Elasticsearch 开发入门 - Python

• Z时代
• 2024-01-10
• 分类：综合

文章转载自：https://elasticstack.blog.csdn.net/article/details/111573923

1. 你需要在你的电脑上安装 python3
2. 你需要安装 docker 18.03.0-ce 或以上的版本

创建一个 python 项目

我们在自己的电脑里创建一个如下的目录：

mkdir python-elasticsearch
cd python-elasticsearch

接着我们在这个目录里安装 elasticsearch 包：

pip3 install elasticsearch

这样我们就安装好了 elasticsearch 包。我们接下来安装 Elastic Stack。

安装 Elasticsearch 及 Kibana

使用 docker 来安装 Elasticsearch 及 Kibana。我们首先来创建一个叫做 docker-compose.yml 的文件：

docker-compose.yml

---
version: "3"
services:
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:7.10.0
    container_name: es01
    environment:
      - node.name=es01
      - cluster.name=docker-cluster
      - bootstrap.memory_lock=true
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
      - discovery.type=single-node
    ulimits:
      memlock:
        soft: -1
        hard: -1
    volumes:
      - esdata:/usr/share/elasticsearch/data
    ports:
      - 9200:9200
  kibana:
    image: docker.elastic.co/kibana/kibana:7.10.0
    ports:
      - 5601:5601
    depends_on:
      - elasticsearch
volumes:
  esdata:
    driver: local

在上面，我们使用了 Elastic Stack 7.10.0 发行版作为实验的版本。在你实际的使用中，你可以根据自己的版本需求而进行修改。

我们必须先启动 docker，然后在命令行中执行：

docker-compose up

上面命令必须执行于 docker-compose.yml 文件所在的目录中。

它将启动 http://localhost:9200 中的 Elasticsearch 和 http://localhost:5601 中的 Kibana。 你可以通过在浏览器中打开链接来进行验证。

连接到 Elasticsearch

首先我们创建一个叫做 main.py 的文件：

main.py

# Import Elasticsearch package
from elasticsearch import Elasticsearch
# Connect to the elastic cluster
es = Elasticsearch([{'host':'localhost','port':9200}])
print(es)

在上面我们使用 elasticsearch 包实例化一个 es。我们使用本地的 Elasticsearch 实例。如果你使用其它地址的 Elasticsearch，请修改上面的地址及端口地址。我们使用如下的命令来运行：

python3 main.py

上面的运行结果为：

$ python3 main.py 
<Elasticsearch([{'host': 'localhost', 'port': 9200}])>

创建索引并导入文档

Elasticsearch 是面向文档的，这意味着它存储了整个对象或文档。 它不仅存储它们，而且索引每个文档的内容以使其可搜索。 在 Elasticsearch 中，你可以对文档进行索引，搜索，排序和过滤。

Elasticsearch 使用 JSON 作为文档的序列化格式。现在让我们开始索引员工文档。在 Elasticsearch 中存储数据的行为称为索引编制。 Elasticsearch 集群可以包含多个索引，而索引又包含一个类型。 这些类型包含多个文档，并且每个文档都有多个字段。

main.py

# Import Elasticsearch package
from elasticsearch import Elasticsearch
# Connect to the elastic cluster
es = Elasticsearch([{'host':'localhost','port':9200}])
print(es)
e1 = {
    "first_name":"nitin",
    "last_name":"panwar",
    "age": 27,
    "about": "Love to play cricket",
    "interests": ['sports','music'],
}
res = es.index(index = 'megacorp', doc_type ='_doc',id=1,body = e1)
print(res)

运行上面的代码，它显示：

<Elasticsearch([{'host': 'localhost', 'port': 9200}])>
{'_index': 'megacorp', '_type': '_doc', '_id': '1', '_version': 1, 'result': 'created', '_shards': {'total': 2, 'successful': 1, 'failed': 0}, '_seq_no': 0, '_primary_term': 1}

