Bucket Aggregations
Group your documents into 'buckets' based on fields like terms, ranges, or dates, enabling faceted search and data categorization.
Bucket Aggregations is a free Elasticsearch & Full Text Search Systems lesson on CoddyKit — lesson 2 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.
Grouping Your Data with Buckets
Welcome to Bucket Aggregations! In Elasticsearch, aggregations allow you to analyze your data.
Bucket aggregations specifically group documents into sets, or 'buckets', based on field values. Think of it like the GROUP BY clause in SQL.
- Categorize data: Group products by brand.
- Faceted search: Show counts for different filters.
- Analyze trends: See activity per day or month.
Finding Unique Values with Terms
The terms aggregation is one of the most common bucket aggregations. It finds the top unique values for a specific field and then counts how many documents fall into each unique value's bucket.
It's incredibly useful for seeing the distribution of categorical data, like product categories, user roles, or country names.
Example: Products by Category
Let's use a terms aggregation to find the top 5 product categories and the count of products in each. We use .keyword for exact matches on text fields.
GET /products/_search
{
"size": 0,
"aggs": {
"top_categories": {
"terms": {
"field": "category.keyword",
"size": 5
}
}
}
}Bucketing by Numeric Ranges
The range aggregation allows you to define custom ranges for numeric or date fields. Documents whose field values fall within a defined range are grouped into that bucket.
This is perfect for creating price tiers (e.g., $0-10, $10-50, $50+) or age groups (e.g., 0-18, 19-65, 65+).
Example: Products by Price Range
Here, we define three price ranges: products under $10, between $10 and $50, and over $50. The size: 0 means we only want aggregation results, not actual search hits.
GET /products/_search
{
"size": 0,
"aggs": {
"price_tiers": {
"range": {
"field": "price",
"ranges": [
{ "to": 10.00 },
{ "from": 10.00, "to": 50.00 },
{ "from": 50.00 }
]
}
}
}
}Grouping Data Over Time
When working with time-series data, the date_histogram aggregation is your best friend. It buckets documents into fixed time intervals like minutes, hours, days, or months.
You specify an interval (e.g., 'day', 'month') and Elasticsearch automatically creates buckets for each period, even if no documents exist for a particular period.
Example: Sales Trends by Month
This aggregation groups sales orders by month, allowing you to easily track monthly sales performance. We assume an order_date field of type date.
GET /sales/_search
{
"size": 0,
"aggs": {
"monthly_sales": {
"date_histogram": {
"field": "order_date",
"calendar_interval": "month"
}
}
}
}Multi-Level Grouping: Nesting Buckets
The true power of bucket aggregations comes from nesting them. You can place one bucket aggregation inside another to create hierarchical groupings.
For example, you might want to see product categories, and then within each category, the price ranges of products. This enables deep, multi-dimensional analysis.
Where Buckets Shine
Bucket aggregations are fundamental for many real-world applications:
- Faceted Search: Allowing users to filter search results by category, brand, price range, etc.
- Data Exploration: Discovering patterns and distributions in your data.
- Dashboards: Building visualizations like bar charts (e.g., sales per month, products per category).
- Reporting: Generating summary reports based on grouped data.
Bucket Aggregation Quiz
You are analyzing user feedback and want to count how many reviews were submitted each day over the past week. Which aggregation type is most suitable?
Bucket Aggregations Recap
Great job! You've learned about the power of Bucket Aggregations in Elasticsearch.
- They group documents into logical 'buckets'.
terms: Groups by unique field values.range: Groups by custom numeric or date ranges.date_histogram: Groups by fixed time intervals.- You can nest them for multi-level analysis.
Next, we'll explore Metric Aggregations, which perform calculations (like sum, avg, min, max) on the data within these buckets!
Frequently asked questions
Is the “Bucket Aggregations” lesson free?
Yes — the full text of “Bucket 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 “Bucket Aggregations”?
Group your documents into 'buckets' based on fields like terms, ranges, or dates, enabling faceted search and data categorization. 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 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Bucket 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