通过HTTP RESTful API 操作elasticsearch搜索数据
样例数据集
这是编造的JSON格式银行客户账号信息文档，文档schema如下：
{
“account_number”: 0,
“balance”: 16623,
“firstname”: “Bradshaw”,
“lastname”: “Mckenzie”,
“age”: 29,
“gender”: “F”,
“address”: “244 Columbus Place”,
“employer”: “Euron”,
“email”: “[email protected]”,
“city”: “Hobucken”,
“state”: “CO”
}
这些数据可以通过www.json-generator.com网站生成
加载样例数据集
下载样例数据集链接
解压数据到指定目录，然后加载到elasticsearch集群
绝对路径：
curl -XPOST 'localhost:9200/bank/account/_bulk?pretty' --data-binary "@/home/cluster/apps/elasticsearch/elasticsearch-1.7.2/test/accounts.json"
相对路径：
curl -XPOST 'localhost:9200/bank/account/_bulk?pretty' --data-binary "@test/accounts.json"curl 'localhost:9200/_cat/indices?v'
结果：
health status index pri rep docs.count docs.deleted store.size pri.store.size
yellow open bank 5 1 1000 0 417.1kb 417.1kb 上面结果，说明我们成功bulk 1000个文档到bank索引中了
搜索数据API
有两种方式：一种方式是通过 REST 请求 URI ，发送搜索参数；另一种是通过REST 请求体，发送搜索参数。而请求体允许你包含更容易表达和可阅读的JSON格式。
- 通过 REST 请求 URI
curl 'localhost:9200/bank/_search?q=*&pretty'
结果：
{
"took" : 63,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 1000,
"max_score" : 1.0,
"hits" : [ {
"_index" : "bank",
"_type" : "account",
"_id" : "1",
"_score" : 1.0, "_source" : {"account_number":1,"balance":39225,"firstname":"Amber","lastname":"Duke","age":32,"gender":"M","address":"880 Holmes Lane","employer":"Pyrami","email":"amberduke@pyrami.com","city":"Brogan","state":"IL"}
}, {
"_index" : "bank",
"_type" : "account",
"_id" : "6",
"_score" : 1.0, "_source" : {"account_number":6,"balance":5686,"firstname":"Hattie","lastname":"Bond","age":36,"gender":"M","address":"671 Bristol Street","employer":"Netagy","email":"hattiebond@netagy.com","city":"Dante","state":"TN"}
}, {
"_index" : "bank",
"_type" : "account",q=*，参数告诉elasticsearch，在bank索引中匹配所有的文档
pretty，参数告诉elasticsearch，返回形式打印JSON结果
- 通过REST 请求体：
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": { "match_all": {} }
}'
结果：
{
"took" : 26,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 1000,
"max_score" : 1.0,
"hits" : [ {
"_index" : "bank",
"_type" : "account",
"_id" : "1",
"_score" : 1.0, "_source" : {"account_number":1,"balance":39225,"firstname":"Amber","lastname":"Duke","age":32,"gender":"M","address":"880 Holmes Lane","employer":"Pyrami","email":"[email protected]","city":"Brogan","state":"IL"}
}, {
"_index" : "bank",
"_type" : "account",
"_id" : "6",
"_score" : 1.0, "_source" : {"account_number":6,"balance":5686,"firstname":"Hattie","lastname":"Bond","age":36,"gender":"M","address":"671 Bristol Street","employer":"Netagy","email":"[email protected]","city":"Dante","state":"TN"}
}, {
"_index" : "bank",
"_type" : "account",
"_id" : "13",与第一种方式不同是在URI中替代传递q=*，使用POST方式提交，请求体包含JSON格式搜索
介绍查询语言
elasticsearch提供JSON格式领域特定语言执行查询。可参考Query DSL。
{
"query": { "match_all": {} }
}query：告诉我们定义查询
match_all：运行简单类型查询指定索引中的所有文档
除了指定查询参数，还可以指定其他参数来影响最终的结果。
- match_all & 只返回第一个文档：
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": { "match_all": {} },
"size": 1
}'
结果：
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 1000,
"max_score" : 1.0,
"hits" : [ {
"_index" : "bank",
"_type" : "account",
"_id" : "4",
"_score" : 1.0,
"_source":{"account_number":4,"balance":27658,"firstname":"Rodriquez","lastname":"Flores","age":31,"gender":"F","address":"986 Wyckoff Avenue","employer":"Tourmania","email":"[email protected]","city":"Eastvale","state":"HI"}
} ]
}
}如果不指定size，默认是返回10条文档信息
- match_all & 返回11到20个文档信息
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": { "match_all": {} },
"from": 10,
"size": 10
}'from：指定文档索引从哪里开始，默认从0开始
size：从from开始，返回多个文档
这feature在实现分页查询很有用
