diff --git a/docs/changelog/118380.yaml b/docs/changelog/118380.yaml
new file mode 100644
index 0000000000000..8b26c871fb172
--- /dev/null
+++ b/docs/changelog/118380.yaml
@@ -0,0 +1,5 @@
+pr: 118380
+summary: Restore original "is within leaf" value in `SparseVectorFieldMapper`
+area: Mapping
+type: bug
+issues: []
diff --git a/docs/reference/quickstart/aggs-tutorial.asciidoc b/docs/reference/quickstart/aggs-tutorial.asciidoc
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+[[aggregations-tutorial]]
+== Analyze eCommerce data with aggregations using Query DSL
+++++
+Basics: Analyze eCommerce data with aggregations
+++++
+
+This hands-on tutorial shows you how to analyze eCommerce data using {es} <> with the `_search` API and Query DSL.
+
+You'll learn how to:
+
+* Calculate key business metrics such as average order value
+* Analyze sales patterns over time
+* Compare performance across product categories
+* Track moving averages and cumulative totals
+
+[discrete]
+[[aggregations-tutorial-requirements]]
+=== Requirements
+
+You'll need:
+
+. A running instance of <>, either on {serverless-full} or together with {kib} on Elastic Cloud Hosted/Self Managed deployments.
+** If you don't have a deployment, you can run the following command in your terminal to set up a <>:
++
+[source,sh]
+----
+curl -fsSL https://elastic.co/start-local | sh
+----
+// NOTCONSOLE
+. 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:
+* Open the *Integrations* pages by searching in the global search field.
+* Search for `sample data` in the **Integrations** search field.
+* Open the *Sample data* page.
+* Select the *Other sample data sets* collapsible.
+* Add the *Sample eCommerce orders* data set.
+This will create and populate an index called `kibana_sample_data_ecommerce`.
+
+[discrete]
+[[aggregations-tutorial-inspect-data]]
+=== Inspect index structure
+
+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 <>:
+
+[source,console]
+----
+GET kibana_sample_data_ecommerce/_mapping
+----
+// TEST[skip:Using Kibana sample data]
+
+The response shows the field mappings for the `kibana_sample_data_ecommerce` index.
+
+.Example response
+[%collapsible]
+==============
+[source,console-response]
+----
+{
+ "kibana_sample_data_ecommerce": {
+ "mappings": {
+ "properties": {
+ "category": {
+ "type": "text",
+ "fields": { <1>
+ "keyword": {
+ "type": "keyword"
+ }
+ }
+ },
+ "currency": {
+ "type": "keyword"
+ },
+ "customer_birth_date": {
+ "type": "date"
+ },
+ "customer_first_name": {
+ "type": "text",
+ "fields": {
+ "keyword": {
+ "type": "keyword",
+ "ignore_above": 256
+ }
+ }
+ },
+ "customer_full_name": {
+ "type": "text",
+ "fields": {
+ "keyword": {
+ "type": "keyword",
+ "ignore_above": 256
+ }
+ }
+ },
+ "customer_gender": {
+ "type": "keyword"
+ },
+ "customer_id": {
+ "type": "keyword"
+ },
+ "customer_last_name": {
+ "type": "text",
+ "fields": {
+ "keyword": {
+ "type": "keyword",
+ "ignore_above": 256
+ }
+ }
+ },
+ "customer_phone": {
+ "type": "keyword"
+ },
+ "day_of_week": {
+ "type": "keyword"
+ },
+ "day_of_week_i": {
+ "type": "integer"
+ },
+ "email": {
+ "type": "keyword"
+ },
+ "event": {
+ "properties": {
+ "dataset": {
+ "type": "keyword"
+ }
+ }
+ },
+ "geoip": {
+ "properties": { <2>
+ "city_name": {
+ "type": "keyword"
+ },
+ "continent_name": {
+ "type": "keyword"
+ },
+ "country_iso_code": {
+ "type": "keyword"
+ },
+ "location": {
+ "type": "geo_point" <3>
+ },
+ "region_name": {
+ "type": "keyword"
+ }
+ }
+ },
+ "manufacturer": {
+ "type": "text",
+ "fields": {
+ "keyword": {
+ "type": "keyword"
+ }
+ }
+ },
+ "order_date": {
+ "type": "date"
+ },
+ "order_id": {
+ "type": "keyword"
+ },
+ "products": {
+ "properties": { <4>
+ "_id": {
+ "type": "text",
+ "fields": {
+ "keyword": {
+ "type": "keyword",
+ "ignore_above": 256
+ }
+ }
+ },
+ "base_price": {
+ "type": "half_float"
