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No EOL
54 KiB
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[[aggregations-tutorial]]
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== Analyze eCommerce data with aggregations using Query DSL
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++++
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<titleabbrev>Basics: Analyze eCommerce data with aggregations</titleabbrev>
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++++
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This hands-on tutorial shows you how to analyze eCommerce data using {es} <<search-aggregations,aggregations>> with the `_search` API and Query DSL.
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You'll learn how to:
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* Calculate key business metrics such as average order value
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* Analyze sales patterns over time
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* Compare performance across product categories
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* Track moving averages and cumulative totals
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[discrete]
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[[aggregations-tutorial-requirements]]
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=== Requirements
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You'll need:
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. A running instance of <<elasticsearch-intro-deploy,{es}>>, either on {serverless-full} or together with {kib} on Elastic Cloud Hosted/Self Managed deployments.
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** If you don't have a deployment, you can run the following command in your terminal to set up a <<run-elasticsearch-locally,local dev environment>>:
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+
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[source,sh]
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----
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curl -fsSL https://elastic.co/start-local | sh
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----
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// NOTCONSOLE
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. The {kibana-ref}/get-started.html#gs-get-data-into-kibana[sample eCommerce data] loaded into {es}. To load sample data follow these steps in your UI:
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* Open the *Integrations* pages by searching in the global search field.
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* Search for `sample data` in the **Integrations** search field.
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* Open the *Sample data* page.
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* Select the *Other sample data sets* collapsible.
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* Add the *Sample eCommerce orders* data set.
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This will create and populate an index called `kibana_sample_data_ecommerce`.
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[discrete]
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[[aggregations-tutorial-inspect-data]]
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=== Inspect index structure
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Before we start analyzing the data, let's examine the structure of the documents in our sample eCommerce index. Run this command to see the field <<elasticsearch-intro-documents-fields-mappings,mappings>>:
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[source,console]
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----
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GET kibana_sample_data_ecommerce/_mapping
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----
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// TEST[skip:Using Kibana sample data]
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The response shows the field mappings for the `kibana_sample_data_ecommerce` index.
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.Example response
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[%collapsible]
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==============
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[source,console-response]
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----
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{
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"kibana_sample_data_ecommerce": {
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"mappings": {
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"properties": {
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"category": {
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"type": "text",
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"fields": { <1>
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"keyword": {
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"type": "keyword"
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}
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}
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},
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"currency": {
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"type": "keyword"
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},
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"customer_birth_date": {
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"type": "date"
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},
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"customer_first_name": {
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"type": "text",
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"fields": {
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"keyword": {
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"type": "keyword",
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"ignore_above": 256
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}
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}
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},
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"customer_full_name": {
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"type": "text",
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"fields": {
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"keyword": {
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"type": "keyword",
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"ignore_above": 256
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}
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}
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},
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"customer_gender": {
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"type": "keyword"
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},
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"customer_id": {
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"type": "keyword"
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},
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"customer_last_name": {
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"type": "text",
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"fields": {
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"keyword": {
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"type": "keyword",
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"ignore_above": 256
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}
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}
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},
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"customer_phone": {
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"type": "keyword"
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},
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"day_of_week": {
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"type": "keyword"
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},
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"day_of_week_i": {
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"type": "integer"
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},
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"email": {
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"type": "keyword"
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},
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"event": {
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"properties": {
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"dataset": {
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"type": "keyword"
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}
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}
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},
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"geoip": {
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"properties": { <2>
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"city_name": {
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"type": "keyword"
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},
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"continent_name": {
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"type": "keyword"
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},
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"country_iso_code": {
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"type": "keyword"
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},
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"location": {
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"type": "geo_point" <3>
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},
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"region_name": {
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"type": "keyword"
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}
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}
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},
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"manufacturer": {
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"type": "text",
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"fields": {
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"keyword": {
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"type": "keyword"
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}
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}
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},
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"order_date": {
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"type": "date"
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},
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"order_id": {
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"type": "keyword"
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},
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"products": {
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"properties": { <4>
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"_id": {
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"type": "text",
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"fields": {
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"keyword": {
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"type": "keyword",
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"ignore_above": 256
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}
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}
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},
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"base_price": {
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"type": "half_float"
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},
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"base_unit_price": {
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"type": "half_float"
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},
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"category": {
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"type": "text",
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"fields": {
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"keyword": {
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"type": "keyword"
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}
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}
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},
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"created_on": {
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"type": "date"
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},
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"discount_amount": {
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"type": "half_float"
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},
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"discount_percentage": {
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"type": "half_float"
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},
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"manufacturer": {
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"type": "text",
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"fields": {
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"keyword": {
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"type": "keyword"
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}
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}
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},
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"min_price": {
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"type": "half_float"
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},
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"price": {
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"type": "half_float"
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},
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"product_id": {
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"type": "long"
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},
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"product_name": {
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"type": "text",
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"fields": {
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"keyword": {
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"type": "keyword"
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}
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},
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"analyzer": "english"
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},
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"quantity": {
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"type": "integer"
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},
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"sku": {
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"type": "keyword"
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},
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"tax_amount": {
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"type": "half_float"
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},
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"taxful_price": {
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"type": "half_float"
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},
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"taxless_price": {
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"type": "half_float"
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},
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"unit_discount_amount": {
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"type": "half_float"
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}
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}
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},
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"sku": {
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"type": "keyword"
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},
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"taxful_total_price": {
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"type": "half_float"
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},
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"taxless_total_price": {
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"type": "half_float"
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},
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"total_quantity": {
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"type": "integer"
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},
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"total_unique_products": {
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"type": "integer"
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},
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"type": {
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"type": "keyword"
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},
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"user": {
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"type": "keyword"
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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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<1> `fields`: Multi-field mapping that allows both full text and exact matching
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<2> `geoip.properties`: Object type field containing location-related properties
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<3> `geoip.location`: Geographic coordinates stored as geo_point for location-based queries
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<4> `products.properties`: Nested structure containing details about items in each order
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==============
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The sample data includes the following <<mapping-types,field data types>>:
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* <<text,`text`>> and <<keyword,`keyword`>> for text fields
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** Most `text` fields have a `.keyword` subfield for exact matching using <<multi-fields,multi-fields>>
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* <<date,`date`>> for date fields
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* 3 <<number,numeric>> types:
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** `integer` for whole numbers
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** `long` for large whole numbers
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** `half_float` for floating-point numbers
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* <<geo-point,`geo_point`>> for geographic coordinates
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* <<object,`object`>> for nested structures such as `products`, `geoip`, `event`
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Now that we understand the structure of our sample data, let's start analyzing it.
