> For the complete documentation index, see [llms.txt](https://vergil-lai.gitbook.io/manticoresearch-manual-zh/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://vergil-lai.gitbook.io/manticoresearch-manual-zh/5-sou-suo/faceted_search.md). # • 分面搜索 分面搜索对于现代搜索应用程序来说，与[自动完成](/manticoresearch-manual-zh/5-sou-suo/autocomplete.md)、[拼写校正](/manticoresearch-manual-zh/5-sou-suo/spell_correction.md)和搜索关键词[高亮显示](/manticoresearch-manual-zh/5-sou-suo/highlighting.md)同样重要，尤其是在电子商务产品中。 ![Faceted search](/files/3eCF83EgqwZwlvu06Oh5) 当处理大量数据和各种相互关联的属性（如尺寸、颜色、制造商或其他因素）时，分面搜索非常有用。查询大量数据时，搜索结果往往包含许多不符合用户预期的条目。分面搜索使终端用户能够明确定义他们希望搜索结果满足的条件。 在 Manticore Search 中，有一种优化机制，它保持原始查询的结果集，并在每次计算分面时重新使用它。由于聚合应用于已计算的文档子集，因此它们的速度很快，总执行时间通常仅比初始查询稍长。可以将分面添加到任何查询中，分面可以是任何属性或表达式。分面结果包括分面值和分面计数。可以通过在查询的最后声明分面，在 SQL `SELECT` 语句中访问分面。 ## 聚合 ### SQL 分面值可以来源于属性、JSON 属性内的 JSON 属性或表达式。分面值也可以使用别名，但**别名必须在所有结果集（主查询结果集和其他分面结果集）中唯一**。分面值是从聚合的属性或表达式中得出的，但也可以来自其他属性或表达式。 ```sql FACET {expr_list} [BY {expr_list} ] [DISTINCT {field_name}] [ORDER BY {expr | FACET()} {ASC | DESC}] [LIMIT [offset,] count] ``` 多个分面声明必须用空格分隔。 ### HTTP JSON 可以在 `aggs` 节点中定义分面： ```json "aggs" : { "group name" : { "terms" : { "field":"attribute name", "size": 1000 } "sort": [ {"attribute name": { "order":"asc" }} ] } } ``` 其中： * `group name` 是分配给聚合的别名 * `field` 值必须包含正在进行分面的属性或表达式的名称 * 可选的 `size` 指定结果中包含的最大桶数。如果未指定，则继承主查询的限制。更多详细信息可以在 [分面结果大小](#Size-of-facet-result) 部分找到。 * 可选的 `sort` 指定属性和/或附加属性数组，语法与[主查询中的 "sort" 参数](/manticoresearch-manual-zh/5-sou-suo/sorting_and_ranking.md#Sorting-via-JSON)相同。 结果集将包含一个带有返回分面的 `aggregations` 节点，其中 `key` 是聚合值，`doc_count` 是聚合计数。 ```json "aggregations": { "group name": { "buckets": [ { "key": 10, "doc_count": 1019 }, { "key": 9, "doc_count": 954 }, { "key": 8, "doc_count": 1021 }, { "key": 7, "doc_count": 1011 }, { "key": 6, "doc_count": 997 } ] } } ``` **SQL:** ```sql SELECT *, price AS aprice FROM facetdemo LIMIT 10 FACET price LIMIT 10 FACET brand_id LIMIT 5; ``` ```sql +------+-------+----------+---------------------+------------+-------------+---------------------------------------+------------+--------+ | id | price | brand_id | title | brand_name | property | j | categories | aprice | +------+-------+----------+---------------------+------------+-------------+---------------------------------------+------------+--------+ | 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | 306 | | 2 | 400 | 10 | Product Three One | Brand Ten | Four_Three | {"prop1":69,"prop2":19,"prop3":"One"} | 13,14 | 400 | ... | 9 | 560 | 6 | Product Two Five | Brand Six | Eight_Two | {"prop1":90,"prop2":84,"prop3":"One"} | 13,14 | 560 | | 10 | 229 | 9 | Product Three Eight | Brand Nine | Seven_Three | {"prop1":84,"prop2":39,"prop3":"One"} | 12,13 | 229 | +------+-------+----------+---------------------+------------+-------------+---------------------------------------+------------+--------+ 10 rows in set (0.00 sec) +-------+----------+ | price | count(*) | +-------+----------+ | 306 | 7 | | 400 | 13 | ... | 229 | 9 | | 595 | 10 | +-------+----------+ 10 rows in set (0.00 sec) +----------+----------+ | brand_id | count(*) | +----------+----------+ | 1 | 1013 | | 10 | 998 | | 5 | 1007 | | 8 | 1033 | | 7 | 965 | +----------+----------+ 5 rows in set (0.00 sec) ``` **JSON:** ```json POST /search -d ' { "index" : "facetdemo", "query" : {"match_all" : {} }, "limit": 5, "aggs" : { "group_property" : { "terms" : { "field":"price" } }, "group_brand_id" : { "terms" : { "field":"brand_id" } } } } ' ``` ```json { "took": 3, "timed_out": false, "hits": { "total": 10000, "hits": [ { "_id": 1, "_score": 1, "_source": { "price": 197, "brand_id": 10, "brand_name": "Brand Ten", "categories": [ 10 ] } }, ... { "_id": 5, "_score": 1, "_source": { "price": 805, "brand_id": 7, "brand_name": "Brand Seven", "categories": [ 11, 12, 13 ] } } ] }, "aggregations": { "group_property": { "buckets": [ { "key": 1000, "doc_count": 11 }, { "key": 999, "doc_count": 12 }, ... { "key": 991, "doc_count": 7 } ] }, "group_brand_id": { "buckets": [ { "key": 10, "doc_count": 1019 }, { "key": 9, "doc_count": 954 }, { "key": 8, "doc_count": 1021 }, { "key": 7, "doc_count": 1011 }, { "key": 6, "doc_count": 997 } ] } } } ``` **PHP:** ```php $index->setName('facetdemo'); $search = $index->search(''); $search->limit(5); $search->facet('price','price'); $search->facet('brand_id','group_brand_id'); $results = $search->get(); ``` ```php Array ( [price] => Array ( [buckets] => Array ( [0] => Array ( [key] => 1000 [doc_count] => 11 ) [1] => Array ( [key] => 999 [doc_count] => 12 ) [2] => Array ( [key] => 998 [doc_count] => 7 ) [3] => Array ( [key] => 997 [doc_count] => 14 ) [4] => Array ( [key] => 996 [doc_count] => 8 ) ) ) [group_brand_id] => Array ( [buckets] => Array ( [0] => Array ( [key] => 10 [doc_count] => 1019 ) [1] => Array ( [key] => 9 [doc_count] => 954 ) [2] => Array ( [key] => 8 [doc_count] => 1021 ) [3] => Array ( [key] => 7 [doc_count] => 1011 ) [4] => Array ( [key] => 6 [doc_count] => 997 ) ) ) ) ``` **Python:** ```python res =searchApi.search({"index":"facetdemo","query":{"match_all":{}},"limit":5,"aggs":{"group_property":{"terms":{"field":"price",}},"group_brand_id":{"terms":{"field":"brand_id"}}}}) ``` ```python {'aggregations': {u'group_brand_id': {u'buckets': [{u'doc_count': 1019, u'key': 10}, {u'doc_count': 954, u'key': 9}, {u'doc_count': 1021, u'key': 8}, {u'doc_count': 1011, u'key': 7}, {u'doc_count': 997, u'key': 6}]}, u'group_property': {u'buckets': [{u'doc_count': 11, u'key': 1000}, {u'doc_count': 12, u'key': 999}, {u'doc_count': 7, u'key': 998}, {u'doc_count': 14, u'key': 997}, {u'doc_count': 8, u'key': 996}]}}, 'hits': {'hits': [{u'_id': u'1', u'_score': 1, u'_source': {u'brand_id': 