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1 filter与query示例1.1 准备测试数据PUT website/_doc/1{ "title": "小白学ES01", "desc": "the first blog about es", "level": 1, "post_date": "2018-10-10", "post_address": { "country": "China", "province": "GuangDong", "city": "GuangZhou" }}PUT website/_doc/2{ "title": "小白学ES02", "desc": "the second blog about es", "level": 3, "post_date": "2018-11-11", "post_address": { "country": "China", "province": "ZheJiang", "city": "HangZhou" }}1.2 搜索测试 搜索条件: 搜索博客等级(level)大于等于2, 同时发布日期(post_date)是2018-11-11的博客: (1) 不使用filter: GET website/_doc/_search{ "query": { "bool": { "must": [ { "match": { "post_date": "2018-11-11" } }, { "range": { "level": { "gte": 2 } } } ] } }}// 结果信息: "hits": { "total": 1, "max_score": 2.0, "hits": [ { "_index": "website2", "_type": "blog", "_id": "2", "_score": 2.0, // 评分为2.0 "_source": { "title": "小白学ES02", "desc": "the second blog about es", "level": 3, "post_date": "2018-11-11", "post_address": { "country": "China", "province": "ZheJiang", "city": "HangZhou" } } } ]}(2) 使用filter: GET website/_doc/_search{ "query": { "bool": { "must": { "match": { "post_date": "2018-11-11" } }, "filter": { "range": { "level": { "gte": 2 } } } } }}// 结果信息: "hits": { "total": 1, "max_score": 1.0, "hits": [ { "_index": "website2", "_type": "blog", "_id": "2", "_score": 1.0, // 评分为1.0 "_source": { "title": "小白学ES02", "desc": "the second blog about es", "level": 3, "post_date": "2018-11-11", "post_address": { "country": "China", "province": "ZheJiang", "city": "HangZhou" } } } ]}2 filter与query的区别filter和query一起使用时, 会先执行filter. 2.1 相关度处理上的不同filter —— 只根据搜索条件过滤出符合的文档, 将这些文档的评分固定为1, 忽略TF/IDF信息, 不计算相关度分数;
query —— 先查询符合搜索条件的文档, 然后计算每个文档对于搜索条件的相关度分数, 再根据评分倒序排序. 建议: - 如果对搜索结果有排序的要求, 要将最匹配的文档排在最前面, 就用query;
- 如果只是根据一定的条件筛选出部分数据, 不关注结果的排序, 就用filter.
2.2 性能上的对比filter 性能更好, 无排序 —— 不计算相关度分数, 不用根据相关度分数进行排序, 同时ES内部还会缓存(cache)比较常用的filter的数据 (使用bitset <0或1> 来记录包含与否). query 性能较差, 有排序 —— 要计算相关度分数, 要根据相关度分数进行排序, 并且没有cache功能. 2.3 对比结论1) 业务关心的、需要根据匹配的相关度进行排序的搜索条件 放在 query 中; 2) 业务不关心、不需要根据匹配的相关度进行排序的搜索条件 放在 filter 中.
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