elasticsearch basic and simple to use in Python

A. Java installation environment and elasticsearch, kibana

First asked jdk 1.8 and above, here elasticsearch (kibana Similarly, try to ensure consistency elasticsearch version, installation and start-up mode is the same) using version 6.5.4.

elasticsearch Description: https://baike.baidu.com/item/elasticsearch/3411206?fr=aladdin

elasticsearch official website: https://www.elastic.co/cn/downloads/

Click to download, and then select the drop-down version of the history of past releases

Finally, click to download

java environment configuration see the blog: https://www.cnblogs.com/Neeo/articles/10368280.html

After installation, if an error does not occur, extract elasticsearch, then enter it in the bin directory, double-click the file to open one of the elasticsearch.bat, then visit 127.0.0.1:9200, if you can see the json data returned by the representatives of the configuration is successful. (9200 is the node where the monitor is turned off, while the 9300 is cluster default listening port).

The reason failed to start properly

II. Part of the file elasticsearch, kibana description

1. Unzip after the config in elasticsearch.yml ES is the configuration file, open it with Notepad.

jvm.options same directory jrel virtual machine configuration.

log4j2.properties same log directory is configured, you can configure the log output level, and so, generally will not be modified. (Apache log4j is a framework for control log)

1. config directory under Kibana kibana.yml store its configuration file.

Three. Kibana of Dev tools in a simple command ES

Data Preparation

PUT s18/doc/2
{
  "name":"yangtao",
  "age": 18,
  "tags":"浪",
  "b":"19970521",
  "sex": "男"
}


PUT s18/doc/1
{
  "name":"egon",
  "age": 20,
  "tags":"认真学习",
  "b":"19970781",
  "sex": "男"
}


PUT s18/doc/3
{
  "name":"sybil",
  "age": 3,
  "tags":"认真学习",
  "b":"19971216",
  "sex": "女"
}

   以 '索引/类型/文档' 的格式
#增 PUT  如果PUT对的数据已存在，那么会进行更新的操作，但是PUT时传的参数是什么，更新之后的数据就是什么，所以需要填写所有的字段
    PUT s18/doc/1   #新增索引s18/doc/1  s18是_index，1是该数据的文档,doc是Type
    {
        "name":"sybil",
        "skill":"fly",
        "hobby":"sleep",
        "tags":"beautiful"
    }
    
#删  DELETE
    DELETE s18  #删除索引（数据库）
    DELETE s18/doc/1  #删除索引s18下type是doc，文档是1的数据
    DELETE s18/doc/_search?q=tags:beautiful  #这是错误的删除方式，要使用search的方式删除需要使用POST
        
#查  GET
    GET s18/doc/1  #查文档为1的数据
    GET s18/doc/_search  #查所有
    GET s18/doc/_search?q=name:sybil  #查名字叫sybil的

#更新指定字段POST，或配合search删除
    POST s18/doc/1/_update
    {
      "doc":{  # 需要指定type，相当于指定表名
        "tags":"美丽冻人"
      }
    }
    
    POST s18/doc/_delete_by_query?q=age:18  #删除年龄为18的数据，不过不推荐使用该方式
 
#查询的两种方式
    1.查询字符串 query string
        GET s18/doc/_search?q=age:18
            
    2. DSL 结构化查询
        GET s18/doc/_search
        {
            "query":{
                "match":{
                    "age":18
                }
            }
        }
        
        GET s18/doc/_search
        {
            "query":{
                "match_all":{}  #因为是查所有，不需要参数
            }
        }
        

1. GET s18/_mapping

1. GET s18/_settings

   

2. GET s18

IV. The ES complex queries

1. Sort sort, paging, bool Boolean queries

#排序 sort  不是所有的字段都能排序，比如名字
    GET s18/doc/_search
        {
            "query":{
                "match_all":{}
            },
            "sort": [  #排序
                {
                    "age":{
                        "order": "asc" #升序，desc降序
                    }
                }
            ]
        }
        
#分页 内部会先排好序，保证翻页后不会出现已经返回过的数据
    GET s18/doc/_search
    {
        "query":{
               "match_all":{}
           },
        "from": 0,  #从0条开始
        "size": 2  #返回2条数据
    }