10, u'brand_name': u'Brand Ten', u'categories': [10], u'price': 197, u'property': u'Six', u'title': u'Product Eight One'}}, {u'_id': u'2', u'_score': 1, u'_source': {u'brand_id': 6, u'brand_name': u'Brand Six', u'categories': [12, 13, 14], u'price': 671, u'property': u'Four', u'title': u'Product Nine Seven'}}, {u'_id': u'3', u'_score': 1, u'_source': {u'brand_id': 3, u'brand_name': u'Brand Three', u'categories': [13, 14, 15], u'price': 92, u'property': u'Six', u'title': u'Product Five Four'}}, {u'_id': u'4', u'_score': 1, u'_source': {u'brand_id': 10, u'brand_name': u'Brand Ten', u'categories': [11], u'price': 713, u'property': u'Five', u'title': u'Product Eight Nine'}}, {u'_id': u'5', u'_score': 1, u'_source': {u'brand_id': 7, u'brand_name': u'Brand Seven', u'categories': [11, 12, 13], u'price': 805, u'property': u'Two', u'title': u'Product Ten Three'}}], 'max_score': None, 'total': 10000}, 'profile': None, 'timed_out': False, 'took': 4} ``` **Javascript:** ```javascript res = await searchApi.search({"index":"facetdemo","query":{"match_all":{}},"limit":5,"aggs":{"group_property":{"terms":{"field":"price",}},"group_brand_id":{"terms":{"field":"brand_id"}}}}); ``` ```javascript {"took":0,"timed_out":false,"hits":{"total":10000,"hits":[{"_id": 1,"_score":1,"_source":{"price":197,"brand_id":10,"brand_name":"Brand Ten","categories":[10],"title":"Product Eight One","property":"Six"}},{"_id": 2,"_score":1,"_source":{"price":671,"brand_id":6,"brand_name":"Brand Six","categories":[12,13,14],"title":"Product Nine Seven","property":"Four"}},{"_id": 3,"_score":1,"_source":{"price":92,"brand_id":3,"brand_name":"Brand Three","categories":[13,14,15],"title":"Product Five Four","property":"Six"}},{"_id": 4,"_score":1,"_source":{"price":713,"brand_id":10,"brand_name":"Brand Ten","categories":[11],"title":"Product Eight Nine","property":"Five"}},{"_id": 5,"_score":1,"_source":{"price":805,"brand_id":7,"brand_name":"Brand Seven","categories":[11,12,13],"title":"Product Ten Three","property":"Two"}}]}} ``` **Java:** ```java aggs = new HashMap(){{ put("group_property", new HashMap(){{ put("terms", new HashMap(){{ put("field","price"); }}); }}); put("group_brand_id", new HashMap(){{ put("terms", new HashMap(){{ put("field","brand_id"); }}); }}); }}; searchRequest = new SearchRequest(); searchRequest.setIndex("facetdemo"); searchRequest.setLimit(5); query = new HashMap(); query.put("match_all",null); searchRequest.setQuery(query); searchRequest.setAggs(aggs); searchResponse = searchApi.search(searchRequest); ``` ```java class SearchResponse { took: 0 timedOut: false aggregations: {group_property={buckets=[{key=1000, doc_count=11}, {key=999, doc_count=12}, {key=998, doc_count=7}, {key=997, doc_count=14}, {key=996, doc_count=8}]}, group_brand_id={buckets=[{key=10, doc_count=1019}, {key=9, doc_count=954}, {key=8, doc_count=1021}, {key=7, doc_count=1011}, {key=6, doc_count=997}]}} hits: class SearchResponseHits { maxScore: null total: 10000 hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two}}] } profile: null } ``` **C#:** ```clike var agg1 = new Aggregation("group_property", "price"); var agg2 = new Aggregation("group_brand_id", "brand_id"); object query = new { match_all=null }; var searchRequest = new SearchRequest("facetdemo", query); searchRequest.Limit = 5; searchRequest.Aggs = new List {agg1, agg2}; var searchResponse = searchApi.Search(searchRequest); ``` ```clike class SearchResponse { took: 0 timedOut: false aggregations: {group_property={buckets=[{key=1000, doc_count=11}, {key=999, doc_count=12}, {key=998, doc_count=7}, {key=997, doc_count=14}, {key=996, doc_count=8}]}, group_brand_id={buckets=[{key=10, doc_count=1019}, {key=9, doc_count=954}, {key=8, doc_count=1021}, {key=7, doc_count=1011}, {key=6, doc_count=997}]}} hits: class SearchResponseHits { maxScore: null total: 10000 hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two}}] } profile: null } ``` ```typescript res = await searchApi.search({ index: 'test', query: { match_all:{} }, aggs: { name_group: { terms: { field : 'name' } }, cat_group: { terms: { field: 'cat' } } } }); ``` ```typescript { "took": 0, "timed_out": false, "hits": { "total": 5, "hits": [ { "_id": 1, "_score": 1, "_source": { "content": "Text 1", "name": "Doc 1", "cat": 1 } }, ... { "_id": 5, "_score": 1, "_source": { "content": "Text 5", "name": "Doc 5", "cat": 4 } } ] }, "aggregations": { "name_group": { "buckets": [ { "key": "Doc 1", "doc_count": 1 }, ... { "key": "Doc 5", "doc_count": 1 } ] }, "cat_group": { "buckets": [ { "key": 1, "doc_count": 2 }, ... { "key": 4, "doc_count": 1 } ] } } } ``` ```go query := map[string]interface{} {} searchRequest.SetQuery(query) aggByName := manticoreclient.NewAggregation() aggTerms := manticoreclient.NewAggregationTerms() aggTerms.SetField("name") aggByName.SetTerms(aggTerms) aggByCat := manticoreclient.NewAggregation() aggTerms.SetField("cat") aggByCat.SetTerms(aggTerms) aggs := map[string]Aggregation{} { "name_group": aggByName, "cat_group": aggByCat } searchRequest.SetAggs(aggs) res, _, _ := apiClient.SearchAPI.Search(context.Background()).SearchRequest(*searchRequest).Execute() ``` ```go { "took": 0, "timed_out": false, "hits": { "total": 5, "hits": [ { "_id": 1, "_score": 1, "_source": { "content": "Text 1", "name": "Doc 1", "cat": 1 } }, ... { "_id": 5, "_score": 1, "_source": { "content": "Text 5", "name": "Doc 5", "cat": 4 } } ] }, "aggregations": { "name_group": { "buckets": [ { "key": "Doc 1", "doc_count": 1 }, ... { "key": "Doc 5", "doc_count": 1 } ] }, "cat_group": { "buckets": [ { "key": 1, "doc_count": 2 }, ... { "key": 4, "doc_count": 1 } ] } } } ``` ### 通过聚合另一个属性进行分面 可以通过对另一个属性或表达式进行聚合来进行分面。