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135 lines
4.2 KiB
Markdown
135 lines
4.2 KiB
Markdown
---
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navigation_title: "Change point"
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mapped_pages:
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- https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-change-point-aggregation.html
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---
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# Change point aggregation [search-aggregations-change-point-aggregation]
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::::{warning}
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This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
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::::
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A sibling pipeline that detects, spikes, dips, and change points in a metric. Given a distribution of values provided by the sibling multi-bucket aggregation, this aggregation indicates the bucket of any spike or dip and/or the bucket at which the largest change in the distribution of values, if they are statistically significant.
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::::{tip}
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It is recommended to use the change point aggregation to detect changes in time-based data, however, you can use any metric to create buckets.
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::::
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## Parameters [change-point-agg-syntax]
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`buckets_path`
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: (Required, string) Path to the buckets that contain one set of values in which to detect a change point. There must be at least 22 bucketed values. Fewer than 1,000 is preferred. For syntax, see [`buckets_path` Syntax](/reference/aggregations/pipeline.md#buckets-path-syntax).
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## Syntax [_syntax_11]
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A `change_point` aggregation looks like this in isolation:
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```js
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{
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"change_point": {
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"buckets_path": "date_histogram>_count" <1>
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}
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}
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```
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1. The buckets containing the values to test against.
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## Response body [change-point-agg-response]
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`bucket`
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: (Optional, object) Values of the bucket that indicates the discovered change point. Not returned if no change point was found. All the aggregations in the bucket are returned as well.
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**Properties of `bucket**:
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`key`
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: (value) The key of the bucket matched. Could be string or numeric.
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`doc_count`
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: (number) The document count of the bucket.
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`type`
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: (object) The found change point type and its related values. Possible types:
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* `dip`: a significant dip occurs at this change point
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* `distribution_change`: the overall distribution of the values has changed significantly
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* `non_stationary`: there is no change point, but the values are not from a stationary distribution
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* `spike`: a significant spike occurs at this point
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* `stationary`: no change point found
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* `step_change`: the change indicates a statistically significant step up or down in value distribution
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* `trend_change`: there is an overall trend change occurring at this point
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## Example [_example_7]
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The following example uses the Kibana sample data logs data set.
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```js
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GET kibana_sample_data_logs/_search
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{
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"aggs": {
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"date":{ <1>
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"date_histogram": {
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"field": "@timestamp",
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"fixed_interval": "1d"
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},
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"aggs": {
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"avg": { <2>
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"avg": {
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"field": "bytes"
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}
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}
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}
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},
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"change_points_avg": { <3>
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"change_point": {
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"buckets_path": "date>avg" <4>
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}
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}
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}
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}
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```
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1. A date histogram aggregation that creates buckets with one day long interval.
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2. A sibling aggregation of the `date` aggregation that calculates the average value of the `bytes` field within every bucket.
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3. The change point detection aggregation configuration object.
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4. The path of the aggregation values to detect change points. In this case, the input of the change point aggregation is the value of `avg` which is a sibling aggregation of `date`.
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The request returns a response that is similar to the following:
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```js
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"change_points_avg" : {
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"bucket" : {
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"key" : "2023-04-29T00:00:00.000Z", <1>
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"doc_count" : 329, <2>
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"avg" : { <3>
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"value" : 4737.209726443769
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}
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},
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"type" : { <4>
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"dip" : {
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"p_value" : 3.8999455212466465e-10, <5>
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"change_point" : 41 <6>
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}
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}
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}
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```
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1. The bucket key that is the change point.
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2. The number of documents in that bucket.
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3. Aggregated values in the bucket.
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4. Type of change found.
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5. The `p_value` indicates how extreme the change is; lower values indicate greater change.
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6. The specific bucket where the change occurs (indexing starts at `0`).
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