为你的爬虫提提速？

Posted 2021-01-13 jclian91

项目介绍

??本文将展示如何利用Pyhton中的异步模块来提高爬虫的效率。
??我们需要爬取的目标为：融360网站上的理财产品信息（https://www.rong360.com/licai-bank/list/p1），页面如下：

我们需要爬取86394条理财产品的信息，每页10条，也就是8640个页面。
??在文章Python爬虫（16）利用Scrapy爬取银行理财产品信息（共12多万条）中，我们使用爬虫框架Scrapy实现了该爬虫，爬取了127130条数据，并存入MongoDB,整个过程耗时3小时。按道理来说，使用Scrapy实现爬虫是较好的选择，但是在速度上，是否能有所提升呢？本文将展示如何利用Pyhton中的异步模块（aiohtpp和asyncio）来提高爬虫的效率。

爬虫项目

??我们的爬虫分两步走：

1. 爬取融360网页上的理财产品信息并存入csv文件；
2. 读取csv文件并存入至mysql数据库。

??首先，我们爬取融360网页上的理财产品信息并存入csv文件，我们使用aiohttp和asyncio来加速爬虫，完整的Python代码如下：

import re
import time
import aiohttp
import asyncio
import pandas as pd
import logging

# 设置日志格式
logging.basicConfig(level = logging.INFO, format=‘%(asctime)s - %(levelname)s: %(message)s‘)
logger = logging.getLogger(__name__)


df = pd.DataFrame(columns=[‘name‘, ‘bank‘, ‘currency‘, ‘startDate‘,                           ‘endDate‘, ‘period‘, ‘proType‘, ‘profit‘, ‘amount‘])

# 异步HTTP请求
async def fetch(sem, session, url):
    async with sem:
        headers = {‘User-Agent‘: ‘Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (Khtml, like Gecko) Chrome/67.0.3396.87 Safari/537.36‘}
        async with session.get(url, headers=headers) as response:
            return await response.text()

# 解析网页
async def parser(html):
    # 利用正则表达式解析网页
    tbody = re.findall(r"<tbody>[sS]*?</tbody>", html)[0]
    trs = re.findall(r"<tr [sS]*?</tr>", tbody)
    for tr in trs:
        tds = re.findall(r"<td[sS]*?</td>", tr)
        name,bank = re.findall(r‘title="(.+?)"‘, ‘‘.join(tds))
        name = name.replace(‘&amp;‘, ‘‘).replace(‘quot;‘, ‘‘)
        currency, startDate, endDate, amount = re.findall(r‘<td>(.+?)</td>‘, ‘‘.join(tds))
        period = ‘‘.join(re.findall(r‘<td class="td7">(.+?)</td>‘, tds[5]))
        proType = ‘‘.join(re.findall(r‘<td class="td7">(.+?)</td>‘, tds[6]))
        profit = ‘‘.join(re.findall(r‘<td class="td8">(.+?)</td>‘, tds[7]))
        df.loc[df.shape[0] + 1] = [name, bank, currency, startDate, endDate,                                    period, proType, profit, amount]

    logger.info(str(df.shape[0])+‘	‘+name)

# 处理网页
async def download(sem, url):
    async with aiohttp.ClientSession() as session:
        try:
            html = await fetch(sem, session, url)
            await parser(html)
        except Exception as err:
            print(err)

# 全部网页
urls = ["https://www.rong360.com/licai-bank/list/p%d"%i for i in range(1, 8641)]

# 统计该爬虫的消耗时间
print(‘*‘ * 50)
t3 = time.time()

# 利用asyncio模块进行异步IO处理
loop = asyncio.get_event_loop()
sem=asyncio.Semaphore(100)
tasks = [asyncio.ensure_future(download(sem, url)) for url in urls]
tasks = asyncio.gather(*tasks)
loop.run_until_complete(tasks)

df.to_csv(‘E://rong360.csv‘)

t4 = time.time()
print(‘总共耗时：%s‘ % (t4 - t3))
print(‘*‘ * 50)

输出的结果如下（中间的输出已省略，以......代替）：

**************************************************
2018-10-17 13:33:50,717 - INFO: 10  金百合第245期
2018-10-17 13:33:50,749 - INFO: 20  金荷恒升2018年第26期
......
2018-10-17 14:03:34,906 - INFO: 86381   翠竹同益1M22期FGAB15015A
2018-10-17 14:03:35,257 - INFO: 86391   润鑫月月盈2号
总共耗时：1787.4312353134155
**************************************************

可以看到，在这个爬虫中，我们爬取了86391条数据，耗时1787.4秒，不到30分钟。虽然数据比预期的少了3条，但这点损失不算什么。来看一眼csv文件中的数据：

??OK，离我们的目标还差一步，将这个csv文件存入至MySQL，具体的操作方法可参考文章：Python之使用Pandas库实现MySQL数据库的读写：https://www.jianshu.com/p/238a13995b2b 。完整的Python代码如下：

