Lesson 4 — Aggregations #

From counts to histograms to composite paging — over sales and logs, with known answers you can verify by hand.

Metric aggregations #

One number out: sum, avg, min, max, stats (all of them at once), cardinality (distinct values):

Four distinct product types; stats gives you min/max/sum/avg/count in one round trip.

Bucket aggregations #

terms builds a bucket per value — the dataset’s known answer is t-shirt 60, hat 30, mug 20, sticker 10:

histogram slices a number into fixed intervals; date_histogram slices time — 92 sales in 2024, 28 in 2025:

Sub-aggregations #

Every bucket runs its own aggs — revenue per type as sum of price inside a terms bucket:

Filtered aggregations #

filter scopes an aggregation to a query; filters builds named buckets — the logs level split (error 25 / warn 25 / info 100):

Paging through buckets #

composite pages through multi-dimensional buckets with an after cursor — for dashboards, not top-10 lists:

What you learned #

• Metric aggs summarize; bucket aggs group; sub-aggs nest.
• size: 0 skips hit assembly — the aggregations ARE the result.
• composite + after cursors for complete bucket walks.

Next: Lesson 5 — relations and hybrid search.