當(dāng)前位置：首頁(yè) > 编程资源 > 编程问答 >内容正文

编程问答

elasticsearch 第一篇(入门篇)

發(fā)布時(shí)間：2025/3/21 编程问答 21 豆豆

生活随笔收集整理的這篇文章主要介紹了 elasticsearch 第一篇(入门篇) 小編覺得挺不錯(cuò)的,現(xiàn)在分享給大家,幫大家做個(gè)參考.

介紹

elasticsearch是一個(gè)高效的、可擴(kuò)展的全文搜索引擎

基本概念

Near Realtime(NRT): es是一個(gè)接近實(shí)時(shí)查詢平臺(tái)，意味從存儲(chǔ)一條數(shù)據(jù)到可以索引到數(shù)據(jù)時(shí)差很小，通常在1s內(nèi)
Cluster: es是一個(gè)分布式、可擴(kuò)展的平臺(tái), 可由一個(gè)或多個(gè)服務(wù)器通過定義的cluster.name（默認(rèn)為elasticsearch）標(biāo)識(shí)共建同一個(gè)集群
Node: 通常一臺(tái)服務(wù)器上部署一臺(tái)es node，作為集群的一部分，用于數(shù)據(jù)的存儲(chǔ)和提供搜索功能，在一個(gè)集群中節(jié)點(diǎn)通過node.name區(qū)分，默認(rèn)在node啟動(dòng)時(shí)隨機(jī)生成一個(gè)的字符串做為節(jié)點(diǎn)名稱，可配置
Index: 類似于關(guān)系型數(shù)據(jù)庫(kù)中的database，用于組織一類功能相似的數(shù)據(jù)，在一個(gè)集群中可以定義任意個(gè)索引，索引的名稱只能由小寫字母組成，在數(shù)據(jù)索引，更新，搜索，刪除時(shí)作為數(shù)據(jù)標(biāo)識(shí)的一部分
Type: 類似于關(guān)系型數(shù)據(jù)庫(kù)中的table，在Index中可以定義多個(gè)Type，原則上一個(gè)Type是由相同屬性組成的數(shù)據(jù)集合
Document: 類似于關(guān)系型數(shù)據(jù)庫(kù)中的record，是數(shù)據(jù)的最基本存儲(chǔ)單元，使用json形式表示，Document在物理上存儲(chǔ)在Index下，但是在邏輯上會(huì)分配到具體的Type下
Shards & Replica:
一個(gè)Index可能存儲(chǔ)大量的數(shù)據(jù)(超過單個(gè)節(jié)點(diǎn)的硬件限制)，不管是數(shù)據(jù)存儲(chǔ)還是數(shù)據(jù)索引，為解決數(shù)據(jù)單節(jié)點(diǎn)存儲(chǔ)并提高并發(fā)，es將每一個(gè)Index物理分為多個(gè)片，從而水平擴(kuò)展存儲(chǔ)容量，提高并發(fā)（可以同時(shí)對(duì)個(gè)shard進(jìn)行索引和搜索）
為防止某個(gè)存儲(chǔ)單元出現(xiàn)故障后數(shù)據(jù)不能索引的情況，es提供將shard進(jìn)行復(fù)制功能，將主shard出現(xiàn)故障后，復(fù)制shard替代主shard進(jìn)行數(shù)據(jù)索引操作，已此方式實(shí)現(xiàn)其高可用性，因?yàn)樵谒阉鲿r(shí)可以使用復(fù)制shard，從而提高的數(shù)據(jù)搜索的并發(fā)性
在Index創(chuàng)建時(shí)可以進(jìn)行分片數(shù)量和復(fù)制數(shù)量的設(shè)置，默認(rèn)創(chuàng)建每個(gè)Index設(shè)置5個(gè)shard和1個(gè)Replica，表示該Index由5個(gè)邏輯存儲(chǔ)單元進(jìn)行存儲(chǔ)，每個(gè)邏輯存儲(chǔ)單元具有一個(gè)復(fù)制節(jié)點(diǎn)進(jìn)行備災(zāi)，注意，shard只能在創(chuàng)建Index時(shí)進(jìn)行設(shè)置，shard數(shù)量與document分配到哪個(gè)shard上存儲(chǔ)有關(guān)(通常使用hash(document _id) % shard num計(jì)算 document存儲(chǔ)在哪個(gè)shard上)
在es將主shard和replic分片在不同的Node上