#布尔查询bool: should(or)  must(and)  must_not(not)，must_not
    GET s18/doc/_search
    {
        "query": {
            "bool": {
                "should": [  #名字是sybil或者年龄18岁
                    {
                        "match": {
                            "name": "sybil"
                        }
                    },
                    {
                        "match": {
                            "age": "18"
                        }
                    }
                ]
            }
        }
    }
    
    '查询性别是男的，年龄18'
    GET s18/doc/_search
    {
        "query": {
            "bool":{
                "must": [
                    {
                        "match": {
                            "age": "18"
                        }
                    },
                    {
                         "match": {
                        "sex": "男"
                        }
                    }
                ]
            }
        }
    }
    
#查询年龄大于19岁的男的  filter尽量用must配合，避免脏数据
    GET s18/doc/_search
    {
        "query": {
            "bool": {
                "must": [
                    {"match": {
                        "sex": "男"
                    }}
                ],
                "filter": {
                    "range": {
                        "age": {
                            "gte": 19,
                            "lte": 20
                        }
                    }
                }
            }
        }
    }
    

2. Highlight highlight query

#查询name是sybil的文档
#高亮查询，查询name是sybil的文档，查询的结果需要在前端才能体现高亮，因为是标签的效果
GET s18/doc/_search
{
  "query": {
    "match": {
      "name": "sybil"
    }
  },
  "highlight": {
    "fields": {
      "name": {}  #查询的结果用<em>标签包裹
    }
  }
}

GET s18/doc/_search
{
  "query": {
    "match": {
      "name": "sybil"  #会将我们这里定义的字段高亮显示
    }
  },
  "highlight": {  #可以使用pre_tags与post_tags自定义标签
    "pre_tags": "<b style='color:red;font-size:20px'>", 
    "post_tags": "<\b>", 
    "fields": {
      "name": {}  #这里指定字段后空着即可
    }
  }
}

3. Results filtered _source

#过滤出查询结果中想要的字段
GET s18/doc/_search
{
  "query": {
    "match": {
      "name": "sybil"
    }
  },
  "_source": "name"  #单个字段
}

GET s18/doc/_search
{
  "query": {
    "match": {
      "name": "sybil"
    }
  },
  "_source": ["name", "age"]  #多个字段
}

4. aggregate query

#sum，查询所有男生的年龄
GET s18/doc/_search
{
  "query": {
    "match": {
      "sex": "男"
    }
  },
  "aggs": {
    "my_sum": {  #这是查询结果的键，可以自定义
      "sum": {  #这是聚合的方法
        "field": "age"  #指定聚合的依据
      }
    }
  }
}

#查询最大年龄的男生
GET s18/doc/_search
{
  "query": {
    "match": {
      "sex": "男"
    }
  },
  "aggs": {
    "my_max": {
      "max": {
        "field": "age"
      }
    }
  }
}

#查询最小年龄的男生
GET s18/doc/_search
{
  "query": {
    "match": {
      "sex": "男"
    }
  },
  "aggs": {
    "my_min": {
      "min": {
        "field": "age"
      }
    }
  }
}

#查询男生的平均年龄
GET s18/doc/_search
{
  "query": {
    "match": {
      "sex": "男"
    }
  },
  "aggs": {
    "my_avg": {
      "avg": {
        "field": "age"
      }
    }
  }
}

#分组，根据年龄，0-10，,0-20
GET s18/doc/_search
{
  "query": {
    "match": {
      "sex": "男"
    }
  },
  "aggs": {
    "my_group": {  #分组名称
      "range": {
        "field": "age",
        "ranges": [
          {  
            "from": 0,  #[0, 10)
            "to": 10
          },
          {
            "from": 10,
            "to": 20
          },
          {
            "from": 20,
            "to": 30
          }
        ]
      }
    }
  }
}

#分组，根据年龄，0-10，,0-20, 对每组年龄求和
GET s18/doc/_search
{
  "query": {
    "match": {
      "sex": "男"
    }
  },
  "aggs": {
    "my_group": {
      "range": {
        "field": "age",
        "ranges": [
          {
            "from": 0,
            "to": 10
          },
          {
            "from": 10,
            "to": 20
          },
          {
            "from": 20,
            "to": 30
          }
        ]
      },
      "aggs": {
        "my_sum": {
          "sum": {
            "field": "age"
          }
        }
      }
    }
  }
}