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[discrete]
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[[aggregations-tutorial-basic-metrics]]
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=== Get key business metrics
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Let's start by calculating important metrics about orders and customers.
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[discrete]
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[[aggregations-tutorial-order-value]]
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==== Get average order size
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Calculate the average order value across all orders in the dataset using the <<search-aggregations-metrics-avg-aggregation,`avg`>> aggregation.
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[source,console]
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----
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GET kibana_sample_data_ecommerce/_search
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{
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"size": 0, <1>
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"aggs": {
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"avg_order_value": { <2>
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"avg": { <3>
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"field": "taxful_total_price"
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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[skip:Using Kibana sample data]
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<1> Set `size` to 0 to avoid returning matched documents in the response and return only the aggregation results
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<2> A meaningful name that describes what this metric represents
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<3> Configures an `avg` aggregation, which calculates a simple arithmetic mean
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.Example response
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[%collapsible]
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==============
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[source,console-result]
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----
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{
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"took": 0,
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"timed_out": false,
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"_shards": {
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"total": 1,
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"successful": 1,
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"skipped": 0,
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"failed": 0
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},
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"hits": {
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"total": {
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"value": 4675, <1>
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"relation": "eq"
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},
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"max_score": null,
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"hits": [] <2>
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},
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"aggregations": {
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"avg_order_value": { <3>
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"value": 75.05542864304813 <4>
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}
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}
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}
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----
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// TEST[skip:Using Kibana sample data]
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<1> Total number of orders in the dataset
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<2> `hits` is empty because we set `size` to 0
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<3> Results appear under the name we specified in the request
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<4> The average order value is calculated dynamically from all the orders in the dataset
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==============
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[discrete]
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[[aggregations-tutorial-order-stats]]
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==== Get multiple order statistics at once
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Calculate multiple statistics about orders in one request using the <<search-aggregations-metrics-stats-aggregation,`stats`>> aggregation.
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[source,console]
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----
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GET kibana_sample_data_ecommerce/_search
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{
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"size": 0,
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"aggs": {
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"order_stats": { <1>
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"stats": { <2>
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"field": "taxful_total_price"
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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[skip:Using Kibana sample data]
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<1> A descriptive name for this set of statistics
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<2> `stats` returns count, min, max, avg, and sum at once
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.Example response
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[%collapsible]
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==============
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[source,console-result]
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----
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{
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"aggregations": {
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"order_stats": {
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"count": 4675, <1>
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"min": 6.98828125, <2>
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"max": 2250, <3>
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"avg": 75.05542864304813, <4>
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"sum": 350884.12890625 <5>
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}
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}
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}
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----
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// TEST[skip:Using Kibana sample data]
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<1> `"count"`: Total number of orders in the dataset
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<2> `"min"`: Lowest individual order value in the dataset
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<3> `"max"`: Highest individual order value in the dataset
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<4> `"avg"`: Average value per order across all orders
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<5> `"sum"`: Total revenue from all orders combined
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==============
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[TIP]
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====
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The <<search-aggregations-metrics-stats-aggregation,stats aggregation>> is more efficient than running individual min, max, avg, and sum aggregations.
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====
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[discrete]
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[[aggregations-tutorial-sales-patterns]]
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=== Analyze sales patterns
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Let's group orders in different ways to understand sales patterns.
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[discrete]
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[[aggregations-tutorial-category-breakdown]]
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==== Break down sales by category
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Group orders by category to see which product categories are most popular, using the <<search-aggregations-bucket-terms-aggregation,`terms`>> aggregation.
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[source,console]
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----
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GET kibana_sample_data_ecommerce/_search
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{
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"size": 0,
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"aggs": {
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"sales_by_category": { <1>
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"terms": { <2>
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"field": "category.keyword", <3>
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"size": 5, <4>
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"order": { "_count": "desc" } <5>
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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[skip:Using Kibana sample data]
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<1> Name reflecting the business purpose of this breakdown
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<2> `terms` aggregation groups documents by field values
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<3> Use <<keyword,`.keyword`>> field for exact matching on text fields
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<4> Limit to top 5 categories
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<5> Order by number of orders (descending)
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.Example response
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[%collapsible]
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==============
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[source,console-result]
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----
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{
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"took": 4,
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"timed_out": false,
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"_shards": {
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"total": 5,
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"successful": 5,
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"skipped": 0,
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"failed": 0
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},
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"hits": {
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"total": {
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"value": 4675,
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"relation": "eq"
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},
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"max_score": null,
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"hits": []
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},
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"aggregations": {
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"sales_by_category": {
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"doc_count_error_upper_bound": 0, <1>
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"sum_other_doc_count": 572, <2>
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"buckets": [ <3>
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{
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"key": "Men's Clothing", <4>
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"doc_count": 2024 <5>
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},
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{
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"key": "Women's Clothing",
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"doc_count": 1903
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},
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{
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"key": "Women's Shoes",
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"doc_count": 1136
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},
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{
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"key": "Men's Shoes",
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"doc_count": 944
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},
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{
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"key": "Women's Accessories",
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"doc_count": 830
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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[skip:Using Kibana sample data]
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<1> Due to Elasticsearch's distributed architecture, when <<search-aggregations-bucket-terms-aggregation,terms aggregations>> run across multiple shards, the doc counts may have a small margin of error. This value indicates the maximum possible error in the counts.