安裝

elasticsearch使用java語(yǔ)言實(shí)現(xiàn)，在使用時(shí)必須安裝java虛擬機(jī)（目前es1.6和1.7版本均可選擇1.8版本java）
下載地址
解壓到安裝目錄?C:\Program Files\elasticsearch
運(yùn)行?cd "C:\Program Files\elasticsearch\bin" && elasticsearch.bat
安裝到服務(wù)?service install elasticsearch
啟動(dòng)服務(wù)?net start elasticsearch
停止服務(wù)?net stop elasticsearch

測(cè)試
訪問地址:?http://localhost:9200
訪問結(jié)果:

 {
  status: 200,
  name: "Smart Alec",
  cluster_name: "elasticsearch",
  version: {
    number: "1.6.0",
    build_hash: "cdd3ac4dde4f69524ec0a14de3828cb95bbb86d0",
    build_timestamp: "2015-06-09T13:36:34Z",
    build_snapshot: false,
    lucene_version: "4.10.4"
  },
  tagline: "You Know, for Search"
}
 

接口

es對(duì)外提供標(biāo)準(zhǔn)RESTAPI接口，使用他進(jìn)行集群的所有操作：

集群、節(jié)點(diǎn)、索引的狀態(tài)和統(tǒng)計(jì)信息查看
管理集群、節(jié)點(diǎn)、索引和類型
執(zhí)行CURD操作（創(chuàng)建，更新，讀取，刪除）和索引
執(zhí)行高級(jí)搜索功能，比如排序，分頁(yè)，篩選，聚合，js腳本執(zhí)行等

格式：curl -X<REST verb> <Node>:<Port>/<Index>/<Type>/<ID>

使用marvel插件

運(yùn)行?cd "C:\Program Files\elasticsearch\bin" && plugin -i elasticsearch/marvel/latest
訪問地址
marvel提供sense工具調(diào)用es的RESTAPI借口,?訪問地址, 以下操作使用sense或使用linux curl命令行練習(xí)

狀態(tài)查詢

集群狀態(tài)查詢
輸入:?GET _cat/health?v
輸出:

1 2	epoch timestamp cluster status node.total node.data shards pri relo init unassign pending_tasks 1442227489 18:44:49 elasticsearch yellow 1 1 50 50 0 0 50 0

說明:
status:表示集群的健康狀態(tài)，值可能為green,yellow,red, green表示主shard和replica(至少一個(gè))正常，yellow表示主shard正常但replica都不正常，red表示有的主shard和replica都有問題
node.total:表示集群中節(jié)點(diǎn)的數(shù)量

節(jié)點(diǎn)狀態(tài)查詢
輸入:?GET /_cat/nodes?v
輸出:
1 2 host ip heap.percent ram.percent load node.role master name silence 192.168.1.111 30 51 d * Thunderbird

查詢所有索引

輸入:?GET /_cat/indices?v
輸出:

 health status index              pri rep docs.count docs.deleted store.size pri.store.size
yellow open   .marvel-2015.09.02   1   1      93564            0     78.4mb         78.4mb
yellow open   .marvel-2015.09.01   1   1      39581            0     45.9mb         45.9mb
 

創(chuàng)建索引

輸入:?PUT /test1?pretty
輸出:

 {
  "acknowledged" : true
}
 

查詢所有索引:

1 2	health status index pri rep docs.count docs.deleted store.size pri.store.size yellow open test1 5 1 0 0 575b 575b

說明:
health:由于只運(yùn)行一個(gè)節(jié)點(diǎn)，replica不能與主shard在同一node中，因此replica不正常，該index的狀態(tài)為yellow
index:為索引名稱
pri:表示主shard個(gè)數(shù)
rep:表示每個(gè)shard的復(fù)制個(gè)數(shù)
docs.count:表示index中document的個(gè)數(shù)

索引、讀取、刪除文檔

索引文檔

方法1:
輸入:
1 2 PUT /test1/user/1?pretty {"name": "silence1"}

輸出:

{ "_index" : "test1 "_type" : "user", "_id" : "1", "_version" : 1, "created" : true }

