python elasticsearch从创建索引到写入数据的全过程
python elasticsearch从创建索引到写入数据
创建索引
from elasticsearch import Elasticsearch es = Elasticsearch('192.168.1.1:9200') mappings = { "mappings": { "type_doc_test": { #type_doc_test为doc_type "properties": { "id": { "type": "long", "index": "false" }, "serial": { "type": "keyword", # keyword不会进行分词,text会分词 "index": "false" # 不建索引 }, #tags可以存json格式,访问tags.content "tags": { "type": "object", "properties": { "content": {"type": "keyword", "index": True}, "dominant_color_name": {"type": "keyword", "index": True}, "skill": {"type": "keyword", "index": True}, } }, "hasTag": { "type": "long", "index": True }, "status": { "type": "long", "index": True }, "createTime": { "type": "date", "format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis" }, "updateTime": { "type": "date", "format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis" } } } } } res = es.indices.create(index = 'index_test',body =mappings)
通过以上代码即可创建es索引
写入一条数据
写入数据需要根据 创建的es索引类型对应的数据结构写入:
from elasticsearch import Elasticsearch es = Elasticsearch('192.168.1.1:9200') action ={ "id": "1111122222", "serial":"版本", #以下tags.content是错误的写法 #"tags.content" :"标签2", #"tags.dominant_color_name": "域名的颜色黄色", #正确的写法如下: "tags":{"content":"标签3","dominant_color_name": "域名的颜色黄色"}, #按照字典的格式写入,如果用上面的那种写法,会直接写成一个tags.content字段。 #而不是在tags中content添加数据,这点需要注意 "tags.skill":"分类信息", "hasTag":"123", "status":"11", "createTime" :"2018-2-2", "updateTime":"2018-2-3", } es.index(index="index_test",doc_type="doc_type_test",body = action)
即可写入一条数据
错误的写入
正确的写入
写入多条数据
from elasticsearch import Elasticsearch from elasticsearch.helpers import bulk es = Elasticsearch('192.168.1.1:9200') ACTIONS = [] action1 ={ "_index": "indes_test", "_type": "doc_type_test", "_id":"bSlegGUBmJ2C8ZCSC1R1", "_source":{ "id": "1111122222", "serial":"版本", "tags.content" :"标签2", "tags.dominant_color_name": "域名的颜色黄色", "tags.skill":"分类信息", "hasTag":"123", "status":"11", "createTime" :"2018-2-2", "updateTime":"2018-2-3", } } action2 ={ "_index": "indes_test", "_type": "doc_type_test", "_id":"bSlegGUBmJ2C8ZCSC1R2", "_source":{ "id": "1111122222", "serial":"版本", "tags.content" :"标签2", "tags.dominant_color_name": "域名的颜色黄色", "tags.skill":"分类信息", "hasTag":"123", "status":"11", "createTime" :"2018-2-2", "updateTime":"2018-2-3", } } ACTIONS.append(action1) ACTIONS.append(action2) res,_ =bulk(es, ACTIONS, index="indes_test", raise_on_error=True) print(res)
这个方式是手动指定了id,如果把”_id”这个参数去掉即可自动生成id数据.
如下:
action2 ={ "_index": "indes_test", "_type": "doc_type_test", "_source":{ "id": "1111122222", "serial":"版本", "tags.content" :"标签2", "tags.dominant_color_name": "域名的颜色黄色", "tags.skill":"分类信息", "hasTag":"123", "status":"11", "createTime" :"2018-2-2", "updateTime":"2018-2-3", } }
删除一条数据
from elasticsearch import Elasticsearch es = Elasticsearch('192.168.1.1:9200') res = es.delete(index="index_test",doc_type="doc_type_test", id ="bSlegGUBmJ2C8ZCSC1R1") print(res)
直接替换id的即可删除所需的id
查询一条数据
from elasticsearch import Elasticsearch es = Elasticsearch('192.168.1.1:9200') res = es.get(index="index_test",doc_type="doc_type_test", id ="bSlegGUBmJ2C8ZCSC1R2") print(res)
直接替换id的即可查询所需的id
查询所有数据
from elasticsearch import Elasticsearch es = Elasticsearch('192.168.1.1:9200') res = es.search(index="index_test",doc_type="doc_type_test") print(res) print(res['hits']['hits'])
通过['hits']参数,可以解析出查询数据的详细内容
根据关键词查找
from elasticsearch import Elasticsearch es = Elasticsearch('192.168.1.1:9200') doc = { "query": { "match": { "_id": "aSlZgGUBmJ2C8ZCSPVRO" } } } res = es.search(index="index_test",doc_type="doc_type_test",body=doc) print(res)
总结
所述是小编给大家介绍的python elasticsearch从创建索引到写入数据的全过程,希望对大家有所帮助,如果大家有任何疑问欢迎给我留言,小编会及时回复大家的!
相关文章
Pandas.DataFrame删除指定行和列(drop)的实现
本文主要介绍了Pandas.DataFrame删除指定行和列(drop)的实现,文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要的朋友们下面随着小编来一起学习学习吧2023-02-02解决Django migrate No changes detected 不能创建表的问题
今天小编就为大家分享一篇解决Django migrate No changes detected 不能创建表的问题,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧2018-05-05
最新评论