132 lines
4.1 KiB
Plaintext
132 lines
4.1 KiB
Plaintext
[[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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==== Syntax
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A `percentiles_bucket` aggregation looks like this in isolation:
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[source,js]
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--------------------------------------------------
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{
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"percentiles_bucket": {
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"buckets_path": "the_sum"
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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.`sum_bucket` Parameters
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|===
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|Parameter Name |Description |Required |Default Value
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|`buckets_path` |The path to the buckets we wish to find the sum for (see <<buckets-path-syntax>> for more
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details) |Required |
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|`gap_policy` |The policy to apply when gaps are found in the data (see <<gap-policy>> for more
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details)|Optional | `skip`
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|`format` |format to apply to the output value of this aggregation |Optional | `null`
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|`percents` |The list of percentiles to calculate |Optional | `[ 1, 5, 25, 50, 75, 95, 99 ]`
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|===
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The following snippet calculates the sum of all the total monthly `sales` buckets:
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[source,js]
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--------------------------------------------------
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POST /sales/_search
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{
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"size": 0,
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"aggs" : {
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"sales_per_month" : {
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"date_histogram" : {
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"field" : "date",
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"interval" : "month"
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},
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"aggs": {
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"sales": {
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"sum": {
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"field": "price"
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}
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}
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}
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},
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"percentiles_monthly_sales": {
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"percentiles_bucket": {
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"buckets_path": "sales_per_month>sales", <1>
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"percents": [ 25.0, 50.0, 75.0 ] <2>
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}
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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// TEST[setup:sales]
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<1> `buckets_path` instructs this percentiles_bucket aggregation that we want to calculate percentiles for
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the `sales` aggregation in the `sales_per_month` date histogram.
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<2> `percents` specifies which percentiles we wish to calculate, in this case, the 25th, 50th and 75th percentiles.
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And the following may be the response:
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[source,js]
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--------------------------------------------------
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{
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"took": 11,
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"timed_out": false,
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"_shards": ...,
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"hits": ...,
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"aggregations": {
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"sales_per_month": {
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"buckets": [
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{
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"key_as_string": "2015/01/01 00:00:00",
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"key": 1420070400000,
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"doc_count": 3,
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"sales": {
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"value": 550.0
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}
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},
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{
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"key_as_string": "2015/02/01 00:00:00",
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"key": 1422748800000,
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"doc_count": 2,
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"sales": {
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"value": 60.0
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}
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},
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{
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"key_as_string": "2015/03/01 00:00:00",
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"key": 1425168000000,
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"doc_count": 2,
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"sales": {
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"value": 375.0
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}
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}
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]
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},
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"percentiles_monthly_sales": {
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"values" : {
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"25.0": 375.0,
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"50.0": 375.0,
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"75.0": 550.0
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}
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}
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}
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}
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--------------------------------------------------
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// TESTRESPONSE[s/"took": 11/"took": $body.took/]
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// TESTRESPONSE[s/"_shards": \.\.\./"_shards": $body._shards/]
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// TESTRESPONSE[s/"hits": \.\.\./"hits": $body.hits/]
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==== Percentiles_bucket implementation
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The Percentile Bucket returns the nearest input data point that is not greater than the requested percentile; it does not
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interpolate between data points.
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The percentiles are calculated exactly and is not an approximation (unlike the Percentiles Metric). This means
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the implementation maintains an in-memory, sorted list of your data to compute the percentiles, before discarding the
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data. You may run into memory pressure issues if you attempt to calculate percentiles over many millions of
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data-points in a single `percentiles_bucket`.
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