+ },
+ "base_unit_price": {
+ "type": "half_float"
+ },
+ "category": {
+ "type": "text",
+ "fields": {
+ "keyword": {
+ "type": "keyword"
+ }
+ }
+ },
+ "created_on": {
+ "type": "date"
+ },
+ "discount_amount": {
+ "type": "half_float"
+ },
+ "discount_percentage": {
+ "type": "half_float"
+ },
+ "manufacturer": {
+ "type": "text",
+ "fields": {
+ "keyword": {
+ "type": "keyword"
+ }
+ }
+ },
+ "min_price": {
+ "type": "half_float"
+ },
+ "price": {
+ "type": "half_float"
+ },
+ "product_id": {
+ "type": "long"
+ },
+ "product_name": {
+ "type": "text",
+ "fields": {
+ "keyword": {
+ "type": "keyword"
+ }
+ },
+ "analyzer": "english"
+ },
+ "quantity": {
+ "type": "integer"
+ },
+ "sku": {
+ "type": "keyword"
+ },
+ "tax_amount": {
+ "type": "half_float"
+ },
+ "taxful_price": {
+ "type": "half_float"
+ },
+ "taxless_price": {
+ "type": "half_float"
+ },
+ "unit_discount_amount": {
+ "type": "half_float"
+ }
+ }
+ },
+ "sku": {
+ "type": "keyword"
+ },
+ "taxful_total_price": {
+ "type": "half_float"
+ },
+ "taxless_total_price": {
+ "type": "half_float"
+ },
+ "total_quantity": {
+ "type": "integer"
+ },
+ "total_unique_products": {
+ "type": "integer"
+ },
+ "type": {
+ "type": "keyword"
+ },
+ "user": {
+ "type": "keyword"
+ }
+ }
+ }
+ }
+}
+----
+<1> `fields`: Multi-field mapping that allows both full text and exact matching
+<2> `geoip.properties`: Object type field containing location-related properties
+<3> `geoip.location`: Geographic coordinates stored as geo_point for location-based queries
+<4> `products.properties`: Nested structure containing details about items in each order
+==============
+
+The sample data includes the following <>:
+
+* <> and <> for text fields
+** Most `text` fields have a `.keyword` subfield for exact matching using <>
+* <> for date fields
+* 3 <> types:
+** `integer` for whole numbers
+** `long` for large whole numbers
+** `half_float` for floating-point numbers
+* <> for geographic coordinates
+* <> for nested structures such as `products`, `geoip`, `event`
+
+Now that we understand the structure of our sample data, let's start analyzing it.
+
+[discrete]
+[[aggregations-tutorial-basic-metrics]]
+=== Get key business metrics
+
+Let's start by calculating important metrics about orders and customers.
+
+[discrete]
+[[aggregations-tutorial-order-value]]
+==== Get average order size
+
+Calculate the average order value across all orders in the dataset using the <> aggregation.
+
+[source,console]
+----
+GET kibana_sample_data_ecommerce/_search
+{
+ "size": 0, <1>
+ "aggs": {
+ "avg_order_value": { <2>
+ "avg": { <3>
+ "field": "taxful_total_price"
+ }
+ }
+ }
+}
+----
+// TEST[skip:Using Kibana sample data]
+<1> Set `size` to 0 to avoid returning matched documents in the response and return only the aggregation results
+<2> A meaningful name that describes what this metric represents
+<3> Configures an `avg` aggregation, which calculates a simple arithmetic mean
+
+.Example response
+[%collapsible]
+==============
+[source,console-result]
+----
+{
+ "took": 0,
+ "timed_out": false,
+ "_shards": {
+ "total": 1,
+ "successful": 1,
+ "skipped": 0,
+ "failed": 0
+ },
+ "hits": {
+ "total": {
+ "value": 4675, <1>
+ "relation": "eq"
+ },
+ "max_score": null,
+ "hits": [] <2>
+ },
+ "aggregations": {
+ "avg_order_value": { <3>
+ "value": 75.05542864304813 <4>
+ }
+ }
+}
+----
+// TEST[skip:Using Kibana sample data]
+<1> Total number of orders in the dataset
+<2> `hits` is empty because we set `size` to 0
+<3> Results appear under the name we specified in the request
+<4> The average order value is calculated dynamically from all the orders in the dataset
+==============
+
+[discrete]
+[[aggregations-tutorial-order-stats]]
+==== Get multiple order statistics at once
+
+Calculate multiple statistics about orders in one request using the <> aggregation.