例如，如果文档同时包含品牌 ID 和名称，我们可以在分面结果中返回品牌名称，但聚合的是品牌 ID。可以通过使用 `FACET {expr1} BY {expr2}` 来实现。 **SQL:** ```sql SELECT * FROM facetdemo FACET brand_name by brand_id; ``` ```sql +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+ | id | price | brand_id | title | brand_name | property | j | categories | +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+ | 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | | 2 | 400 | 10 | Product Three One | Brand Ten | Four_Three | {"prop1":69,"prop2":19,"prop3":"One"} | 13,14 | .... | 19 | 855 | 1 | Product Seven Two | Brand One | Eight_Seven | {"prop1":63,"prop2":78,"prop3":"One"} | 10,11,12 | | 20 | 31 | 9 | Product Four One | Brand Nine | Ten_Four | {"prop1":79,"prop2":42,"prop3":"One"} | 12,13,14 | +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+ 20 rows in set (0.00 sec) +-------------+----------+ | brand_name | count(*) | +-------------+----------+ | Brand One | 1013 | | Brand Ten | 998 | | Brand Five | 1007 | | Brand Nine | 944 | | Brand Two | 990 | | Brand Six | 1039 | | Brand Three | 1016 | | Brand Four | 994 | | Brand Eight | 1033 | | Brand Seven | 965 | +-------------+----------+ 10 rows in set (0.00 sec) ``` ### 无重复分面 如果需要从 `FACET` 返回的桶中移除重复项，可以使用 `DISTINCT field_name`，其中 `field_name` 是你想要进行去重的字段。如果你在对分布式表进行 FACET 查询且不确定表中的 ID 是否唯一（表应是本地的并且具有相同的架构），它也可以是 `id`（这是默认值）。 如果在查询中有多个 FACET 声明，`field_name` 应该在所有声明中保持一致。 `DISTINCT` 会在 `count(*)` 列之前返回一个额外的列 `count(distinct ...)`，这样你可以在不需要另一个查询的情况下获取两种结果。 **SQL:** ```sql SELECT brand_name, property FROM facetdemo FACET brand_name distinct property; ``` ```sql +-------------+----------+ | brand_name | property | +-------------+----------+ | Brand Nine | Four | | Brand Ten | Four | | Brand One | Five | | Brand Seven | Nine | | Brand Seven | Seven | | Brand Three | Seven | | Brand Nine | Five | | Brand Three | Eight | | Brand Two | Eight | | Brand Six | Eight | | Brand Ten | Four | | Brand Ten | Two | | Brand Four | Ten | | Brand One | Nine | | Brand Four | Eight | | Brand Nine | Seven | | Brand Four | Five | | Brand Three | Four | | Brand Four | Two | | Brand Four | Eight | +-------------+----------+ 20 rows in set (0.00 sec) +-------------+--------------------------+----------+ | brand_name | count(distinct property) | count(*) | +-------------+--------------------------+----------+ | Brand Nine | 3 | 3 | | Brand Ten | 2 | 3 | | Brand One | 2 | 2 | | Brand Seven | 2 | 2 | | Brand Three | 3 | 3 | | Brand Two | 1 | 1 | | Brand Six | 1 | 1 | | Brand Four | 4 | 5 | +-------------+--------------------------+----------+ 8 rows in set (0.00 sec) ``` ### 表达式上的分面 分面可以聚合表达式。一个经典的例子是通过特定的范围对价格进行分段： ```sql SELECT * FROM facetdemo FACET INTERVAL(price,200,400,600,800) AS price_range ; ``` ```sql +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+ | id | price | brand_id | title | brand_name | property | j | categories | price_range | +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+ | 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | 1 | ... +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+ 20 rows in set (0.00 sec) +-------------+----------+ | price_range | count(*) | +-------------+----------+ | 0 | 1885 | | 3 | 1973 | | 4 | 2100 | | 2 | 1999 | | 1 | 2043 | +-------------+----------+ 5 rows in set (0.01 sec) ``` ```json POST /search -d ' { "index": "facetdemo", "query": { "match_all": {} }, "expressions": { "price_range": "INTERVAL(price,200,400,600,800)" }, "aggs": { "group_property": { "terms": { "field": "price_range" } } } } ``` ```json { "took": 3, "timed_out": false, "hits": { "total": 10000, "hits": [ { "_id": 1, "_score": 1, "_source": { "price": 197, "brand_id": 10, "brand_name": "Brand Ten", "categories": [ 10 ], "price_range": 0 } }, ... { "_id": 20, "_score": 1, "_source": { "price": 227, "brand_id": 3, "brand_name": "Brand Three", "categories": [ 12, 13 ], "price_range": 1 } } ] }, "aggregations": { "group_property": { "buckets": [ { "key": 4, "doc_count": 2100 }, { "key": 3, "doc_count": 1973 }, { "key": 2, "doc_count": 1999 }, { "key": 1, "doc_count": 2043 }, { "key": 0, "doc_count": 1885 } ] } } } ``` ```php $index->setName('facetdemo'); $search = $index->search(''); $search->limit(5); $search->expression('price_range','INTERVAL(price,200,400,600,800)'); $search->facet('price_range','group_property'); $results = $search->get(); print_r($results->getFacets()); ``` ```php Array ( [group_property] => Array ( [buckets] => Array ( [0] => Array ( [key] => 4 [doc_count] => 2100 ) [1] => Array ( [key] => 3 [doc_count] => 1973 ) [2] => Array ( [key] => 2 [doc_count] => 1999 ) [3] => Array ( [key] => 1 [doc_count] => 2043 ) [4] => Array ( [key] => 0 [doc_count] => 1885 ) ) ) ) ``` ```python res =searchApi.search({"index":"facetdemo","query":{"match_all":{}},"expressions":{"price_range":"INTERVAL(price,200,400,600,800)"},"aggs":{"group_property":{"terms":{"field":"price_range"}}}}) ``` ```python {'aggregations': {u'group_brand_id': {u'buckets': [{u'doc_count': 1019, u'key': 10}, {u'doc_count': 954, u'key': 9}, {u'doc_count': 1021, u'key': 8}, {u'doc_count': 1011, u'key': 7}, {u'doc_count': 997, u'key': 6}]}, u'group_property': {u'buckets': [{u'doc_count': 11, u'key': 1000}, {u'doc_count': 12, u'key': 999}, {u'doc_count': 7, u'key': 998}, {u'doc_count': 14, u'key': 997}, {u'doc_count': 8, u'key': 996}]}}, 'hits': {'hits': [{u'_id': u'1', u'_score': 1, u'_source': {u'brand_id': 10, u'brand_name': u'Brand Ten', u'categories': [10], u'price': 197, u'property': u'Six', u'title': u'Product Eight One'}}, {u'_id': u'2', u'_score': 1, u'_source': {u'brand_id': 6, u'brand_name': u'Brand Six', u'categories': [12, 13, 14], u'price': 671, u'property': u'Four', u'title': u'Product Nine Seven'}}, {u'_id': u'3', u'_score': 