Elasticsearch学习记录(入门篇)
1、 Elasticsearch的请求与结果
请求结构
curl -X<VERB> '<PROTOCOL>://<HOST>:<PORT>/<PATH>?<QUERY_STRING>' -d '<BODY>'
- VERB HTTP方法：GET, POST, PUT, HEAD, DELETE
- PROTOCOL http或者https协议（只有在Elasticsearch前面有https代理的时候可用）
- HOST Elasticsearch集群中的任何一个节点的主机名，如果是在本地的节点，那么就叫localhost
- PORT Elasticsearch HTTP服务所在的端口，默认为9200
- PATH API路径（例如_count将返回集群中文档的数量），PATH可以包含多个组件，例如_cluster/stats或者_nodes/stats/jvm
- QUERY_STRING 一些可选的查询请求参数，例如?pretty参数将使请求返回更加美观易读的JSON数据
BODY 一个JSON格式的请求主体（如果请求需要的话）
PUT创建(索引创建)
$ curl -XPUT 'http://localhost:9200/megacorp/employee/3?pretty' -d ' { "first_name" : "Douglas", "last_name" : "Fir", "age" : 35, "about": "I like to build cabinets", "interests": [ "forestry" ] } ’{ "_index" : "megacorp", "_type" : "employee", "_id" : "3", "_version" : 1, "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "created" : true }GET请求(搜索)
检索文档
$ curl -XGET 'http://localhost:9200/megacorp/employee/1?pretty'{ "_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" ] } }简单搜索
使用
megacorp索引和employee类型，但是我们在结尾使用关键字_search来取代原来的文档ID。响应内容的hits数组中包含了我们所有的三个文档。默认情况下搜索会返回前10个结果。$ curl -XGET 'http://localhost:9200/megacorp/employee/_search?pretty'{ "took" : 2, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "failed" : 0 }, "hits" : { "total" : 3, "max_score" : 1.0, "hits" : [ { "_index" : "megacorp", "_type" : "employee", "_id" : "2", "_score" : 1.0, "_source" : { "first_name" : "Jane", "last_name" : "Smith", "age" : 32, "about" : "I like to collect rock albums", "interests" : [ "music" ] } }, { "_index" : "megacorp", "_type" : "employee", "_id" : "1", "_score" : 1.0, "_source" : { "first_name" : "John", "last_name" : "Smith", "age" : 25, "about" : "I love to go rock climbing", "interests" : [ "sports", "music" ] } }, { "_index" : "megacorp", "_type" : "employee", "_id" : "3", "_score" : 1.0, "_source" : { "first_name" : "Douglas", "last_name" : "Fir", "age" : 35, "about" : "I like to build cabinets", "interests" : [ "forestry" ] } } ] } }接下来，让我们搜索姓氏中包含“Smith”的员工。我们将在命令行中使用轻量级的搜索方法。这种方法常被称作查询字符串(query string)搜索，因为我们像传递URL参数一样去传递查询语句：
$ curl -XGET 'http://localhost:9200/megacorp/employee/_search?q=last_name:Smith&pretty'{ "took" : 4, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "failed" : 0 }, "hits" : { "total" : 2, "max_score" : 0.30685282, "hits" : [ { "_index" : "megacorp", "_type" : "employee", "_id" : "2", "_score" : 0.30685282, "_source" : { "first_name" : "Jane", "last_name" : "Smith", "age" : 32, "about" : "I like to collect rock albums", "interests" : [ "music" ] } }, { "_index" : "megacorp", "_type" : "employee", "_id" : "1", "_score" : 0.30685282, "_source" : { "first_name" : "John", "last_name" : "Smith", "age" : 25, "about" : "I love to go rock climbing", "interests" : [ "sports", "music" ] } } ] } }使用DSL语句查询
查询字符串搜索便于通过命令行完成特定(ad hoc)的搜索，但是它也有局限性（参阅简单搜索章节）。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:
$ curl -XGET 'http://localhost:9200/megacorp/employee/_search?pretty' -d ' { "query" : { "match" : { "last_name" : "Smith" } } } '更复杂的搜索
我们让搜索稍微再变的复杂一些。我们依旧想要找到姓氏为“Smith”的员工，但是我们只想得到年龄大于30岁的员工。我们的语句将添加过滤器(filter),它使得我们高效率的执行一个结构化搜索：
$ curl -XGET 'http://localhost:9200/megacorp/employee/_search?pretty' -d ' { "query" : { "filtered" : { "filter" : { "range" : { "age" : { "gt" : 30 } --<1> } }, "query" : { "match" : { "last_name" : "smith" --<2> } } } } } '
- <1> 这部分查询属于区间过滤器(range filter),它用于查找所有年龄大于30岁的数据——gt为"greater than"的缩写。
- <2> 这部分查询与之前的match语句(query)一致。
{ "took" : 2, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "failed" : 0 }, "hits" : { "total" : 1, "max_score" : 0.30685282, "hits" : [ { "_index" : "megacorp", "_type" : "employee", "_id" : "2", "_score" : 0.30685282, "_source" : { "first_name" : "Jane", "last_name" : "Smith", "age" : 32, "about" : "I like to collect rock albums", "interests" : [ "music" ] } } ] } }全文搜索
到目前为止搜索都很简单：搜索特定的名字，通过年龄筛选。让我们尝试一种更高级的搜索，全文搜索——一种传统数据库很难实现的功能。
我们将会搜索所有喜欢“rock climbing”的员工：