方法2:
輸入:
1 2 POST /test1/user/2?pretty {"name": "silence2"}

輸出:

 {
  "_index" : "test1",
  "_type" : "user",
  "_id" : "2",
  "_version" : 1,
  "created" : true
}
 

方法3:
輸入:
1 2 POST /test1/user?pretty {"name": "silence3"}

輸出:

 {
  "_index" : "test1",
  "_type" : "user",
  "_id" : "AU_MdQoXRYiHSIs7UGBQ",
  "_version" : 1,
  "created" : true
}
 

說明: 在索引文檔時(shí)若需要指定文檔ID值則需要使用PUT或者POST提交數(shù)據(jù)并顯示指定ID值，若需要由es自動(dòng)生成ID，則需要使用POST提交數(shù)據(jù)

讀取文檔：
輸入:?GET /test1/user/1?pretty
輸出:

 {
  "_index" : "test1",
  "_type" : "user",
  "_id" : "1",
  "_version" : 1,
  "found" : true,
  "_source":{"name": "silence1"}
}
 

說明:
_index,_type:表示文檔存儲(chǔ)的Index和Type信息
_id:表示文檔的編號(hào)
_version:表示文檔的版本號(hào)，主要用于并發(fā)處理時(shí)使用樂觀鎖防止臟數(shù)據(jù)
found:表示請(qǐng)求的文檔是否存在
_souce:格式為json，為文檔的內(nèi)容

注意:在之前我們并未創(chuàng)建user的Type，在進(jìn)行文檔索引時(shí)自動(dòng)創(chuàng)建了user，在es中可以不顯示的創(chuàng)建Index和Type而使用默認(rèn)參數(shù)或者根據(jù)提交數(shù)據(jù)自定義，但不建議這么使用，在不清楚可能導(dǎo)致什么情況時(shí)顯示創(chuàng)建Index和Type并設(shè)置參數(shù)

刪除文檔:
輸入:?DELETE /test1/user/1?pretty
輸出:

 {
  "found" : true,
  "_index" : "test1",
  "_type" : "user",
  "_id" : "1",
  "_version" : 2
}
 

再次讀取文檔輸出:

 {
  "_index" : "test1",
  "_type" : "user",
  "_id" : "1",
  "found" : false
}
 

刪除索引

輸入:?DELETE /test1?pretty
輸出:

 {
  "acknowledged" : true
}
 

修改文檔

初始化文檔輸入:

1 2	PUT /test1/user/1?pretty {"name" : "silence2", "age":28}

修改文檔輸入:

1 2	PUT /test1/user/1?pretty {"name" : "silence1"}

讀取文檔輸出:

 {
  "_index" : "test1",
  "_type" : "user",
  "_id" : "1",
  "_version" : 2,
  "found" : true,
  "_source":{"name" : "silence1"}
}
 

更新文檔

更新數(shù)據(jù)輸入:

1 2	POST /test1/user/1/_update?pretty {"doc" : {"name" : "silence3", "age":28}}

讀取數(shù)據(jù)輸出:

 {
  "_index" : "test1",
  "_type" : "user",
  "_id" : "1",
  "_version" : 3,
  "found" : true,
  "_source":{"name":"silence3","age":28}
}
 

更新文檔輸入:

1 2	POST /test1/user/1/_update?pretty {"script" : "ctx._source.age += 1"}

讀取文檔輸出:

 {
  "_index" : "test1",
  "_type" : "user",
  "_id" : "1",
  "_version" : 4,
  "found" : true,
  "_source":{"name":"silence3","age":29}
}
 

說明：需要POST使用script則必須在elasticsearch/config/elasticsearch.yml配置script.groovy.sandbox.enabled: true
修改(PUT)和更新(POST+_update)的區(qū)別在于修改使用提交的文檔覆蓋es中的文檔，更新使用提交的參數(shù)值覆蓋es中文檔對(duì)應(yīng)的參數(shù)值

根據(jù)查詢刪除文檔

輸入:

1 2	DELETE /test1/user/_query?pretty {"query" : {"match" : {"name" : "silence3"}}}

輸出:

 {
  "_indices" : {
    "test1" : {
      "_shards" : {
        "total" : 5,
        "successful" : 5,
        "failed" : 0
      }
    }
  }
}
 