Mapping the mapping 5. ES

#自定义索引的映射
PUT s2
{
  "mappings": {
    "doc":{  #类型
      "properties":{  #文档的属性
        "name":{
          "type":"text"
        },
        "age":{
          "type":"long"
        },
        "desc":{
          "type":"text"
        }
      }
    }
  }
}

#如果给该索引加文档时，额外增加了字段，那么mappings会自动增加该字段，使其能够成为查询条件
GET s2/_mapping
PUT s2/doc/2
{
  "name":"catmao",
  "age":30,
  "desc":"beautiful",
  "skill":"sleep"
}
GET s2/_mapping  #再次执行会发现字段多了skill

#这是由mappings的dynamic的三种状态，三种状态时，均可以缺省字段
dynamic为true时特征如上。

dynamic为false时，PUT添加的数据有额外字段时，Mapping不会自动添加，该字段也无法成为查询的条件。

dynamic为strict时，添加的数据不能有额外的字段，会直接报错
    PUT s6
    {
      "mappings": {
        "doc":{
          "dynamic":"strict",
          "properties":{
            "name":{
              "type":"text"
            }
          }
        }
      }
    }
#mapping的ignore_above，不会为超过该设定字符长度的字符串设定索引与存储，即无法成为有效的查询条件，仅对type为keyword的字段有效。
https://www.cnblogs.com/Neeo/articles/10789701.html
    PUT s7
    {
      "mappings": {
        "doc":{
          "properties":{
            "title":{
              "type":"keyword",
              "ignore_above":10
            }
          }
        }
      }
    }

    PUT s7/doc/2
    {
      "title":"从手机、平板电脑、路由器"
    }
    PUT s7/doc/1
    {
      "title":"1234567"
    }
    GET s7/doc/_search
    {
      "query": {
        "match": {
          "title": "1234567"
        }
      }
    }
    
#mappings参数之index,设置为false后，ES不会为该字段建立索引，其实是不会做分词
#mappings的index参数
    PUT s8
    {
      "mappings": {
        "doc":{
          "properties":{
            "t1":{
              "type":"text",
              "index":"true"
            },
            "t2":{
              "type":"text",
              "index":"false"
            }
          }
        }
      }
    }

    PUT s8/doc/1
    {
      "t1":"论母猪的厂前保养",
      "t2":"论母猪的厂后保养"
    }

    GET s8/doc/_search
    {
      "query": {
        "match": {
          "t2": "母猪"
        }
      }
    }
    
#mappings的copy_to，把一个字段的值复制给另一个，可以减少一次查询的次数
    PUT s9
    {
      "mappings": {
        "doc":{
          "properties":{
            "t1":{
              "type":"text",
              "copy_to":"full_name"  #复制给多个字段 ["f1", "f2"]
            },
            "t2":{
              "type":"text",
              "copy_to":"full_name"
            },
            "full_name":{
              "type":"text"
            }
          }
        }
      }
    }

    PUT s9/doc/1
    {
      "t1":"xxx",
      "t2":"ooo"
    }

    GET s9/doc/_search
    {
      "query": {
        "match": {
          "full_name": "xxx"
        }
      }
    }

6. nested property

#嵌套类型
    PUT w1
    {
      "mappings": {
        "doc":{
          "properties":{
            "name":{
              "type":"text"
            },
            "age":{
              "type":"long"
            },
             "info":{  #info字段嵌套两个字段
                "properties":{
                  "addr":{
                    "type":"text"
                  },
                  "tel":{
                    "type":"long"
                  }
                }
        }
          }
        }
      }
    }

    PUT w1/doc/1  #插入一条数据
    {
      "name":"tom",
      "age":18,
      "info":{  
        "addr":"北京",
        "tel":"10010"
      }
    }

    GET w1/doc/_search
    {
      "query": {
        "match": {
          "info.tel": "10010"  #以嵌套字段的属性为查询条件时，直接点语法即可
        }
      }
    }