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<2> Count of documents in categories beyond the requested size.
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<3> Array of category buckets, ordered by count.
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<4> Category name.
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<5> Number of orders in this category.
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==============
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[discrete]
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[[aggregations-tutorial-daily-sales]]
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==== Track daily sales patterns
|
|
|
|
Group orders by day to track daily sales patterns using the <<search-aggregations-bucket-datehistogram-aggregation,`date_histogram`>> aggregation.
|
|
|
|
[source,console]
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----
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GET kibana_sample_data_ecommerce/_search
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{
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"size": 0,
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"aggs": {
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"daily_orders": { <1>
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"date_histogram": { <2>
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"field": "order_date",
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"calendar_interval": "day", <3>
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"format": "yyyy-MM-dd", <4>
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"min_doc_count": 0 <5>
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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[skip:Using Kibana sample data]
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<1> Descriptive name for the time-series aggregation results.
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<2> The `date_histogram` aggregration groups documents into time-based buckets, similar to terms aggregation but for dates.
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<3> Uses <<calendar_and_fixed_intervals,calendar and fixed time intervals>> to handle months with different lengths. `"day"` ensures consistent daily grouping regardless of timezone.
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<4> Formats dates in response using <<mapping-date-format,date patterns>> (e.g. "yyyy-MM-dd"). Refer to <<date-math,date math expressions>> for additional options.
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<5> When `min_doc_count` is 0, returns buckets for days with no orders, useful for continuous time series visualization.
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|
.Example response
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|
[%collapsible]
|
|
==============
|
|
[source,console-result]
|
|
----
|
|
{
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|
"took": 2,
|
|
"timed_out": false,
|
|
"_shards": {
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"total": 5,
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|
"successful": 5,
|
|
"skipped": 0,
|
|
"failed": 0
|
|
},
|
|
"hits": {
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|
"total": {
|
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"value": 4675,
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"relation": "eq"
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},
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"max_score": null,
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|
"hits": []
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},
|
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"aggregations": {
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"daily_orders": { <1>
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"buckets": [ <2>
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{
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"key_as_string": "2024-11-28", <3>
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"key": 1732752000000, <4>
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"doc_count": 146 <5>
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},
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{
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"key_as_string": "2024-11-29",
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"key": 1732838400000,
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"doc_count": 153
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},
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{
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"key_as_string": "2024-11-30",
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"key": 1732924800000,
|
|
"doc_count": 143
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-01",
|
|
"key": 1733011200000,
|
|
"doc_count": 140
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-02",
|
|
"key": 1733097600000,
|
|
"doc_count": 139
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-03",
|
|
"key": 1733184000000,
|
|
"doc_count": 157
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-04",
|
|
"key": 1733270400000,
|
|
"doc_count": 145
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-05",
|
|
"key": 1733356800000,
|
|
"doc_count": 152
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-06",
|
|
"key": 1733443200000,
|
|
"doc_count": 163
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-07",
|
|
"key": 1733529600000,
|
|
"doc_count": 141
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-08",
|
|
"key": 1733616000000,
|
|
"doc_count": 151
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-09",
|
|
"key": 1733702400000,
|
|
"doc_count": 143
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-10",
|
|
"key": 1733788800000,
|
|
"doc_count": 143
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-11",
|
|
"key": 1733875200000,
|
|
"doc_count": 142
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-12",
|
|
"key": 1733961600000,
|
|
"doc_count": 161
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-13",
|
|
"key": 1734048000000,
|
|
"doc_count": 144
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-14",
|
|
"key": 1734134400000,
|
|
"doc_count": 157
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-15",
|
|
"key": 1734220800000,
|
|
"doc_count": 158
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-16",
|
|
"key": 1734307200000,
|
|
"doc_count": 144
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-17",
|
|
"key": 1734393600000,
|
|
"doc_count": 151
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-18",
|
|
"key": 1734480000000,
|
|
"doc_count": 145
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-19",
|
|
"key": 1734566400000,
|
|
"doc_count": 157
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-20",
|
|
"key": 1734652800000,
|
|
"doc_count": 158
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-21",
|
|
"key": 1734739200000,
|
|
"doc_count": 153
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-22",
|
|
"key": 1734825600000,
|
|
"doc_count": 165
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-23",
|
|
"key": 1734912000000,
|
|
"doc_count": 153
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-24",
|
|
"key": 1734998400000,
|
|
"doc_count": 158
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-25",
|
|
"key": 1735084800000,
|
|
"doc_count": 160
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-26",
|
|
"key": 1735171200000,
|
|
"doc_count": 159
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-27",
|
|
"key": 1735257600000,
|
|
"doc_count": 152
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-28",
|
|
"key": 1735344000000,
|
|
"doc_count": 142
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
----
|
|
// TEST[skip:Using Kibana sample data]
|
|
<1> Results of our named aggregation "daily_orders"
|
|
<2> Time-based buckets from date_histogram aggregation
|
|
<3> `key_as_string` is the human-readable date for this bucket
|
|
<4> `key` is the same date represented as the Unix timestamp for this bucket
|
|
<5> `doc_count` counts the number of documents that fall into this time bucket
|
|
==============
|
|
|
|
[discrete]
|
|
[[aggregations-tutorial-combined-analysis]]
|
|
=== Combine metrics with groupings
|
|
|
|
Now let's calculate <<search-aggregations-metrics,metrics>> within each group to get deeper insights.
|
|
|
|
[discrete]
|
|
[[aggregations-tutorial-category-metrics]]
|
|
==== Compare category performance
|
|
|
|
Calculate metrics within each category to compare performance across categories.