獲取文檔數(shù)量

輸入:?GET /test1/user/_count?pretty
輸出:

 {
  "count" : 0,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "failed" : 0
  }
}
 

批量操作

輸入:

 POST /test1/user/_bulk?pretty
{"index" : {"_id" : 1}}
{"name" : "silence1"}
{"index" : {"_id" : 2}}
{"name" : "silence2"}
{"index" : {}}
{"name" : "silence3"}
{"index" : {}}
{"name" : "silence4"}
 

輸入:

 POST /test1/user/_bulk?pretty
{"update" : {"_id" : 1}}
{"doc" : {"age" : 28}}
{"delete" : {"_id" : 2}}
 

通過文件導(dǎo)入數(shù)據(jù):?curl -XPOST "localhost:9200/test1/account/_bulk?pretty" --data-binary @accounts.json

Query查詢

查詢可以通過兩種方式進(jìn)行，一種為使用查詢字符串進(jìn)行提交參數(shù)查詢，一種為使用RESTAPI提交requesbody提交參數(shù)查詢

獲取所有文檔輸入:?GET /test1/user/_search?q=*&pretty

 POST /test1/user/_search?pretty
{
  "query" : {"match_all" : {}}
}
 

輸出：

 {
   "took": 2,
   "timed_out": false,
   "_shards": {
      "total": 5,
      "successful": 5,
      "failed": 0
   },
   "hits": {
      "total": 3,
      "max_score": 1,
      "hits": [
         {
            "_index": "test1",
            "_type": "user",
            "_id": "1",
            "_score": 1,
            "_source": {
               "name": "silence1",
               "age": 28
            }
         },
         {
            "_index": "test1",
            "_type": "user",
            "_id": "AU_M2zgwLNdQvgqQS3MP",
            "_score": 1,
            "_source": {
               "name": "silence3"
            }
         },
         {
            "_index": "test1",
            "_type": "user",
            "_id": "AU_M2zgwLNdQvgqQS3MQ",
            "_score": 1,
            "_source": {
               "name": "silence4"
            }
         }
      ]
   }
}
 

說明:
took: 執(zhí)行查詢的時(shí)間(單位為毫秒)
timed_out: 執(zhí)行不能超時(shí)
_shards: 提示有多少shard參與查詢以及查詢成功和失敗shard數(shù)量
hits: 查詢結(jié)果
hits.total: 文檔總數(shù)
_score, max_score: 為文檔與查詢條件匹配度和最大匹配度

Query SDL

輸入:

 POST /test1/account/_search?pretty
{
  "query" : {"match_all":{}},
  "size": 2,
  "from" : 6,
  "sort" : {
    "age" : {"order" : "asc"} 
  }
}
 

說明:
query: 用于定義查詢條件過濾
match_all: 表示查詢所有文檔
size: 表示查詢返回文檔數(shù)量，若未設(shè)置默認(rèn)為10
from: 表示開始位置, es使用0作為開始索引，常與size組合進(jìn)行分頁(yè)查詢，若未設(shè)置默認(rèn)為0
sort: 用于設(shè)置排序?qū)傩院鸵?guī)則

使用_source設(shè)置查詢結(jié)果返回的文檔屬性
輸入:
1 2 3 4 5 6 7 POST /test1/account/_search?pretty { "query": { "match_all": {} }, "_source":["firstname", "lastname", "age"] }

輸出:

 {
   "took": 5,
   "timed_out": false,
   "_shards": {
      "total": 5,
      "successful": 5,
      "failed": 0
   },
   "hits": {
      "total": 1000,
      "max_score": 1,
      "hits": [
         {
            "_index": "test1",
            "_type": "account",
            "_id": "4",
            "_score": 1,
            "_source": {
               "firstname": "Rodriquez",
               "age": 31,
               "lastname": "Flores"
            }
         },
         {
            "_index": "test1",
            "_type": "account",
            "_id": "9",
            "_score": 1,
            "_source": {
               "firstname": "Opal",
               "age": 39,
               "lastname": "Meadows"
            }
         }
      ]
   }
}
 

使用match設(shè)置查詢匹配值
輸入:
1 2 3 4 5 6 7 POST /test1/account/_search?pretty { "query": { "match": {"address" : "986 Wyckoff Avenue"} }, "size" : 2 }

輸出:

