Python对elasticsearch的CRUD

　　　　　　　　　　　　　　　　　　　　　　　

一.官网提供的Elasticsearch的Python接口包

　　1.github地址：https://github.com/elastic/elasticsearch-dsl-py

　　2.安装：pip install elasticsearch-dsl

　　3.有很多api，使用可参考github中的文档

二.定义写入es的Pipeline：

　　1.生成索引，type及映射：

　　　　有可能会报IllegalOperation异常，访问本地9200端口查看es版本，然后将python中的elasticsearch和elasticsearch-dsl改成相近版本即可

# _*_ encoding:utf-8 _*_
__author__ = 'LYQ'
__date__ = '2018/10/29 11:02'
#新版本把DocType改为Docment
from datetime import datetime
from elasticsearch_dsl import DocType,Date, Nested, Boolean, \
    analyzer, Completion, Keyword, Text, Integer
from elasticsearch_dsl.connections import connections

# es连接到本地，可以连接到多台服务器
connections.create_connection(hosts=["localhost"])

class ArticleType(DocType):
    "定义es映射"
    # 以ik解析
    title = Text(analyzer="ik_max_word")
    create_date = Date()
    # 不分析
    url = Keyword()
    url_object_id = Keyword()
    front_image_url = Keyword()
    front_image_path = Keyword()
    praise_nums = Integer()
    fav_nums = Integer()
    comment_nums = Integer()
    tags = Text(analyzer="ik_max_word")
    content = Text(analyzer="ik_max_word")

    class Meta:
        #定义索引和type
        index = "jobbole"
        doc_type = "artitle"

if __name__ == "__main__":
#调用init()方法便能生成相应所应和映射
    ArticleType.init()

　　2.创建相应item：

#导入定义的es映射
from models.es import ArticleType
from w3lib.html import remove_tags

class ElasticsearchPipeline(object):
    """
    数据写入elasticsearch，定义pipeline，记得配置进setting
    """
class ElasticsearchPipeline(object):
    """
    数据写入elasticsearch
    """
class ElasticsearchPipeline(object):
    """
    数据写入elasticsearch
    """

    def process_item(self, item, spider):
        #将定义的elasticsearch映射实列化
        articletype=ArticleType()
        articletype.title= item["title"]
        articletype.create_date = item["create_date"]
        articletype.url = item["url"]
        articletype.front_image_url = item["front_image_url"]
        if "front_image_path" in item:
            articletype.front_image_path = item["front_image_path"]
        articletype.praise_nums = item["praise_nums"]
        articletype.fav_nums = item["fav_nums"]
        articletype.comment_nums = item["comment_nums"]
        articletype.tags = item["tags"]
        articletype.content = remove_tags(item["content"])
        articletype.meta.id = item["url_object_id"]

        articletype.save()
        return item

查看9100端口，数据插入成功

　　

class JobboleArticleSpider(scrapy.Item):
    ......

    def save_to_es(self):
          "在item中分别定义存入es，方便不同的字段的保存"
        articletype = ArticleType()
        articletype.title = self["title"]
        articletype.create_date = self["create_date"]
        articletype.url = self["url"]
        articletype.front_image_url = self["front_image_url"]
        if "front_image_path" in self:
            articletype.front_image_path = self["front_image_path"]
        articletype.praise_nums = self["praise_nums"]
        articletype.fav_nums = self["fav_nums"]
        articletype.comment_nums = self["comment_nums"]
        articletype.tags = self["tags"]
        articletype.content = remove_tags(self["content"])
        articletype.meta.id = self["url_object_id"]

        articletype.save()    

class ElasticsearchPipeline(object):
    """
    数据写入elasticsearch
    """

    def process_item(self, item, spider):
        # 将定义的elasticsearch映射实列化
       #调用item中的方法
        item.save_to_es()
        return item

三.搜索建议：

　　实质调用anylyer接口如下：

GET _analyze
{
  "analyzer": "ik_max_word",
  "text"    : "Python网络基础学习"
}

　　es文件中：

from elasticsearch_dsl.analysis import CustomAnalyzer as _CustomAnalyzer

esc=connections.create_connection(ArticleType._doc_type.using)

class Customanalyzer(_CustomAnalyzer):
    """自定义analyser"""

    def get_analysis_definition(self):
        # 重写该函数返回空字典
        return {}

ik_analyser = Customanalyzer("ik_max_word", filter=["lowercase"])

class ArticleType(DocType):
    "定义es映射"
    suggest = Completion(analyzer=ik_analyser)
   ......

生成该字段的信息

　　　2.item文件：

......
from models.es import esc
def get_suggest(index, info_tuple):
    """根据字符串和权重生成搜索建议数组"""
    used_words = set()
    suggests = []
    for text, weight in info_tuple:
        if text:
            # 调用es得analyer接口分析字符串
            # 返回解析后得分词数据
            words = esc.indices.analyze(index=index, analyer="ik_max_word", params={"filter": ["lowercase"]}, body=text)
            # 生成式过滤掉长度为1的
            anylyzed_words = set([r["token"] for r in words if len(r) > 1])
            # 去重
            new_words = anylyzed_words - used_words
        else:
            new_words = set()
        if new_words:
            suggests.append({"input": list(new_words), "weight": weight})
　　return suggests
class JobboleArticleSpider(scrapy.Item):
   ......
    def save_to_es(self):
        articletype = ArticleType()
        .......# 生成搜索建议字段，以及字符串和权重
        articletype.suggest = get_suggest(ArticleType._doc_type.index,((articletype.title,1),(articletype.tags,7)) )

        articletype.save()