1, u'_source': {u'brand_id': 3, u'brand_name': u'Brand Three', u'categories': [13, 14, 15], u'price': 92, u'property': u'Six', u'title': u'Product Five Four'}}, {u'_id': u'4', u'_score': 1, u'_source': {u'brand_id': 10, u'brand_name': u'Brand Ten', u'categories': [11], u'price': 713, u'property': u'Five', u'title': u'Product Eight Nine'}}, {u'_id': u'5', u'_score': 1, u'_source': {u'brand_id': 7, u'brand_name': u'Brand Seven', u'categories': [11, 12, 13], u'price': 805, u'property': u'Two', u'title': u'Product Ten Three'}}], 'max_score': None, 'total': 10000}, 'profile': None, 'timed_out': False, 'took': 0} ``` ```javascript res = await searchApi.search({"index":"facetdemo","query":{"match_all":{}},"expressions":{"price_range":"INTERVAL(price,200,400,600,800)"},"aggs":{"group_property":{"terms":{"field":"price_range"}}}}); ``` ```javascript {"took":0,"timed_out":false,"hits":{"total":10000,"hits":[{"_id": 1,"_score":1,"_source":{"price":197,"brand_id":10,"brand_name":"Brand Ten","categories":[10],"title":"Product Eight One","property":"Six","price_range":0}},{"_id": 2,"_score":1,"_source":{"price":671,"brand_id":6,"brand_name":"Brand Six","categories":[12,13,14],"title":"Product Nine Seven","property":"Four","price_range":3}},{"_id": 3,"_score":1,"_source":{"price":92,"brand_id":3,"brand_name":"Brand Three","categories":[13,14,15],"title":"Product Five Four","property":"Six","price_range":0}},{"_id": 4,"_score":1,"_source":{"price":713,"brand_id":10,"brand_name":"Brand Ten","categories":[11],"title":"Product Eight Nine","property":"Five","price_range":3}},{"_id": 5,"_score":1,"_source":{"price":805,"brand_id":7,"brand_name":"Brand Seven","categories":[11,12,13],"title":"Product Ten Three","property":"Two","price_range":4}},{"_id": 6,"_score":1,"_source":{"price":420,"brand_id":2,"brand_name":"Brand Two","categories":[10,11],"title":"Product Two One","property":"Six","price_range":2}},{"_id": 7,"_score":1,"_source":{"price":412,"brand_id":9,"brand_name":"Brand Nine","categories":[10],"title":"Product Four Nine","property":"Eight","price_range":2}},{"_id": 8,"_score":1,"_source":{"price":300,"brand_id":9,"brand_name":"Brand Nine","categories":[13,14,15],"title":"Product Eight Four","property":"Five","price_range":1}},{"_id": 9,"_score":1,"_source":{"price":728,"brand_id":1,"brand_name":"Brand One","categories":[11],"title":"Product Nine Six","property":"Four","price_range":3}},{"_id": 10,"_score":1,"_source":{"price":622,"brand_id":3,"brand_name":"Brand Three","categories":[10,11],"title":"Product Six Seven","property":"Two","price_range":3}},{"_id": 11,"_score":1,"_source":{"price":462,"brand_id":5,"brand_name":"Brand Five","categories":[10,11],"title":"Product Ten Two","property":"Eight","price_range":2}},{"_id": 12,"_score":1,"_source":{"price":939,"brand_id":7,"brand_name":"Brand Seven","categories":[12,13],"title":"Product Nine Seven","property":"Six","price_range":4}},{"_id": 13,"_score":1,"_source":{"price":948,"brand_id":8,"brand_name":"Brand Eight","categories":[12],"title":"Product Ten One","property":"Six","price_range":4}},{"_id": 14,"_score":1,"_source":{"price":900,"brand_id":9,"brand_name":"Brand Nine","categories":[12,13,14],"title":"Product Ten Nine","property":"Three","price_range":4}},{"_id": 15,"_score":1,"_source":{"price":224,"brand_id":3,"brand_name":"Brand Three","categories":[13],"title":"Product Two Six","property":"Four","price_range":1}},{"_id": 16,"_score":1,"_source":{"price":713,"brand_id":10,"brand_name":"Brand Ten","categories":[12],"title":"Product Two Four","property":"Six","price_range":3}},{"_id": 17,"_score":1,"_source":{"price":510,"brand_id":2,"brand_name":"Brand Two","categories":[10],"title":"Product Ten Two","property":"Seven","price_range":2}},{"_id": 18,"_score":1,"_source":{"price":702,"brand_id":10,"brand_name":"Brand Ten","categories":[12,13],"title":"Product Nine One","property":"Three","price_range":3}},{"_id": 19,"_score":1,"_source":{"price":836,"brand_id":4,"brand_name":"Brand Four","categories":[10,11,12],"title":"Product Four Five","property":"Two","price_range":4}},{"_id": 20,"_score":1,"_source":{"price":227,"brand_id":3,"brand_name":"Brand Three","categories":[12,13],"title":"Product Three Four","property":"Ten","price_range":1}}]}} ``` ```java searchRequest = new SearchRequest(); expressions = new HashMap(){{ put("price_range","INTERVAL(price,200,400,600,800)"); }}; searchRequest.setExpressions(expressions); aggs = new HashMap(){{ put("group_property", new HashMap(){{ put("terms", new HashMap(){{ put("field","price_range"); }}); }}); }}; searchRequest.setIndex("facetdemo"); searchRequest.setLimit(5); query = new HashMap(); query.put("match_all",null); searchRequest.setQuery(query); searchRequest.setAggs(aggs); searchResponse = searchApi.search(searchRequest); ``` ```java class SearchResponse { took: 0 timedOut: false aggregations: {group_property={buckets=[{key=4, doc_count=2100}, {key=3, doc_count=1973}, {key=2, doc_count=1999}, {key=1, doc_count=2043}, {key=0, doc_count=1885}]}} hits: class SearchResponseHits { maxScore: null total: 10000 hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six, price_range=0}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four, price_range=3}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six, price_range=0}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five, price_range=3}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two, price_range=4}}] } profile: null } ``` ```clike var expr = new Dictionary { {"price_range", "INTERVAL(price,200,400,600,800"} } ; var agg = new Aggregation("group_property", "price_range"); object query = new { match_all=null }; var searchRequest = new SearchRequest("facetdemo", query); searchRequest.Limit = 5; searchRequest.Expressions = new List {expr}; searchRequest.Aggs = new List {agg}; var searchResponse = searchApi.Search(searchRequest); ``` ```clike class SearchResponse { took: 0 timedOut: false aggregations: {group_property={buckets=[{key=4, doc_count=2100}, {key=3, doc_count=1973}, {key=2, doc_count=1999}, {key=1, doc_count=2043}, {key=0, doc_count=1885}]}} hits: class SearchResponseHits { maxScore: null total: 10000 hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six, price_range=0}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four, price_range=3}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six, price_range=0}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five, price_range=3}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two, price_range=4}}] } profile: null } ``` ```typecript res = await searchApi.search({ index: 'test', query: { match_all:{} }, expressions: { cat_range: "INTERVAL(cat,1,3)" } aggs: { expr_group: { terms: { field : 'cat_range' } } } }); ``` ```typescript { "took": 0, "timed_out": false, "hits": { "total": 5, "hits": [ { "_id": 1, "_score": 1, "_source": { "content": "Text 1", "name": "Doc 1", "cat": 1, "cat_range": 1 } }, ... { "_id": 5, "_score": 1, "_source": { "content": "Text 5", "name": "Doc 5", "cat": 4, "cat_range": 2, } } ] }, "aggregations": { "expr_group": { "buckets": [ { "key": 0, "doc_count": 0 }, { "key": 1, "doc_count": 3 }, { "key": 2, "doc_count": 2 } ] } } } ``` ```go query := map[string]interface{} {} searchRequest.SetQuery(query) exprs := map[string]string{} { "cat_range": "INTERVAL(cat,1,3)" } searchRequest.SetExpressions(exprs) aggByExpr := manticoreclient.NewAggregation() aggTerms := manticoreclient.NewAggregationTerms() aggTerms.SetField("cat_range") aggByExpr.SetTerms(aggTerms) aggs := map[string]Aggregation{} { "expr_group": aggByExpr } searchRequest.SetAggs(aggs) res, _, _ := apiClient.SearchAPI.Search(context.Background()).SearchRequest(*searchRequest).Execute() ``` ```go { "took": 0, "timed_out": false, "hits": { "total": 5, "hits": [ { "_id": 1, "_score": 1, "_source": { "content": "Text 1", "name": "Doc 1", "cat": 1, "cat_range": 1 } }, ... { "_id": 5, "_score": 1, "_source": { "content": "Text 5", "name": "Doc 5", "cat": 4, "cat_range": 2 } } ] }, "aggregations": { "expr_group": { "buckets": [ { "key": 0, "doc_count": 0 }, { "key": 1, "doc_count": 3 }, { "key": 2, "doc_count": 2 } ] } } } ``` ### 多级分组上的分面 分面可以在多级分组上进行聚合，其结果集与执行多级分组查询时的结果相同： ```sql SELECT *,INTERVAL(price,200,400,600,800) AS price_range FROM facetdemo FACET price_range AS price_range,brand_name ORDER BY brand_name asc; ``` ```sql +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+ | id | price | brand_id | title | brand_name | property | j | categories | price_range | +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+ | 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | 1 | ... +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+ 20 rows in set (0.00 sec) +--------------+-------------+----------+ | fprice_range | brand_name | count(*) | +--------------+-------------+----------+ | 1 | Brand Eight | 197 | | 4 | Brand Eight | 235 | | 3 | Brand Eight | 203 | | 2 | Brand Eight | 201 | | 0 | Brand Eight | 197 | | 4 | Brand Five | 230 | | 2 | Brand Five | 197 | | 1 | Brand Five | 204 | | 3 | Brand Five | 193 | | 0 | Brand Five | 183 | | 1 | Brand Four | 195 | ... ``` ### 在直方图值上进行分面 分面可以通过构造固定大小的区间来对直方图值进行聚合。关键函数为： ```sql key_of_the_bucket = interval + offset * floor ( ( value - offset ) / interval ) ``` 直方图参数 `interval` 必须为正数，参数 `offset` 必须为正数且小于 `interval`。默认情况下，区间以数组形式返回。使用 `keyed` 参数可以使响应变为一个包含区间键的字典。 ```sql SELECT COUNT(*), HISTOGRAM(price, {hist_interval=100}) as price_range FROM facets GROUP BY price_range ORDER BY price_range ASC; ``` ```sql +----------+-------------+ | count(*) | price_range | +----------+-------------+ | 5 | 0 | | 5 | 100 | | 1 | 300 | | 4 | 400 | | 1 | 500 | | 3 | 700 | | 1 | 900 | +----------+-------------+ ``` ```json POST /search -d ' { "size": 0, "index": "facets", "aggs": { "price_range": { "histogram": { "field": "price", "interval": 300 } } } }' ``` ```json { "took": 0, "timed_out": false, "hits": { "total": 20, "total_relation": "eq", "hits": [] }, "aggregations": { "price_range": { "buckets": [ { "key": 0, "doc_count": 10 }, { "key": 300, "doc_count": 6 }, { "key": 600, "doc_count": 3 }, { "key": 900, "doc_count": 1 } ] } } } ``` ```json POST /search -d ' { "size": 0, "index": "facets", "aggs": { "price_range": { "histogram": { "field": "price", "interval": 300, "keyed": true } } } }' ``` ```json { "took": 0, "timed_out": false, "hits": { "total": 20, "total_relation": "eq", "hits": [] }, "aggregations": { "price_range": { "buckets": { "0": { "key": 0, "doc_count": 10 }, "300": { "key": 300, "doc_count": 6 }, "600": { "key": 600, "doc_count": 3 }, "900": { "key": 900, "doc_count": 1 } } } } } ``` ### 在直方图日期值上进行分面 分面可以对日期值直方图进行聚合，类似于普通直方图。区别在于间隔是使用日期或时间表达式指定的。这类表达式需要特殊支持，因为时间间隔不总是固定长度的。值将通过以下键函数四舍五入到最近的区间： ```sql key_of_the_bucket = interval * floor ( value / interval ) ``` 直方图参数 `calendar_interval` 能够理解不同月份的天数不同。可接受的时间间隔描述在 [date\_histogram](https://vergil-lai.gitbook.io/manticoresearch-manual-zh/5-sou-suo/pages/ZFnOuCZuKPed5WkofXei#DATE_HISTOGRAM\(\)) 表达式中。默认情况下，区间以数组形式返回。使用 `keyed` 参数可以使响应变为一个包含区间键的字典。 ```sql SELECT count(*), DATE_HISTOGRAM(tm, {calendar_interval='month'}) AS months FROM idx_dates GROUP BY months ORDER BY months ASC ``` ```sql +----------+------------+ | count(*) | months | +----------+------------+ | 442 | 1485907200 | | 744 | 1488326400 | | 720 | 1491004800 | | 230 | 1493596800 | +----------+------------+ ``` ```json POST /search -d ' { "index": "idx_dates", "size": 0, "aggs": { "months": { "date_histogram": { "field": "tm", "keyed": true, "calendar_interval": "month" } } } }' ``` ```json { "timed_out": false, "hits": { "total": 2136, "total_relation": "eq", "hits": [] }, "aggregations": { "months": { "buckets": { "2017-02-01T00:00:00": { "key": 1485907200, "key_as_string": "2017-02-01T00:00:00", "doc_count": 442 }, "2017-03-01T00:00:00": { "key": 1488326400, "key_as_string": "2017-03-01T00:00:00", "doc_count": 744 }, "2017-04-01T00:00:00": { "key": 1491004800, "key_as_string": "2017-04-01T00:00:00", "doc_count": 720 }, "2017-05-01T00:00:00": { "key": 1493596800, "key_as_string": "2017-05-01T00:00:00", "doc_count": 230 } } } } } ``` ### 在一组范围上进行分面 分面可以对一组范围进行聚合。值将根据区间范围进行检查，每个区间包括 `from` 值，但排除 `to` 值。 将 `keyed` 属性设置为 `true` 时，响应将变为一个包含区间键的字典，而不是数组。 ```sql SELECT COUNT(*), RANGE(price, {range_to=150},{range_from=150,range_to=300},{range_from=300}) price_range FROM facets GROUP BY price_range ORDER BY price_range ASC; ``` ```sql +----------+-------------+ | count(*) | price_range | +----------+-------------+ | 8 | 0 | | 2 | 1 | | 10 | 2 | +----------+-------------+ ``` ```json POST /search -d ' { "size": 0, "index": "facets", "aggs": { "price_range": { "range": { "field": "price", "ranges": [ { "to": 99 }, { "from": 99, "to": 550 }, { "from": 550 } ] } } } }' ``` ```json { "took": 0, "timed_out": false, "hits": { "total": 20, "total_relation": "eq", "hits": [] }, "aggregations": { "price_range": { "buckets": [ { "key": "*-99", "to": 99, "doc_count": 5 }, { "key": "99-550", "from": 99, "to": 550, "doc_count": 11 }, { "key": "550-*", "from": 550, "doc_count": 4 } ] } } } ``` ```json POST /search -d ' { "size":0, "index":"facets", "aggs":{ "price_range":{ "range":{ "field":"price", "keyed":true, "ranges":[ { "from":100, "to":399 }, { "from":399 } ] } } } }' ``` ```json { "took": 0, "timed_out": false, "hits": { "total": 20, "total_relation": "eq", "hits": [] }, "aggregations": { "price_range": { "buckets": { "100-399": { "from": 100, "to": 399, "doc_count": 6 }, "399-*": { "from": 399, "doc_count": 9 } } } } } ``` ### 在一组日期范围上进行分面。 分面可以对一组日期范围进行聚合，类似于普通的范围聚合。不同之处在于，`from` 和 `to` 值可以使用 [日期运算](/manticoresearch-manual-zh/functions/date_and_time_functions.md#Date-math) 表达式来表示。此聚合包括每个范围的 `from` 值，排除 `to` 值。将 `keyed` 属性设置为 `true` 时，响应将变为一个包含区间键的字典，而不是数组。 ```sql SELECT COUNT(*), DATE_RANGE(tm, {range_to='2017||+2M/M'},{range_from='2017||+2M/M',range_to='2017||+5M/M'},{range_from='2017||+5M/M'}) AS points FROM idx_dates GROUP BY points ORDER BY points ASC; ``` ```sql +----------+--------+ | count(*) | points | +----------+--------+ | 442 | 0 | | 1464 | 1 | | 230 | 2 | +----------+--------+ ``` ```json POST /search -d ' { "index": "idx_dates", "size": 0, "aggs": { "points": { "date_range": { "field": "tm", "keyed": true, "ranges": [ { "to": "2017||+2M/M" }, { "from": "2017||+2M/M", "to": "2017||+4M/M" }, { "from": "2017||+4M/M", "to": "2017||+5M/M" }, { "from": "2017||+5M/M" } ] } } } }' ``` ```json { "timed_out": false, "hits": { "total": 2136, "total_relation": "eq", "hits": [] }, "aggregations": { "points": { "buckets": { "*-2017-03-01T00:00:00": { "to": "2017-03-01T00:00:00", "doc_count": 442 }, "2017-03-01T00:00:00-2017-04-01T00:00:00": { "from": "2017-03-01T00:00:00", "to": "2017-04-01T00:00:00", "doc_count": 744 }, "2017-04-01T00:00:00-2017-05-01T00:00:00": { "from": "2017-04-01T00:00:00", "to": "2017-05-01T00:00:00", "doc_count": 720 }, "2017-05-01T00:00:00-*": { "from": "2017-05-01T00:00:00", "doc_count": 230 } } } } } ``` ### 分面结果的排序 分面支持类似于标准查询的 `ORDER BY` 子句。每个分面可以有自己的排序，分面的排序不会影响主结果集的排序，主结果集的排序由主查询的 `ORDER BY` 确定。排序可以基于属性名称、计数（使用 `COUNT(*)`），或者使用特殊的 `FACET()` 函数，该函数提供聚合后的数据值。默认情况下，带有 `ORDER BY COUNT(*)` 的查询将按降序排列。 **SQL:** ```sql SELECT * FROM facetdemo FACET brand_name BY brand_id ORDER BY FACET() ASC FACET brand_name BY brand_id ORDER BY brand_name ASC FACET brand_name BY brand_id order BY COUNT(*) DESC; FACET brand_name BY brand_id order BY COUNT(*); ``` ```sql +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+ | id | price | brand_id | title | brand_name | property | j | categories | +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+ | 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | ... | 20 | 31 | 9 | Product Four One | Brand Nine | Ten_Four | {"prop1":79,"prop2":42,"prop3":"One"} | 12,13,14 | +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+ 20 rows in set (0.01 sec) +-------------+----------+ | brand_name | count(*) | +-------------+----------+ | Brand One | 1013 | | Brand Two | 990 | | Brand Three | 1016 | | Brand Four | 994 | | Brand Five | 1007 | | Brand Six | 1039 | | Brand Seven | 965 | | Brand Eight | 1033 | | Brand Nine | 944 | | Brand Ten | 998 | +-------------+----------+ 10 rows in set (0.01 sec) +-------------+----------+ | brand_name | count(*) | +-------------+----------+ | Brand Eight | 1033 | | Brand Five | 1007 | | Brand Four | 994 | | Brand Nine | 944 | | Brand One | 1013 | | Brand Seven | 965 | | Brand Six | 1039 | | Brand Ten | 998 | | Brand Three | 1016 | | Brand Two | 990 | +-------------+----------+ 10 rows in set (0.01 sec) +-------------+----------+ | brand_name | count(*) | +-------------+----------+ | Brand Six | 1039 | | Brand Eight | 1033 | | Brand Three | 1016 | | Brand One | 1013 | | Brand Five | 1007 | | Brand Ten | 998 | | Brand Four | 994 | | Brand Two | 990 | | Brand Seven | 965 | | Brand Nine | 944 | +-------------+----------+ 10 rows in set (0.01 sec) ``` **JSON:** ```json POST /search -d ' { "index":"table_name", "aggs":{ "group_property":{ "terms":{ "field":"a" }, "sort":[ { "count(*)":{ "order":"desc" } } ] } } }' ``` ```json { "took": 0, "timed_out": false, "hits": { "total": 6, "total_relation": "eq", "hits": [ { "_id": 1515697460415037554, "_score": 1, "_source": { "a": 1 } }, { "_id": 1515697460415037555, "_score": 1, "_source": { "a": 2 } }, { "_id": 1515697460415037556, "_score": 1, "_source": { "a": 2 } }, { "_id": 1515697460415037557, "_score": 1, "_source": { "a": 3 } }, { "_id": 1515697460415037558, "_score": 1, "_source": { "a": 3 } }, { "_id": 1515697460415037559, "_score": 1, "_source": { "a": 3 } } ] }, "aggregations": { "group_property": { "buckets": [ { "key": 3, "doc_count": 3 }, { "key": 2, "doc_count": 2 }, { "key": 1, "doc_count": 1 } ] } } } ``` ### 分面结果的大小 默认情况下，每个分面结果集限制为 20 个值。