才能够上面的代码的 result 字段的结果 created 中，可以看出来一个新的文档已经被生成。

我们可以通过 Kibana 来进行查看

我们在 Console 中打入如下的命令：

GET megacorp/_search

从上面，我们可以看出来已经被导入的文档。

在上面的例子中，我们指定了一个文档的 id 为 1。在实际的使用中，指定 id 会带来导入效率的降低，因为在写入时，需要检查该 id 的文档是否已经存在，如果不存在就创建新的文档。如果已经存在就更新原有的文档。

我们可以使用如下的例子来创建一个不指定 id 的文档：

main.py

# Import Elasticsearch package
from elasticsearch import Elasticsearch
# Connect to the elastic cluster
es = Elasticsearch([{'host':'localhost','port':9200}])
print(es)
e1 = {
    "first_name":"nitin",
    "last_name":"panwar",
    "age": 27,
    "about": "Love to play cricket",
    "interests": ['sports','music'],
}
res = es.index(index = 'megacorp', doc_type ='_doc', body = e1)
print(res)

运行上面的代码：

<Elasticsearch([{'host': 'localhost', 'port': 9200}])>
{'_index': 'megacorp', '_type': '_doc', '_id': 'Fg-VjXYBP6HMK-G4GNQV', '_version': 1, 'result': 'created', '_shards': {'total': 2, 'successful': 1, 'failed': 0}, '_seq_no': 1, '_primary_term': 1}

上面显示 result 为 created，表明一个新的文档已经生成。同样，我们可以在 Kibana 中使用：

GET megacorp/_search

来查询已经生成文档：

    {
      "took" : 660,
      "timed_out" : false,
      "_shards" : {
        "total" : 1,
        "successful" : 1,
        "skipped" : 0,
        "failed" : 0
      },
      "hits" : {
        "total" : {
          "value" : 2,
          "relation" : "eq"
        },
        "max_score" : 1.0,
        "hits" : [
          {
            "_index" : "megacorp",
            "_type" : "_doc",
            "_id" : "1",
            "_score" : 1.0,
            "_source" : {
              "first_name" : "nitin",
              "last_name" : "panwar",
              "age" : 27,
              "about" : "Love to play cricket",
              "interests" : [
                "sports",
                "music"
              ]
            }
          },
          {
            "_index" : "megacorp",
            "_type" : "_doc",
            "_id" : "Fg-VjXYBP6HMK-G4GNQV",
            "_score" : 1.0,
            "_source" : {
              "first_name" : "nitin",
              "last_name" : "panwar",
              "age" : 27,
              "about" : "Love to play cricket",
              "interests" : [
                "sports",
                "music"
              ]
            }
          }
        ]
      }
    }

在上面所示的第二个文档的 _id 是一个随机分配的文档。

从上面的创建索引的过程来看，它非常之简单！ 无需先执行任何管理任务，例如创建索引或指定每个字段包含的数据类型。 我们可以直接为文档建立索引。

接下来，我们插入更多的文档：

main.py

# Import Elasticsearch package
from elasticsearch import Elasticsearch
# Connect to the elastic cluster
es = Elasticsearch([{'host':'localhost','port':9200}])
print(es)
e1 = {
    "first_name":"nitin",
    "last_name":"panwar",
    "age": 27,
    "about": "Love to play cricket",
    "interests": ['sports','music'],
}
res = es.index(index = 'megacorp', doc_type ='_doc', id = 1, body = e1)
print(res['result'])
e2 = {
    "first_name" :  "Jane",
    "last_name" :   "Smith",
    "age" :         32,
    "about" :       "I like to collect rock albums",
    "interests":  [ "music" ]
}
e3 = {
    "first_name" :  "Douglas",
    "last_name" :   "Fir",
    "age" :         35,
    "about":        "I like to build cabinets",
    "interests":  [ "forestry" ]
}
res = es.index(index = 'megacorp', doc_type = '_doc', id = 2,body = e2)
print(res['result'])
res = es.index(index = 'megacorp', doc_type = '_doc', id = 3, body = e3)
print(res['result'])

