# 为什么相同的文本搜索结果的\_score不同？ **URL:** \ **Category:** 中文提问与讨论\ **Created:** [March 10, 2017, 10:59am UTC](https://discuss.elastic.co/t/-score/78135 "2017-03-10T10:59:42Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![watson](https://avatars.discourse-cdn.com/v4/letter/w/ce73a5/32.png) [@watson](https://discuss.elastic.co/u/watson)\ **Post date:** [March 10, 2017, 10:59am UTC](https://discuss.elastic.co/t/-score/78135/1 "2017-03-10T10:59:42Z")
\*\*Elasticsearch version2.3.3: Plugins installed: [head,ik] \*\*JVM versionopenjdk version "1.8.0\_111": \*\*OS versionCentOS release 6.8 (Final): +++++++++++++++++++++++++++++++++++++++++ my mapping： "mappings": { "weixinMedia": { "\_all": { "analyzer": "ik\_max\_word" }, "properties": { "mediaScore": { "type": "float" }, "weixinId": { "type": "string" }, "pi": { "type": "long" }, "mediaNameLen": { "type": "integer" }, "pmid": { "index": "not\_analyzed", "type": "string" }, "mediaName": { "analyzer": "ik\_max\_word", "term\_vector": "with\_positions\_offsets", "boost": 2, "type": "string" } } }, +++++++++++++++++++++++++++++++++++++++++ my request: { "track\_scores" : "true", "sort" : { "\_script" : { "type" : "number", "script" : "\_score+(doc.pi.value/100.0)", "order" : "desc" } }, "query" : { "match\_phrase" : { "mediaName" : "微美食" } } } +++++++++++++++++++++++++++++++++++++++++ expect result: { "\_index" : "media\_b", "\_type" : "weixinMedia", "\_id" : "MzA5NTgyOTUyNA==", "\_score" : 17.916512, "\_source" : { "mediaName" : "微美食", "pmid" : "MzA5NTgyOTUyNA==", "mediaNameLen" : 3, "mediaScore" : 2.2, "pi" : 600 }, "sort" : [23.91651153564453] }, { "\_index" : "media\_b", "\_type" : "weixinMedia", "\_id" : "MjM5NzMxOTM4MQ==", "\_score" : 17.916512, "\_source" : { "mediaName" : "微美食", "pmid" : "MjM5NzMxOTM4MQ==", "mediaNameLen" : 3, "mediaScore" : 2.062, "pi" : 506 }, "sort" : [22.97651153564453] }, { "\_index" : "media\_b", "\_type" : "weixinMedia", "\_id" : "MjM5OTg0OTcwMg==", "\_score" : 17.916512, "\_source" : { "mediaName" : "微美食西安", "pmid" : "MjM5OTg0OTcwMg==", "mediaNameLen" : 5, "mediaScore" : 2.308, "pi" : 629 }, "sort" : [24.20651153564453] }, +++++++++++++++++++++++++++++++++++++++++ actual result with explain: { "\_shard" : 4, "\_node" : "uSTOGHNUSjGNFweC7RynRQ", "\_index" : "media\_b", "\_type" : "weixinMedia", "\_id" : "MjM5OTg0OTcwMg==", "\_score" : 17.916512, "\_source" : { "mediaName" : "微美食西安", "pmid" : "MjM5OTg0OTcwMg==", "mediaNameLen" : 5, "mediaScore" : 2.308, "pi" : 629 }, "sort" : [24.20651153564453], "\_explanation" : { "value" : 17.916512, "description" : "sum of:", "details" : [ { "value" : 17.916512, "description" : "weight(mediaName:"微 美食 食" in 161368) [PerFieldSimilarity], result of:", "details" : [ { "value" : 17.916512, "description" : "fieldWeight in 161368, product of:", "details" : [ { "value" : 1.0, "description" : "tf(freq=1.0), with freq of:", "details" : [ { "value" : 1.0, "description" : "phraseFreq=1.0", "details" : [] } ] }, { "value" : 17.916512, "description" : "idf(), sum of:", "details" : [ { "value" : 4.92588, "description" : "idf(docFreq=9932, maxDocs=503580)", "details" : [] }, { "value" : 7.0545344, "description" : "idf(docFreq=1181, maxDocs=503580)", "details" : [] }, { "value" : 5.9360976, "description" : "idf(docFreq=3616, maxDocs=503580)", "details" : [] "value" : 1.0, "description" : "fieldNorm(doc=161368)", "details" : [] "value" : 0.0, "description" : "match on required clause, product of:", "details" : [ { "value" : 0.0, "description" : "# clause", "details" : [] }, { "value" : 0.055814438, "description" : "\_type:weixinMedia, product of:", "details" : [ { "value" : 1.0, "description" : "boost", "details" : [] }, { "value" : 0.055814438, "description" : "queryNorm", "details" : [] "\_shard" : 3, "\_node" : "uSTOGHNUSjGNFweC7RynRQ", "\_index" : "media\_b", "\_type" : "weixinMedia", "\_id" : "MzA5NTgyOTUyNA==", "\_score" : 17.796759, "\_source" : { "mediaName" : "微美食", "pmid" : "MzA5NTgyOTUyNA==", "mediaNameLen" : 3, "mediaScore" : 2.2, "pi" : 600 }, "sort" : [23.7967586517334], "\_explanation" : { "value" : 17.796759, "description" : "sum of:", "details" : [ { "value" : 17.796759, "description" : "weight(mediaName:"微 美食 食" in 120379) [PerFieldSimilarity], result of:", "details" : [ { "value" : 17.796759, "description" : "score(doc=120379,freq=1.0), product of:", "details" : [ { "value" : 0.99999994, "description" : "queryWeight, product of:", "details" : [ { "value" : 