 {
   "took": 1,
   "timed_out": false,
   "_shards": {
      "total": 5,
      "successful": 5,
      "failed": 0
   },
   "hits": {
      "total": 216,
      "max_score": 4.1231737,
      "hits": [
         {
            "_index": "test1",
            "_type": "account",
            "_id": "4",
            "_score": 4.1231737,
            "_source": {
               "account_number": 4,
               "balance": 27658,
               "firstname": "Rodriquez",
               "lastname": "Flores",
               "age": 31,
               "gender": "F",
               "address": "986 Wyckoff Avenue",
               "employer": "Tourmania",
               "email": "rodriquezflores@tourmania.com",
               "city": "Eastvale",
               "state": "HI"
            }
         },
         {
            "_index": "test1",
            "_type": "account",
            "_id": "34",
            "_score": 0.59278774,
            "_source": {
               "account_number": 34,
               "balance": 35379,
               "firstname": "Ellison",
               "lastname": "Kim",
               "age": 30,
               "gender": "F",
               "address": "986 Revere Place",
               "employer": "Signity",
               "email": "ellisonkim@signity.com",
               "city": "Sehili",
               "state": "IL"
            }
         }
      ]
   }
}
 

說明：根據(jù)查詢結(jié)果可見在查詢結(jié)果中并非只查詢address包含”986 Wyckoff Avenue”的文檔，而是包含986,wychoff,Avenue三個(gè)詞中任意一個(gè)，這就是es分詞的強(qiáng)大之處
可見查詢結(jié)果中_score(與查詢條件匹配度)按從大到小的順序排列
此時(shí)你可能想要值查詢address包含”986 Wyckoff Avenue”的文檔，怎么辦呢？使用match_phrase
輸入:

 POST /test1/account/_search?pretty
{
  "query": {
    "match_phrase": {"address" : "986 Wyckoff Avenue"}
  }
}
 

可能你已經(jīng)注意到, 以上query中只有一個(gè)條件，若存在多個(gè)條件，我們必須使用bool query將多個(gè)條件進(jìn)行組合
輸入:

 POST /test1/account/_search?pretty
{
  "query": {
    "bool" : {
      "must":[
        {"match_phrase": {"address" : "986 Wyckoff Avenue"}},
        {"match" : {"age" : 31}}  
      ]
    }
  }
}
 

說明: 查詢所有條件都滿足的結(jié)果

輸入:

 POST /test1/account/_search
{
  "query": {
    "bool" : {
      "should":[
        {"match_phrase": {"address" : "986 Wyckoff Avenue"}},
        {"match_phrase": {"address" : "963 Neptune Avenue"}}
      ]
    }
  }
}
 

說明: 查詢有一個(gè)條件滿足的結(jié)果
輸入:

 POST /test1/account/_search
{
  "query": {
    "bool" : {
      "must_not":[
        {"match": {"city" : "Eastvale"}},
        {"match": {"city" : "Olney"}}
      ]
    }
  }
}
 

說明: 查詢有條件都不滿足的結(jié)果

在Query SDL中可以將must, must_not和should組合使用
輸入:

 POST /test1/account/_search
{
  "query": {
    "bool" : {
      "must": [{
        "match" : {"age":20}
      }],
      "must_not":[
        {"match": {"city" : "Steinhatchee"}}
      ]
    }
  }
}
 

Filters 查詢

在使用Query 查詢時(shí)可以看到在查詢結(jié)果中都有_score值, _score值需要進(jìn)行計(jì)算, 在某些情況下我們并不需要_socre值，在es中提供了Filters查詢，它類似于Query查詢，但是效率較高，原因:

不需要對(duì)查詢結(jié)果進(jìn)行_score值的計(jì)算

Filters可以被緩存在內(nèi)存中，可被重復(fù)搜索從而提高查詢效率

range 過濾器，用于設(shè)置條件在某個(gè)范圍內(nèi)
輸入:

 POST /test1/account/_search?pretty
{
  "query": {
    "filtered":{
      "query": {
        "match_all" : {}
      },
      "filter": {
        "range" : {
          "age" : {
            "gte" : 20,
            "lt" : 28
          }
        }
      }
    }
  }
}
 

判斷使用filter還是使用query的最簡(jiǎn)單方法就是是否關(guān)注_score值，若關(guān)注則使用query，若不關(guān)注則使用filter