上面的运行结果为：

<Elasticsearch([{'host': 'localhost', 'port': 9200}])>
updated
created
created

从上面可以看出来：第一个文档插入时，由于 id 为 1 的文档已经是存在，再次进行插入时，返回的结果为 updated，而对于下面的两个文档来说，它们都是第一次被创建所以是 created。

获取一个文档

在 Elasticsearch 中这很容易。 我们只需执行一个 HTTP GET 请求并指定文档的地址-索引，类型和 ID。 使用这三段信息，我们可以返回原始 JSON 文档。

main.py

# Import Elasticsearch package
from elasticsearch import Elasticsearch
# Connect to the elastic cluster
es = Elasticsearch([{'host':'localhost','port':9200}])
print(es)
e1 = {
    "first_name":"nitin",
    "last_name":"panwar",
    "age": 27,
    "about": "Love to play cricket",
    "interests": ['sports','music'],
}
res = es.index(index = 'megacorp', doc_type ='_doc', id = 1, body = e1)
print(res['result'])
e2 = {
    "first_name" :  "Jane",
    "last_name" :   "Smith",
    "age" :         32,
    "about" :       "I like to collect rock albums",
    "interests":  [ "music" ]
}
e3 = {
    "first_name" :  "Douglas",
    "last_name" :   "Fir",
    "age" :         35,
    "about":        "I like to build cabinets",
    "interests":  [ "forestry" ]
}
res = es.index(index = 'megacorp', doc_type = '_doc', id = 2,body = e2)
print(res['result'])
res = es.index(index = 'megacorp', doc_type = '_doc', id = 3, body = e3)
print(res['result'])
res = es.get(index='megacorp', doc_type = '_doc', id = 3)
print(res)

在上面，我们添加了获取 id 为 3 的文档：

    res = es.get(index='megacorp', doc_type = '_doc', id = 3)
    print(res)

运行上面的代码：

<Elasticsearch([{'host': 'localhost', 'port': 9200}])>
updated
updated
updated
{'_index': 'megacorp', '_type': '_doc', '_id': '3', '_version': 5, '_seq_no': 15, '_primary_term': 1, 'found': True, '_source': {'first_name': 'Douglas', 'last_name': 'Fir', 'age': 35, 'about': 'I like to build cabinets', 'interests': ['forestry']}}

你在 _source 字段中可以发现之前输入文档的内容。

删除一个文档

我们可以使用如下的代码来删除一个文档：

res = es.delete(index = 'megacorp',doc_type='_doc', id = 3)
print(res['result'])

在上面，我们删除 id 为 3 的文档。

main.py

# Import Elasticsearch package
from elasticsearch import Elasticsearch
# Connect to the elastic cluster
es = Elasticsearch([{'host':'localhost','port':9200}])
print(es)
e1 = {
    "first_name":"nitin",
    "last_name":"panwar",
    "age": 27,
    "about": "Love to play cricket",
    "interests": ['sports','music'],
}
res = es.index(index = 'megacorp', doc_type ='_doc', id = 1, body = e1)
print(res['result'])
e2 = {
    "first_name" :  "Jane",
    "last_name" :   "Smith",
    "age" :         32,
    "about" :       "I like to collect rock albums",
    "interests":  [ "music" ]
}
e3 = {
    "first_name" :  "Douglas",
    "last_name" :   "Fir",
    "age" :         35,
    "about":        "I like to build cabinets",
    "interests":  [ "forestry" ]
}
res = es.index(index = 'megacorp', doc_type = '_doc', id = 2,body = e2)
print(res['result'])
res = es.index(index = 'megacorp', doc_type = '_doc', id = 3, body = e3)
print(res['result'])
res = es.get(index='megacorp', doc_type = '_doc', id = 3)
print(res)
# Delete a doc with id = 3
res = es.delete(index = 'megacorp',doc_type='_doc', id = 3)
print(res['result'])

