152 lines
4.5 KiB
Plaintext
152 lines
4.5 KiB
Plaintext
[[search-aggregations-pipeline-bucket-script-aggregation]]
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=== Bucket Script Aggregation
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experimental[]
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A parent pipeline aggregation which executes a script which can perform per bucket computations on specified metrics
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in the parent multi-bucket aggregation. The specified metric must be numeric and the script must return a numeric value.
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==== Syntax
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A `bucket_script` aggregation looks like this in isolation:
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[source,js]
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--------------------------------------------------
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{
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"bucket_script": {
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"buckets_path": {
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"my_var1": "the_sum", <1>
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"my_var2": "the_value_count"
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},
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"script": "my_var1 / my_var2"
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}
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}
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--------------------------------------------------
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<1> Here, `my_var1` is the name of the variable for this buckets path to use in the script, `the_sum` is the path to
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the metrics to use for that variable.
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.`bucket_script` Parameters
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|===
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|Parameter Name |Description |Required |Default Value
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|`script` |The script to run for this aggregation. The script can be inline, file or indexed. (see <<modules-scripting>>
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for more details) |Required |
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|`buckets_path` |A map of script variables and their associated path to the buckets we wish to use for the variable
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(see <<buckets-path-syntax>> for more 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, defaults to `skip` |
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|`format` |format to apply to the output value of this aggregation |Optional, defaults to `null` |
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|===
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The following snippet calculates the ratio percentage of t-shirt sales compared to total sales each month:
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[source,js]
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--------------------------------------------------
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{
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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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"total_sales": {
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"sum": {
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"field": "price"
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}
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},
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"t-shirts": {
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"filter": {
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"term": {
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"type": "t-shirt"
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}
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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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"t-shirt-percentage": {
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"bucket_script": {
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"buckets_path": {
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"tShirtSales": "t-shirts>sales",
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"totalSales": "total_sales"
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},
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"script": "tShirtSales / totalSales * 100"
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}
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}
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}
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}
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}
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}
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--------------------------------------------------
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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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"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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"total_sales": {
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"value": 50
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},
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"t-shirts": {
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"doc_count": 2,
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"sales": {
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"value": 10
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}
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},
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"t-shirt-percentage": {
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"value": 20
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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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"total_sales": {
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"value": 60
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},
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"t-shirts": {
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"doc_count": 1,
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"sales": {
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"value": 15
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}
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},
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"t-shirt-percentage": {
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"value": 25
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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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"total_sales": {
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"value": 40
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},
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"t-shirts": {
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"doc_count": 1,
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"sales": {
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"value": 20
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}
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},
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"t-shirt-percentage": {
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"value": 50
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}
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}
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]
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}
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}
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}
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--------------------------------------------------
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