# -*- coding: utf-8 -*-

# 导入必要模块
import pandas as pd
from sqlalchemy import create_engine

# 初始化数据库连接，使用pymysql模块
engine = create_engine(‘mysql+pymysql://root:******@localhost:33061/test‘, echo=True)

print("Read CSV file...")
# 读取本地CSV文件
df = pd.read_csv("E://rong360.csv", sep=‘,‘, encoding=‘gb18030‘)

# 将新建的DataFrame储存为MySQL中的数据表，不储存index列
df.to_sql(‘rong360‘,
          con=engine,
          index= False,
          index_label=‘name‘
          )

print("Write to MySQL successfully!")

输出结果如下（耗时十几秒）：

Read CSV file...
2018-10-17 15:07:02,447 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE ‘sql_mode‘
2018-10-17 15:07:02,447 INFO sqlalchemy.engine.base.Engine {}
2018-10-17 15:07:02,452 INFO sqlalchemy.engine.base.Engine SELECT DATABASE()
2018-10-17 15:07:02,452 INFO sqlalchemy.engine.base.Engine {}
2018-10-17 15:07:02,454 INFO sqlalchemy.engine.base.Engine show collation where `Charset` = ‘utf8mb4‘ and `Collation` = ‘utf8mb4_bin‘
2018-10-17 15:07:02,454 INFO sqlalchemy.engine.base.Engine {}
2018-10-17 15:07:02,455 INFO sqlalchemy.engine.base.Engine SELECT CAST(‘test plain returns‘ AS CHAR(60)) AS anon_1
2018-10-17 15:07:02,456 INFO sqlalchemy.engine.base.Engine {}
2018-10-17 15:07:02,456 INFO sqlalchemy.engine.base.Engine SELECT CAST(‘test unicode returns‘ AS CHAR(60)) AS anon_1
2018-10-17 15:07:02,456 INFO sqlalchemy.engine.base.Engine {}
2018-10-17 15:07:02,457 INFO sqlalchemy.engine.base.Engine SELECT CAST(‘test collated returns‘ AS CHAR CHARACTER SET utf8mb4) COLLATE utf8mb4_bin AS anon_1
2018-10-17 15:07:02,457 INFO sqlalchemy.engine.base.Engine {}
2018-10-17 15:07:02,458 INFO sqlalchemy.engine.base.Engine DESCRIBE `rong360`
2018-10-17 15:07:02,458 INFO sqlalchemy.engine.base.Engine {}
2018-10-17 15:07:02,459 INFO sqlalchemy.engine.base.Engine ROLLBACK
2018-10-17 15:07:02,462 INFO sqlalchemy.engine.base.Engine 
CREATE TABLE rong360 (
    `Unnamed: 0` BIGINT, 
    name TEXT, 
    bank TEXT, 
    currency TEXT, 
    `startDate` TEXT, 
    `endDate` TEXT, 
    enduration TEXT, 
    `proType` TEXT, 
    profit TEXT, 
    amount TEXT
)


