Elasticsearch学习记录(入门篇)
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 rock climbing" ]
}
} ]
}
}##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。