运行上面的代码：

<Elasticsearch([{'host': 'localhost', 'port': 9200}])>
updated
updated
updated
{'_index': 'megacorp', '_type': '_doc', '_id': '3', '_version': 6, '_seq_no': 18, '_primary_term': 1, 'found': True, '_source': {'first_name': 'Douglas', 'last_name': 'Fir', 'age': 35, 'about': 'I like to build cabinets', 'interests': ['forestry']}}
deleted

从上面我们可以看出来 id 为 3 的文档已经被删除了。

搜索文档

首先让我们搜索之前所有的文档：

main.py

# Import Elasticsearch package
from elasticsearch import Elasticsearch
# Connect to the elastic cluster
es = Elasticsearch([{'host':'localhost','port':9200}])
print(es)
e1 = {
    "first_name":"nitin",
    "last_name":"panwar",
    "age": 27,
    "about": "Love to play cricket",
    "interests": ['sports','music'],
}
res = es.index(index = 'megacorp', doc_type ='_doc', id = 1, body = e1)
print(res['result'])
e2 = {
    "first_name" :  "Jane",
    "last_name" :   "Smith",
    "age" :         32,
    "about" :       "I like to collect rock albums",
    "interests":  [ "music" ]
}
e3 = {
    "first_name" :  "Douglas",
    "last_name" :   "Fir",
    "age" :         35,
    "about":        "I like to build cabinets",
    "interests":  [ "forestry" ]
}
res = es.index(index = 'megacorp', doc_type = '_doc', id = 2,body = e2)
print(res['result'])
res = es.index(index = 'megacorp', doc_type = '_doc', id = 3, body = e3)
print(res['result'])
res = es.get(index='megacorp', doc_type = '_doc', id = 3)
print(res)
# Delete a doc with id = 3
res = es.delete(index = 'megacorp',doc_type='_doc', id = 3)
print(res['result'])
# Search all of the available documents
res = es.search(index = 'megacorp', body = {'query': {"match_all": {}} } )
print(res['hits'])

在上面最后的部分：

res = es.search(index = 'megacorp', body = {'query': {"match_all": {}} } )
print(res['hits'])

它搜索所有的文档。在这里，我们使用 match_all 搜索。运行上面的代码显示：

<Elasticsearch([{'host': 'localhost', 'port': 9200}])>
updated
updated
created
{'_index': 'megacorp', '_type': '_doc', '_id': '3', '_version': 1, '_seq_no': 26, '_primary_term': 1, 'found': True, '_source': {'first_name': 'Douglas', 'last_name': 'Fir', 'age': 35, 'about': 'I like to build cabinets', 'interests': ['forestry']}}
deleted
{'total': {'value': 4, 'relation': 'eq'}, 'max_score': 1.0, 'hits': [{'_index': 'megacorp', '_type': '_doc', '_id': 'Fg-VjXYBP6HMK-G4GNQV', '_score': 1.0, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': 'Fw-bjXYBP6HMK-G4GNTa', '_score': 1.0, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': '1', '_score': 1.0, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': '2', '_score': 1.0, '_source': {'first_name': 'Jane', 'last_name': 'Smith', 'age': 32, 'about': 'I like to collect rock albums', 'interests': ['music']}}]}

