurskii
evanfreed
Robert
KangYongKyun
Zachary Tong
Shane Connelly