一、数据情况
purchase记录每个用户的购买信息；
PUT purchase { "mappings":{ "properties":{ "id":{ "type":"keyword" }, "name":{ "type":"text" }, "goods":{ "properties":{ "id":{ "type":"keyword" }, "name":{ "type":"text" } } } } } } index 三个document
PUT purchase/_doc/1 { "id":1, "name":"sam", "goods":[ {"id":"g1","name":"ipad"}, {"id":"g2","name":"iphone"} ] } PUT purchase/_doc/2 { "id":2, "name":"coco", "goods":[ {"id":"g1","name":"ipad"}, {"id":"g2","name":"iphone"}, {"id":"g3","name":"ipod"} ] } PUT purchase/_doc/3 { "id":3, "name":"jim", "goods":[ {"id":"g1","name":"ipad"}, {"id":"g2","name":"iphone"}, {"id":"g3","name":"ipod"}, {"id":"g4","name":"TV"} ] } 查看索引数据情况
POST purchase/_search { "query": { "match_all": {} } } { "took":331, "timed_out":false, "_shards":{ "total":1, "successful":1, "skipped":0, "failed":0 }, "hits":{ "total":{ "value":3, "relation":"eq" }, "max_score":1, "hits":[ { "_index":"purchase", "_id":"1", "_score":1, "_source":{ "id":1, "name":"sam", "goods":[ { "id":"g1", "name":"ipad" }, { "id":"g2", "name":"iphone" } ] } }, { "_index":"purchase", "_id":"2", "_score":1, "_source":{ "id":2, "name":"coco", "goods":[ { "id":"g1", "name":"ipad" }, { "id":"g2", "name":"iphone" }, { "id":"g3", "name":"ipod" } ] } }, { "_index":"purchase", "_id":"3", "_score":1, "_source":{ "id":3, "name":"jim", "goods":[ { "id":"g1", "name":"ipad" }, { "id":"g2", "name":"iphone" }, { "id":"g3", "name":"ipod" }, { "id":"g4", "name":"TV" } ] } } ] } } 二、查询需求
我们需要查询购买过某种商品的顾客，一般我们可以通过ui的业务逻辑得到需要筛选的一些商品的id，由于id字段是一个不需要分词的keyword字段，所以我们会直接使用term级别的查询；
POST purchase/_search { "query": { "terms": { "goods.id": [ "g2", "g3", "g4" ] } } } 我们可以看到查询结果中的三条记录的权重打分都是1；正常情况下购买商品越多的客户，相对来说价值更大即命中的权重得分越大；
{ "took":0, "timed_out":false, "_shards":{ "total":1, "successful":1, "skipped":0, "failed":0 }, "hits":{ "total":{ "value":3, "relation":"eq" }, "max_score":1, "hits":[ { "_index":"purchase", "_id":"1", "_score":1, "_source":{ "id":1, "name":"sam", "goods":[ { "id":"g1", "name":"ipad" }, { "id":"g2", "name":"iphone" } ] } }, { "_index":"purchase", "_id":"2", "_score":1, "_source":{ "id":2, "name":"coco", "goods":[ { "id":"g1", "name":"ipad" }, { "id":"g2", "name":"iphone" }, { "id":"g3", "name":"ipod" } ] } }, { "_index":"purchase", "_id":"3", "_score":1, "_source":{ "id":3, "name":"jim", "goods":[ { "id":"g1", "name":"ipad" }, { "id":"g2", "name":"iphone" }, { "id":"g3", "name":"ipod" }, { "id":"g4", "name":"TV" } ] } } ] } } 三、terms查询分析
我们使用_explain分析一下terms查询怎么打分的；
POST purchase/_explain/3 { "query": { "terms": { "goods.id": [ "g2", "g3", "g4" ] } } } 我们可以看到elasticsearch最终使用ConstantScore查询重写的terms查询，此查询默认权重打分为1；
{ "_index" : "purchase", "_id" : "3", "matched" : true, "explanation" : { "value" : 1.0, "description" : "ConstantScore(goods.id:g2 goods.id:g3 goods.id:g4)", "details" : [ ] } } terms提供的查询参数十分有限，其中涉及权重的只有boost，但是这只是针对整个terms查询，而不是内部的子查询；