可以通过 `LIMIT` 子句单独控制每个分面的分面值数量，格式可以是 `LIMIT count` 返回一定数量的值，或使用偏移量格式 `LIMIT offset, count`。 最大可返回的分面值数量受查询的 `max_matches` 设置限制。如果你想实现动态的 `max_matches`（将 `max_matches` 限制为偏移量 + 每页数量以提高性能），需要注意的是，过低的 `max_matches` 值可能会影响分面值的数量。在这种情况下，应使用足够覆盖分面值数量的最小 `max_matches` 值。 **SQL:** ```sql SELECT * FROM facetdemo FACET brand_name BY brand_id ORDER BY FACET() ASC LIMIT 0,1 FACET brand_name BY brand_id ORDER BY brand_name ASC LIMIT 2,4 FACET brand_name BY brand_id order BY COUNT(*) DESC LIMIT 4; ``` ```sql +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+ | id | price | brand_id | title | brand_name | property | j | categories | +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+ | 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | ... | 20 | 31 | 9 | Product Four One | Brand Nine | Ten_Four | {"prop1":79,"prop2":42,"prop3":"One"} | 12,13,14 | +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+ 20 rows in set (0.01 sec) +-------------+----------+ | brand_name | count(*) | +-------------+----------+ | Brand One | 1013 | +-------------+----------+ 1 rows in set (0.01 sec) +-------------+----------+ | brand_name | count(*) | +-------------+----------+ | Brand Four | 994 | | Brand Nine | 944 | | Brand One | 1013 | | Brand Seven | 965 | +-------------+----------+ 4 rows in set (0.01 sec) +-------------+----------+ | brand_name | count(*) | +-------------+----------+ | Brand Six | 1039 | | Brand Eight | 1033 | | Brand Three | 1016 | +-------------+----------+ 3 rows in set (0.01 sec) ``` ```json POST /search -d ' { "index" : "facetdemo", "query" : {"match_all" : {} }, "limit": 5, "aggs" : { "group_property" : { "terms" : { "field":"price", "size":1, } }, "group_brand_id" : { "terms" : { "field":"brand_id", "size":3 } } } } ' ``` ```json { "took": 3, "timed_out": false, "hits": { "total": 10000, "hits": [ { "_id": 1, "_score": 1, "_source": { "price": 197, "brand_id": 10, "brand_name": "Brand Ten", "categories": [ 10 ] } }, ... { "_id": 5, "_score": 1, "_source": { "price": 805, "brand_id": 7, "brand_name": "Brand Seven", "categories": [ 11, 12, 13 ] } } ] }, "aggregations": { "group_property": { "buckets": [ { "key": 1000, "doc_count": 11 } ] }, "group_brand_id": { "buckets": [ { "key": 10, "doc_count": 1019 }, { "key": 9, "doc_count": 954 }, { "key": 8, "doc_count": 1021 } ] } } } ``` ```php $index->setName('facetdemo'); $search = $index->search(''); $search->limit(5); $search->facet('price','price',1); $search->facet('brand_id','group_brand_id',3); $results = $search->get(); print_r($results->getFacets()); ``` ```php Array ( [price] => Array ( [buckets] => Array ( [0] => Array ( [key] => 1000 [doc_count] => 11 ) ) ) [group_brand_id] => Array ( [buckets] => Array ( [0] => Array ( [key] => 10 [doc_count] => 1019 ) [1] => Array ( [key] => 9 [doc_count] => 954 ) [2] => Array ( [key] => 8 [doc_count] => 1021 ) ) ) ) ``` ```python res =searchApi.search({"index":"facetdemo","query":{"match_all":{}},"limit":5,"aggs":{"group_property":{"terms":{"field":"price","size":1,}},"group_brand_id":{"terms":{"field":"brand_id","size":3}}}}) ``` ```python {'aggregations': {u'group_brand_id': {u'buckets': [{u'doc_count': 1019, u'key': 10}, {u'doc_count': 954, u'key': 9}, {u'doc_count': 1021, u'key': 8}]}, u'group_property': {u'buckets': [{u'doc_count': 11, u'key': 1000}]}}, 'hits': {'hits': [{u'_id': u'1', u'_score': 1, u'_source': {u'brand_id': 10, u'brand_name': u'Brand Ten', u'categories': [10], u'price': 197, u'property': u'Six', u'title': u'Product Eight One'}}, {u'_id': u'2', u'_score': 1, u'_source': {u'brand_id': 6, u'brand_name': u'Brand Six', u'categories': [12, 13, 14], u'price': 671, u'property': u'Four', u'title': u'Product Nine Seven'}}, {u'_id': u'3', u'_score': 1, u'_source': {u'brand_id': 3, u'brand_name': u'Brand Three', u'categories': [13, 14, 15], u'price': 92, u'property': u'Six', u'title': u'Product Five Four'}}, {u'_id': u'4', u'_score': 1, u'_source': {u'brand_id': 10, u'brand_name': u'Brand Ten', u'categories': [11], u'price': 713, u'property': u'Five', u'title': u'Product Eight Nine'}}, {u'_id': u'5', u'_score': 1, u'_source': {u'brand_id': 7, u'brand_name': u'Brand Seven', u'categories': [11, 12, 13], u'price': 805, u'property': u'Two', u'title': u'Product Ten Three'}}], 'max_score': None, 'total': 10000}, 'profile': None, 'timed_out': False, 'took': 0} ``` ```javascript res = await searchApi.search({"index":"facetdemo","query":{"match_all":{}},"limit":5,"aggs":{"group_property":{"terms":{"field":"price","size":1,}},"group_brand_id":{"terms":{"field":"brand_id","size":3}}}}); ``` ```javascript {"took":0,"timed_out":false,"hits":{"total":10000,"hits":[{"_id": 1,"_score":1,"_source":{"price":197,"brand_id":10,"brand_name":"Brand Ten","categories":[10],"title":"Product Eight One","property":"Six"}},{"_id": 2,"_score":1,"_source":{"price":671,"brand_id":6,"brand_name":"Brand Six","categories":[12,13,14],"title":"Product Nine Seven","property":"Four"}},{"_id": 3,"_score":1,"_source":{"price":92,"brand_id":3,"brand_name":"Brand