python elasticsearch环境搭建详解

发布时间：2020-10-22 15:20:10 作者：古月月月胡
来源：脚本之家阅读：171

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="ftech460",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("ftech460","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("ftech460","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亿速云的支持。

python elasticsearch环境搭建详解

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