python中elasticsearch_dsl模块用法详解
elasticsearch-dsl是基于elasticsearch-py封装实现的，提供了更简便的操作elasticsearch的方法。
安装：
install elasticsearch_ds
连接elasticsearch
from elasticsearch_dsl import connections, Search es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20) print(es)
还可以通过alias给连接设置别名，后续可以通过别名来引用该连接，默认别名为default。
from elasticsearch_dsl import connections, Search # 方式一：连接es es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20) print(es) # 方式二：连接es connections.create_connection(alias="my_new_connection", hosts=["127.0.0.1:9200"], timeout=20)
elasticsearch_dsl.Search
search对象代表整个搜索请求，包括：queries、filters、aggregations、sort、pagination、additional parameters、associated client。
API被设置为可链接的即和用.连续操作。search对象是不可变的，除了聚合，对对象的所有更改都将导致创建包含该更改的浅表副本。
当初始化Search对象时，传递elasticsearch客户端作为using的参数
示例代码1：
from elasticsearch_dsl import connections, Search
# 方式一：连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
# 方式二：连接es
connections.create_connection(alias="my_new_connection", hosts=["127.0.0.1:9200"], timeout=20)
# 不使用别名使用
res = Search(using=es).index("test_index").query()
# print(res)
for data in res:
print(data.to_dict())
print("*" * 100)
# 使用别名后这样使用
res2 = Search(using="my_new_connection").index('test_index').query()
# print(e)
for data in res2:
print(data.to_dict())
运行结果
示例代码2：
from elasticsearch_dsl import connections, Search
# 方式一：连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
# 不使用别名使用
res = Search(using=es).index("test_index").query()
# print(res)
for data in res:
print(data.to_dict())
print("*" * 100)
# 书写方式一：按条件查询数据
res2 = Search(using=es).index("test_index").query("match", name="张三") # 查询时注意分词器的使用
for data in res2:
print(data.to_dict())
print("*" * 100)
# 书写方式二：按条件查询数据
res3 = Search(using=es).index("test_index").query({"match": {"name": "张三"}})
for data in res3:
print(data.to_dict())
运行结果：
在上述执行execute方法将请求发送给elasticsearch：
response = res.execute()
不需要执行execute()方法，迭代后可以通过to_dict()方法将Search对象序列化为一个dict对象，这样可以方便调试。
query方法
查询，参数可以是Q对象，也可以是query模块中的一些类，还可以是自已写上如何查询。
示例代码1：
from elasticsearch_dsl import connections, Search, Q import time # 方式一：连接es es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20) # print(es) res = Search(using=es, index="test_index").query().query() # 当调用.query()方法多次时，内部会使用&操作符 print(res.to_dict())
运行结果：
filter方法
在过滤上下文中添加查询，可以使用filter()函数来使之变的简单。
示例代码1：
from elasticsearch_dsl import connections, Search, Q
# 方式一：连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
# res = Search(using=es).index("test_index").filter({"match": {"name": "北"}})
# res = Search(using=es).index("test_index").filter("terms", tags=["name", "id"])
res = Search(using=es).index("test_index").query("bool", filter=[
Q("terms", tags=["name", "id"])]) # 上面代码在背后会产生一个bool查询，并将指定的条件查询放入到filter分支
print(res)
for data in res:
print(data.to_dict())
示例代码2：
from elasticsearch_dsl import connections, Search, Q
import time
# 方式一：连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
# 范围查询
# res = Search(using=es, index="test_index").filter("range", timestamp={"gte": 0, "lt": time.time()}).query({"match": {"name": "北"}})
res = Search(using=es, index="test_index").filter("range", id={"gte": 1, "lte": 4}).query({"match": {"name": "北"}})
print(res)
for data in res:
print(data.to_dict())
# 普通过滤
res2 = Search(using=es, index="test_index").filter("terms", id=["2", "4"]).execute()
print(res2)
for data in res2:
print(data.to_dict())
运行结果：
index方法
指定索引
usring方法
指定哪个elasticsearch
elasticsearch_dsl.query
该库为所有的Elasticsearch查询类型都提供了类。以关键字参数传递所有的参数，最终会把参数序列化后传递给Elasticsearch，这意味着在原始查询和它对应的dsl之间有这一个清理的一对一的映射。
示例代码：
from elasticsearch_dsl import connections, Search, Q
from elasticsearch_dsl.query import MultiMatch, Match