- match_all and 根据account balance 降序排序 & 返回10个文档（默认10个）
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": { "match_all": {} },
"sort": { "balance": { "order": "desc" } }
}'执行搜索
默认的，我们搜索返回完整的JSON文档。而source（_source字段搜索点击量）。如果我们不想返回完整的JSON文档，我们可以使用source返回指定字段。
- 返回 account_number and balance：
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": { "match_all": {} },
"_source": ["account_number", "balance"]
}'这样操作有点类似于SQL SELECT FROM field lis
match 查询，可作为基本字段搜索查询
- 返回 account_number=20:
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": { "match": { "account_number": 20 } }
}'- 返回 address=mill：
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": { "match": { "address": "mill" } }
}'- 返回 address=mill or address=lane：
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": { "match": { "address": "mill lane" } }
}'- 返回 短语匹配 address=mill lane：
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": { "match_phrase": { "address": "mill lane" } }
}'布尔值(bool)查询
- 返回 匹配address=mill & address=lane：
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": {
"bool": {
"must": [
{ "match": { "address": "mill" } },
{ "match": { "address": "lane" } }
]
}
}
}'must:要求所有条件都要满足（类似于&&）
- 返回 匹配address=mill or address=lane：
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": {
"bool": {
"should": [
{ "match": { "address": "mill" } },
{ "match": { "address": "lane" } }
]
}
}
}'should：任何一个满足就可以（类似于||）
- 返回 不匹配address=mill & address=lane：
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": {
"bool": {
"must_not": [
{ "match": { "address": "mill" } },
{ "match": { "address": "lane" } }
]
}
}
}'must_not:所有条件都不能满足（类似于! (&&)）
- 返回 age=40 & state!=ID
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": {
"bool": {
"must": [
{ "match": { "age": "40" } }
],
"must_not": [
{ "match": { "state": "ID" } }
]
}
}
}'执行过滤器
文档中score(_score字段是搜索结果)。score是一个数字型的，是一种相对方法匹配查询文档结果。分数越高，搜索关键字与该文档相关性越高；越低，搜索关键字与该文档相关性越低。
在elasticsearch中所有的搜索都会触发相关性分数计算。如果我们不使用相关性分数计算，那要使用另一种查询能力，构建过滤器。
过滤器是类似于查询的概念,除了得以优化,更快的执行速度的两个主要原因:
1. 过滤器不计算得分，所以他们比执行查询的速度
2. 过滤器可缓存在内存中，允许重复搜索
为了便于理解过滤器，先介绍过滤器搜索(like match_all, match, bool, etc.)，可以与其他的普通查询搜索组合一个过滤器。
range filter,允许我们通过一个范围值来过滤文档，一般用于数字或日期过滤
使用过滤器搜索返回 balances[ 20000,30000]。换句话说，balance>=20000 && balance<=30000
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"query": {
"filtered": {
"query": { "match_all": {} },
"filter": {
"range": {
"balance": {
"gte": 20000,
"lte": 30000
}
}
}
}
}
}'
结果：
{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 217,
"max_score" : 1.0,
"hits" : [ {
"_index" : "bank",
"_type" : "account",
"_id" : "4",
"_score" : 1.0,
"_source":{"account_number":4,"balance":27658,"firstname":"Rodriquez","lastname":"Flores","age":31,"gender":"F","address":"986 Wyckoff Avenue","employer":"Tourmania","email":"[email protected]","city":"Eastvale","state":"HI"}
}, {
"_index" : "bank",
"_type" : "account",
"_id" : "9",
"_score" : 1.0,
"_source":{"account_number":9,"balance":24776,"firstname":"Opal","lastname":"Meadows","age":39,"gender":"M","address":"963 Neptune Avenue","employer":"Cedward","email":"[email protected]","city":"Olney","state":"OH"}
}, {
"_index" : "bank",
"_type" : "account",
"_id" : "11",
"_score" : 1.0,
"_source":{"account_number":11,"balance":20203,"firstname":"Jenkins","lastname":"Haney","age":20,"gender":"M","address":"740 Ferry Place","employer":"Qimonk","email":"[email protected]","city":"Steinhatchee","state":"GA"}
}, {
"_index" : "bank",
"_type" : "account",
"_id" : "42",
"_score" : 1.0,
