183 lines
5.8 KiB
Plaintext
183 lines
5.8 KiB
Plaintext
[role="xpack"]
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[testenv="basic"]
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[[search-aggregations-pipeline-normalize-aggregation]]
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=== Normalize Aggregation
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A parent pipeline aggregation which calculates the specific normalized/rescaled value for a specific bucket value.
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Values that cannot be normalized, will be skipped using the <<gap-policy, skip gap policy>>.
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==== Syntax
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A `normalize` aggregation looks like this in isolation:
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[source,js]
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--------------------------------------------------
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{
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"normalize": {
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"buckets_path": "normalized",
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"method": "percent_of_sum"
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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[[normalize_pipeline-params]]
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.`normalize_pipeline` Parameters
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[options="header"]
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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 normalize (see <<buckets-path-syntax, `buckets_path` syntax>> for more details) |Required |
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|`method` | The specific <<normalize_pipeline-method, method>> to apply | Required |
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|`format` |format to apply to the output value of this aggregation |Optional |`null`
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|===
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==== Methods
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[[normalize_pipeline-method]]
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The Normalize Aggregation supports multiple methods to transform the bucket values. Each method definition will use
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the following original set of bucket values as examples: `[5, 5, 10, 50, 10, 20]`.
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_rescale_0_1_::
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This method rescales the data such that the minimum number is zero, and the maximum number is 1, with the rest normalized
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linearly in-between.
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x' = (x - min_x) / (max_x - min_x)
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[0, 0, .1111, 1, .1111, .3333]
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_rescale_0_100_::
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This method rescales the data such that the minimum number is zero, and the maximum number is 1, with the rest normalized
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linearly in-between.
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x' = 100 * (x - min_x) / (max_x - min_x)
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[0, 0, 11.11, 100, 11.11, 33.33]
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_percent_of_sum_::
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This method normalizes each value so that it represents a percentage of the total sum it attributes to.
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x' = x / sum_x
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[5%, 5%, 10%, 50%, 10%, 20%]
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_mean_::
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This method normalizes such that each value is normalized by how much it differs from the average.
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x' = (x - mean_x) / (max_x - min_x)
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[4.63, 4.63, 9.63, 49.63, 9.63, 9.63, 19.63]
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_zscore_::
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This method normalizes such that each value represents how far it is from the mean relative to the standard deviation
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x' = (x - mean_x) / stdev_x
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[-0.68, -0.68, -0.39, 1.94, -0.39, 0.19]
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_softmax_::
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This method normalizes such that each value is exponentiated and relative to the sum of the exponents of the original values.
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x' = e^x / sum_e_x
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[2.862E-20, 2.862E-20, 4.248E-18, 0.999, 9.357E-14, 4.248E-18]
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==== Example
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The following snippet calculates the percent of total sales for each month:
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[source,console]
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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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"calendar_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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"percent_of_total_sales": {
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"normalize": {
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"buckets_path": "sales", <1>
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"method": "percent_of_sum", <2>
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"format": "00.00%" <3>
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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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// TEST[setup:sales]
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<1> `buckets_path` instructs this normalize aggregation to use the output of the `sales` aggregation for rescaling
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<2> `method` sets which rescaling to apply. In this case, `percent_of_sum` will calculate the sales value as a percent of all sales
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in the parent bucket
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<3> `format` influences how to format the metric as a string using Java's `DecimalFormat` pattern. In this case, multiplying by 100
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and adding a '%'
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And the following may be the response:
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[source,console-result]
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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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"percent_of_total_sales": {
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"value": 0.5583756345177665,
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"value_as_string": "55.84%"
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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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"percent_of_total_sales": {
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"value": 0.06091370558375635,
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"value_as_string": "06.09%"
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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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"percent_of_total_sales": {
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"value": 0.38071065989847713,
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"value_as_string": "38.07%"
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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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// 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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