|
|
|
|
[source,console]
|
|
----
|
|
GET kibana_sample_data_ecommerce/_search
|
|
{
|
|
"size": 0,
|
|
"aggs": {
|
|
"categories": {
|
|
"terms": {
|
|
"field": "category.keyword",
|
|
"size": 5,
|
|
"order": { "total_revenue": "desc" } <1>
|
|
},
|
|
"aggs": { <2>
|
|
"total_revenue": { <3>
|
|
"sum": {
|
|
"field": "taxful_total_price"
|
|
}
|
|
},
|
|
"avg_order_value": { <4>
|
|
"avg": {
|
|
"field": "taxful_total_price"
|
|
}
|
|
},
|
|
"total_items": { <5>
|
|
"sum": {
|
|
"field": "total_quantity"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
----
|
|
// TEST[skip:Using Kibana sample data]
|
|
<1> Order categories by their total revenue instead of count
|
|
<2> Define metrics to calculate within each category
|
|
<3> Total revenue for the category
|
|
<4> Average order value in the category
|
|
<5> Total number of items sold
|
|
|
|
.Example response
|
|
[%collapsible]
|
|
==============
|
|
[source,console-result]
|
|
----
|
|
{
|
|
"aggregations": {
|
|
"categories": {
|
|
"buckets": [
|
|
{
|
|
"key": "Men's Clothing", <1>
|
|
"doc_count": 2179, <2>
|
|
"total_revenue": { <3>
|
|
"value": 156729.453125
|
|
},
|
|
"avg_order_value": { <4>
|
|
"value": 71.92726898715927
|
|
},
|
|
"total_items": { <5>
|
|
"value": 8716
|
|
}
|
|
},
|
|
{
|
|
"key": "Women's Clothing",
|
|
"doc_count": 2262,
|
|
...
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
----
|
|
// TEST[skip:Using Kibana sample data]
|
|
<1> Category name
|
|
<2> Number of orders
|
|
<3> Total revenue for this category
|
|
<4> Average order value for this category
|
|
<5> Total quantity of items sold
|
|
==============
|
|
|
|
[discrete]
|
|
[[aggregations-tutorial-daily-metrics]]
|
|
==== Analyze daily sales performance
|
|
|
|
Let's combine metrics to track daily trends: daily revenue, unique customers, and average basket size.
|
|
|
|
[source,console]
|
|
----
|
|
GET kibana_sample_data_ecommerce/_search
|
|
{
|
|
"size": 0,
|
|
"aggs": {
|
|
"daily_sales": {
|
|
"date_histogram": {
|
|
"field": "order_date",
|
|
"calendar_interval": "day",
|
|
"format": "yyyy-MM-dd"
|
|
},
|
|
"aggs": {
|
|
"revenue": { <1>
|
|
"sum": {
|
|
"field": "taxful_total_price"
|
|
}
|
|
},
|
|
"unique_customers": { <2>
|
|
"cardinality": {
|
|
"field": "customer_id"
|
|
}
|
|
},
|
|
"avg_basket_size": { <3>
|
|
"avg": {
|
|
"field": "total_quantity"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
----
|
|
// TEST[skip:Using Kibana sample data]
|
|
<1> Daily revenue
|
|
<2> Uses the <<search-aggregations-metrics-cardinality-aggregation,`cardinality`>> aggregation to count unique customers per day
|
|
<3> Average number of items per order
|
|
|
|
.Example response
|
|
[%collapsible]
|
|
==============
|
|
[source,console-result]
|
|
----
|
|
{
|
|
"took": 119,
|
|
"timed_out": false,
|
|
"_shards": {
|
|
"total": 5,
|
|
"successful": 5,
|
|
"skipped": 0,
|
|
"failed": 0
|
|
},
|
|
"hits": {
|
|
"total": {
|
|
"value": 4675,
|
|
"relation": "eq"
|
|
},
|
|
"max_score": null,
|
|
"hits": []
|
|
},
|
|
"aggregations": {
|
|
"daily_sales": {
|
|
"buckets": [
|
|
{
|
|
"key_as_string": "2024-11-14",
|
|
"key": 1731542400000,
|
|
"doc_count": 146,
|
|
"unique_customers": { <1>
|
|
"value": 42
|
|
},
|
|
"revenue": { <2>
|
|
"value": 10578.53125
|
|
},
|
|
"avg_basket_size": { <3>
|
|
"value": 2.1780821917808217
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-15",
|
|
"key": 1731628800000,
|
|
"doc_count": 153,
|
|
"unique_customers": {
|
|
"value": 44
|
|
},
|
|
"revenue": {
|
|
"value": 10448
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.183006535947712
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-16",
|
|
"key": 1731715200000,
|
|
"doc_count": 143,
|
|
"unique_customers": {
|
|
"value": 45
|
|
},
|
|
"revenue": {
|
|
"value": 10283.484375
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.111888111888112
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-17",
|
|
"key": 1731801600000,
|
|
"doc_count": 140,
|
|
"unique_customers": {
|
|
"value": 42
|
|
},
|
|
"revenue": {
|
|
"value": 10145.5234375
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.142857142857143
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-18",
|
|
"key": 1731888000000,
|
|
"doc_count": 139,
|
|
"unique_customers": {
|
|
"value": 42
|
|
},
|
|
"revenue": {
|
|
"value": 12012.609375
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.158273381294964
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-19",
|
|
"key": 1731974400000,
|
|
"doc_count": 157,
|
|
"unique_customers": {
|
|
"value": 43
|
|
},
|
|
"revenue": {
|
|
"value": 11009.45703125
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.0955414012738856
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-20",
|
|
"key": 1732060800000,
|
|
"doc_count": 145,
|
|
"unique_customers": {
|
|
"value": 44
|
|
},
|
|
"revenue": {
|
|
"value": 10720.59375
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.179310344827586
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-21",
|
|
"key": 1732147200000,
|
|
"doc_count": 152,
|
|
"unique_customers": {
|
|
"value": 43
|
|
},
|
|
"revenue": {
|
|
"value": 11185.3671875
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.1710526315789473
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-22",
|
|
"key": 1732233600000,
|
|
"doc_count": 163,
|
|
"unique_customers": {
|
|
"value": 44
|
|
},
|
|
"revenue": {
|
|
"value": 13560.140625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.2576687116564416
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-23",
|
|
"key": 1732320000000,
|
|
"doc_count": 141,
|
|
"unique_customers": {
|
|
"value": 45
|
|
},
|
|
"revenue": {
|
|
"value": 9884.78125
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.099290780141844
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-24",
|
|
"key": 1732406400000,
|
|
"doc_count": 151,
|
|
"unique_customers": {
|
|
"value": 44
|
|
},
|
|
"revenue": {
|
|
