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A single aggregation answers one question. Real analytics often need layered answers: average price per category, or top brands per region. Elasticsearch lets you nest aggregations inside one another.

This lesson shows how to compose them.

Any bucket aggregation can contain a sub-aggs block. The sub-aggregation runs once per bucket, operating only on the documents in that bucket.

This is the core mechanism for multi-dimensional analytics.

Here we group documents by category, then compute the average price within each group. The metric aggregation lives inside the bucket aggregation's aggs.

Each bucket in by_category now carries an avg_price value alongside its doc_count. You get the category name, how many docs it holds, and its average price in one response.

You can nest bucket aggregations too. Group by region, then by brand within each region. This produces a two-level breakdown.

Deeply nested terms aggregations can explode combinatorially. A region with 50 brands across 20 regions yields up to 1,000 buckets. Use the size parameter to limit returned buckets and protect memory.

A bucket can hold several sub-aggregations at once. Here each category reports both its average and maximum price.

When data uses the nested field type, you must enter that scope with a nested aggregation before aggregating on its inner fields. It points at a path.

Inside a nested aggregation you can jump back to the parent document scope using reverse_nested. This lets you count distinct parent docs that contain a matching nested element.

Order parent buckets by a nested sub-metric using order. Here categories are sorted by their average price, descending.

Keep nesting shallow when possible, always set size on inner terms, and put the most selective bucket aggregation first to reduce the work done by deeper levels.

You learned to compose aggregations:

• Any bucket aggregation can hold sub-aggs that run per bucket.
• Nest metrics in buckets, or buckets in buckets for multi-level breakdowns.
• Use the nested and reverse_nested aggregations to traverse nested document scopes.
• Control bucket explosion with size and order buckets by sub-metrics with order.