POST purchase/_explain/3 { "query": { "terms": { "goods.id": [ "g2", "g3", "g4" ], "boost":2 } } } { "_index" : "purchase", "_id" : "3", "matched" : true, "explanation" : { "value" : 2.0, "description" : "ConstantScore(goods.id:g2 goods.id:g3 goods.id:g4)^2.0", "details" : [ ] } } 四、构建子查询打分
match是elasticsearch提供的一个跟terms类似的查询，由于goods.id的type是keyword，所以需要给match指定一个查询时的analyzer，才能保证输入的几个id分开作为不同的查询；
POST purchase/_search { "query": { "match": { "goods.id": { "query": "g2 g3 g4", "analyzer":"standard" } } } } { "took" : 1, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 3, "relation" : "eq" }, "max_score" : 2.178501, "hits" : [ { "_index" : "purchase", "_id" : "3", "_score" : 2.178501, "_source" : { "id" : 3, "name" : "jim", "goods" : [ { "id" : "g1", "name" : "ipad" }, { "id" : "g2", "name" : "iphone" }, { "id" : "g3", "name" : "ipod" }, { "id" : "g4", "name" : "TV" } ] } }, { "_index" : "purchase", "_id" : "2", "_score" : 0.8298607, "_source" : { "id" : 2, "name" : "coco", "goods" : [ { "id" : "g1", "name" : "ipad" }, { "id" : "g2", "name" : "iphone" }, { "id" : "g3", "name" : "ipod" } ] } }, { "_index" : "purchase", "_id" : "1", "_score" : 0.18360566, "_source" : { "id" : 1, "name" : "sam", "goods" : [ { "id" : "g1", "name" : "ipad" }, { "id" : "g2", "name" : "iphone" } ] } } ] } } 通过查看文档3的打分情况，我们可以看到elasticsearch先针对每个关键字计算打分，然后将三项打分的和作为最终的打分；在这里我们也可以看到elasticsearch内部会自动将match查询rewrite为三个子查询；
POST purchase/_explain/3 { "query": { "match": { "goods.id": { "query": "g2 g3 g4", "analyzer":"standard" } } } } { "_index" : "purchase", "_id" : "3", "matched" : true, "explanation" : { "value" : 2.178501, "description" : "sum of:", "details" : [ { "value" : 0.18360566, "description" : "weight(goods.id:g2 in 2) [PerFieldSimilarity], result of:", "details" : [] }, { "value" : 0.646255, "description" : "weight(goods.id:g3 in 2) [PerFieldSimilarity], result of:", "details" : [] }, { "value" : 1.3486402, "description" : "weight(goods.id:g4 in 2) [PerFieldSimilarity], result of:", "details" : [] } ] } } 我们也可以通过bool查询，使用它的should在查询之前手动组建多个子查询；
POST purchase/_search { "query": { "bool": { "should": [ {"term": {"goods.id": "g2"}}, {"term": {"goods.id": "g3"}}, {"term": {"goods.id": "g4"}} ], "minimum_should_match": 1 } } } { "took" : 1, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 3, "relation" : "eq" }, "max_score" : 2.178501, "hits" : [ { "_index" : "purchase", "_id" : "3", "_score" : 2.178501, "_source" : { "id" : 3, "name" : "jim", "goods" : [ { "id" : "g1", "name" : "ipad" }, { "id" : "g2", "name" : "iphone" }, { "id" : "g3", "name" : "ipod" }, { "id" : "g4", "name" : "TV" } ] } }, { "_index" : "purchase", "_id" : "2", "_score" : 0.8298607, "_source" : { "id" : 2, "name" : "coco", "goods" : [ { "id" : "g1", "name" : "ipad" }, { "id" : "g2", "name" : "iphone" }, { "id" : "g3", "name" : "ipod" } ] } }, { "_index" : "purchase", "_id" : "1", "_score" : 0.18360566, "_source" : { "id" : 1, "name" : "sam", "goods" : [ { "id" : "g1", "name" : "ipad" }, { "id" : "g2", "name" : "iphone" } ] } } ] } } 在bool查询中，通过查看文档3的打分情况，我们可以看到elasticsearch也是先针对每个关键字计算打分，然后将三项打分的和作为最终的打分；