"value": 11075.65625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.0927152317880795
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-25",
|
|
"key": 1732492800000,
|
|
"doc_count": 143,
|
|
"unique_customers": {
|
|
"value": 41
|
|
},
|
|
"revenue": {
|
|
"value": 10323.8515625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.167832167832168
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-26",
|
|
"key": 1732579200000,
|
|
"doc_count": 143,
|
|
"unique_customers": {
|
|
"value": 44
|
|
},
|
|
"revenue": {
|
|
"value": 10369.546875
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.167832167832168
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-27",
|
|
"key": 1732665600000,
|
|
"doc_count": 142,
|
|
"unique_customers": {
|
|
"value": 46
|
|
},
|
|
"revenue": {
|
|
"value": 11711.890625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.1971830985915495
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-28",
|
|
"key": 1732752000000,
|
|
"doc_count": 161,
|
|
"unique_customers": {
|
|
"value": 43
|
|
},
|
|
"revenue": {
|
|
"value": 12612.6640625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.1180124223602483
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-29",
|
|
"key": 1732838400000,
|
|
"doc_count": 144,
|
|
"unique_customers": {
|
|
"value": 42
|
|
},
|
|
"revenue": {
|
|
"value": 10176.87890625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.0347222222222223
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-30",
|
|
"key": 1732924800000,
|
|
"doc_count": 157,
|
|
"unique_customers": {
|
|
"value": 43
|
|
},
|
|
"revenue": {
|
|
"value": 11480.33203125
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.159235668789809
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-01",
|
|
"key": 1733011200000,
|
|
"doc_count": 158,
|
|
"unique_customers": {
|
|
"value": 42
|
|
},
|
|
"revenue": {
|
|
"value": 11533.265625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.0822784810126582
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-02",
|
|
"key": 1733097600000,
|
|
"doc_count": 144,
|
|
"unique_customers": {
|
|
"value": 43
|
|
},
|
|
"revenue": {
|
|
"value": 10499.8125
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.201388888888889
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-03",
|
|
"key": 1733184000000,
|
|
"doc_count": 151,
|
|
"unique_customers": {
|
|
"value": 40
|
|
},
|
|
"revenue": {
|
|
"value": 12111.6875
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.172185430463576
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-04",
|
|
"key": 1733270400000,
|
|
"doc_count": 145,
|
|
"unique_customers": {
|
|
"value": 40
|
|
},
|
|
"revenue": {
|
|
"value": 10530.765625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.0965517241379312
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-05",
|
|
"key": 1733356800000,
|
|
"doc_count": 157,
|
|
"unique_customers": {
|
|
"value": 43
|
|
},
|
|
"revenue": {
|
|
"value": 11872.5625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.1464968152866244
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-06",
|
|
"key": 1733443200000,
|
|
"doc_count": 158,
|
|
"unique_customers": {
|
|
"value": 42
|
|
},
|
|
"revenue": {
|
|
"value": 12109.453125
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.151898734177215
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-07",
|
|
"key": 1733529600000,
|
|
"doc_count": 153,
|
|
"unique_customers": {
|
|
"value": 42
|
|
},
|
|
"revenue": {
|
|
"value": 11057.40625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.111111111111111
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-08",
|
|
"key": 1733616000000,
|
|
"doc_count": 165,
|
|
"unique_customers": {
|
|
"value": 42
|
|
},
|
|
"revenue": {
|
|
"value": 13095.609375
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.1818181818181817
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-09",
|
|
"key": 1733702400000,
|
|
"doc_count": 153,
|
|
"unique_customers": {
|
|
"value": 41
|
|
},
|
|
"revenue": {
|
|
"value": 12574.015625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.2287581699346406
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-10",
|
|
"key": 1733788800000,
|
|
"doc_count": 158,
|
|
"unique_customers": {
|
|
"value": 42
|
|
},
|
|
"revenue": {
|
|
"value": 11188.1875
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.151898734177215
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-11",
|
|
"key": 1733875200000,
|
|
"doc_count": 160,
|
|
"unique_customers": {
|
|
"value": 42
|
|
},
|
|
"revenue": {
|
|
"value": 12117.65625
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.20625
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-12",
|
|
"key": 1733961600000,
|
|
"doc_count": 159,
|
|
"unique_customers": {
|
|
"value": 45
|
|
},
|
|
"revenue": {
|
|
"value": 11558.25
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.1823899371069184
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-13",
|
|
"key": 1734048000000,
|
|
"doc_count": 152,
|
|
"unique_customers": {
|
|
"value": 45
|
|
},
|
|
"revenue": {
|
|
"value": 11921.1171875
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.289473684210526
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-14",
|
|
"key": 1734134400000,
|
|
"doc_count": 142,
|
|
"unique_customers": {
|
|
"value": 45
|
|
},
|
|
"revenue": {
|
|
"value": 11135.03125
|
|
},
|
|
"avg_basket_size": {
|
|
"value": 2.183098591549296
|
|
}
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
----
|
|
// TEST[skip:Using Kibana sample data]
|
|
==============
|
|
|
|
[discrete]
|
|
[[aggregations-tutorial-trends]]
|
|
=== Track trends and patterns
|
|
|
|
You can use <<search-aggregations-pipeline,pipeline aggregations>> on the results of other aggregations.
|
|
Let's analyze how metrics change over time.
|
|
|
|
[discrete]
|
|
[[aggregations-tutorial-moving-average]]
|
|
==== Smooth out daily fluctuations
|
|
|
|
Moving averages help identify trends by reducing day-to-day noise in the data.
