Pipeline Aggregations
Chain aggregations together to perform calculations on the results of other aggregations, creating sophisticated analytical pipelines.
Pipeline Aggregations is a free Elasticsearch & Full Text Search Systems lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Elasticsearch & Full Text Search Systems learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Intro to Pipeline Aggregations
Welcome to Pipeline Aggregations! So far, we've learned about metric and bucket aggregations.
Pipeline aggregations take things a step further. They operate on the results of other aggregations, allowing you to chain them together for more sophisticated analytics.
Beyond Basic Aggregations
Think of basic aggregations as giving you statistics within each bucket (e.g., total sales per product category).
Pipeline aggregations, on the other hand, allow you to calculate statistics across those buckets or over time. For example, finding the overall total of 'total sales per category'.
Chaining Aggregations
The core idea is chaining. A pipeline aggregation receives its input from another aggregation's output.
- It processes the results of 'sibling' or 'parent' aggregations.
- This enables multi-stage analysis, where one calculation feeds into the next.
sum_bucket Aggregation
One common pipeline aggregation is sum_bucket. It calculates the sum of a specified metric across all sibling buckets.
Use case: You've grouped sales by product category and calculated the total sales for each category. Now you want to find the grand total sales across all categories.
sum_bucket in Action
This example first gets total sales per category, then uses sum_bucket to sum those category totals into an overall total. You can send this JSON to Elasticsearch using curl -XGET 'localhost:9200/your_index/_search?pretty' -H 'Content-Type: application/json' -d'...'
{ "size": 0,
"aggs": {
"sales_by_category": {
"terms": { "field": "product_category.keyword" },
"aggs": {
"total_sales_per_category": { "sum": { "field": "sales_amount" } }
}
},
"overall_total_sales": {
"sum_bucket": {
"buckets_path": "sales_by_category>total_sales_per_category"
}
}
}
}avg_bucket Aggregation
Similar to sum_bucket, the avg_bucket aggregation calculates the average of a specific metric across all sibling buckets.
Use case: After getting the total sales for each product category, you might want to know the average total sales amount *per category*.
avg_bucket in Action
Here, we use avg_bucket to calculate the average of the 'total sales per category' values. Notice the buckets_path remains the same, pointing to the aggregation whose results we want to average.
{ "size": 0,
"aggs": {
"sales_by_category": {
"terms": { "field": "product_category.keyword" },
"aggs": {
"total_sales_per_category": { "sum": { "field": "sales_amount" } }
}
},
"average_sales_per_category": {
"avg_bucket": {
"buckets_path": "sales_by_category>total_sales_per_category"
}
}
}
}min_bucket & max_bucket
Elasticsearch also provides min_bucket and max_bucket pipeline aggregations.
min_bucket: Finds the minimum value among the specified metric from all sibling buckets.max_bucket: Finds the maximum value among the specified metric from all sibling buckets.
These work just like sum_bucket and avg_bucket, simply changing the operation.
Understanding buckets_path
The buckets_path parameter is crucial for pipeline aggregations. It tells Elasticsearch which aggregation's output to use as input.
- It uses a
>separator for nested aggregations (e.g.,parent_agg>child_agg). - You can also reference the document count of a bucket using
_count(e.g.,sales_by_category>_count).
Pipeline Aggregation Check
Let's check your understanding of pipeline aggregations!
Pipeline Aggregations Recap
Great job! In this lesson, you learned about:
- What pipeline aggregations are and why they're powerful.
- How they allow you to perform calculations across the results of other aggregations.
- Key types like
sum_bucket,avg_bucket,min_bucket, andmax_bucket. - The importance of the
buckets_pathparameter for referencing aggregation outputs.
Pipeline aggregations enable a deeper level of data analysis in Elasticsearch!
Frequently asked questions
Is the “Pipeline Aggregations” lesson free?
Yes — the full text of “Pipeline Aggregations” is free to read here on the web, and the Elasticsearch & Full Text Search Systems course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Elasticsearch & Full Text Search Systems course, upgrade to CoddyKit PRO.
What will I learn in “Pipeline Aggregations”?
Chain aggregations together to perform calculations on the results of other aggregations, creating sophisticated analytical pipelines. You practise Elasticsearch & Full Text Search Systems with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Elasticsearch & Full Text Search Systems?
No prior experience is required. Elasticsearch & Full Text Search Systems on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Pipeline Aggregations” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Elasticsearch & Full Text Search Systems lesson?
Yes. Every Elasticsearch & Full Text Search Systems lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Metric Aggregations
- Bucket Aggregations
- Pipeline Aggregations
- Nested and Sub-Aggregations