测试需要用到的数据：
PUT /us/user/1?pretty=1 { "email" : "john@smith.com", "name" : "John Smith", "username" : "@john" } PUT /gb/user/2?pretty=1 { "email" : "mary@jones.com", "name" : "Mary Jones", "username" : "@mary" } PUT /gb1/tweet/3?pretty=1 { "date" : "2014-09-13", "name" : "Mary Jones", "tweet" : "Elasticsearch means full text search has never been so easy", "user_id" : 2 } PUT /us1/tweet/4?pretty=1 { "date" : "2014-09-14", "name" : "John Smith", "tweet" : "@mary it is not just text, it does everything", "user_id" : 1 } PUT /gb1/tweet/5?pretty=1 { "date" : "2014-09-15", "name" : "Mary Jones", "tweet" : "However did I manage before Elasticsearch?", "user_id" : 2 } PUT /us1/tweet/6?pretty=1 { "date" : "2014-09-16", "name" : "John Smith", "tweet" : "The Elasticsearch API is really easy to use", "user_id" : 1 } PUT /gb1/tweet/7?pretty=1 { "date" : "2014-09-17", "name" : "Mary Jones", "tweet" : "The Query DSL is really powerful and flexible", "user_id" : 2 } PUT /us1/tweet/8?pretty=1 { "date" : "2014-09-18", "name" : "John Smith", "user_id" : 1 } PUT /gb1/tweet/9?pretty=1 { "date" : "2014-09-19", "name" : "Mary Jones", "tweet" : "Geo-location aggregations are really cool", "user_id" : 2 } PUT /us1/tweet/10?pretty=1 { "date" : "2014-09-20", "name" : "John Smith", "tweet" : "Elasticsearch surely is one of the hottest new NoSQL products", "user_id" : 1 } PUT /gb1/tweet/11?pretty=1 { "date" : "2014-09-21", "name" : "Mary Jones", "tweet" : "Elasticsearch is built for the cloud, easy to scale", "user_id" : 2 } PUT /us1/tweet/12?pretty=1 { "date" : "2014-09-22", "name" : "John Smith", "tweet" : "Elasticsearch and I have left the honeymoon stage, and I still love her.", "user_id" : 1 } PUT /gb1/tweet/13?pretty=1 { "date" : "2014-09-23", "name" : "Mary Jones", "tweet" : "So yes, I am an Elasticsearch fanboy", "user_id" : 2 } PUT /us1/tweet/14?pretty=1 { "date" : "2014-09-24", "name" : "John Smith", "tweet" : "How many more cheesy tweets do I have to write?", "user_id" : 1 }
空搜索：
GET /_search
结果：
{ "took" : 5, "timed_out" : false, "_shards" : { "total" : 34, "successful" : 34, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 28, "max_score" : 1.0, "hits" : [ { "_index" : ".kibana_1", "_type" : "doc", "_id" : "space:default", "_score" : 1.0, "_source" : { "space" : { "name" : "Default", "description" : "This is your default space!", "color" : "#00bfb3", "_reserved" : true }, "type" : "space", "updated_at" : "2019-03-11T01:39:09.342Z" } }, { "_index" : ".kibana_1", "_type" : "doc", "_id" : "config:6.6.1", "_score" : 1.0, "_source" : { "config" : { "buildNum" : 19513 }, "type" : "config", "updated_at" : "2019-03-11T01:39:15.676Z" } }, { "_index" : ".kibana_1", "_type" : "doc", "_id" : "telemetry:telemetry", "_score" : 1.0, "_source" : { "telemetry" : { "enabled" : false }, "type" : "telemetry", "updated_at" : "2019-03-11T05:17:03.176Z" } }, { "_index" : "us1", "_type" : "tweet", "_id" : "14", "_score" : 1.0, "_source" : { "date" : "2014-09-24", "name" : "John Smith", "tweet" : "How many more cheesy tweets do I have to write?", "user_id" : 1 } }, { "_index" : "website", "_type" : "blog", "_id" : "bf7xa2kBUi3i0pMAwG44", "_score" : 1.0, "_source" : { "title" : "My first blog entry", "text" : "Just trying this out...", "date" : "2014/01/01" } }, { "_index" : "website", "_type" : "blog", "_id" : "123", "_score" : 1.0, "_source" : { "title" : "But we can update it" } }, { "_index" : "gb1", "_type" : "tweet", "_id" : "5", "_score" : 1.0, "_source" : { "date" : "2014-09-15", "name" : "Mary Jones", "tweet" : "However did I manage before Elasticsearch?", "user_id" : 2 } }, { "_index" : "gb1", "_type" : "tweet", "_id" : "9", "_score" : 1.0, "_source" : { "date" : "2014-09-19", "name" : "Mary Jones", "tweet" : "Geo-location aggregations are really cool", "user_id" : 2 } }, { "_index" : "us1", "_type" : "tweet", "_id" : "8", "_score" : 1.0, "_source" : { "date" : "2014-09-18", "name" : "John Smith", "user_id" : 1 } }, { "_index" : "us1", "_type" : "tweet", "_id" : "10", "_score" : 1.0, "_source" : { "date" : "2014-09-20", "name" : "John Smith", "tweet" : "Elasticsearch surely is one of the hottest new NoSQL products", "user_id" : 1 } } ] } }
hits
- 响应中最重要的部分是
hits，它包含了total字段来表示匹配到的文档总数，hits数组还包含了匹配到的前10条数据。
hits数组中的每个结果都包含_index、_type和文档的_id字段，被加入到_source字段中这意味着在搜索结果中我们将可以直接使用全部文档。这不像其他搜索引擎只返回文档ID，需要你单独去获取文档。
- 每个节点都有一个
_score字段，这是相关性得分(relevance score)，它衡量了文档与查询的匹配程度。默认的，返回的结果中关联性最大的文档排在首位；这意味着，它是按照_score降序排列的。这种情况下，我们没有指定任何查询，所以所有文档的相关性是一样的，因此所有结果的_score都是取得一个中间值1。
max_score指的是所有文档匹配查询中_score的最大值。
took
took告诉我们整个搜索请求花费的毫秒数。
shards
_shards节点告诉我们参与查询的分片数（total字段），有多少是成功的（successful字段），有多少的是失败的（failed字段）。
timeout
time_out值告诉我们查询超时与否。一般的，搜索请求不会超时。如果响应速度比完整的结果更重要，你可以定义timeout参数为10或者10ms（10毫秒），或者1s（1秒）。
GET /_search?timeout=10ms
Elasticsearch将返回在请求超时前收集到的结果。