142 lines
4.0 KiB
Plaintext
142 lines
4.0 KiB
Plaintext
[[search-aggregations-metrics-stats-aggregation]]
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=== Stats Aggregation
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A `multi-value` metrics aggregation that computes stats over numeric values extracted from the aggregated documents. These values can be extracted either from specific numeric fields in the documents, or be generated by a provided script.
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The stats that are returned consist of: `min`, `max`, `sum`, `count` and `avg`.
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Assuming the data consists of documents representing exams grades (between 0 and 100) of students
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[source,js]
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--------------------------------------------------
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POST /exams/_search?size=0
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{
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"aggs" : {
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"grades_stats" : { "stats" : { "field" : "grade" } }
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}
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}
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--------------------------------------------------
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// CONSOLE
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// TEST[setup:exams]
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The above aggregation computes the grades statistics over all documents. The aggregation type is `stats` and the `field` setting defines the numeric field of the documents the stats will be computed on. The above will return the following:
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[source,js]
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--------------------------------------------------
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{
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...
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"aggregations": {
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"grades_stats": {
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"count": 2,
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"min": 50.0,
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"max": 100.0,
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"avg": 75.0,
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"sum": 150.0
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}
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}
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}
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--------------------------------------------------
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// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
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The name of the aggregation (`grades_stats` above) also serves as the key by which the aggregation result can be retrieved from the returned response.
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==== Script
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Computing the grades stats based on a script:
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[source,js]
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--------------------------------------------------
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POST /exams/_search?size=0
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{
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"aggs" : {
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"grades_stats" : {
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"stats" : {
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"script" : {
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"lang": "painless",
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"inline": "doc['grade'].value"
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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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// CONSOLE
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// TEST[setup:exams]
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This will interpret the `script` parameter as an `inline` script with the `painless` script language and no script parameters. To use a stored script use the following syntax:
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[source,js]
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--------------------------------------------------
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POST /exams/_search?size=0
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{
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"aggs" : {
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"grades_stats" : {
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"stats" : {
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"script" : {
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"stored": "my_script",
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"params" : {
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"field" : "grade"
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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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// CONSOLE
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// TEST[setup:exams,stored_example_script]
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===== Value Script
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It turned out that the exam was way above the level of the students and a grade correction needs to be applied. We can use a value script to get the new stats:
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[source,js]
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--------------------------------------------------
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POST /exams/_search?size=0
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{
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"aggs" : {
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"grades_stats" : {
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"stats" : {
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"field" : "grade",
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"script" : {
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"lang": "painless",
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"inline": "_value * params.correction",
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"params" : {
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"correction" : 1.2
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}
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}
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}
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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// TEST[setup:exams]
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==== Missing value
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The `missing` parameter defines how documents that are missing a value should be treated.
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By default they will be ignored but it is also possible to treat them as if they
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had a value.
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[source,js]
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--------------------------------------------------
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POST /exams/_search?size=0
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{
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"aggs" : {
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"grades_stats" : {
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"stats" : {
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"field" : "grade",
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"missing": 0 <1>
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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:exams]
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<1> Documents without a value in the `grade` field will fall into the same bucket as documents that have the value `0`.
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