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Python中Scrapy+adbapi提高数据库写入效率实现

让我为您详细讲解“Python中Scrapy+adbapi提高数据库写入效率实现”的完整攻略。

让我为您详细讲解“Python中Scrapy+adbapi提高数据库写入效率实现”的完整攻略。

1. Scrapy简介

Scrapy是一个开源的Python网络爬虫框架,它可以轻松地从网页中提取所需要的数据。Scrapy自带的Item Pipeline功能可以方便地将爬取到的数据存储到各种类型的数据库中。

2. adbapi介绍

adbapi是Twisted框架中一个用于连接数据库的API。使用adbapi可以较为方便地在异步的Twisted环境下连接和操作数据库。

3. Scrapy如何使用adbapi

Scrapy使用adbapi存储数据的步骤如下:

  1. 安装Twisted和adbapi。
pip install twisted
pip install psycopg2

这里以PostgreSQL数据库为例,如果您使用的是其他类型的数据库,需要相应地安装对应的驱动。

  1. 在Scrapy项目的settings.py配置文件中添加数据库配置信息。
DATABASE = {
    'drivername': 'postgresql',
    'host': 'localhost',
    'port': '5432',
    'username': 'postgres',
    'password': 'password',
    'database': 'scrapy_db'
}
  1. 在Scrapy项目中创建一个pipeline,在pipeline中使用adbapi连接数据库,并将数据存储到数据库中。
from scrapy.exceptions import DropItem
from scrapy import log
from twisted.enterprise import adbapi
import psycopg2

class PostgresPipeline(object):
    def __init__(self, dbpool):
        self.dbpool = dbpool

    @classmethod
    def from_settings(cls, settings):
        dbargs = dict(
            host=settings['PG_HOST'],
            db=settings['PG_DBNAME'],
            user=settings['PG_USER'],
            password=settings['PG_PASSWORD']
        )
        dbpool = adbapi.ConnectionPool('psycopg2', **dbargs)
        return cls(dbpool)

    def process_item(self, item, spider):
        # run db query in thread pool
        query = self.dbpool.runInteraction(self.do_insert, item)
        query.addErrback(self.handle_error)

        return item

    def handle_error(self, e):
        log.err(e)

    def do_insert(self, cursor, item):
        # execute insert statement
        insert_sql = """
                    INSERT INTO mytable (name,age,gender)
                    VALUES (%s, %s, %s)
                """
        cursor.execute(insert_sql, (item['name'], item['age'], item['gender']))

在上面的代码中,我们使用PostgreSQL作为数据库,在pipeline的from_settings方法中使用adbapi创建一个连接池,然后在process_item方法中,通过runInteraction方法将数据插入到数据库中。

4. 示例说明

示例一

假设我们需要从一个论坛中爬取用户信息,存储到PostgreSQL数据库中。我们可以先定义一个Item:

# items.py
import scrapy

class UserItem(scrapy.Item):
    name = scrapy.Field()
    age = scrapy.Field()
    gender = scrapy.Field()

然后定义一个Spider,在Spider中提取用户信息,并通过pipeline将数据存储到数据库中:

# spiders/users.py
import scrapy
from myproject.items import UserItem

class UsersSpider(scrapy.Spider):
    name = "users"
    allowed_domains = ["example.com"]
    start_urls = [
        "http://example.com/users/1",
        "http://example.com/users/2",
        "http://example.com/users/3",
    ]

    def parse(self, response):
        user = UserItem()
        user['name'] = response.css('div.name::text').get()
        user['age'] = response.css('div.age::text').get()
        user['gender'] = response.css('div.gender::text').get()

        yield user

最后,在pipeline中使用adbapi将数据存储到数据库中:

# pipelines.py
from scrapy.exceptions import DropItem
from scrapy import log
from twisted.enterprise import adbapi
import psycopg2

class PostgresPipeline(object):
    def __init__(self, dbpool):
        self.dbpool = dbpool

    @classmethod
    def from_settings(cls, settings):
        dbargs = dict(
            host=settings['PG_HOST'],
            db=settings['PG_DBNAME'],
            user=settings['PG_USER'],
            password=settings['PG_PASSWORD']
        )
        dbpool = adbapi.ConnectionPool('psycopg2', **dbargs)
        return cls(dbpool)

    def process_item(self, item, spider):
        # run db query in thread pool
        query = self.dbpool.runInteraction(self.do_insert, item)
        query.addErrback(self.handle_error)

        return item

    def handle_error(self, e):
        log.err(e)

    def do_insert(self, cursor, item):
        # execute insert statement
        insert_sql = """
                    INSERT INTO mytable (name,age,gender)
                    VALUES (%s, %s, %s)
                """
        cursor.execute(insert_sql, (item['name'], item['age'], item['gender']))

示例二

假设我们需要从一个网站中爬取商品信息,并存储到MySQL数据库中。我们可以先定义一个Item:

# items.py
import scrapy

class ProductItem(scrapy.Item):
    title = scrapy.Field()
    price = scrapy.Field()
    description = scrapy.Field()

然后定义一个Spider,在Spider中提取商品信息,并通过pipeline将数据存储到数据库中:

# spiders/products.py
import scrapy
from myproject.items import ProductItem

class ProductsSpider(scrapy.Spider):
    name = "products"
    allowed_domains = ["example.com"]
    start_urls = [
        "http://example.com/products/1",
        "http://example.com/products/2",
        "http://example.com/products/3",
    ]

    def parse(self, response):
        product = ProductItem()
        product['title'] = response.css('h1.title::text').get()
        product['price'] = response.css('span.price::text').get()
        product['description'] = response.css('div.description::text').get()

        yield product

最后,在pipeline中使用adbapi将数据存储到数据库中:

# pipelines.py
from scrapy.exceptions import DropItem
from scrapy import log
from twisted.enterprise import adbapi
import pymysql

class MySQLPipeline(object):
    def __init__(self, dbpool):
        self.dbpool = dbpool

    @classmethod
    def from_settings(cls, settings):
        dbargs = dict(
            host=settings['MYSQL_HOST'],
            db=settings['MYSQL_DBNAME'],
            user=settings['MYSQL_USER'],
            password=settings['MYSQL_PASSWORD'],
            charset='utf8mb4',
            cursorclass=pymysql.cursors.DictCursor
        )
        dbpool = adbapi.ConnectionPool('pymysql', **dbargs)
        return cls(dbpool)

    def process_item(self, item, spider):
        # run db query in thread pool
        query = self.dbpool.runInteraction(self.do_insert, item)
        query.addErrback(self.handle_error)

        return item

    def handle_error(self, e):
        log.err(e)

    def do_insert(self, cursor, item):
        # execute insert statement
        insert_sql = """
                    INSERT INTO mytable (title,price,description)
                    VALUES (%s, %s, %s)
                """
        cursor.execute(insert_sql, (item['title'], item['price'], item['description']))

总结

通过以上的操作,我们成功地使用Scrapy和adbapi将数据存储到数据库中,实现了快速高效的数据库写入。Scrapy+adbapi的组合可以有效地提高数据的存储速度,减少数据库连接的开销,适用于需要高效处理大量数据存储的场景。

本文标题为:Python中Scrapy+adbapi提高数据库写入效率实现

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