从上面的 value 为 4 来看，总共有4个文档。它们在 hits 字段中被展示。

现在，让我们搜索姓氏为 nitin 的用户名。

match 操作符

main.py

# Import Elasticsearch package
from elasticsearch import Elasticsearch
# Connect to the elastic cluster
es = Elasticsearch([{'host':'localhost','port':9200}])
print(es)
e1 = {
    "first_name":"nitin",
    "last_name":"panwar",
    "age": 27,
    "about": "Love to play cricket",
    "interests": ['sports','music'],
}
res = es.index(index = 'megacorp', doc_type ='_doc', id = 1, body = e1)
print(res['result'])
e2 = {
    "first_name" :  "Jane",
    "last_name" :   "Smith",
    "age" :         32,
    "about" :       "I like to collect rock albums",
    "interests":  [ "music" ]
}
e3 = {
    "first_name" :  "Douglas",
    "last_name" :   "Fir",
    "age" :         35,
    "about":        "I like to build cabinets",
    "interests":  [ "forestry" ]
}
res = es.index(index = 'megacorp', doc_type = '_doc', id = 2,body = e2)
print(res['result'])
res = es.index(index = 'megacorp', doc_type = '_doc', id = 3, body = e3)
print(res['result'])
res = es.get(index='megacorp', doc_type = '_doc', id = 3)
print(res)
# Delete a doc with id = 3
res = es.delete(index = 'megacorp',doc_type='_doc', id = 3)
print(res['result'])
# Search all of the available documents
res = es.search(index = 'megacorp', body = {'query': {"match_all": {}} } )
print(res['hits'])
# Search for a document with first_name = nitin
res= es.search(index = 'megacorp', body = {'query':{'match':{'first_name':'nitin'}}})
print(res['hits']['hits'])

我们使用了如下的代码来进行匹配：

# Search for a document with first_name = nitin
res= es.search(index = 'megacorp', body = {'query':{'match':{'first_name':'nitin'}}})
print(res['hits']['hits'])

运行上面的代码：

<Elasticsearch([{'host': 'localhost', 'port': 9200}])>
updated
updated
created
{'_index': 'megacorp', '_type': '_doc', '_id': '3', '_version': 1, '_seq_no': 30, '_primary_term': 1, 'found': True, '_source': {'first_name': 'Douglas', 'last_name': 'Fir', 'age': 35, 'about': 'I like to build cabinets', 'interests': ['forestry']}}
deleted
{'total': {'value': 4, 'relation': 'eq'}, 'max_score': 1.0, 'hits': [{'_index': 'megacorp', '_type': '_doc', '_id': 'Fg-VjXYBP6HMK-G4GNQV', '_score': 1.0, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': 'Fw-bjXYBP6HMK-G4GNTa', '_score': 1.0, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': '1', '_score': 1.0, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_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': '_doc', '_id': 'Fg-VjXYBP6HMK-G4GNQV', '_score': 0.99542797, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': 'Fw-bjXYBP6HMK-G4GNTa', '_score': 0.99542797, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': '1', '_score': 0.99542797, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}]

在上面输出结果的最后部分显示了搜索的结果。

bool 操作符

bool 使用字典，其中至少包含 must，should 和 must_not 中的一个，每个字典都包含匹配列表或其他进一步的搜索运算符。

res= es.search(index = 'megacorp', body = {
        'query':{
            'bool':{
                'must':[{
                        'match':{
                            'first_name':'nitin'
                        }
                    }]
            }
        }
    })
print(res['hits']['hits'])

main.py

# Import Elasticsearch package
from elasticsearch import Elasticsearch
# Connect to the elastic cluster
es = Elasticsearch([{'host':'localhost','port':9200}])
#print(es)
e1 = {
    "first_name":"nitin",
    "last_name":"panwar",
    "age": 27,
    "about": "Love to play cricket",
    "interests": ['sports','music'],
}
res = es.index(index = 'megacorp', doc_type ='_doc', id = 1, body = e1)
#print(res['result'])
e2 = {
    "first_name" :  "Jane",
    "last_name" :   "Smith",
    "age" :         32,
    "about" :       "I like to collect rock albums",
    "interests":  [ "music" ]
}
e3 = {
    "first_name" :  "Douglas",
    "last_name" :   "Fir",
    "age" :         35,
    "about":        "I like to build cabinets",
    "interests":  [ "forestry" ]
}
res = es.index(index = 'megacorp', doc_type = '_doc', id = 2,body = e2)
#print(res['result'])
res = es.index(index = 'megacorp', doc_type = '_doc', id = 3, body = e3)
#print(res['result'])
res = es.get(index='megacorp', doc_type = '_doc', id = 3)
#print(res)
# Delete a doc with id = 3
res = es.delete(index = 'megacorp',doc_type='_doc', id = 3)
#print(res['result'])
# Search all of the available documents
res = es.search(index = 'megacorp', body = {'query': {"match_all": {}} } )
#print(res['hits'])
# Search for a document with first_name = nitin
res= es.search(index = 'megacorp', body = {'query':{'match':{'first_name':'nitin'}}})
#print(res['hits']['hits'])
res= es.search(index = 'megacorp', body = {
        'query':{
            'bool':{
                'must':[{
                        'match':{
                            'first_name':'nitin'
                        }
                    }]
            }
        }
    })
print(res['hits']['hits'])