"_source":{"account_number":42,"balance":21137,"firstname":"Harding","lastname":"Hobbs","age":26,"gender":"F","address":"474 Ridgewood Place","employer":"Xth","email":"[email protected]","city":"Heil","state":"ND"}
}, {
"_index" : "bank",
"_type" : "account",
"_id" : "54",
"_score" : 1.0,
"_source":{"account_number":54,"balance":23406,"firstname":"Angel","lastname":"Mann","age":22,"gender":"F","address":"229 Ferris Street","employer":"Amtas","email":"[email protected]","city":"Calverton","state":"WA"}
}, {
"_index" : "bank",
"_type" : "account",
"_id" : "66",
"_score" : 1.0,
"_source":{"account_number":66,"balance":25939,"firstname":"Franks","lastname":"Salinas","age":28,"gender":"M","address":"437 Hamilton Walk","employer":"Cowtown","email":"[email protected]","city":"Chase","state":"VT"}
}, {
"_index" : "bank",
"_type" : "account",
"_id" : "92",
"_score" : 1.0,
"_source":{"account_number":92,"balance":26753,"firstname":"Gay","lastname":"Brewer","age":34,"gender":"M","address":"369 Ditmars Street","employer":"Savvy","email":"[email protected]","city":"Moquino","state":"HI"}
}, {
"_index" : "bank",
"_type" : "account",
"_id" : "100",
"_score" : 1.0,
"_source":{"account_number":100,"balance":29869,"firstname":"Madden","lastname":"Woods","age":32,"gender":"F","address":"696 Ryder Avenue","employer":"Slumberia","email":"[email protected]","city":"Deercroft","state":"ME"}
}, {
"_index" : "bank",
"_type" : "account",
"_id" : "105",
"_score" : 1.0,
过滤查询包含match_all查询(查询部分)和一系列过滤(过滤部分)。可以代替任何其他查询到查询部分以及其他过滤器过滤部分。在上述情况下,过滤器范围智能，因为文档落入range所有匹配“平等”,即。比另一个更相关,没有文档。
一般情况，最明智的方式决定是否使用filter or query，就看你是否关心相关性分数。如果相关性不重要，那就使用filter，否则就使用query。
queries and filters很类似于关系型数据库中的 “SELECT WHERE clause”
执行聚合
聚合提供从你的数据中分组和提取统计能力。
类似于关系型数据中的SQL GROUP BY和SQL 聚合函数。
在Elasticsearch,你有能力执行搜索返回命中结果,同时拆分命中结果，然后统一返回结果。当你使用简单的API运行搜索和多个聚合，然后返回所有结果避免网络带宽过大的情况是高效的。
- 根据state分组，降序统计top 10 state
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"size": 0,
"aggs": {
"group_by_state": {
"terms": {
"field": "state"
}
}
}
}'
结果：
"hits" : {
"total" : 1000,
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"group_by_state" : {
"buckets" : [ {
"key" : "al",
"doc_count" : 21
}, {
"key" : "tx",
"doc_count" : 17
}, {
"key" : "id",
"doc_count" : 15
}, {
"key" : "ma",
"doc_count" : 15
}, {
"key" : "md",
"doc_count" : 15
}, {
"key" : "pa",
"doc_count" : 15
}, {
"key" : "dc",
"doc_count" : 14
}, {
"key" : "me",
"doc_count" : 14
}, {
"key" : "mo",
"doc_count" : 14
}, {
"key" : "nd",
"doc_count" : 14
} ]
}
}
}类似于关系型数据库
SELECT state, COUNT(*) FROM bank GROUP BY state ORDER BY COUNT(*) DESCsize=0 不是展示搜索结果命中数，因为我只是想要看聚合结果
- 根据state计算账户平均balance，降序统计top 10 state
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"size": 0,
"aggs": {
"group_by_state": {
"terms": {
"field": "state"
},
"aggs": {
"average_balance": {
"avg": {
"field": "balance"
}
}
}
}
}
}'注意嵌套average_balance聚合group_by_state内聚合。这是一个常见的模式,所有的聚合。您可以嵌套内聚合聚合任意提取旋转汇总时,你需要从你的数据。
- 降序排序平均 balance：
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"size": 0,
"aggs": {
"group_by_state": {
"terms": {
"field": "state",
"order": {
"average_balance": "desc"
}
},
"aggs": {
"average_balance": {
"avg": {
"field": "balance"
}
}
}
}
}
}'- 聚合年龄分区间(ages 20-29, 30-39, and 40-49),聚合性别，最后平均balance 展示最终结果
curl -XPOST 'localhost:9200/bank/_search?pretty' -d '
{
"size": 0,
"aggs": {
"group_by_age": {
"range": {
"field": "age",
"ranges": [
{
"from": 20,
"to": 30
},
{
"from": 30,
"to": 40
},
{
"from": 40,
"to": 50
}
]
},
"aggs": {
"group_by_gender": {
"terms": {
"field": "gender"
},
"aggs": {
"average_balance": {
"avg": {
"field": "balance"
}
}
}
}
}
}
}
}'更多关于聚合操作，请参考这篇文章
尊重原创，拒绝转载
http://blog.csdn.net/stark_summer/article/details/48830493