|
|
Let's observe sales trends more clearly by smoothing daily revenue variations, using the <<search-aggregations-pipeline-movfn-aggregation,Moving Function>> aggregation.
|
|
|
|
[source,console]
|
|
----
|
|
GET kibana_sample_data_ecommerce/_search
|
|
{
|
|
"size": 0,
|
|
"aggs": {
|
|
"daily_sales": {
|
|
"date_histogram": {
|
|
"field": "order_date",
|
|
"calendar_interval": "day"
|
|
},
|
|
"aggs": {
|
|
"daily_revenue": { <1>
|
|
"sum": {
|
|
"field": "taxful_total_price"
|
|
}
|
|
},
|
|
"smoothed_revenue": { <2>
|
|
"moving_fn": { <3>
|
|
"buckets_path": "daily_revenue", <4>
|
|
"window": 3, <5>
|
|
"script": "MovingFunctions.unweightedAvg(values)" <6>
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
----
|
|
// TEST[skip:Using Kibana sample data]
|
|
<1> Calculate daily revenue first.
|
|
<2> Create a smoothed version of the daily revenue.
|
|
<3> Use `moving_fn` for moving window calculations.
|
|
<4> Reference the revenue from our date histogram.
|
|
<5> Use a 3-day window — use different window sizes to see trends at different time scales.
|
|
<6> Use the built-in unweighted average function in the `moving_fn` aggregation.
|
|
|
|
.Example response
|
|
[%collapsible]
|
|
==============
|
|
[source,console-result]
|
|
----
|
|
{
|
|
"took": 13,
|
|
"timed_out": false,
|
|
"_shards": {
|
|
"total": 5,
|
|
"successful": 5,
|
|
"skipped": 0,
|
|
"failed": 0
|
|
},
|
|
"hits": {
|
|
"total": {
|
|
"value": 4675,
|
|
"relation": "eq"
|
|
},
|
|
"max_score": null,
|
|
"hits": []
|
|
},
|
|
"aggregations": {
|
|
"daily_sales": {
|
|
"buckets": [
|
|
{
|
|
"key_as_string": "2024-11-14T00:00:00.000Z", <1>
|
|
"key": 1731542400000,
|
|
"doc_count": 146, <2>
|
|
"daily_revenue": { <3>
|
|
"value": 10578.53125
|
|
},
|
|
"smoothed_revenue": { <4>
|
|
"value": null
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-15T00:00:00.000Z",
|
|
"key": 1731628800000,
|
|
"doc_count": 153,
|
|
"daily_revenue": {
|
|
"value": 10448
|
|
},
|
|
"smoothed_revenue": { <5>
|
|
"value": 10578.53125
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-16T00:00:00.000Z",
|
|
"key": 1731715200000,
|
|
"doc_count": 143,
|
|
"daily_revenue": {
|
|
"value": 10283.484375
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 10513.265625
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-17T00:00:00.000Z",
|
|
"key": 1731801600000,
|
|
"doc_count": 140,
|
|
"daily_revenue": {
|
|
"value": 10145.5234375
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 10436.671875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-18T00:00:00.000Z",
|
|
"key": 1731888000000,
|
|
"doc_count": 139,
|
|
"daily_revenue": {
|
|
"value": 12012.609375
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 10292.3359375
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-19T00:00:00.000Z",
|
|
"key": 1731974400000,
|
|
"doc_count": 157,
|
|
"daily_revenue": {
|
|
"value": 11009.45703125
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 10813.872395833334
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-20T00:00:00.000Z",
|
|
"key": 1732060800000,
|
|
"doc_count": 145,
|
|
"daily_revenue": {
|
|
"value": 10720.59375
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11055.86328125
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-21T00:00:00.000Z",
|
|
"key": 1732147200000,
|
|
"doc_count": 152,
|
|
"daily_revenue": {
|
|
"value": 11185.3671875
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11247.553385416666
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-22T00:00:00.000Z",
|
|
"key": 1732233600000,
|
|
"doc_count": 163,
|
|
"daily_revenue": {
|
|
"value": 13560.140625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 10971.805989583334
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-23T00:00:00.000Z",
|
|
"key": 1732320000000,
|
|
"doc_count": 141,
|
|
"daily_revenue": {
|
|
"value": 9884.78125
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11822.033854166666
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-24T00:00:00.000Z",
|
|
"key": 1732406400000,
|
|
"doc_count": 151,
|
|
"daily_revenue": {
|
|
"value": 11075.65625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11543.4296875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-25T00:00:00.000Z",
|
|
"key": 1732492800000,
|
|
"doc_count": 143,
|
|
"daily_revenue": {
|
|
"value": 10323.8515625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11506.859375
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-26T00:00:00.000Z",
|
|
"key": 1732579200000,
|
|
"doc_count": 143,
|
|
"daily_revenue": {
|
|
"value": 10369.546875
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 10428.096354166666
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-27T00:00:00.000Z",
|
|
"key": 1732665600000,
|
|
"doc_count": 142,
|
|
"daily_revenue": {
|
|
"value": 11711.890625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 10589.684895833334
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-28T00:00:00.000Z",
|
|
"key": 1732752000000,
|
|
"doc_count": 161,
|
|
"daily_revenue": {
|
|
"value": 12612.6640625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 10801.763020833334
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-29T00:00:00.000Z",
|
|
"key": 1732838400000,
|
|
"doc_count": 144,
|
|
"daily_revenue": {
|
|
"value": 10176.87890625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11564.700520833334
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-30T00:00:00.000Z",
|
|
"key": 1732924800000,
|
|
"doc_count": 157,
|
|
"daily_revenue": {
|
|
"value": 11480.33203125
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11500.477864583334
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-01T00:00:00.000Z",
|
|
"key": 1733011200000,
|
|
"doc_count": 158,
|
|
"daily_revenue": {
|
|
"value": 11533.265625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11423.291666666666
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-02T00:00:00.000Z",
|
|
"key": 1733097600000,
|
|
"doc_count": 144,
|
|
"daily_revenue": {
|
|
"value": 10499.8125
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11063.4921875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-03T00:00:00.000Z",
|
|
"key": 1733184000000,
|
|
"doc_count": 151,
|
|
"daily_revenue": {
|
|