运行上面的代码：

[{'_index': 'megacorp', '_type': '_doc', '_id': 'Fg-VjXYBP6HMK-G4GNQV', '_score': 1.0159205, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': 'Fw-bjXYBP6HMK-G4GNTa', '_score': 1.0159205, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': '1', '_score': 1.0159205, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}]

filter 操作符

让我们的搜索更加复杂。 我们仍然希望找到所有姓氏为 nitin 的员工，但我们只希望年龄在 30 岁以上的员工。我们的查询将略有变化以适应过滤器，这使我们可以高效地执行结构化搜索：

res= es.search(index = 'megacorp', body = {
        'query':{
            'bool':{
                'must':{
                    'match':{
                        'first_name':'nitin'
                    }
                },
                "filter":{
                    "range":{
                        "age":{
                            "gt":25
                        }
                    }
                }
            }
        }
    })
print(res['hits']['hits'])

我们添加上面的代码到 main.py 中，并运行：

[{'_index': 'megacorp', '_type': '_doc', '_id': 'Fg-VjXYBP6HMK-G4GNQV', '_score': 1.0296195, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': 'Fw-bjXYBP6HMK-G4GNTa', '_score': 1.0296195, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': '1', '_score': 1.0296195, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}]

从上面我们可以看出来只有年龄大于25的，并且 first_name 为 nitin 的文档被搜索到。如果我们把年龄设置为 27岁，那么我们将不会搜索到任何的文档：

res= es.search(index = 'megacorp', body = {
        'query':{
            'bool':{
                'must':{
                    'match':{
                        'first_name':'nitin'
                    }
                },
                "filter":{
                    "range":{
                        "age":{
                            "gt":27
                        }
                    }
                }
            }
        }
    })
print(res['hits']['hits'])

上面的搜索将会生成：

[]

