Related to issue #77823
This does the following:
- Updates several asciidoc files that contained code snippets with
invalid JSON, most involving unnecessary trailing commas.
- Makes the switch from the Groovy JSON parser to the Jackson parser,
pursuant to the general goal of eliminating Groovy dependence.
- Makes testing of JSON validity at build time more strict.
Note that this update still allows backslash escaping for any
character. Currently that matters because of the file
"docs/reference/ml/anomaly-detection/apis/get-datafeed-stats.asciidoc",
specifically this part:
"attributes" : {
"ml.machine_memory" :
"$body.datafeeds.0.node.attributes.ml\.machine_memory",
"ml.max_open_jobs" : "512"
}
It's not clear to me what change, if any, is appropriate there. So,
I've left in the escaped period and configured the parser to ignore
it for the time being.
This adds a new pipeline aggregation for calculating Kolmogorov–Smirnov test for a given sample and buckets path.
For now, the buckets path resolution needs to be `_count`. But, this may be relaxed in the future.
It accepts a parameter `fractions` that indicates the distribution of documents from some other pre-calculated sample.
This particular version of the K-S test is Two-sample, meaning, it calculates if the `fractions` and the distribution of `_count` values in the buckets_path are taken from the same distribution.
This in combination with the hypothesis alternatives (`less`, `greater`, `two_sided`) and sampling logic (`upper_tail`, `lower_tail`, `uniform`) allow for flexibility and usefulness when comparing two samples and determining the likelihood of them being from the same overall distribution.
Usage:
```
POST correlate_latency/_search?size=0&filter_path=aggregations
{
"aggs": {
"buckets": {
"terms": { <1>
"field": "version",
"size": 2
},
"aggs": {
"latency_ranges": {
"range": { <2>
"field": "latency",
"ranges": [
{ "to": 0.0 },
{ "from": 0, "to": 105 },
{ "from": 105, "to": 225 },
{ "from": 225, "to": 445 },
{ "from": 445, "to": 665 },
{ "from": 665, "to": 885 },
{ "from": 885, "to": 1115 },
{ "from": 1115, "to": 1335 },
{ "from": 1335, "to": 1555 },
{ "from": 1555, "to": 1775 },
{ "from": 1775 }
]
}
},
"ks_test": { <3>
"bucket_count_ks_test": {
"buckets_path": "latency_ranges>_count",
"alternative": ["less", "greater", "two_sided"]
}
}
}
}
}
}
```
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
* [ML] add new bucket_correlation aggregation with initial count_correlation function (#72133)
This commit adds a new pipeline aggregation that allows correlation within the aggregation frame work in bucketed values.
The initial function is a `count_correlation` function. The purpose of which is to correlate the count in a consistent number of buckets with a pre calculated indicator. The indicator and the aggregated buckets should related to the same metrics with in documents.
Example for correlating terms within a `service.version.keyword` with latency percentiles. The percentiles and provided correlation indicator both refer to the same source data where the indicator was previously calculated.:
```
GET apm-7.12.0-transaction-generated/_search
{
"size": 0,
"aggs": {
"field_terms": {
"terms": {
"field": "service.version.keyword",
"size": 20
},
"aggs": {
"latency_range": {
"range": {
"field": "transaction.duration.us",
"ranges": [<snip>],
"keyed": true
}
},
"correlation": {
"bucket_correlation": {
"buckets_path": "latency_range>_count",
"count_correlation": {
"indicator": {
"expectations": [<snip>],
"doc_count": 20000
}
}
}
}
}
}
}
}
```
* [ML] renames */inference* apis to */trained_models* (#63097)
This commit renames all `inference` CRUD APIs to `trained_models`.
This aligns with internal terminology, documentation, and use-cases.
Moves the search sort docs from the deprecated 'Request Body Search'
page to a new subpage of 'Run a search'.
No substantive changes were made to the content.
Per 49554 I added standard deviation sampling and variance sampling to the extended stats interface.
Closes#49554
Co-authored-by: Igor Motov <igor@motovs.org>
Co-authored-by: andrewjohnson2 <aj114114@gmail.com>
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.
