分面搜索的聚合
使用 Elasticsearch 聚合，在搜索结果旁构建分面、直方图和汇总指标。
分面搜索的聚合 是 CoddyKit 上的免费 Elasticsearch & Full Text Search Systems 课时。 这是第 4 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Elasticsearch & Full Text Search Systems 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Elasticsearch & Full Text Search Systems 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
Beyond Matching
Search finds documents; aggregations summarize them. They power faceted navigation, dashboards, and analytics, all in the same request as the search.
Two Families
Aggregations come in two main families:
- Bucket aggregations group documents (e.g. by category)
- Metric aggregations compute numbers (e.g. average price)
A Terms Facet
The terms aggregation produces a bucket per distinct value with counts, the classic facet.
GET /products/_search
{ "size": 0, "aggs": {
"by_category": { "terms": { "field": "category" } }
}}size 0 Trick
Setting size: 0 returns no document hits, only the aggregation results, which is efficient when you only want the facets.
Aggregate on keyword
Aggregations run on non-analyzed fields. Use the keyword field, not the analyzed text field, or you will bucket on individual tokens.
{ "terms": { "field": "brand.keyword" } }Metric Aggregations
Metrics like avg, min, max, and sum compute a single value over matching documents.
"aggs": { "avg_price": { "avg": { "field": "price" } } }Histograms
A histogram buckets numeric values into fixed intervals, great for price ranges.
"aggs": { "price_ranges": {
"histogram": { "field": "price", "interval": 100 }
}}Date Histograms
A date_histogram groups by calendar intervals like day or month, ideal for time-series charts.
"aggs": { "per_day": { "date_histogram": {
"field": "created", "calendar_interval": "day"
}}}Nesting Aggregations
You can nest a metric inside a bucket: average price within each category. Nesting is how rich facets are built.
"aggs": { "by_cat": {
"terms": { "field": "category" },
"aggs": { "avg_price": { "avg": { "field": "price" } } }
}}Facets Respect the Query
Aggregations are computed over the documents that match the query and filters, so facet counts naturally reflect the current search context.
Cardinality for Distinct Counts
The cardinality metric gives an approximate distinct-value count, far cheaper than exact counting on large data.
"aggs": { "unique_brands": {
"cardinality": { "field": "brand.keyword" } } }Quick Check
Why aggregate on a keyword field instead of a text field?
Recap
You learned aggregations for faceted search: bucket aggregations like terms and histograms, metric aggregations like avg and cardinality, nesting metrics inside buckets, the size: 0 trick, and why facets use keyword fields.
常见问题解答
「分面搜索的聚合」课时是免费的吗？
是的 — 「分面搜索的聚合」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Elasticsearch & Full Text Search Systems 课程的其余内容，请升级到 CoddyKit PRO。 Elasticsearch & Full Text Search Systems 课程共包含 4 节课。
「分面搜索的聚合」这节课中我会学到什么？
使用 Elasticsearch 聚合，在搜索结果旁构建分面、直方图和汇总指标。 你通过在浏览器中直接运行的动手代码来练习 Elasticsearch & Full Text Search Systems，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Elasticsearch & Full Text Search Systems 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Elasticsearch & Full Text Search Systems 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 4 节课，共 4 节。
「分面搜索的聚合」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Elasticsearch & Full Text Search Systems 课中编写并运行代码吗？
能。每节 Elasticsearch & Full Text Search Systems 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。