17.79676, "description" : "idf(), sum of:", "details" : [ { "value" : 4.93295, "description" : "idf(docFreq=10996, maxDocs=561478)", "details" : [] "value" : 6.9785085, "description" : "idf(docFreq=1421, maxDocs=561478)", "details" : [] }, { "value" : 5.885302, "description" : "idf(docFreq=4242, maxDocs=561478)", "details" : [] "value" : 0.05619, "description" : "queryNorm", "details" : [] "value" : 17.79676, "description" : "fieldWeight in 120379, product of:", "details" : [ { "value" : 1.0, "description" : "tf(freq=1.0), with freq of:", "details" : [ { "value" : 1.0, "description" : "phraseFreq=1.0", "details" : [] "value" : 17.79676, "description" : "idf(), sum of:", "details" : [ { "value" : 4.93295, "description" : "idf(docFreq=10996, maxDocs=561478)", "details" : [] }, { "value" : 6.9785085, "description" : "idf(docFreq=1421, maxDocs=561478)", "details" : [] }, { "value" : 5.885302, "description" : "idf(docFreq=4242, maxDocs=561478)", "details" : [] } ] }, { "value" : 1.0, "description" : "fieldNorm(doc=120379)", "details" : [] "value" : 0.0, "description" : "match on required clause, product of:", "details" : [ { "value" : 0.0, "description" : "# clause", "details" : [] }, { "value" : 0.05619, "description" : "\_type:weixinMedia, product of:", "details" : [ { "value" : 1.0, "description" : "boost", "details" : [] }, { "value" : 0.05619, "description" : "queryNorm", "details" : [] "\_shard" : 4, "\_node" : "uSTOGHNUSjGNFweC7RynRQ", "\_index" : "media\_b", "\_type" : "weixinMedia", "\_id" : "MjM5NzMxOTM4MQ==", "\_score" : 17.916512, "\_source" : { "mediaName" : "微美食", "pmid" : "MjM5NzMxOTM4MQ==", "mediaNameLen" : 3, "mediaScore" : 2.062, "pi" : 506 }, "sort" : [22.97651153564453], "\_explanation" : { "value" : 17.916512, "description" : "sum of:", "details" : [ { "value" : 17.916512, "description" : "weight(mediaName:"微 美食 食" in 364138) [PerFieldSimilarity], result of:", "details" : [ { "value" : 17.916512, "description" : "fieldWeight in 364138, product of:", "details" : [ { "value" : 1.0, "description" : "tf(freq=1.0), with freq of:", "details" : [ { "value" : 1.0, "description" : "phraseFreq=1.0", "details" : [] } ] }, { "value" : 17.916512, "description" : "idf(), sum of:", "details" : [ { "value" : 4.92588, "description" : "idf(docFreq=9932, maxDocs=503580)", "details" : [] }, { "value" : 7.0545344, "description" : "idf(docFreq=1181, maxDocs=503580)", "details" : [] }, { "value" : 5.9360976, "description" : "idf(docFreq=3616, maxDocs=503580)", "details" : [] } ] }, { "value" : 1.0, "description" : "fieldNorm(doc=364138)", "details" : [] +++++++++++++++++++++++++++++++++++++++++ 不知道为什么搜索出来两个“微美食”的评分不同，评分最高的不是完全匹配的，这个是为什么呢？ thanks! ---
**Author:** ![medcl.net](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/medcl.net/32/4414_2.png) [@medcl.net](https://discuss.elastic.co/u/medcl.net)\ **Post date:** [March 20, 2017, 12:38pm UTC](https://discuss.elastic.co/t/-score/78135/2 "2017-03-20T12:38:49Z")
> [@watson](#): > > "script" : "\_score+(doc.pi.value/100.0)", 你的评分公式不是自己指定了会使用pi这个值么，看返回，这些pi值是不同的 ---
**Author:** ![watson](https://avatars.discourse-cdn.com/v4/letter/w/ce73a5/32.png) [@watson](https://discuss.elastic.co/u/watson)\ **Post date:** [March 27, 2017, 8:45am UTC](https://discuss.elastic.co/t/-score/78135/3 "2017-03-27T08:45:14Z")
我说的不同是es得出的\_score值不同，不是我计算的结果。 es得出的\_score的值为什么不同？ "index" : "mediab", "\_type" : "weixinMedia", "\_id" : "MzA5NTgyOTUyNA==", " **`_score`**" : 17.916512, "\_source" : { "mediaName" : "微美食", "pmid" : "MzA5NTgyOTUyNA==", "mediaNameLen" : 3, "mediaScore" : 2.2, "pi" : 600 }, "sort" : [23.91651153564453] } ---
**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\ **Post date:** [April 24, 2017, 8:45am UTC](https://discuss.elastic.co/t/-score/78135/4 "2017-04-24T08:45:20Z")
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**Author:** ![medcl.net](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/medcl.net/32/4414_2.png) [@medcl.net](https://discuss.elastic.co/u/medcl.net)\ **Post date:** [May 29, 2017, 3:35pm UTC](https://discuss.elastic.co/t/-score/78135/5 "2017-05-29T15:35:15Z")
> [@watson](#): > > "\_shard" : 3, > "\_node" : "uSTOGHNUSjGNFweC7RynRQ", > "index" : "mediab", > "\_type" : "weixinMedia", > "\_id" : "MzA5NTgyOTUyNA==", > "\_score" : 17.796759, > "\_source" : { > "mediaName" : "微美食", 1.首先要知道es底层按分片来存储索引，每个索引各种的评分和该shard索引内的倒排表有关，所有每个分片的数据都是不一样的，打出来的分不一定是一样的，尽管他们的term一样。 2.全文检索针对的是field分词之后的结果，所以他们都匹配上就能查询出来，而评分默认是按各自shard内的评分算出来的。