python elasticsearch环境搭建详解
作者：古月月月胡
在本篇文章里小编给大家整理的是关于python elasticsearch环境搭建的相关知识点内容，有需要的朋友们可以参考下。
windows下载zip
linux下载tar
下载地址：https://www.elastic.co/downloads/elasticsearch
解压后运行：bin/elasticsearch (or bin\elasticsearch.bat on Windows)
检查是否成功：访问 http://localhost:9200
linux下不能以root用户运行，
普通用户运行报错：
java.nio.file.AccessDeniedException
原因：当前用户没有执行权限
解决方法： chown linux用户名 elasticsearch安装目录 -R
例如：chown ealsticsearch /data/wwwroot/elasticsearch-6.2.4 -R
PS：其他Java软件报.AccessDeniedException错误也可以同样方式解决，给 执行用户相应的目录权限即可
2|0代码实例
如下的代码实现类似链家网小区搜索功能。
从文件读取小区及地址信息写入es,然后通过小区所在城市code及搜索关键字 匹配到对应小区。
代码主要包含三部分内容：
1.创建索引
2.用bulk将批量数据存储到es
3.数据搜索
注意：
代码的es版本交低2.xx版本，高版本在创建的索引数据类型有所不同
#coding:utf8
from __future__ import unicode_literals
import os
import time
import config
from datetime import datetime
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk
class ElasticSearch():
def __init__(self, index_name,index_type,ip ="127.0.0.1"):
'''
:param index_name: 索引名称
:param index_type: 索引类型
'''
self.index_name =index_name
self.index_type = index_type
# 无用户名密码状态
#self.es = Elasticsearch([ip])
#用户名密码状态
self.es = Elasticsearch([ip],http_auth=('elastic', 'password'),port=9200)
def create_index(self,index_name="ftech360",index_type="community"):
'''
创建索引,创建索引名称为ott，类型为ott_type的索引
:param ex: Elasticsearch对象
:return:
'''
#创建映射
_index_mappings = {
"mappings": {
self.index_type: {
"properties": {
"city_code": {
"type": "string",
# "index": "not_analyzed"
},
"name": {
"type": "string",
# "index": "not_analyzed"
},
"address": {
"type": "string",
# "index": "not_analyzed"
}
}
}
}
}
if self.es.indices.exists(index=self.index_name) is True:
self.es.indices.delete(index=self.index_name)
res = self.es.indices.create(index=self.index_name, body=_index_mappings)
print res
def build_data_dict(self):
name_dict = {}
with open(os.path.join(config.datamining_dir,'data_output','house_community.dat')) as f:
for line in f:
line_list = line.decode('utf-8').split('\t')
community_code = line_list[6]
name = line_list[7]
city_code = line_list[0]
name_dict[community_code] = (name,city_code)
address_dict = {}
with open(os.path.join(config.datamining_dir,'data_output','house_community_detail.dat')) as f:
for line in f:
line_list = line.decode('utf-8').split('\t')
community_code = line_list[6]
address = line_list[10]
address_dict[community_code] = address
return name_dict,address_dict
def bulk_index_data(self,name_dict,address_dict):
'''
用bulk将批量数据存储到es
:return:
'''
list_data = []
for community_code, data in name_dict.items():
tmp = {}
tmp['code'] = community_code
tmp['name'] = data[0]
tmp['city_code'] = data[1]
if community_code in address_dict:
tmp['address'] = address_dict[community_code]
else:
tmp['address'] = ''
list_data.append(tmp)
ACTIONS = []
for line in list_data:
action = {
"_index": self.index_name,
"_type": self.index_type,
"_id": line['code'], #_id 小区code
"_source": {
"city_code": line['city_code'],
"name": line['name'],
"address": line['address']
}
}
ACTIONS.append(action)
# 批量处理
success, _ = bulk(self.es, ACTIONS, index=self.index_name, raise_on_error=True)
#单条写入 单条写入速度很慢
#self.es.index(index=self.index_name,doc_type="doc_type_test",body = action)
print('Performed %d actions' % success)
def delete_index_data(self,id):
'''
删除索引中的一条
:param id:
:return:
'''
res = self.es.delete(index=self.index_name, doc_type=self.index_type, id=id)
print res
def get_data_id(self,id):
res = self.es.get(index=self.index_name, doc_type=self.index_type,id=id)
# # 输出查询到的结果
print res['_source']['city_code'], res['_id'], res['_source']['name'], res['_source']['address']
def get_data_by_body(self, name, city_code):
# doc = {'query': {'match_all': {}}}
doc = {
"query": {
"bool":{
"filter":{
"term":{
"city_code": city_code
}
},
"must":{
"multi_match": {
"query": name,
"type":"phrase_prefix",
"fields": ['name^3', 'address'],
"slop":1,
}
}
}
}
}
_searched = self.es.search(index=self.index_name, doc_type=self.index_type, body=doc)
data = _searched['hits']['hits']
return data
if __name__=='__main__':
#数据插入es
obj = ElasticSearch("ftech360","community")
obj.create_index()
name_dict, address_dict = obj.build_data_dict()
obj.bulk_index_data(name_dict,address_dict)
#从es读取数据
obj2 = ElasticSearch("ftech360","community")
obj2.get_data_by_body(u'保利','510100')
以上就是全部知识点内容，感谢大家的阅读和对脚本之家的支持。