POST purchase/_explain/3 { "query": { "bool": { "should": [ {"term": {"goods.id": "g2"}}, {"term": {"goods.id": "g3"}}, {"term": {"goods.id": "g4"}} ], "minimum_should_match": 1 } } } { "_index" : "purchase", "_id" : "3", "matched" : true, "explanation" : { "value" : 2.178501, "description" : "sum of:", "details" : [ { "value" : 0.18360566, "description" : "weight(goods.id:g2 in 2) [PerFieldSimilarity], result of:", "details" : [] }, { "value" : 0.646255, "description" : "weight(goods.id:g3 in 2) [PerFieldSimilarity], result of:", "details" : [] }, { "value" : 1.3486402, "description" : "weight(goods.id:g4 in 2) [PerFieldSimilarity], result of:", "details" : [] } ] } } 五、控制子查询的打分
不管是elasticsearch自动组建子查询，还是我们自己手动构建子查询，elasticsearch都会针对每个查询做相关性的打分计算，这对于一般的语义化关键字搜索是没有问题的；
我们这里的搜索条件goods.id一般是没有任何语义的，不同的值打分应该是一样的；这样我们只能使用bool+constant_score+term来手动构建查询语句；
POST purchase/_search { "query": { "bool": { "should": [ {"constant_score": {"filter": {"term": {"goods.id": "g2"}}}}, {"constant_score": {"filter": {"term": {"goods.id": "g3"}}}}, {"constant_score": {"filter": {"term": {"goods.id": "g4"}}}} ], "minimum_should_match": 1 } } } { "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 3, "relation" : "eq" }, "max_score" : 3.0, "hits" : [ { "_index" : "purchase", "_id" : "3", "_score" : 3.0, "_source" : { "id" : 3, "name" : "jim", "goods" : [ { "id" : "g1", "name" : "ipad" }, { "id" : "g2", "name" : "iphone" }, { "id" : "g3", "name" : "ipod" }, { "id" : "g4", "name" : "TV" } ] } }, { "_index" : "purchase", "_id" : "2", "_score" : 2.0, "_source" : { "id" : 2, "name" : "coco", "goods" : [ { "id" : "g1", "name" : "ipad" }, { "id" : "g2", "name" : "iphone" }, { "id" : "g3", "name" : "ipod" } ] } }, { "_index" : "purchase", "_id" : "1", "_score" : 1.0, "_source" : { "id" : 1, "name" : "sam", "goods" : [ { "id" : "g1", "name" : "ipad" }, { "id" : "g2", "name" : "iphone" } ] } } ] } } 我们看下文档3的打分情况，每一个命中项的打分都是固定的1，最终的打分命中项的和；
POST purchase/_explain/3 { "query": { "bool": { "should": [ {"constant_score": {"filter": {"term": {"goods.id": "g2"}}}}, {"constant_score": {"filter": {"term": {"goods.id": "g3"}}}}, {"constant_score": {"filter": {"term": {"goods.id": "g4"}}}} ], "minimum_should_match": 1 } } } { "_index" : "purchase", "_id" : "3", "matched" : true, "explanation" : { "value" : 3.0, "description" : "sum of:", "details" : [ { "value" : 1.0, "description" : "ConstantScore(goods.id:g2)", "details" : [ ] }, { "value" : 1.0, "description" : "ConstantScore(goods.id:g3)", "details" : [ ] }, { "value" : 1.0, "description" : "ConstantScore(goods.id:g4)", "details" : [ ] } ] } } 
