说明

我发现写的文章和代码太多了，有时候搜索起来很不方便。所以我打算给自己上一个全文搜素。初步搞的化功能尽量简单，时间尽量短。
以下部分2小时内就可以完成。

内容

1 安装

使用docker安装，很快。参考,里面有安装还有查询的常见格式。

# 安装
docker pull elasticsearch
# 运行
docker run -d --name ES01 \
    -e ES_JAVA_OPTS="-Xms256m -Xmx256m"\
    -p 9200:9200\
    -p 9300:9300\
    elasticsearch

使用浏览器输入对应的地址http://YOURIP:9200

{
  "name" : "j_s_TwY",
  "cluster_name" : "elasticsearch",
  "cluster_uuid" : "UDTgWZu4QVqolgsY2csYWQ",
  "version" : {
    "number" : "5.6.12",
    "build_hash" : "cfe3d9f",
    "build_date" : "2018-09-10T20:12:43.732Z",
    "build_snapshot" : false,
    "lucene_version" : "6.6.1"
  },
  "tagline" : "You Know, for Search"
}

2 连接

对我来说肯定是使用python进行操作，这部分参考这篇文章

!pip install elasticsearch -i https://mirrors.aliyun.com/pypi/simple/

无密码状态适合用于内网或者测试；有密码的可以用在外网。

3 写入

这部分参考这篇文章。
下面那个index函数好像就写入了。

# 基于上面的连接 es 
body1={
    "first_name" : "John",
    "last_name" :  "Smith",
    "age" :        25,
    "about" :      "I love to go rock climbing",
    "interests": [ "sports", "music" ]
}


#余下代码为写入三段数据
body2={
    "first_name" :  "Jane",
    "last_name" :   "Smith",
    "age" :         32,
    "about" :       "I like to collect rock albums",
    "interests":  [ "music" ]
}

body3={
    "first_name" :  "Douglas",
    "last_name" :   "Fir",
    "age" :         35,
    "about":        "I like to build cabinets",
    "interests":  [ "forestry" ]
}

# 好像这里已经写入了

res1 = es.index(index="megacorp", doc_type='employee', id=1,body=body1)
res2 = es.index(index="megacorp", doc_type='employee', id=2,body=body2)
res3 = es.index(index="megacorp", doc_type='employee', id=3,body=body3)

res1 
{'_index': 'megacorp',
 '_type': 'employee',
 '_id': '1',
 '_version': 1,
 'result': 'created',
 '_shards': {'total': 2, 'successful': 1, 'failed': 0},
 'created': True}

4 查询

4.1 精确匹配

# 查询
bb1={
    "query" : {
               "match" : {"last_name" : "Smith" }
                }
     }


rt1= es.search(index="megacorp", body=bb1)

{'took': 1,
 'timed_out': False,
 '_shards': {'total': 5, 'successful': 5, 'skipped': 0, 'failed': 0},
 'hits': {'total': 2,
  'max_score': 0.2876821,
  'hits': [{'_index': 'megacorp',
    '_type': 'employee',
    '_id': '2',
    '_score': 0.2876821,
    '_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': 0.2876821,
    '_source': {'first_name': 'John',
     'last_name': 'Smith',
     'age': 25,
     'about': 'I love to go rock climbing',
     'interests': ['sports', 'music']}}]}}

4.2 过滤查找

bb2={
    "query" : {
                "bool": {
                           "must": { "match" : { "last_name" : "smith" }
                                   },
                           "filter": {
                                         "range" : {
                                                      "age" : { "gt" : 30 } 
                                                    }
                                     }
                         }
             }
}
rt2= es.search(index="megacorp", body=bb2)

{'took': 19,
 'timed_out': False,
 '_shards': {'total': 5, 'successful': 5, 'skipped': 0, 'failed': 0},
 'hits': {'total': 1,
  'max_score': 0.2876821,
  'hits': [{'_index': 'megacorp',
    '_type': 'employee',
    '_id': '2',
    '_score': 0.2876821,
    '_source': {'first_name': 'Jane',
     'last_name': 'Smith',
     'age': 32,
     'about': 'I like to collect rock albums',
     'interests': ['music']}}]}}

4.3 全文匹配

all_search={
    "query" : {
        "match" : {
            "about" : "rock climbing"
        }
    }
}
rt3= es.search(index="megacorp", body=all_search)
rt3
{'took': 4,
 'timed_out': False,
 '_shards': {'total': 5, 'successful': 5, 'skipped': 0, 'failed': 0},
 'hits': {'total': 2,
  'max_score': 0.53484553,
  'hits': [{'_index': 'megacorp',
    '_type': 'employee',
    '_id': '1',
    '_score': 0.53484553,
    '_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': 0.26742277,
    '_source': {'first_name': 'Jane',
     'last_name': 'Smith',
     'age': 32,
     'about': 'I like to collect rock albums',
     'interests': ['music']}}]}}

4.4 短语匹配

phrase={
    "query" : {
        "match_phrase" : {
            "about" : "rock climbing"
        }
    }
}
rt4= es.search(index="megacorp", body=phrase)
rt4
{'took': 1,
 'timed_out': False,
 '_shards': {'total': 5, 'successful': 5, 'skipped': 0, 'failed': 0},
 'hits': {'total': 1,
  'max_score': 0.53484553,
  'hits': [{'_index': 'megacorp',
    '_type': 'employee',
    '_id': '1',
    '_score': 0.53484553,
    '_source': {'first_name': 'John',
     'last_name': 'Smith',
     'age': 25,
     'about': 'I love to go rock climbing',
     'interests': ['sports', 'music']}}]}}

5 Next

• 1 构造实用的文章存储和检索
• 2 了解es的原理

标签：index,about,last,name,Python,megacorp,全栈,score,elasticsearch
来源： https://blog.csdn.net/yukai08008/article/details/118460649