"value": 12111.6875
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11171.13671875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-04T00:00:00.000Z",
|
|
"key": 1733270400000,
|
|
"doc_count": 145,
|
|
"daily_revenue": {
|
|
"value": 10530.765625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11381.588541666666
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-05T00:00:00.000Z",
|
|
"key": 1733356800000,
|
|
"doc_count": 157,
|
|
"daily_revenue": {
|
|
"value": 11872.5625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11047.421875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-06T00:00:00.000Z",
|
|
"key": 1733443200000,
|
|
"doc_count": 158,
|
|
"daily_revenue": {
|
|
"value": 12109.453125
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11505.005208333334
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-07T00:00:00.000Z",
|
|
"key": 1733529600000,
|
|
"doc_count": 153,
|
|
"daily_revenue": {
|
|
"value": 11057.40625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11504.260416666666
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-08T00:00:00.000Z",
|
|
"key": 1733616000000,
|
|
"doc_count": 165,
|
|
"daily_revenue": {
|
|
"value": 13095.609375
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11679.807291666666
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-09T00:00:00.000Z",
|
|
"key": 1733702400000,
|
|
"doc_count": 153,
|
|
"daily_revenue": {
|
|
"value": 12574.015625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 12087.489583333334
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-10T00:00:00.000Z",
|
|
"key": 1733788800000,
|
|
"doc_count": 158,
|
|
"daily_revenue": {
|
|
"value": 11188.1875
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 12242.34375
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-11T00:00:00.000Z",
|
|
"key": 1733875200000,
|
|
"doc_count": 160,
|
|
"daily_revenue": {
|
|
"value": 12117.65625
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 12285.9375
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-12T00:00:00.000Z",
|
|
"key": 1733961600000,
|
|
"doc_count": 159,
|
|
"daily_revenue": {
|
|
"value": 11558.25
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11959.953125
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-13T00:00:00.000Z",
|
|
"key": 1734048000000,
|
|
"doc_count": 152,
|
|
"daily_revenue": {
|
|
"value": 11921.1171875
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11621.364583333334
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-14T00:00:00.000Z",
|
|
"key": 1734134400000,
|
|
"doc_count": 142,
|
|
"daily_revenue": {
|
|
"value": 11135.03125
|
|
},
|
|
"smoothed_revenue": {
|
|
"value": 11865.674479166666
|
|
}
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
----
|
|
// TEST[skip:Using Kibana sample data]
|
|
<1> Date of the bucket is in default ISO format because we didn't specify a format
|
|
<2> Number of orders for this day
|
|
<3> Raw daily revenue before smoothing
|
|
<4> First day has no smoothed value as it needs previous days for the calculation
|
|
<5> Moving average starts from second day, using a 3-day window
|
|
==============
|
|
|
|
[TIP]
|
|
====
|
|
Notice how the smoothed values lag behind the actual values - this is because they need previous days' data to calculate. The first day will always be null when using moving averages.
|
|
====
|
|
|
|
[discrete]
|
|
[[aggregations-tutorial-cumulative]]
|
|
==== Track running totals
|
|
|
|
Track running totals over time using the <<search-aggregations-pipeline-cumulative-sum-aggregation,`cumulative_sum`>> aggregation.
|
|
|
|
[source,console]
|
|
----
|
|
GET kibana_sample_data_ecommerce/_search
|
|
{
|
|
"size": 0,
|
|
"aggs": {
|
|
"daily_sales": {
|
|
"date_histogram": {
|
|
"field": "order_date",
|
|
"calendar_interval": "day"
|
|
},
|
|
"aggs": {
|
|
"revenue": {
|
|
"sum": {
|
|
"field": "taxful_total_price"
|
|
}
|
|
},
|
|
"cumulative_revenue": { <1>
|
|
"cumulative_sum": { <2>
|
|
"buckets_path": "revenue" <3>
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
----
|
|
// TEST[skip:Using Kibana sample data]
|
|
<1> Name for our running total
|
|
<2> `cumulative_sum` adds up values across buckets
|
|
<3> Reference the revenue we want to accumulate
|
|
|
|
.Example response
|
|
[%collapsible]
|
|
==============
|
|
[source,console-result]
|
|
----
|
|
{
|
|
"took": 4,
|
|
"timed_out": false,
|
|
"_shards": {
|
|
"total": 5,
|
|
"successful": 5,
|
|
"skipped": 0,
|
|
"failed": 0
|
|
},
|
|
"hits": {
|
|
"total": {
|
|
"value": 4675,
|
|
"relation": "eq"
|
|
},
|
|
"max_score": null,
|
|
"hits": []
|
|
},
|
|
"aggregations": {
|
|
"daily_sales": { <1>
|
|
"buckets": [ <2>
|
|
{
|
|
"key_as_string": "2024-11-14T00:00:00.000Z", <3>
|
|
"key": 1731542400000,
|
|
"doc_count": 146,
|
|
"revenue": { <4>
|
|
"value": 10578.53125
|
|
},
|
|
"cumulative_revenue": { <5>
|
|
"value": 10578.53125
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-15T00:00:00.000Z",
|
|
"key": 1731628800000,
|
|
"doc_count": 153,
|
|
"revenue": {
|
|
"value": 10448
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 21026.53125
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-16T00:00:00.000Z",
|
|
"key": 1731715200000,
|
|
"doc_count": 143,
|
|
"revenue": {
|
|
"value": 10283.484375
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 31310.015625
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-17T00:00:00.000Z",
|
|
"key": 1731801600000,
|
|
"doc_count": 140,
|
|
"revenue": {
|
|
"value": 10145.5234375
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 41455.5390625
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-18T00:00:00.000Z",
|
|
"key": 1731888000000,
|
|
"doc_count": 139,
|
|
"revenue": {
|
|
"value": 12012.609375
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 53468.1484375
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-19T00:00:00.000Z",
|
|
"key": 1731974400000,
|
|
"doc_count": 157,
|
|
"revenue": {
|
|
"value": 11009.45703125
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 64477.60546875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-20T00:00:00.000Z",
|
|
"key": 1732060800000,
|
|
"doc_count": 145,
|
|
"revenue": {
|
|
"value": 10720.59375