# 方式一：连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
# 相对与{"multi_match": {"query": "ha", "fields": ["firstname", "lastname"]}}
m1 = MultiMatch(query="Ha", fields=["firstname", "lastname"])
res = Search(using=es, index="test_index").query(m1)
print(res)
for data in res:
print(data.to_dict())
# 相当于{"match": {"firstname": {"query": "Hughes"}}}
m2 = Match(firstname={"query": "Hughes"})
res = Search(using=es, index="test_index").query(m2)
print(res)
for data in res:
print(data.to_dict())
elasticsearch_dsl.Q
使用快捷方式Q通过命名参数或者原始dict类型数据来构建一个查询实例。Q的格式一般是Q("查询类型", 字段="xxx")或Q("查询类型", query="xxx", fields=["字段1", "字段2"])
示例代码：
from elasticsearch_dsl import connections, Search, Q
from elasticsearch_dsl.query import MultiMatch, Match
# 方式一：连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
# q = Q("match", city="Summerfield")
q = Q("multi_match", query="Summerfield", fields=["city", "firstname"])
res = Search(using=es, index="test_index").query(q)
print(res)
for data in res:
print(data.to_dict())
查询对象可以通过逻辑运算符组合起来：
Q("match", title="python") | Q("match", title="django")
# {"bool": {"should": [...]}}
Q("match", title="python") & Q("match", title="django")
# {"bool": {"must": [...]}}
~Q("match", title="python")
# {"bool": {"must_not": [...]}}
示例代码：
from elasticsearch_dsl import connections, Search, Q
# 方式一：连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
# q = Q("multi_match", query="123.244.101.255", fields=["clientip", "timestamp"])
q = Q('match', name='张') | Q("match", name="北")
res = Search(using=es, index="test_index").query(q)
# print(res)
for data in res:
print(data.to_dict(), data.name)
print("*" * 100)
q = Q('match', name='张') & Q("match", name="北")
res = Search(using=es, index="test_index").query(q)
# print(res)
for data in res:
print(data.to_dict(), data.name)
print("*" * 100)
q = ~Q('match', name='张')
res = Search(using=es, index="test_index").query(q)
# print(res)
for data in res:
print(data.to_dict(), data.name)
运行结果：
嵌套类型
有时候需要引用一个在其他字段中的字段，例如多字段（title.keyword）或者在一个json文档中的address.city。为了方便，Q允许你使用双下划线‘__’代替关键词参数中的‘.’
示例代码：
from elasticsearch_dsl import connections, Search, Q
# 方式一：连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
# res = Search(using=es, index="test_index").query("match", address__city="北京")
res = Search(using=es, index="test_index").filter("term", address__city="北京")
# print(res)
for data in res:
print(data.to_dict(), data.name)
查询
示例代码：
from elasticsearch_dsl import Search
from elasticsearch import Elasticsearch
# 连接es
es = Elasticsearch(hosts=["127.0.0.1:9200"], sniffer_timeout=60, timeout=30)
# 获取es中所有的索引
# 返回类型为字典，只返回索引名
index_name = es.cat.indices(format="json", h="index")
print(index_name)
# 查询多个索引
es_multi_index = Search(using=es, index=["personal_info_5000000", "grade", "test_index"])
print(es_multi_index.execute())
# 查询一个索引
es_one_index = Search(using=es, index="test_index")
print(es_one_index.execute())
print("*" * 100)
# 条件查询1
es_search1 = es_one_index.filter("range", id={"gte": 1, "lt": 5})
print(es_search1.execute())
# 条件查询2
es_search2 = es_one_index.filter("term", name="张")
print(es_search2.execute())
print("*" * 100)
# 结果转换为字典
es_search3 = es_search2.to_dict()
print(es_search3)
es_search4 = es_search2.execute().to_dict()
print(es_search4)
运行结果：
案例分析
创建索引：
from elasticsearch_dsl import Search
from elasticsearch import Elasticsearch
# 连接es
es = Elasticsearch(hosts=["127.0.0.1:9200"], sniffer_timeout=60, timeout=30)
body = {
"mappings": {
"properties": {
"account_number": {
"type": "integer"
},
"balance": {
"type": "integer"
},
"firstname": {
"type": "text"
},
"lastname": {
"type": "text"
},
"age": {
"type": "integer"
},
"gender": {
"type": "keyword"
},
"address": {
"type": "text"
},
"employer": {
"type": "text"