全文搜索

到目前为止，搜索非常简单。让我们尝试更高级的全文本搜索。 在开始下一种搜索之前，让我再插入一个文档。

main.py

# Import Elasticsearch package
from elasticsearch import Elasticsearch
# Connect to the elastic cluster
es = Elasticsearch([{'host':'localhost','port':9200}])
#print(es)
e1 = {
    "first_name":"nitin",
    "last_name":"panwar",
    "age": 27,
    "about": "Love to play cricket",
    "interests": ['sports','music'],
}
res = es.index(index = 'megacorp', doc_type ='_doc', id = 1, body = e1)
#print(res['result'])
e2 = {
    "first_name" :  "Jane",
    "last_name" :   "Smith",
    "age" :         32,
    "about" :       "I like to collect rock albums",
    "interests":  [ "music" ]
}
e3 = {
    "first_name" :  "Douglas",
    "last_name" :   "Fir",
    "age" :         35,
    "about":        "I like to build cabinets",
    "interests":  [ "forestry" ]
}
res = es.index(index = 'megacorp', doc_type = '_doc', id = 2,body = e2)
#print(res['result'])
res = es.index(index = 'megacorp', doc_type = '_doc', id = 3, body = e3)
#print(res['result'])
res = es.get(index='megacorp', doc_type = '_doc', id = 3)
#print(res)
# Delete a doc with id = 3
res = es.delete(index = 'megacorp',doc_type='_doc', id = 3)
#print(res['result'])
# Search all of the available documents
res = es.search(index = 'megacorp', body = {'query': {"match_all": {}} } )
#print(res['hits'])
# Search for a document with first_name = nitin
res= es.search(index = 'megacorp', body = {'query':{'match':{'first_name':'nitin'}}})
#print(res['hits']['hits'])
res= es.search(index = 'megacorp', body = {
        'query':{
            'bool':{
                'must':[{
                        'match':{
                            'first_name':'nitin'
                        }
                    }]
            }
        }
    })
# print(res['hits']['hits'])
res= es.search(index = 'megacorp', body = {
        'query':{
            'bool':{
                'must':{
                    'match':{
                        'first_name':'nitin'
                    }
                },
                "filter":{
                    "range":{
                        "age":{
                            "gt":27
                        }
                    }
                }
            }
        }
    })
# print(res['hits']['hits'])
e4 = {
    "first_name":"asd",
    "last_name":"pafdfd",
    "age": 27,
    "about": "Love to play football",
    "interests": ['sports','music'],
}
res = es.index(index = 'megacorp', doc_type = '_doc', id = 4, body = e4)
print(res['result'])
res = es.search( index = 'megacorp', body = {
        'query':{
            'match':{
                "about":"play cricket"
            }
        }
    })
for hit in res['hits']['hits']:
    print(hit['_source']['about'])
    print(hit['_score'])
    print('**********************')

在上面，我们添加了一个 id 为 4 的文档，进行全文搜索。在上面的示例中，它返回4个文档，但是得分不同。它的运行结果为：

updated
Love to play cricket
1.9652195
**********************
Love to play cricket
1.9652195
**********************
Love to play cricket
1.9652195
**********************
Love to play football
0.74101156
**********************

Phrase search

在一个字段中查找单个单词很好，但是有时你想要匹配短语中单词的确切顺序。

res= es.search(index = 'megacorp', body = {
        'query':{
            'match_phrase':{
                "about":"play cricket"
            }
        }
    })
for hit in res['hits']['hits']:
    print(hit['_source']['about'])
    print(hit['_score'])
    print('**********************')

上面代码的显示结果为：

Love to play cricket
2.0281231
**********************
Love to play cricket
2.0281231
**********************
Love to play cricket
2.0281231
**********************

聚合

Elasticsearch 具有称为聚合的功能，该功能使你可以对数据进行复杂的分析。 它与 SQ L中的 “Group By” 相似，但功能更强大。

res= es.search(index = 'megacorp', body = {
        "aggs": {
            "all_interests": {
            "terms": { "field": "interests.keyword" }
            }
        }
    })
print(res)

上面的代码执行的结果是：

 {'took': 1, 'timed_out': False, '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}, 'hits': {'total': {'value': 5, 'relation': 'eq'}, 'max_score': 1.0, 'hits': [{'_index': 'megacorp', '_type': '_doc', '_id': 'Fg-VjXYBP6HMK-G4GNQV', '_score': 1.0, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': 'Fw-bjXYBP6HMK-G4GNTa', '_score': 1.0, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_id': '1', '_score': 1.0, '_source': {'first_name': 'nitin', 'last_name': 'panwar', 'age': 27, 'about': 'Love to play cricket', 'interests': ['sports', 'music']}}, {'_index': 'megacorp', '_type': '_doc', '_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': '_doc', '_id': '4', '_score': 1.0, '_source': {'first_name': 'asd', 'last_name': 'pafdfd', 'age': 27, 'about': 'Love to play football', 'interests': ['sports', 'music']}}]}, 'aggregations': {'all_interests': {'doc_count_error_upper_bound': 0, 'sum_other_doc_count': 0, 'buckets': [{'key': 'music', 'doc_count': 5}, {'key': 'sports', 'doc_count': 4}]}}}

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