Redis methods and application examples for implementing distributed crawlers

With the popularization of the Internet and the increasing scale of data, the application of crawler technology is becoming more and more widespread. However, as the amount of data continues to expand, single-machine crawlers are no longer able to meet actual needs. Distributed crawler technology emerged as the times require, among which Redis is a very excellent distributed crawler tool. This article will introduce the method and application examples of Redis to implement distributed crawlers.

1. The principle of Redis distributed crawler

Redis is a non-relational database. In distributed crawlers, it is used as a data cache and queue, and is an important means to achieve distribution. Task allocation is performed by implementing a first-in-first-out (FIFO) queue.

In Redis, you can use the List type to implement a queue. Redis provides LPUSH and RPUSH commands to insert data into the head and tail of the queue. At the same time, LPOP and RPOP commands are also provided to pop the data in the queue and delete the popped data.

Through Redis, task distribution of multiple crawler processes can be achieved to improve crawler efficiency and speed.

2. Specific implementation of Redis distributed crawler

1. Use Redis to store URLs to be crawled

When crawling web page data, you must first Determine the URL queue to be crawled. When using Redis, we can add the URL to be crawled to the end of the queue through RPUSH. At the same time, the LPOP command is used to pop the queue from the head and obtain the URL to be crawled.

The specific code is as follows:

import redis

# 初始化Redis数据库
client = redis.Redis(host='localhost', port=6379, db=0)

# 将待抓取的URL加入到队列末尾
client.rpush('url_queue', 'http://www.example.com')

# 从队列头部弹出URL
url = client.lpop('url_queue')

2. Crawler process and task allocation

In a distributed crawler, tasks need to be assigned to multiple crawler processes. In order to achieve distributed task distribution, multiple queues can be created in Redis, and each crawler process obtains tasks from different queues. When allocating tasks, the Round-robin algorithm is used to achieve even distribution of tasks.

The specific code is as follows:

import redis

# 初始化Redis数据库
client = redis.Redis(host='localhost', port=6379, db=0)

# 定义爬虫进程个数
num_spiders = 3

# 将任务分配给爬虫进程
for i in range(num_spiders):
    url = client.lpop('url_queue_%d' % i)
    if url:
        # 启动爬虫进程进行任务处理
        process_url(url)

3. Storage of crawler data

In a distributed crawler, the crawler data needs to be stored in the same database. In order to achieve data aggregation and analysis. At this point, Redis's Hash data type can play an important role. Use Redis's Hash array to store the number and content of the crawler data to facilitate subsequent data processing and statistics.

The specific code is as follows:

import redis

# 初始化Redis数据库
client = redis.Redis(host='localhost', port=6379, db=0)

# 存储爬虫数据
def save_data(data):
    client.hset('data', data['id'], json.dumps(data))

3. Application examples of Redis distributed crawler

Redis distributed crawler technology is widely used, including data mining, search engines, finance analysis and other fields. The following uses the Redis-based distributed crawler framework Scrapy-Redis as an example to introduce the implementation of distributed crawlers.

1. Install Scrapy-Redis

Scrapy-Redis is a distributed crawler tool developed based on the Scrapy framework, which can realize data sharing and task distribution among multiple crawler processes. When doing distributed crawling, Scrapy-Redis needs to be installed.

pip install scrapy-redis

2. Configuring Scrapy-Redis and Redis

When crawling Scrapy-Redis, you need to configure Scrapy-Redis and Redis. The settings of Scrapy-Redis are similar to the Scrapy framework and can be set in the settings.py file. Scrapy-Redis needs to use Redis to implement task queues and data sharing, so it is necessary to configure the relevant information of the Redis database.

# Scrapy-Redis配置
SCHEDULER = "scrapy_redis.scheduler.Scheduler"  # 使用Redis调度（Scheduler）
DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"  # 使用Redis去重（Dupefilter）

# Redis数据库配置
REDIS_URL = 'redis://user:password@localhost:6379'

3. Writing Scrapy-Redis crawler code

When performing Scrapy-Redis crawler, the main code implementation is similar to the Scrapy framework. The only difference is that you need to use the RedisSpider class provided by Scrapy-Redis to replace the original Spider class to implement operations and task distribution on the Redis database.

import scrapy
from scrapy_redis.spiders import RedisSpider


class MySpider(RedisSpider):
    """Spider that reads urls from redis queue (myspider:start_urls)."""
    name = 'myspider_redis'
    redis_key = 'myspider:start_urls'

    def parse(self, response):
        """This function parses a sample response. Some contracts are mingled
        with this docstring.

        @url http://www.example.com/
        @returns items 1
        @returns requests 1
        """
        item = MyItem()
        item['title'] = response.xpath('//title/text()').extract_first()
        yield item

4. Summary

Implementing a distributed crawler can not only improve the efficiency and speed of the crawler, but also avoid the risk of single point failure. As a very excellent data caching and queuing tool, Redis can play a very good role in distributed crawlers. Through the methods and application examples of Redis implementing distributed crawlers introduced above, you can better understand the implementation of distributed crawlers and the advantages of Redis.

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