python elasticsearch环境搭建详解

Python  /  管理员 发布于 5年前   353

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:utf8from __future__ import unicode_literalsimport osimport timeimport configfrom datetime import datetimefrom elasticsearch import Elasticsearchfrom elasticsearch.helpers import bulkclass 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，感谢大家的阅读和的支持。