Three","categories":[13,14,15],"title":"Product Five Four","property":"Six"}},{"_id": 4,"_score":1,"_source":{"price":713,"brand_id":10,"brand_name":"Brand Ten","categories":[11],"title":"Product Eight Nine","property":"Five"}},{"_id": 5,"_score":1,"_source":{"price":805,"brand_id":7,"brand_name":"Brand Seven","categories":[11,12,13],"title":"Product Ten Three","property":"Two"}}]}} ``` ```java searchRequest = new SearchRequest(); aggs = new HashMap(){{ put("group_property", new HashMap(){{ put("terms", new HashMap(){{ put("field","price"); put("size",1); }}); }}); put("group_brand_id", new HashMap(){{ put("terms", new HashMap(){{ put("field","brand_id"); put("size",3); }}); }}); }}; searchRequest.setIndex("facetdemo"); searchRequest.setLimit(5); query = new HashMap(); query.put("match_all",null); searchRequest.setQuery(query); searchRequest.setAggs(aggs); searchResponse = searchApi.search(searchRequest); ``` ```java class SearchResponse { took: 0 timedOut: false aggregations: {group_property={buckets=[{key=1000, doc_count=11}]}, group_brand_id={buckets=[{key=10, doc_count=1019}, {key=9, doc_count=954}, {key=8, doc_count=1021}]}} hits: class SearchResponseHits { maxScore: null total: 10000 hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two}}] } profile: null } ``` ```clike var agg1 = new Aggregation("group_property", "price"); agg1.Size = 1; var agg2 = new Aggregation("group_brand_id", "brand_id"); agg2.Size = 3; agg2.Size = 100; object query = new { match_all=null }; var searchRequest = new SearchRequest("facetdemo", query); searchRequest.Aggs = new List {agg1, agg2}; var searchResponse = searchApi.Search(searchRequest); ``` ```clike class SearchResponse { took: 0 timedOut: false aggregations: {group_property={buckets=[{key=1000, doc_count=11}]}, group_brand_id={buckets=[{key=10, doc_count=1019}, {key=9, doc_count=954}, {key=8, doc_count=1021}]}} hits: class SearchResponseHits { maxScore: null total: 10000 hits: [{_id=1, _score=1, _source={price=197, brand_id=10, brand_name=Brand Ten, categories=[10], title=Product Eight One, property=Six}}, {_id=2, _score=1, _source={price=671, brand_id=6, brand_name=Brand Six, categories=[12, 13, 14], title=Product Nine Seven, property=Four}}, {_id=3, _score=1, _source={price=92, brand_id=3, brand_name=Brand Three, categories=[13, 14, 15], title=Product Five Four, property=Six}}, {_id=4, _score=1, _source={price=713, brand_id=10, brand_name=Brand Ten, categories=[11], title=Product Eight Nine, property=Five}}, {_id=5, _score=1, _source={price=805, brand_id=7, brand_name=Brand Seven, categories=[11, 12, 13], title=Product Ten Three, property=Two}}] } profile: null } ``` ```typescript res = await searchApi.search({ index: 'test', query: { match_all:{} }, aggs: { name_group: { terms: { field : 'name', size: 1 } }, cat_group: { terms: { field: 'cat' } } } }); ``` ```typescript { "took": 0, "timed_out": false, "hits": { "total": 5, "hits": [ { "_id": 1, "_score": 1, "_source": { "content": "Text 1", "name": "Doc 1", "cat": 1 } }, ... { "_id": 5, "_score": 1, "_source": { "content": "Text 5", "name": "Doc 5", "cat": 4 } } ] }, "aggregations": { "name_group": { "buckets": [ { "key": "Doc 1", "doc_count": 1 } ] }, "cat_group": { "buckets": [ { "key": 1, "doc_count": 2 }, ... { "key": 4, "doc_count": 1 } ] } } } ``` ```go query := map[string]interface{} {} searchRequest.SetQuery(query) aggByName := manticoreclient.NewAggregation() aggTerms := manticoreclient.NewAggregationTerms() aggTerms.SetField("name") aggByName.SetTerms(aggTerms) aggByName.SetSize(1) aggByCat := manticoreclient.NewAggregation() aggTerms.SetField("cat") aggByCat.SetTerms(aggTerms) aggs := map[string]Aggregation{} { "name_group": aggByName, "cat_group": aggByCat } searchRequest.SetAggs(aggs) res, _, _ := apiClient.SearchAPI.Search(context.Background()).SearchRequest(*searchRequest).Execute() ``` ```go { "took": 0, "timed_out": false, "hits": { "total": 5, "hits": [ { "_id": 1, "_score": 1, "_source": { "content": "Text 1", "name": "Doc 1", "cat": 1 } }, ... { "_id": 5, "_score": 1, "_source": { "content": "Text 5", "name": "Doc 5", "cat": 4 } } ] }, "aggregations": { "name_group": { "buckets": [ { "key": "Doc 1", "doc_count": 1 } ] }, "cat_group": { "buckets": [ { "key": 1, "doc_count": 2 }, ... { "key": 4, "doc_count": 1 } ] } } } ``` ### 返回的结果集 使用 SQL 进行带分面的搜索时，会返回多个结果集。所使用的 MySQL 客户端/库/连接器 **必须** 支持多个结果集，才能访问分面结果集。 ### 性能 在内部，`FACET` 是执行多查询的简写，其中第一个查询包含主要的搜索查询，批次中的其余查询则分别进行聚类。与多查询情况类似，分面搜索的查询优化可以生效，意味着搜索查询只执行一次，分面基于搜索查询结果进行操作，每个分面仅增加一小部分时间到总查询时间。 要检查分面搜索是否以优化模式运行，可以查看 [查询日志](/manticoresearch-manual-zh/ri-zhi/query_logging.md)，所有已记录的查询中都会包含一个 `xN` 字符串，其中 `N` 是在优化组中运行的查询数量。或者，你可以检查 [SHOW META](/manticoresearch-manual-zh/jie-dian-xin-xi-yu-guan-li/show_meta.md) 语句的输出，其中会显示一个 `multiplier` 指标: ```sql SELECT * FROM facetdemo FACET brand_id FACET price FACET categories; SHOW META LIKE 'multiplier'; ``` ```sql +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+ | id | price | brand_id | title | brand_name | property | j | categories | +------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+ | 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | ... +----------+----------+ | brand_id | count(*) | +----------+----------+ | 1 | 1013 | ... +-------+----------+ | price | count(*) | +-------+----------+ | 306 | 7 | ... +------------+----------+ | categories | count(*) | +------------+----------+ | 10 | 2436 | ... +---------------+-------+ | Variable_name | Value | +---------------+-------+ | multiplier | 4 | +---------------+-------+ 1 row in set (0.00 sec) ``` --- # Agent Instructions This documentation is published with GitBook. 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