2018-10-17 15:07:02,462 INFO sqlalchemy.engine.base.Engine {}
2018-10-17 15:07:02,867 INFO sqlalchemy.engine.base.Engine COMMIT
2018-10-17 15:07:02,909 INFO sqlalchemy.engine.base.Engine BEGIN (implicit)
2018-10-17 15:07:03,973 INFO sqlalchemy.engine.base.Engine INSERT INTO rong360 (`Unnamed: 0`, name, bank, currency, `startDate`, `endDate`, enduration, `proType`, profit, amount) VALUES (%(Unnamed: 0)s, %(name)s, %(bank)s, %(currency)s, %(startDate)s, %(endDate)s, %(enduration)s, %(proType)s, %(profit)s, %(amount)s)
2018-10-17 15:07:03,974 INFO sqlalchemy.engine.base.Engine ({‘Unnamed: 0‘: 1, ‘name‘: ‘龙信20183773‘, ‘bank‘: ‘龙江银行‘, ‘currency‘: ‘人民币‘, ‘startDate‘: ‘2018-10-12‘, ‘endDate‘: ‘2018-10-14‘, ‘enduration‘: ‘99天‘, ‘proType‘: ‘不保本‘, ‘profit‘: ‘4.8%‘, ‘amount‘: ‘5万‘}, {‘Unnamed: 0‘: 2, ‘name‘: ‘福瀛家NDHLCS20180055B‘, ‘bank‘: ‘宁波东海银行‘, ‘currency‘: ‘人民币‘, ‘startDate‘: ‘2018-10-12‘, ‘endDate‘: ‘2018-10-17‘, ‘enduration‘: ‘179天‘, ‘proType‘: ‘保证收益‘, ‘profit‘: ‘4.8%‘, ‘amount‘: ‘5万‘}, {‘Unnamed: 0‘: 3, ‘name‘: ‘薪鑫乐2018年第6期‘, ‘bank‘: ‘无为农商行‘, ‘currency‘: ‘人民币‘, ‘startDate‘: ‘2018-10-12‘, ‘endDate‘: ‘2018-10-21‘, ‘enduration‘: ‘212天‘, ‘proType‘: ‘不保本‘, ‘profit‘: ‘4.8%‘, ‘amount‘: ‘5万‘}, {‘Unnamed: 0‘: 4, ‘name‘: ‘安鑫MTLC18165‘, ‘bank‘: ‘民泰商行‘, ‘currency‘: ‘人民币‘, ‘startDate‘: ‘2018-10-12‘, ‘endDate‘: ‘2018-10-15‘, ‘enduration‘: ‘49天‘, ‘proType‘: ‘不保本‘, ‘profit‘: ‘4.75%‘, ‘amount‘: ‘5万‘}, {‘Unnamed: 0‘: 5, ‘name‘: ‘农银私行·如意ADRY181115A‘, ‘bank‘: ‘农业银行‘, ‘currency‘: ‘人民币‘, ‘startDate‘: ‘2018-10-12‘, ‘endDate‘: ‘2018-10-16‘, ‘enduration‘: ‘90天‘, ‘proType‘: ‘不保本‘, ‘profit‘: ‘4.75%‘, ‘amount‘: ‘100万‘}, {‘Unnamed: 0‘: 6, ‘name‘: ‘稳健成长(2018)176期‘, ‘bank‘: ‘威海市商业银行‘, ‘currency‘: ‘人民币‘, ‘startDate‘: ‘2018-10-12‘, ‘endDate‘: ‘2018-10-15‘, ‘enduration‘: ‘91天‘, ‘proType‘: ‘不保本‘, ‘profit‘: ‘4.75%‘, ‘amount‘: ‘5万‘}, {‘Unnamed: 0‘: 7, ‘name‘: ‘季季红J18071‘, ‘bank‘: ‘温州银行‘, ‘currency‘: ‘人民币‘, ‘startDate‘: ‘2018-10-12‘, ‘endDate‘: ‘2018-10-16‘, ‘enduration‘: ‘96天‘, ‘proType‘: ‘不保本‘, ‘profit‘: ‘4.75%‘, ‘amount‘: ‘1万‘}, {‘Unnamed: 0‘: 8, ‘name‘: ‘私人银行客户84618042‘, ‘bank‘: ‘兴业银行‘, ‘currency‘: ‘人民币‘, ‘startDate‘: ‘2018-10-12‘, ‘endDate‘: ‘2018-10-17‘, ‘enduration‘: ‘99天‘, ‘proType‘: ‘不保本‘, ‘profit‘: ‘4.75%‘, ‘amount‘: ‘50万‘}  ... displaying 10 of 86391 total bound parameter sets ...  {‘Unnamed: 0‘: 86390, ‘name‘: ‘润鑫月月盈3号RX1M003‘, ‘bank‘: ‘珠海华润银行‘, ‘currency‘: ‘人民币‘, ‘startDate‘: ‘2015-06-24‘, ‘endDate‘: ‘2015-06-30‘, ‘enduration‘: ‘35天‘, ‘proType‘: ‘不保本‘, ‘profit‘: ‘4.5%‘, ‘amount‘: ‘5万‘}, {‘Unnamed: 0‘: 86391, ‘name‘: ‘润鑫月月盈2号‘, ‘bank‘: ‘珠海华润银行‘, ‘currency‘: ‘人民币‘, ‘startDate‘: ‘2015-06-17‘, ‘endDate‘: ‘2015-06-23‘, ‘enduration‘: ‘35天‘, ‘proType‘: ‘不保本‘, ‘profit‘: ‘4.4%‘, ‘amount‘: ‘5万‘})
2018-10-17 15:07:14,106 INFO sqlalchemy.engine.base.Engine COMMIT
Write to MySQL successfully!

??如果你还不放心，也许我们可以看一眼MySQL中的数据：

总结

??让我们来比较该爬虫与使用Scrapy的爬虫。使用Scrap用的爬虫爬取了127130条数据，耗时3小时，该爬虫爬取86391条数据，耗时半小时。如果是同样的数据量，那么Scrapy爬取86391条数据耗时约2小时，该爬虫仅用了Scrapy爬虫的四分之一的时间就出色地完成了任务。
??最后，让我们看看前十名的银行及理财产品数量（按理财产品数量从高到低排列），输入以下MySQL命令：

use test;
SELECT bank, count(*) as product_num 
FROM rong360
GROUP BY bank
ORDER BY product_num DESC
LIMIT 10;

输出结果如下：