Elastic
Tutorial: Analyze eCommerce data with aggregations using Query DSL
This hands-on tutorial shows you how to analyze eCommerce data using Elasticsearch aggregations 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
Requirements ¶
You’ll need:
- A running instance of Elasticsearch, either on Elastic Cloud Serverless or together with Kibana 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 local dev environment:
curl -fsSL https://elastic.co/start-local | sh
- If you don’t have a deployment, you can run the following command in your terminal to set up a local dev environment:
- The sample eCommerce data loaded into Elasticsearch. To load sample data follow these steps in your UI:
- Open the Integrations pages by searching in the global search field.
- Search for
sample datain 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.
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 mappings:
GET kibana_sample_data_ecommerce/_mapping
The response shows the field mappings for the kibana_sample_data_ecommerce index.
Example 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"
}
}
}
}
}
-
fields: Multi-field mapping that allows both full text and exact matching -
geoip.properties: Object type field containing location-related properties -
geoip.location: Geographic coordinates stored as geo_point for location-based queries -
products.properties: Nested structure containing details about items in each order
The sample data includes the following field data types:
-
textandkeywordfor text fields- Most
textfields have a.keywordsubfield for exact matching using multi-fields
- Most
-
datefor date fields - 3 numeric types:
-
integerfor whole numbers -
longfor large whole numbers -
half_floatfor floating-point numbers
-
-
geo_pointfor geographic coordinates -
objectfor nested structures such asproducts,geoip,event
Now that we understand the structure of our sample data, let’s start analyzing it.
Get key business metrics ¶
Let’s start by calculating important metrics about orders and customers.
Get average order size ¶
Calculate the average order value across all orders in the dataset using the avg aggregation.
GET kibana_sample_data_ecommerce/_search
{
"size": 0, 1
"aggs": {
"avg_order_value": { 2
"avg": { 3
"field": "taxful_total_price"
}
}
}
}
- Set
sizeto 0 to avoid returning matched documents in the response and return only the aggregation results - A meaningful name that describes what this metric represents
- Configures an
avgaggregation, which calculates a simple arithmetic mean
Example response
{
"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
}
}
}
- Total number of orders in the dataset
-
hitsis empty because we setsizeto 0 - Results appear under the name we specified in the request
- The average order value is calculated dynamically from all the orders in the dataset
Get multiple order statistics at once ¶
Calculate multiple statistics about orders in one request using the stats aggregation.
GET kibana_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"order_stats": { 1
"stats": { 2
"field": "taxful_total_price"
}
}
}
}
- A descriptive name for this set of statistics
-
statsreturns count, min, max, avg, and sum at once
Example response
{
"aggregations": {
"order_stats": {
"count": 4675, 1
"min": 6.98828125, 2
"max": 2250, 3
"avg": 75.05542864304813, 4
"sum": 350884.12890625 5
}
}
}
-
"count": Total number of orders in the dataset -
"min": Lowest individual order value in the dataset -
"max": Highest individual order value in the dataset -
"avg": Average value per order across all orders -
"sum": Total revenue from all orders combined
Tip
The stats aggregation is more efficient than running individual min, max, avg, and sum aggregations.
Analyze sales patterns ¶
Let’s group orders in different ways to understand sales patterns.
Break down sales by category ¶
Group orders by category to see which product categories are most popular, using the terms aggregation.
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
}
}
}
}
- Name reflecting the business purpose of this breakdown
-
termsaggregation groups documents by field values - Use
.keywordfield for exact matching on text fields - Limit to top 5 categories
- Order by number of orders (descending)
Example response
{
"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
}
]
}
}
}
- Due to Elasticsearch’s distributed architecture, when 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.
- Count of documents in categories beyond the requested size.
- Array of category buckets, ordered by count.
- Category name.
- Number of orders in this category.
Track daily sales patterns ¶
Group orders by day to track daily sales patterns using the date_histogram aggregation.
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
}
}
}
}
- Descriptive name for the time-series aggregation results.
- The
date_histogramaggregation groups documents into time-based buckets, similar to terms aggregation but for dates. - Uses calendar and fixed time intervals to handle months with different lengths.
"day"ensures consistent daily grouping regardless of timezone. - Formats dates in response using date patterns (e.g. "yyyy-MM-dd"). Refer to date math expressions for additional options.
- When
min_doc_countis 0, returns buckets for days with no orders, useful for continuous time series visualization.
Example response
{
"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
}
]
}
}
}
- Results of our named aggregation "daily_orders"
- Time-based buckets from date_histogram aggregation
-
key_as_stringis the human-readable date for this bucket -
keyis the same date represented as the Unix timestamp for this bucket -
doc_countcounts the number of documents that fall into this time bucket
Combine metrics with groupings ¶
Now let’s calculate metrics within each group to get deeper insights.
Compare category performance ¶
Calculate metrics within each category to compare performance across categories.
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"
}
}
}
}
}
}
- Order categories by their total revenue instead of count
- Define metrics to calculate within each category
- Total revenue for the category
- Average order value in the category
- Total number of items sold
Example response
{
"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,
...
}
]
}
}
}
- Category name
- Number of orders
- Total revenue for this category
- Average order value for this category
- Total quantity of items sold
Analyze daily sales performance ¶
Let’s combine metrics to track daily trends: daily revenue, unique customers, and average basket size.
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"
}
}
}
}
}
}
- Daily revenue
- Uses the
cardinalityaggregation to count unique customers per day - Average number of items per order
Example response
{
"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": {
"value": 42
},
"revenue": {
"value": 10578.53125
},
"avg_basket_size": {
"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
}
}
]
}
}
}
Track trends and patterns ¶
You can use pipeline aggregations on the results of other aggregations. Let’s analyze how metrics change over time.
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 Moving Function aggregation.
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
}
}
}
}
}
}
- Calculate daily revenue first.
- Create a smoothed version of the daily revenue.
- Use
moving_fnfor moving window calculations. - Reference the revenue from our date histogram.
- Use a 3-day window — use different window sizes to see trends at different time scales.
- Use the built-in unweighted average function in the
moving_fnaggregation.
Example response
{
"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
}
}
]
}
}
}
- Date of the bucket is in default ISO format because we didn’t specify a format
- Number of orders for this day
- Raw daily revenue before smoothing
- First day has no smoothed value as it needs previous days for the calculation
- 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.
Track running totals ¶
Track running totals over time using the cumulative_sum aggregation.
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
}
}
}
}
}
}
- Name for our running total
-
cumulative_sumadds up values across buckets - Reference the revenue we want to accumulate
Example response
{
"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
}
}
]
}
}
}
-
daily_sales: Results from our daily sales date histogram -
buckets: Array of time-based buckets -
key_as_string: Date for this bucket (in ISO format since no format specified) -
revenue: Daily revenue for this date -
cumulative_revenue: Running total of revenue up to this date
Next steps ¶
Refer to the aggregations reference for more details on all available aggregation types.