The primary and slice 7.settings

#主分片一旦设置无法更改，复制分片可以
PUT w2
{
  "mappings": {
    "doc":{
      "properties":{
        "title":{
          "type":"text"
        }
      }
    }
  }, 
  "settings": {  #通过settings设置
    "number_of_shards": 3,  #主分片数量，默认为5
    "number_of_replicas": 3  #复制分片数量，默认为1
  }
}

GET w2

8.match series

Blog address https://www.cnblogs.com/Neeo/articles/10578482.html

#数据准备
PUT t1/doc/1
{
  "title":"中国是世界上人口最多的国家"
}
PUT t1/doc/2
{
  "title":"美国是世界上军事实力强大多的国家"
}
PUT t1/doc/3
{
  "title":"北京是中国 的首都"
}

Use match the query "China"

#会发现美国也包含在其中，这是因为match内部会散列分词，内部是采用标准的分析器，中国会分为中和国
GET t1/doc/_search
{
  "query": {
    "match": {
      "title": "中国"
    }
  }
}

We want China as a phrase, we need match_phrase

#这样就不好分词，把中国当成一个短语去查询
GET t1/doc/_search
{
  "query": {
    "match_phrase": {
      "title": "中国"
      "slop": 1  #该参数可以指定分词的间隔
    }
  }
}

The most left-prefix query match_phrase_prefix

PUT t2/doc/1
{
  "title":"beautiful girl"
}

PUT t2/doc/2
{
  "title":"beautiful so"
}

GET t2/doc/_search
{
  "query": {
    "match_phrase_prefix": {
      "title": "bea"  #只要有单词以bea开头
    }
  }
}

multi_match multi-field queries, we can complete the work match_phrase and match_phrase_prefix using very flexible.

PUT t3/doc/1
{
  "t1":"beautiful girl",
  "t2":"beautiful so"
}

#查找字段t1和t2都包含beautiful的文档
GET t3/doc/_search
{
  "query": {
    "multi_match": {
      "query": "beautiful",
      "fields": ["t1", "t2"]
    }
  }
}

V. the process of analyzing data elasticsearch Talk

See blog https://www.cnblogs.com/Neeo/p/10304892.html , comes with installation ik word

5.1 ik installation problems

5.2 ik word test

#ik分词的测试
GET _analyze
{
  "analyzer": "standard",
  "text":"上海自来水来自海上"
}

GET _analyze
{
  "analyzer": "ik_smart",
  "text":"上海自来水来自海上"
}

#粒度更细，一般存储时建议使用粒度粗的（意义更大），ik_max_word会将文档做最细粒度的拆分
GET _analyze
{
  "analyzer": "ik_max_word",
  "text":"上海自来水来自海上"
}

Six. Python operation Elasticsearch

6.1 python connection ES

doc_type default doc, so you can not write, but the proposal is written on, just in case

'1. 安装elasticsearch模块'
pip install elasticsearch
# 豆瓣源
pip install -i https://pypi.doubanio.com/simple/             elasticsearch
        
'2. 连接的方式'
from elasticsearch import  Elasticsearch
# es = Elasticsearch()    # 默认连接本地elasticsearch
# es = Elasticsearch(['127.0.0.1:9200'])  # 连接本地9200端口
es = Elasticsearch(
    ["192.168.1.10", "192.168.1.11", "192.168.1.12"], # 连接集群，以列表的形式存放各节点的IP地址
    sniff_on_start=True,    # 连接前测试
    sniff_on_connection_fail=True,  # 节点无响应时刷新节点
    sniff_timeout=60    # 设置超时时间
)

'3. 配置忽略响应状态码'
es = Elasticsearch(['127.0.0.1:9200'],ignore=400)  # 忽略返回的400状态码
es = Elasticsearch(['127.0.0.1:9200'],ignore=[400, 405, 502])  # 以列表的形式忽略多个状态码

