Elasticsearch之高亮查詢
一 前言
如果返回的結果集中很多符合條件的結果，那怎么能一眼就能看到我們想要的那個結果呢？比如下面網站所示的那樣，我們搜索elasticsearch，在結果集中，將所有elasticsearch高亮顯示？
如上圖我們搜索百度一樣。我們該怎么做呢？
二 准備數據
PUT lqz/doc/4 { "name":"石頭", "age":29, "from":"gu", "desc":"粗中有細，狐假虎威", "tags":["粗", "大","猛"] }
三 默認高亮顯示
我們來查詢：
GET lqz/doc/_search { "query": { "match": { "name": "石頭" } }, "highlight": { "fields": { "name": {} } } } #我們使用highlight屬性來實現結果高亮顯示，需要的字段名稱添加到fields內即可，elasticsearch會自動幫我們實現高亮。
結果如下： { "took" : 1, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 1, "max_score" : 1.5098256, "hits" : [ { "_index" : "lqz", "_type" : "doc", "_id" : "4", "_score" : 1.5098256, "_source" : { "name" : "石頭", "age" : 29, "from" : "gu", "desc" : "粗中有細，狐假虎威", "tags" : [ "粗", "大", "猛" ] }, "highlight" : { "name" : [ "<em>石</em><em>頭</em>" ] } } ] } }
上例中，elasticsearch會自動將檢索結果用標簽包裹起來，用於在頁面中渲染。
四 自定義高亮顯示
GET lqz/chengyuan/_search { "query": { "match": { "from": "gu" } }, "highlight": { "pre_tags": "<b class='key' style='color:red'>", "post_tags": "</b>", "fields": { "from": {} } } } 上例中，在highlight中，pre_tags用來實現我們的自定義標簽的前半部分，在這里，我們也可以為自定義的標簽添加屬性和樣式。post_tags實現標簽的后半部分，組成一個完整的標簽。至於標簽中的內容，則還是交給fields來完成。
{ "took" : 1, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 1, "max_score" : 0.5753642, "hits" : [ { "_index" : "lqz", "_type" : "chengyuan", "_id" : "1", "_score" : 0.5753642, "_source" : { "name" : "老二", "age" : 30, "sex" : "male", "birth" : "1070-10-11", "from" : "gu", "desc" : "皮膚黑，武器長，性格直", "tags" : [ "黑", "長", "直" ] }, "highlight" : { "name" : [ "<b class='key' style='color:red'>老</b><b class='key' style='color:red'>二</b>" ] } } ] } }
需要注意的是：自定義標簽中屬性或樣式中的逗號一律用英文狀態的單引號表示，應該與外部elasticsearch語法的雙引號區分開。
前后端分離，你怎么處理？把<b class='key' style='color:red'>串直接以json格式返回，前端自行渲染
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# 查詢`from`是`gu`的人的平均年齡。 # select max(age) as my_avg from user； GET lqz/doc/_search { "query": { "match": { "from": "gu" } }, "aggs": { "my_avg": { "avg": { "field": "age" } } }, "_source": ["name", "age"] }
{ "took" : 1, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 3, "max_score" : 0.6931472, "hits" : [ { "_index" : "lqz", "_type" : "doc", "_id" : "4", "_score" : 0.6931472, "_source" : { "name" : "石頭", "age" : 29 } }, { "_index" : "lqz", "_type" : "doc", "_id" : "1", "_score" : 0.2876821, "_source" : { "name" : "顧老二", "age" : 30 } }, { "_index" : "lqz", "_type" : "doc", "_id" : "3", "_score" : 0.2876821, "_source" : { "name" : "龍套偏房", "age" : 22 } } ] }, "aggregations" : { "my_avg" : { "value" : 27.0 } } }
上例中，在查詢結果的最后是平均值信息，可以看到是27歲。
雖然我們已經使用_source對字段做了過濾，但是還不夠。我不想看都有哪些數據，只想看平均值怎么辦？別忘了size!
GET lqz/doc/_search { "query": { "match": { "from": "gu" } }, "aggs": { "my_avg": { "avg": { "field": "age" } } }, "size": 0, "_source": ["name", "age"] }
上例中，只需要在原來的查詢基礎上，增加一個size就可以了，輸出幾條結果，我們寫上0，就是輸出0條查詢結果。
{ "took" : 8, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 3, "max_score" : 0.0, "hits" : [ ] }, "aggregations" : { "my_avg" : { "value" : 27.0 } } }
GET lqz/doc/_search { "query": { "match": { "from": "gu" } }, "aggs": { "my_max": { "max": { "field": "age" } } }, "size": 0 }
GET lqz/doc/_search { "query": { "match": { "from": "gu" } }, "aggs": { "my_min": { "min": { "field": "age" } } }, "size": 0 }
sum
# 求年齡總和
GET lqz/doc/_search { "query": { "match": { "from": "gu" } }, "aggs": { "my_sum": { "sum": { "field": "age" } } }, "size": 0 }
GET lqz/doc/_search { "size": 0, "query": { "match_all": {} }, "aggs": { "age_group": { "range": { "field": "age", "ranges": [ { "from": 15, "to": 20 }, { "from": 20, "to": 25 }, { "from": 25, "to": 30 } ] }, "aggs": { "my_avg": { "avg": { "field": "age" } } } } } }
{ "took" : 1, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 5, "max_score" : 0.0, "hits" : [ ] }, "aggregations" : { "age_group" : { "buckets" : [ { "key" : "15.0-20.0", "from" : 15.0, "to" : 20.0, "doc_count" : 1, "my_avg" : { "value" : 18.0 } }, { "key" : "20.0-25.0", "from" : 20.0, "to" : 25.0, "doc_count" : 1, "my_avg" : { "value" : 22.0 } }, { "key" : "25.0-30.0", "from" : 25.0, "to" : 30.0, "doc_count" : 2, "my_avg" : { "value" : 27.0 } } ] } } }