},
"email": {
"type": "text"
},
"province": {
"type": "text"
},
"state": {
"type": "text"
}
}
}
}
# 创建 index
es.indices.create(index="account_info", body=body)
查看索引：
使用kibana批量生成数据：
POST account_info/_bulk
{"index": {"_index":"account_info"}}
{"account_number":1,"balance":20,"firstname":"三","lastname":"张","age":25,"gender":"男","address":"北京朝阳","employer":"AAA","email":"[email protected]","province":"北京","state":"正常"}
{"index": {"_index":"account_info"}}
{"account_number":2,"balance":70,"firstname":"二","lastname":"张","age":26,"gender":"男","address":"北京海淀","employer":"AAA","email":"[email protected]","province":"北京","state":"正常"}
{"index": {"_index":"account_info"}}
{"account_number":3,"balance":80,"firstname":"四","lastname":"张","age":27,"gender":"女","address":"辽宁朝阳","employer":"BBB","email":"[email protected]","province":"辽宁","state":"正常"}
{"index": {"_index":"account_info"}}
{"account_number":4,"balance":60,"firstname":"五","lastname":"张","age":28,"gender":"男","address":"山东青岛","employer":"AAA","email":"[email protected]","province":"山东","state":"正常"}
{"index": {"_index":"account_info"}}
{"account_number":5,"balance":40,"firstname":"六","lastname":"张","age":29,"gender":"女","address":"山东济南","employer":"AAA","email":"[email protected]","province":"山东","state":"正常"}
{"index": {"_index":"account_info"}}
{"account_number":6,"balance":50,"firstname":"七","lastname":"张","age":30,"gender":"男","address":"河北唐山","employer":"BBB","email":"[email protected]","province":"河北","state":"正常"}
{"index": {"_index":"account_info"}}
{"account_number":7,"balance":30,"firstname":"一","lastname":"张","age":31,"gender":"女","address":"河北石家庄","employer":"AAA","email":"[email protected]","province":"河北","state":"正常"}
查看生成的数据：
根据条件查询：
1.查询balance在40~70的信息
from elasticsearch_dsl import connections, Search, Q
# 连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
s = Search(using=es, index="account_info")
# 查询balance在40~70的信息
q = Q("range", balance={"gte": 40, "lte": 70})
res = s.query(q)
for data in res:
print(data.to_dict())
print("共查到%d条数据" % res.count())
2.查询balance在40~70的男性信息
from elasticsearch_dsl import connections, Search, Q
# 连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
s = Search(using=es, index="account_info")
# 查询balance在40~70的信息
q1 = Q("range", balance={"gte": 40, "lte": 70})
# 男性
q2 = Q("term", gender="男")
# and
q = q1 & q2
res = s.query(q)
for data in res:
print(data.to_dict())
print("共查到%d条数据" % res.count())
3.省份为北京、25或30岁的男性信息
from elasticsearch_dsl import connections, Search, Q
# 连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
s = Search(using=es, index="account_info")
# 方式一：
# 省份为北京
q1 = Q("match", province="北京")
# 25或30岁的男性信息
q2 = Q("bool", must=[Q("terms", age=[25, 30]), Q("term", gender="男")])
# and
q = q1 & q2
res = s.query(q)
for data in res:
print(data.to_dict())
print("共查到%d条数据" % res.count())
print("*" * 100)
# 方式二
# 省份为北京
q1 = Q("match", province="北京")
# 25或30岁的信息
# q2 = Q("bool", must=[Q("terms", age=[25, 30]), Q("term", gender="男")])
q2 = Q("term", age=25) | Q("term", age=30)
# 男性
q3 = Q("term", gender="男")
res = s.query(q1).query(q2).query(q3) # 多次query就是& ==> and 操作
for data in res:
print(data.to_dict())
print("共查到%d条数据" % res.count())
4.地址中有“山”字，年龄不在25~28岁的女性信息
from elasticsearch_dsl import connections, Search, Q
# 连接es
es = connections.create_connection(hosts=["127.0.0.1:9200"], timeout=20)
# print(es)
s = Search(using=es, index="account_info")
# 地址中有“山”字且为女性
q1 = Q("match", address="山") & Q("match", gender="女")
# 年龄在25~28岁
q2 = ~Q("range", age={"gte": 25, "lte": 28})
# 使用filter过滤
# query和filter的前后关系都行
res = s.filter(q2).query(q1)
for data in res:
print(data.to_dict())
print("共查到%d条数据" % res.count())
谢谢支持~