数据写入elasticsearch中

 

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