'4. 简单的示例'
from elasticsearch import  Elasticsearch
es = Elasticsearch()    # 默认连接本地elasticsearch
# print(es.ping())  #可以用该方法查看是否连接成功，如果群集已启动，则返回True，否则返回False
print(es.index(index='p1', doc_type='doc', id=1, body={'name': "sybil", "age": 18}))  #index方法，如果索引存在则更新，否则增加新记录
print(es.get(index='p1', doc_type='doc', id=1))

6.2 python operating ES

# search方法使用的较多，因为可以跟复杂查询
'对返回的信息的几种过滤方法（过滤出想要显示的信息）'
body = {
    "query": {
        "match": {
            "name": "lou22"
        }
    }
}
# 返回的是一个大字典
print(es.search(index='p1', body=body))
# 可以使用filter_path过滤出其中想要的信息
print(es.search(index='p1', body=body, filter_path=['hits.hits']))
# 查询出来的信息主要保存在_source中  {'hits': {'hits': [{'_source': {'name': 'lou22'}}]}}
print(es.search(index='p1', body=body, filter_path=['hits.hits._source']))
# 也可以指定多个要显示的信息 {'hits': {'total': 1, 'hits': [{'_source': {'name': 'lou22'}}]}}
print(es.search(index='p1', body=body, filter_path=['hits.hits._source', 'hits.total']))
# 可以用*指定返回hits下所有的内容  *是语法，表示所有
print(es.search(index='p1', body=body, filter_path=['hits.*']))
# 可以用*指定返回hits下的hits中所有的内容
print(es.search(index='p1', body=body, filter_path=['hits.hits._*']))


'过滤出结果中想要的字段，_source'
print(es.search(index='py3', doc_type='doc', body={"query": {"match":{"age": 20}}}))  # 一般查询
print(es.search(index='py3', doc_type='doc', body={"query": {"match":{"age": 19}}},_source=['name', 'age']))  # 结果字段过滤
print(es.search(index='py3', doc_type='doc', body={"query": {"match":{"age": 19}}},_source_exclude  =[ 'age']))  #除了age字段之外的所有字段都要
print(es.search(index='py3', doc_type='doc', body={"query": {"match":{"age": 19}}},_source_include =[ 'age']))  #只要age字段，同_source差不多

get_source direct return data dictionary

# 直接返回数据字典  {'name': 'lou22'}
print(es.get_source(index='p1', doc_type='doc', id=1))

count count the number of query results

# 新建几条数据
for i in range(2, 11):
    print(es.index(index='p1', doc_type='doc', body={"name": "lou%s" % i}))

# 指定用于查询的方式
body = {
    "query": {
        "match": {
            "name": "lou2"
        }
    }
}
#查询回来的结果就是字典，可以采用字典的操作方式
print(es.count(index='p1', doc_type='doc', body=body))['count']  # 1
print(es.count(index='w2'))  # {'count': 6, '_shards': {'total': 5, 'successful': 5, 'skipped': 0, 'failed': 0}}
print(es.count(index='w2', doc_type='doc')) # {'count': 6, '_shards': {'total': 5, 'successful': 5, 'skipped': 0, 'failed': 0}}

es.create create an index (the index does not exist) and add a new data, there is the new index only (can only add, will repeat the error

#其实内部调用了es.index，一般不使用create方法
print(es.create(index='py3', doc_type='doc', id='1', body={"name": '王五', "age": 20}))
print(es.get(index='py3', doc_type='doc', id='1'))

es.delete与es.delete_by_query

#es.delete，删除指定的文档。比如删除文章id为4的文档，但不能删除索引，如果想要删除索引，还需要es.indices.delete来处理
print(es.delete(index='py3', doc_type='doc', id='4'))

#es.delete_by_query，删除与查询匹配的所有文档
print(es.delete_by_query(index='py3', doc_type='doc', body={"query": {"match":{"age": 20}}}))

es.exists与es.info

# es.exists，查询elasticsearch中是否存在指定的文档，返回一个布尔值
print(es.exists(index='py3', doc_type='doc', id='1'))

#es.info，获取当前集群的基本信息，一般使用kibana查看，因为比较直观
print(es.info())