$ curl -XGET 'http://localhost:9200/megacorp/employee/_search?pretty' -d ' { "query" : { "match" : { "about" : "rock climbing" } } } '你可以看到我们使用了之前的
match查询，从about字段中搜索"rock climbing"，我们得到了两个匹配文档：{ "took" : 3, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "failed" : 0 }, "hits" : { "total" : 2, "max_score" : 0.16273327, "hits" : [ { "_index" : "megacorp", "_type" : "employee", "_id" : "1", "_score" : 0.16273327,<1> "_source" : { "first_name" : "John", "last_name" : "Smith", "age" : 25, "about" : "I love to go rock climbing", "interests" : [ "sports", "music" ] } }, { "_index" : "megacorp", "_type" : "employee", "_id" : "2", "_score" : 0.016878016,<2> "_source" : { "first_name" : "Jane", "last_name" : "Smith", "age" : 32, "about" : "I like to collect rock albums", "interests" : [ "music" ] } } ] } }
- <1><2> 结果相关性评分。
默认情况下，Elasticsearch根据结果相关性评分来对结果集进行排序，所谓的「结果相关性评分」就是文档与查询条件的匹配程度。很显然，排名第一的
John Smith的about字段明确的写到“rock climbing”但是为什么
Jane Smith也会出现在结果里呢？原因是“rock”在她的abuot字段中被提及了。因为只有“rock”被提及而“climbing”没有，所以她的_score要低于John。短语搜索
目前我们可以在字段中搜索单独的一个词，这挺好的，但是有时候你想要确切的匹配若干个单词或者短语(phrases)。例如我们想要查询同时包含"rock"和"climbing"（并且是相邻的）的员工记录。
要做到这个，我们只要将
match查询变更为match_phrase查询即可:$ curl -XGET 'http://localhost:9200/megacorp/employee/_search?pretty' -d ' { "query" : { "match_phrase" : { "about" : "rock climbing" } } } '{ "took" : 16, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "failed" : 0 }, "hits" : { "total" : 1, "max_score" : 0.23013961, "hits" : [ { "_index" : "megacorp", "_type" : "employee", "_id" : "1", "_score" : 0.23013961, "_source" : { "first_name" : "John", "last_name" : "Smith", "age" : 25, "about" : "I love to go rock climbing", "interests" : [ "sports", "music" ] } } ] } }高亮我们的搜索
很多应用喜欢从每个搜索结果中高亮(highlight)匹配到的关键字，这样用户可以知道为什么这些文档和查询相匹配。在Elasticsearch中高亮片段是非常容易的。
让我们在之前的语句上增加
highlight参数：$ curl -XGET 'http://localhost:9200/megacorp/employee/_search?pretty' -d ' { "query" : { "match_phrase" : { "about" : "rock climbing" } }, "highlight": { "fields" : { "about" : {} } } } '当我们运行这个语句时，会命中与之前相同的结果，但是在返回结果中会有一个新的部分叫做
highlight，这里包含了来自about字段中的文本，并且用<em></em>来标识匹配到的单词。{ "took" : 33, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "failed" : 0 }, "hits" : { "total" : 1, "max_score" : 0.23013961, "hits" : [ { "_index" : "megacorp", "_type" : "employee", "_id" : "1", "_score" : 0.23013961, "_source" : { "first_name" : "John", "last_name" : "Smith", "age" : 25, "about" : "I love to go rock climbing", "interests" : [ "sports", "music" ] }, "highlight" : { "about" : [ "I love to go <em>rock</em> <em>climbing</em>" ] } } ] } }聚合
分析
最后，我们还有一个需求需要完成：允许管理者在职员目录中进行一些分析。 Elasticsearch有一个功能叫做聚合(aggregations)，它允许你在数据上生成复杂的分析统计。它很像SQL中的
GROUP BY但是功能更强大。$ curl -XGET 'http://localhost:9200/megacorp/employee/_search?pretty' -d ' { "aggs": { "all_interests": { "terms": { "field": "interests" } } } } '查询结果：
{... "aggregations" : { "all_interests" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "music", "doc_count" : 2 }, { "key" : "forestry", "doc_count" : 1 }, { "key" : "sports", "doc_count" : 1 } ] } } }这些数据并没有被预先计算好，它们是实时的从匹配查询语句的文档中动态计算生成的。
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：
$ curl -XGET 'http://localhost:9200/megacorp/employee/_search?pretty' -d ' { "query": { "match": { "last_name": "smith" } }, "aggs": { "all_interests": { "terms": { "field": "interests" } } } } 'all_interests聚合已经变成只包含和查询语句相匹配的文档了：
... "all_interests": { "buckets": [ { "key": "music", "doc_count": 2 }, { "key": "sports", "doc_count": 1 } ] }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：
$ curl -XGET 'http://localhost:9200/megacorp/employee/_search?pretty' -d ' { "aggs" : { "all_interests" : { "terms" : { "field" : "interests" }, "aggs" : { "avg_age" : { "avg" : { "field" : "age" } } } } } } '虽然这次返回的聚合结果有些复杂，但仍然很容易理解：
... "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 } } ] }该聚合结果比之前的聚合结果要更加丰富。我们依然得到了兴趣以及数量（指具有该兴趣的员工人数）的列表，但是现在每个兴趣额外拥有
avg_age字段来显示具有该兴趣员工的平均年龄。
来源：https://www.cnblogs.com/mr-cc/p/5762261.html