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 75198.19921875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-21T00:00:00.000Z",
|
|
"key": 1732147200000,
|
|
"doc_count": 152,
|
|
"revenue": {
|
|
"value": 11185.3671875
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 86383.56640625
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-22T00:00:00.000Z",
|
|
"key": 1732233600000,
|
|
"doc_count": 163,
|
|
"revenue": {
|
|
"value": 13560.140625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 99943.70703125
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-23T00:00:00.000Z",
|
|
"key": 1732320000000,
|
|
"doc_count": 141,
|
|
"revenue": {
|
|
"value": 9884.78125
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 109828.48828125
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-24T00:00:00.000Z",
|
|
"key": 1732406400000,
|
|
"doc_count": 151,
|
|
"revenue": {
|
|
"value": 11075.65625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 120904.14453125
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-25T00:00:00.000Z",
|
|
"key": 1732492800000,
|
|
"doc_count": 143,
|
|
"revenue": {
|
|
"value": 10323.8515625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 131227.99609375
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-26T00:00:00.000Z",
|
|
"key": 1732579200000,
|
|
"doc_count": 143,
|
|
"revenue": {
|
|
"value": 10369.546875
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 141597.54296875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-27T00:00:00.000Z",
|
|
"key": 1732665600000,
|
|
"doc_count": 142,
|
|
"revenue": {
|
|
"value": 11711.890625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 153309.43359375
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-28T00:00:00.000Z",
|
|
"key": 1732752000000,
|
|
"doc_count": 161,
|
|
"revenue": {
|
|
"value": 12612.6640625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 165922.09765625
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-29T00:00:00.000Z",
|
|
"key": 1732838400000,
|
|
"doc_count": 144,
|
|
"revenue": {
|
|
"value": 10176.87890625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 176098.9765625
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-11-30T00:00:00.000Z",
|
|
"key": 1732924800000,
|
|
"doc_count": 157,
|
|
"revenue": {
|
|
"value": 11480.33203125
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 187579.30859375
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-01T00:00:00.000Z",
|
|
"key": 1733011200000,
|
|
"doc_count": 158,
|
|
"revenue": {
|
|
"value": 11533.265625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 199112.57421875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-02T00:00:00.000Z",
|
|
"key": 1733097600000,
|
|
"doc_count": 144,
|
|
"revenue": {
|
|
"value": 10499.8125
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 209612.38671875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-03T00:00:00.000Z",
|
|
"key": 1733184000000,
|
|
"doc_count": 151,
|
|
"revenue": {
|
|
"value": 12111.6875
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 221724.07421875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-04T00:00:00.000Z",
|
|
"key": 1733270400000,
|
|
"doc_count": 145,
|
|
"revenue": {
|
|
"value": 10530.765625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 232254.83984375
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-05T00:00:00.000Z",
|
|
"key": 1733356800000,
|
|
"doc_count": 157,
|
|
"revenue": {
|
|
"value": 11872.5625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 244127.40234375
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-06T00:00:00.000Z",
|
|
"key": 1733443200000,
|
|
"doc_count": 158,
|
|
"revenue": {
|
|
"value": 12109.453125
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 256236.85546875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-07T00:00:00.000Z",
|
|
"key": 1733529600000,
|
|
"doc_count": 153,
|
|
"revenue": {
|
|
"value": 11057.40625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 267294.26171875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-08T00:00:00.000Z",
|
|
"key": 1733616000000,
|
|
"doc_count": 165,
|
|
"revenue": {
|
|
"value": 13095.609375
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 280389.87109375
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-09T00:00:00.000Z",
|
|
"key": 1733702400000,
|
|
"doc_count": 153,
|
|
"revenue": {
|
|
"value": 12574.015625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 292963.88671875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-10T00:00:00.000Z",
|
|
"key": 1733788800000,
|
|
"doc_count": 158,
|
|
"revenue": {
|
|
"value": 11188.1875
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 304152.07421875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-11T00:00:00.000Z",
|
|
"key": 1733875200000,
|
|
"doc_count": 160,
|
|
"revenue": {
|
|
"value": 12117.65625
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 316269.73046875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-12T00:00:00.000Z",
|
|
"key": 1733961600000,
|
|
"doc_count": 159,
|
|
"revenue": {
|
|
"value": 11558.25
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 327827.98046875
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-13T00:00:00.000Z",
|
|
"key": 1734048000000,
|
|
"doc_count": 152,
|
|
"revenue": {
|
|
"value": 11921.1171875
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 339749.09765625
|
|
}
|
|
},
|
|
{
|
|
"key_as_string": "2024-12-14T00:00:00.000Z",
|
|
"key": 1734134400000,
|
|
"doc_count": 142,
|
|
"revenue": {
|
|
"value": 11135.03125
|
|
},
|
|
"cumulative_revenue": {
|
|
"value": 350884.12890625
|
|
}
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
----
|
|
// TEST[skip:Using Kibana sample data]
|
|
<1> `daily_sales`: Results from our daily sales date histogram
|
|
<2> `buckets`: Array of time-based buckets
|
|
<3> `key_as_string`: Date for this bucket (in ISO format since no format specified)
|
|
<4> `revenue`: Daily revenue for this date
|
|
<5> `cumulative_revenue`: Running total of revenue up to this date
|
|
==============
|
|
|
|
[discrete]
|
|
[[aggregations-tutorial-next-steps]]
|
|
=== Next steps
|
|
|
|
Refer to the <<search-aggregations,aggregations reference>> for more details on all available aggregation types. |