6.3 es.indices operation of the index

Up es.indices.create create indexes Elasticsearch in use

body = {
    "mappings": {
        "doc": {
            "dynamic": "strict",  #设置严格模式
            "properties": {
                "title": {
                    "type": "text",
                    "analyzer": "ik_max_word"
                },
                "url": {
                    "type": "text"
                },
                "action_type": {
                    "type": "text"
                },
                "content": {
                    "type": "text"
                }
            }
        }
    }
}
es.indices.create('py4', body=body)

es.indices.analyze, return segmentation results

es.indices.analyze(body={'analyzer': "ik_max_word", "text": "皮特和茱丽当选“年度模范情侣”Brad Pitt and Angelina Jolie"})

es.indices.delete, delete the index Elasticsearch

print(es.indices.delete(index='py4'))
print(es.indices.delete(index='w3'))    # {'acknowledged': True}

es.indices.put_alias, create an alias for one or more indexes, query multiple indexes, they can use this alias

print(es.indices.put_alias(index='py4', name='py4_alias'))  # 为单个索引创建别名
print(es.indices.put_alias(index=['py3', 'py2'], name='py23_alias'))  # 为多个索引创建同一个别名，联查用

es.indices.delete_alias, delete one or more aliases

print(es.indices.delete_alias(index='alias1'))
print(es.indices.delete_alias(index=['alias1, alias2']))

es.indices.get_mapping, retrieval index or index / type mapping definition

print(es.indices.get_mapping(index='py4'))

es.indices.get_settings, retrieving one or more (or all) of the set index

print(es.indices.get_settings(index='py4'))

es.indices.get, allows retrieval of information about one or more indexes

print(es.indices.get(index='py2'))    # 查询指定索引是否存在
print(es.indices.get(index=['py2', 'py3']))

es.indices.get_alias, retrieving one or more aliases

print(es.indices.get_alias(index='py2'))
print(es.indices.get_alias(index=['py2', 'py3']))

mapping information es.indices.get_field_mapping, retrieve specific fields

print(es.indices.get_field_mapping(fields='url', index='py4', doc_type='doc'))
print(es.indices.get_field_mapping(fields=['url', 'title'], index='py4', doc_type='doc'))

Other

# es.indices.delete_alias，删除特定别名。
# es.indices.exists，返回一个布尔值，指示给定的索引是否存在。
# es.indices.exists_type，检查索引/索引中是否存在类型/类型。
# es.indices.flus，明确的刷新一个或多个索引。
# es.indices.get_field_mapping，检索特定字段的映射。
# es.indices.get_template，按名称检索索引模板。
# es.indices.open，打开一个封闭的索引以使其可用于搜索。
# es.indices.close，关闭索引以从群集中删除它的开销。封闭索引被阻  止进行读/写操作。
# es.indices.clear_cache，清除与一个或多个索引关联的所有缓存或特定缓存。
# es.indices.put_alias，为特定索引/索引创建别名。
# es.indices.get_uprade，监控一个或多个索引的升级程度。
# es.indices.put_mapping，注册特定类型的特定映射定义。
# es.indices.put_settings，实时更改特定索引级别设置。
# es.indices.put_template，创建一个索引模板，该模板将自动应用于创建的新索引。
# es.indices.rollove，当现有索引被认为太大或太旧时，翻转索引API将别名转移到新索引。API接受单个别名和条件列表。别名必须仅指向单个索引。如果索引满足指定条件，则创建新索引并切换别名以指向新别名。
# es.indices.segments，提供构建Lucene索引（分片级别）的低级别段信息

6.4 Cluster cluster-related

es.cluster.get_settigns, get cluster setup

print(es.cluster.get_settings())

es.cluster.health, get a very simple state about the health of the cluster

print(es.cluster.health())

es.cluster.state, obtain comprehensive information on the state of the entire cluster

print(es.cluster.state())

Information es.cluster.stats, returns the current node cluster

print(es.cluster.stats())