聚合分析

es提供Aggregations支持分組和聚合查詢，類似于關(guān)系型數(shù)據(jù)庫(kù)中的GROUP BY和聚合函數(shù)，在ES調(diào)用聚合RESTAPI時(shí)返回結(jié)果包含文檔查詢結(jié)果和聚合結(jié)果，也可以返回多個(gè)聚合結(jié)果，從而簡(jiǎn)化API調(diào)用和減少網(wǎng)絡(luò)流量使用
輸入:

 POST /test1/account/_search?pretty
{
  "size" : 0,
  "aggs" : {
    "group_by_gender" : {
      "terms" : {"field":"gender"}
    }
  }
}
 

輸出:

 {
   "took": 1,
   "timed_out": false,
   "_shards": {
      "total": 5,
      "successful": 5,
      "failed": 0
   },
   "hits": {
      "total": 1000,
      "max_score": 0,
      "hits": []
   },
   "aggregations": {
      "group_by_gender": {
         "doc_count_error_upper_bound": 0,
         "sum_other_doc_count": 0,
         "buckets": [
            {
               "key": "m",
               "doc_count": 507
            },
            {
               "key": "f",
               "doc_count": 493
            }
         ]
      }
   }
}
 

說明:
size: 返回文檔查詢結(jié)果數(shù)量
aggs: 用于設(shè)置聚合分類
terms: 設(shè)置group by屬性值

輸入:

 POST /test1/account/_search?pretty
{
  "size" : 0,
  "aggs" : {
    "group_by_gender" : {
      "terms" : {
        "field":"state",
        "order" : {"avg_age":"desc"},
        "size" : 3
      },
      "aggs" : {
        "avg_age" : {
          "avg" : {"field" : "age"}
        },
        "max_age" : {
          "max" : {"field": "age"}
        },
        "min_age" : {
          "min": {"field":"age"}
        }
      }
    }
  }
}
 

輸出:

 {
   "took": 9,
   "timed_out": false,
   "_shards": {
      "total": 5,
      "successful": 5,
      "failed": 0
   },
   "hits": {
      "total": 1000,
      "max_score": 0,
      "hits": []
   },
   "aggregations": {
      "group_by_gender": {
         "doc_count_error_upper_bound": -1,
         "sum_other_doc_count": 992,
         "buckets": [
            {
               "key": "de",
               "doc_count": 1,
               "max_age": {
                  "value": 37
               },
               "avg_age": {
                  "value": 37
               },
               "min_age": {
                  "value": 37
               }
            },
            {
               "key": "il",
               "doc_count": 3,
               "max_age": {
                  "value": 39
               },
               "avg_age": {
                  "value": 36.333333333333336
               },
               "min_age": {
                  "value": 32
               }
            },
            {
               "key": "in",
               "doc_count": 4,
               "max_age": {
                  "value": 39
               },
               "avg_age": {
                  "value": 36
               },
               "min_age": {
                  "value": 34
               }
            }
         ]
      }
   }
}
 

說明:根據(jù)state進(jìn)行分類，并查詢每種分類所有人員的最大，最小，平均年齡, 查詢結(jié)果按平均年齡排序并返回前3個(gè)查詢結(jié)果
若需要按照分類總數(shù)進(jìn)行排序時(shí)可以使用_count做為sort的field值
在聚合查詢時(shí)通過size設(shè)置返回的TOP數(shù)量，默認(rèn)為10

在聚合查詢中可任意嵌套聚合語(yǔ)句進(jìn)行查詢
輸入:

 POST /test1/account/_search?pretty
{
  "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" : {
            "group_by_balance" :{
              "range" : {
                "field":"balance",
                "ranges" : [{
                  "to" : 5000
                }, {
                  "from" : 5000
                }
                ]
              }
            }
          }
        }
      }
    }
  }
}
 

輸出:

