在Elasticsearch中存储数据的行为就叫做索引(indexing)，不过在索引之前，我们需要明确数据应该存储在哪里。在Elasticsearch中，文档归属于一种类型(type),而这些类型存在于索引(index)中,这样说可能不是很好理解,这里贴出一张es和关系型数据库的对应图,就懂了 第一步:创建员工目录,为每个员工的文档(document)建立索引，每个文档包含了相应员工的所有信息。 每个文档的类型为employee employee类型归属于索引megacorp megacorp索引存储在Elasticsearch集群中。
PUT /megacorp/employee/1 { "first_name" : "John", "last_name" : "Smith", "age" : 25, "about" : "I love to go rock climbing", "interests": [ "sports", "music" ] }上图代表的意思是在一个叫megacorp的索引(index)中建立了一个叫 employee的类型(type),它的ID是1,整个json部分代表的是这个id为1的文档(document). 同样的,我们再次插入两个document.
PUT /megacorp/employee/2 { "first_name" : "Jane", "last_name" : "Smith", "age" : 32, "about" : "I like to collect rock albums", "interests": [ "music" ] } PUT /megacorp/employee/3 { "first_name" : "Douglas", "last_name" : "Fir", "age" : 35, "about": "I like to build cabinets", "interests": [ "forestry" ] }搜索,上一步中插入了3个document,我们可以通过http get来获取它们的信息.
GET /megacorp/employee/1==
{ "_index" : "megacorp", "_type" : "employee", "_id" : "1", "_version" : 1, "found" : true, "_source" : { "first_name" : "John", "last_name" : "Smith", "age" : 25, "about" : "I love to go rock climbing", "interests": [ "sports", "music" ] } } _index--索引名称_type–类型名称 _id–id(这个id可以自己指定也可以自动生成) _version–版本号,每次改动会+1 found–true表示在document存在 _source–document的全部内容
轻量级搜索,查询字符串(query string)
GET /megacorp/employee/_search?q=last_name:Smith=
{ "took": 6, //花费的时间(毫秒) "timed_out": false, //是否超时 "_shards": { ... }, //分片相关,后续介绍 "hits": { //存放所有命中 "total": 2, //命中document的个数 "max_score": 0.30685282, //最高命中分 "hits": [ { ... "_source": { "first_name": "John", "last_name": "Smith", "age": 25, "about": "I love to go rock climbing", "interests": [ "sports", "music" ] } }, { ... "_source": { "first_name": "Jane", "last_name": "Smith", "age": 32, "about": "I like to collect rock albums", "interests": [ "music" ] } } ] } }Elasticsearch提供丰富且灵活的查询语言叫做DSL查询(Query DSL),它允许你构建更加复杂、强大的查询。DSL(Domain Specific Language特定领域语言)以JSON请求体的形式出现。我们可以这样表示之前关于“Smith”的查询:
GET /megacorp/employee/_search { "query" : { "match" : { "last_name" : "Smith" } } }更复杂的搜索,使用过滤器(filter)来实现sql中where的效果,比如:你想要搜索一个叫Smith,且年龄大于30的员工,可以这么检索.
GET /megacorp/employee/_search { "query" : { "filtered" : { "filter" : { "range" : { "age" : { "gt" : 30 } } }, "query" : { "match" : { "last_name" : "Smith" } } } } }短语搜索(phrases),match_phrase与match的区别在于,前者会把rock climbing(搜索条件)作为一个整体,而后者会命中rock balabala climbing
GET /megacorp/employee/_search { "query" : { "match_phrase" : { "about" : "rock climbing" } } }聚合(aggregations)，它允许你在数据上生成复杂的分析统计,类似于sql中的group by.
GET /megacorp/employee/_search { "aggs": { "all_interests": { "terms": { "field": "interests" } } } }==
{ ... "hits": { ... }, "aggregations": { "all_interests": { "buckets": [ { "key": "music", "doc_count": 2 }, { "key": "forestry", "doc_count": 1 }, { "key": "sports", "doc_count": 1 } ] } } }如果我们想知道所有姓"Smith"的人最大的共同点（兴趣爱好），我们只需要增加合适的语句既可：
GET /megacorp/employee/_search { "query": { "match": { "last_name": "smith" } }, "aggs": { "all_interests": { "terms": { "field": "interests" } } } }聚合也允许分级汇总。例如，让我们统计每种兴趣下职员的平均年龄
GET /megacorp/employee/_search { "aggs" : { "all_interests" : { "terms" : { "field" : "interests" }, "aggs" : { "avg_age" : { "avg" : { "field" : "age" } } } } } }===
... "all_interests": { "buckets": [ { "key": "music", "doc_count": 2, "avg_age": { "value": 28.5 } }, { "key": "forestry", "doc_count": 1, "avg_age": { "value": 35 } }, { "key": "sports", "doc_count": 1, "avg_age": { "value": 25 } } ] }