6.5 Node node-related

es.nodes.info, return information nodes in the cluster

print(es.nodes.info())  # 返回所节点
print(es.nodes.info(node_id='node1'))   # 指定一个节点
print(es.nodes.info(node_id=['node1', 'node2']))   # 指定多个节点列表

es.nodes.stats, obtain statistical information nodes in the cluster

print(es.nodes.stats())
print(es.nodes.stats(node_id='node1'))
print(es.nodes.stats(node_id=['node1', 'node2']))

es.nodes.hot_threads, get thread information specified node

print(es.nodes.hot_threads(node_id='node1'))
print(es.nodes.hot_threads(node_id=['node1', 'node2']))

es.nodes.usage, acquisition function nodes in the cluster using the information

print(es.nodes.usage())
print(es.nodes.usage(node_id='node1'))
print(es.nodes.usage(node_id=['node1', 'node2']))

6.6 Cat one kind of query

es.cat.aliases, alias information return

#name要返回的以逗号分隔的别名列表。
#formatAccept标头的简短版本，例如json，yaml
print(es.cat.aliases(name='py23_alias'))
print(es.cat.aliases(name='py23_alias', format='json'))

es.cat.allocation, returns slice usage

print(es.cat.allocation())
print(es.cat.allocation(node_id=['node1']))
print(es.cat.allocation(node_id=['node1', 'node2'], format='json'))

es.cat.count, Count provides quick access to the document count for the entire cluster or individual indexes

print(es.cat.count())  # 集群内的文档总数
print(es.cat.count(index='py3'))  # 指定索引文档总数
print(es.cat.count(index=['py3', 'py2'], format='json'))  # 返回两个索引文档和

es.cat.fielddata, displays information about the currently loaded fielddata based on each node. For some data query efficiency, will be placed in memory, fielddata to control what data should be placed in memory, and this es.cat.fielddatais what is now query data in memory, the size of the data and other information

print(es.cat.fielddata())
print(es.cat.fielddata(format='json', bytes='b'))
#bytes显示字节值的单位，有效选项为：'b'，'k'，'kb'，'m'，'mb'，'g'，'gb'，'t'，'tb' ，'p'，'pb'
#formatAccept标头的简短版本，例如json，yaml

es.cat.health, from the cluster healthfilter out simple health information inside the cluster

print(es.cat.health())
print(es.cat.health(format='json'))

es.cat.help, returns es.cathelp information

print(es.cat.help())

es.cat.indices, the index return information

print(es.cat.indices())
print(es.cat.indices(index='py3'))
print(es.cat.indices(index='py3', format='json'))

es.cat.master, returns the IP cluster master node, node name and IP binding

print(es.cat.master())
print(es.cat.master(format='json'))

es.cat.nodeattrs, return a custom attribute nodes

print(es.cat.nodeattrs())
print(es.cat.nodeattrs(format='json'))

es.cat.nodes, return topological node, the information is often useful when viewing the entire cluster, especially in large clusters. I have a number of qualified node

print(es.cat.nodes())
print(es.cat.nodes(format='json'))

es.cat.plugins, returns to the plug information node

print(es.cat.plugins())
print(es.cat.plugins(format='json'))

es.cat.segments, returns information about each index of Lucene

print(es.cat.segments())
print(es.cat.segments(index='py3'))
print(es.cat.segments(index='py3', format='json'))

es.cat.shards, which returns information pieces which node comprises

print(es.cat.shards())
print(es.cat.shards(index='py3'))
print(es.cat.shards(index='py3', format='json'))

es.cat.thread_pool, get information about the thread pool

print(es.cat.thread_pool())

6.7 Snapshot Snapshot related

# es.snapshot.create，在存储库中创建快照。
    repository 存储库名称。
    snapshot快照名称。
    body快照定义。
# es.snapshot.delete，从存储库中删除快照。
# es.snapshot.create_repository。注册共享文件系统存储库。
# es.snapshot.delete_repository，删除共享文件系统存储库。
# es.snapshot.get，检索有关快照的信息。
# es.snapshot.get_repository，返回有关已注册存储库的信息。
# es.snapshot.restore，恢复快照。
# es.snapshot.status，返回有关所有当前运行快照的信息。通过指定存储库名称，可以将结果限制为特定存储库。
# es.snapshot.verify_repository，返回成功验证存储库的节点列表，如果验证过程失败，则返回错误消息

6.8 Task-related tasks

# es.tasks.get，检索特定任务的信息。
# es.tasks.cancel，取消任务。
# es.tasks.list，任务列表