+
+[source,console]
+----
+GET kibana_sample_data_ecommerce/_search
+{
+ "size": 0,
+ "aggs": {
+ "order_stats": { <1>
+ "stats": { <2>
+ "field": "taxful_total_price"
+ }
+ }
+ }
+}
+----
+// TEST[skip:Using Kibana sample data]
+<1> A descriptive name for this set of statistics
+<2> `stats` returns count, min, max, avg, and sum at once
+
+.Example response
+[%collapsible]
+==============
+[source,console-result]
+----
+{
+ "aggregations": {
+ "order_stats": {
+ "count": 4675, <1>
+ "min": 6.98828125, <2>
+ "max": 2250, <3>
+ "avg": 75.05542864304813, <4>
+ "sum": 350884.12890625 <5>
+ }
+ }
+}
+----
+// TEST[skip:Using Kibana sample data]
+<1> `"count"`: Total number of orders in the dataset
+<2> `"min"`: Lowest individual order value in the dataset
+<3> `"max"`: Highest individual order value in the dataset
+<4> `"avg"`: Average value per order across all orders
+<5> `"sum"`: Total revenue from all orders combined
+==============
+
+[TIP]
+====
+The <> is more efficient than running individual min, max, avg, and sum aggregations.
+====
+
+[discrete]
+[[aggregations-tutorial-sales-patterns]]
+=== Analyze sales patterns
+
+Let's group orders in different ways to understand sales patterns.
+
+[discrete]
+[[aggregations-tutorial-category-breakdown]]
+==== Break down sales by category
+
+Group orders by category to see which product categories are most popular, using the <> aggregation.
+
+[source,console]
+----
+GET kibana_sample_data_ecommerce/_search
+{
+ "size": 0,
+ "aggs": {
+ "sales_by_category": { <1>
+ "terms": { <2>
+ "field": "category.keyword", <3>
+ "size": 5, <4>
+ "order": { "_count": "desc" } <5>
+ }
+ }
+ }
+}
+----
+// TEST[skip:Using Kibana sample data]
+<1> Name reflecting the business purpose of this breakdown
+<2> `terms` aggregation groups documents by field values
+<3> Use <> field for exact matching on text fields
+<4> Limit to top 5 categories
+<5> Order by number of orders (descending)
+
+.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": {
+ "sales_by_category": {
+ "doc_count_error_upper_bound": 0, <1>
+ "sum_other_doc_count": 572, <2>
+ "buckets": [ <3>
+ {
+ "key": "Men's Clothing", <4>
+ "doc_count": 2024 <5>
+ },
+ {
+ "key": "Women's Clothing",
+ "doc_count": 1903
+ },
+ {
+ "key": "Women's Shoes",
+ "doc_count": 1136
+ },
+ {
+ "key": "Men's Shoes",
+ "doc_count": 944
+ },
+ {
+ "key": "Women's Accessories",
+ "doc_count": 830
+ }
+ ]
+ }
+ }
+}
+----
+// TEST[skip:Using Kibana sample data]
+<1> Due to Elasticsearch's distributed architecture, when <> run across multiple shards, the doc counts may have a small margin of error. This value indicates the maximum possible error in the counts.
+<2> Count of documents in categories beyond the requested size.
+<3> Array of category buckets, ordered by count.
+<4> Category name.
+<5> Number of orders in this category.
+==============
+
+[discrete]
+[[aggregations-tutorial-daily-sales]]
+==== Track daily sales patterns
+
+Group orders by day to track daily sales patterns using the <> aggregation.