 {
   "took": 1,
   "timed_out": false,
   "_shards": {
      "total": 5,
      "successful": 5,
      "failed": 0
   },
   "hits": {
      "total": 1000,
      "max_score": 0,
      "hits": []
   },
   "aggregations": {
      "group_by_age": {
         "buckets": [
            {
               "key": "20.0-30.0",
               "from": 20,
               "from_as_string": "20.0",
               "to": 30,
               "to_as_string": "30.0",
               "doc_count": 451,
               "group_by_gender": {
                  "doc_count_error_upper_bound": 0,
                  "sum_other_doc_count": 0,
                  "buckets": [
                     {
                        "key": "m",
                        "doc_count": 232,
                        "group_by_balance": {
                           "buckets": [
                              {
                                 "key": "*-5000.0",
                                 "to": 5000,
                                 "to_as_string": "5000.0",
                                 "doc_count": 9
                              },
                              {
                                 "key": "5000.0-*",
                                 "from": 5000,
                                 "from_as_string": "5000.0",
                                 "doc_count": 223
                              }
                           ]
                        }
                     },
                     {
                        "key": "f",
                        "doc_count": 219,
                        "group_by_balance": {
                           "buckets": [
                              {
                                 "key": "*-5000.0",
                                 "to": 5000,
                                 "to_as_string": "5000.0",
                                 "doc_count": 20
                              },
                              {
                                 "key": "5000.0-*",
                                 "from": 5000,
                                 "from_as_string": "5000.0",
                                 "doc_count": 199
                              }
                           ]
                        }
                     }
                  ]
               }
            },
            {
               "key": "30.0-40.0",
               "from": 30,
               "from_as_string": "30.0",
               "to": 40,
               "to_as_string": "40.0",
               "doc_count": 504,
               "group_by_gender": {
                  "doc_count_error_upper_bound": 0,
                  "sum_other_doc_count": 0,
                  "buckets": [
                     {
                        "key": "f",
                        "doc_count": 253,
                        "group_by_balance": {
                           "buckets": [
                              {
                                 "key": "*-5000.0",
                                 "to": 5000,
                                 "to_as_string": "5000.0",
                                 "doc_count": 26
                              },
                              {
                                 "key": "5000.0-*",
                                 "from": 5000,
                                 "from_as_string": "5000.0",
                                 "doc_count": 227
                              }
                           ]
                        }
                     },
                     {
                        "key": "m",
                        "doc_count": 251,
                        "group_by_balance": {
                           "buckets": [
                              {
                                 "key": "*-5000.0",
                                 "to": 5000,
                                 "to_as_string": "5000.0",
                                 "doc_count": 21
                              },
                              {
                                 "key": "5000.0-*",
                                 "from": 5000,
                                 "from_as_string": "5000.0",
                                 "doc_count": 230
                              }
                           ]
                        }
                     }
                  ]
               }
            },
            {
               "key": "40.0-50.0",
               "from": 40,
               "from_as_string": "40.0",
               "to": 50,
               "to_as_string": "50.0",
               "doc_count": 45,
               "group_by_gender": {
                  "doc_count_error_upper_bound": 0,
                  "sum_other_doc_count": 0,
                  "buckets": [
                     {
                        "key": "m",
                        "doc_count": 24,
                        "group_by_balance": {
                           "buckets": [
                              {
                                 "key": "*-5000.0",
                                 "to": 5000,
                                 "to_as_string": "5000.0",
                                 "doc_count": 3
                              },
                              {
                                 "key": "5000.0-*",
                                 "from": 5000,
                                 "from_as_string": "5000.0",
                                 "doc_count": 21
                              }
                           ]
                        }
                     },
                     {
                        "key": "f",
                        "doc_count": 21,
                        "group_by_balance": {
                           "buckets": [
                              {
                                 "key": "*-5000.0",
                                 "to": 5000,
                                 "to_as_string": "5000.0",
                                 "doc_count": 0
                              },
                              {
                                 "key": "5000.0-*",
                                 "from": 5000,
                                 "from_as_string": "5000.0",
                                 "doc_count": 21
                              }
                           ]
                        }
                     }
                  ]
               }
            }
         ]
      }
   }
}
 

使用head插件

運(yùn)行?cd "C:\Program Files\elasticsearch\bin" && plugin -install mobz/elasticsearch-head
訪問地址

from:?http://imsilence.github.io/2015/09/14/elasticsearch/elasticsearch_01/

總結(jié)

以上是生活随笔為你收集整理的elasticsearch 第一篇(入门篇)的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。

上一篇： elasticsearch 第二篇(配置
下一篇： elasticsearch 第四篇(AP