如我所说。您的问题是您的文本被分析，elasticsearch总是在令牌级别聚合。因此，为了解决该问题，必须将字段值索引为单个标记。有两种选择：
- 不分析它们
- 使用关键字分析器+小写（不区分大小写的aggs）为它们编制索引
因此，将使用小写过滤器并删除重音符号（
ö => o以及
ß =>ss您的字段的其他字段，以创建自定义关键字分析器）来进行设置，以便将它们用于聚合（
raw和
keyword）：
PUT /test{ "settings": { "analysis": { "analyzer": { "my_analyzer_keyword": { "type": "custom", "tokenizer": "keyword", "filter": [ "asciifolding", "lowercase" ] } } } }, "mappings": { "data": { "properties": { "products_filter": { "type": "nested", "properties": { "filter_name": { "type": "string", "analyzer": "standard", "fields": { "raw": { "type": "string", "index": "not_analyzed" }, "keyword": { "type": "string", "analyzer": "my_analyzer_keyword" } } }, "filter_value": { "type": "string", "analyzer": "standard", "fields": { "raw": { "type": "string", "index": "not_analyzed" }, "keyword": { "type": "string", "analyzer": "my_analyzer_keyword" } } } } } } } }}测试文件，您给了我们：
PUT /test/data/1{ "products_filter": [ { "filter_name": "Rahmengröße", "filter_value": "33,5 cm" }, { "filter_name": "color", "filter_value": "gelb" }, { "filter_name": "Rahmengröße", "filter_value": "39,5 cm" }, { "filter_name": "Rahmengröße", "filter_value": "45,5 cm" } ]}这将是查询以使用
raw字段进行汇总：
GET /test/_search{ "size": 0, "aggs": { "Nesting": { "nested": { "path": "products_filter" }, "aggs": { "raw_names": { "terms": { "field": "products_filter.filter_name.raw", "size": 0 }, "aggs": { "raw_values": { "terms": { "field": "products_filter.filter_value.raw", "size": 0 } } } } } } }}它确实带来了预期的结果（带有过滤器名称的存储桶和带有其值的子存储桶）：
{ "took": 1, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 1, "max_score": 0, "hits": [] }, "aggregations": { "Nesting": { "doc_count": 4, "raw_names": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "Rahmengröße", "doc_count": 3, "raw_values": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "33,5 cm", "doc_count": 1 }, { "key": "39,5 cm", "doc_count": 1 }, { "key": "45,5 cm", "doc_count": 1 } ] } }, { "key": "color", "doc_count": 1, "raw_values": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "gelb", "doc_count": 1 } ] } } ] } } }}另外，您可以将field与关键字分析器（以及一些规范化）结合使用，以获得更通用且不区分大小写的结果：
GET /test/_search{ "size": 0, "aggs": { "Nesting": { "nested": { "path": "products_filter" }, "aggs": { "keyword_names": { "terms": { "field": "products_filter.filter_name.keyword", "size": 0 }, "aggs": { "keyword_values": { "terms": { "field": "products_filter.filter_value.keyword", "size": 0 } } } } } } }}结果就是：
{ "took": 1, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 1, "max_score": 0, "hits": [] }, "aggregations": { "Nesting": { "doc_count": 4, "keyword_names": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "rahmengrosse", "doc_count": 3, "keyword_values": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "33,5 cm", "doc_count": 1 }, { "key": "39,5 cm", "doc_count": 1 }, { "key": "45,5 cm", "doc_count": 1 } ] } }, { "key": "color", "doc_count": 1, "keyword_values": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "gelb", "doc_count": 1 } ] } } ] } } }}