+
+[source,console]
+----
+GET kibana_sample_data_ecommerce/_search
+{
+ "size": 0,
+ "aggs": {
+ "daily_orders": { <1>
+ "date_histogram": { <2>
+ "field": "order_date",
+ "calendar_interval": "day", <3>
+ "format": "yyyy-MM-dd", <4>
+ "min_doc_count": 0 <5>
+ }
+ }
+ }
+}
+----
+// TEST[skip:Using Kibana sample data]
+<1> Descriptive name for the time-series aggregation results.
+<2> The `date_histogram` aggregration groups documents into time-based buckets, similar to terms aggregation but for dates.
+<3> Uses <> to handle months with different lengths. `"day"` ensures consistent daily grouping regardless of timezone.
+<4> Formats dates in response using <> (e.g. "yyyy-MM-dd"). Refer to <> for additional options.
+<5> When `min_doc_count` is 0, returns buckets for days with no orders, useful for continuous time series visualization.
+
+.Example response
+[%collapsible]
+==============
+[source,console-result]
+----
+{
+ "took": 2,
+ "timed_out": false,
+ "_shards": {
+ "total": 5,
+ "successful": 5,
+ "skipped": 0,
+ "failed": 0
+ },
+ "hits": {
+ "total": {
+ "value": 4675,
+ "relation": "eq"
+ },
+ "max_score": null,
+ "hits": []
+ },
+ "aggregations": {
+ "daily_orders": { <1>
+ "buckets": [ <2>
+ {
+ "key_as_string": "2024-11-28", <3>
+ "key": 1732752000000, <4>
+ "doc_count": 146 <5>
+ },
+ {
+ "key_as_string": "2024-11-29",
+ "key": 1732838400000,
+ "doc_count": 153
+ },
+ {
+ "key_as_string": "2024-11-30",
+ "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 <> 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 <> 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 <> 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 <> 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 <> 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 <> for more details on all available aggregation types.
\ No newline at end of file
diff --git a/docs/reference/quickstart/index.asciidoc b/docs/reference/quickstart/index.asciidoc
index 31ba67e1b7a60..330582956c457 100644
--- a/docs/reference/quickstart/index.asciidoc
+++ b/docs/reference/quickstart/index.asciidoc
@@ -26,6 +26,7 @@ Alternatively, refer to our <>. Learn about indices, documents, and mappings, and perform a basic search using the Query DSL.
* <>. Learn about different options for querying data, including full-text search and filtering, using the Query DSL.
* <>: Learn how to query and aggregate your data using {esql}.
+* <>. Learn how to analyze data using different types of aggregations, including metrics, buckets, and pipelines.
* <>: Learn how to create embeddings for your data with `semantic_text` and query using the `semantic` query.
** <>: Learn how to combine semantic search with full-text search.
* <>: Learn how to ingest dense vector embeddings into {es}.
@@ -41,3 +42,4 @@ If you're interested in using {es} with Python, check out Elastic Search Labs:
include::getting-started.asciidoc[]
include::full-text-filtering-tutorial.asciidoc[]
+include::aggs-tutorial.asciidoc[]
diff --git a/server/src/main/java/org/elasticsearch/index/mapper/vectors/SparseVectorFieldMapper.java b/server/src/main/java/org/elasticsearch/index/mapper/vectors/SparseVectorFieldMapper.java
index d0a8dfae4f242..19ae649c12a83 100644
--- a/server/src/main/java/org/elasticsearch/index/mapper/vectors/SparseVectorFieldMapper.java
+++ b/server/src/main/java/org/elasticsearch/index/mapper/vectors/SparseVectorFieldMapper.java
@@ -171,6 +171,7 @@ public void parse(DocumentParserContext context) throws IOException {
);
}
+ final boolean isWithinLeaf = context.path().isWithinLeafObject();
String feature = null;
try {
// make sure that we don't expand dots in field names while parsing
@@ -205,7 +206,7 @@ public void parse(DocumentParserContext context) throws IOException {
context.addToFieldNames(fieldType().name());
}
} finally {
- context.path().setWithinLeafObject(false);
+ context.path().setWithinLeafObject(isWithinLeaf);
}
}