A distributed crawler actually refers to a crawler that uses multiple computers to collect data from the Internet in a distributed manner. It can decompose large-scale tasks into several small-scale tasks, which can be processed in parallel by multiple computers, which greatly improves efficiency and speed.
Distributed crawlers have many advantages: to solve the problem of low efficiency of stand-alone crawlers, distributed crawlers can assign tasks to multiple nodes for parallel processing, greatly improving efficiency and speed. It can save bandwidth and memory resources, because multiple nodes can process data at the same time, which can avoid the pressure brought by a single node crawler. It can be highly scalable and flexible. If you need to add a large number of nodes to process tasks, you can also modify the deployment strategy and program logic.
Python has many excellent distributed multi-topic web crawler frameworks, here are a few of them:
1. Scrapy: Scrapy is one of the most popular crawler frameworks in Python, which supports distributed crawling and multi-theme crawling. Scrapy uses the Twisted asynchronous network framework, which can efficiently handle a large number of requests and responses.
2. PySpider: PySpider is a lightweight distributed crawler framework that supports multi-theme crawling and distributed crawling. PySpider uses the Tornado asynchronous network framework, which can efficiently handle a large number of requests and responses.
3. Scrapyd: Scrapyd is a distributed deployment tool for Scrapy, which can deploy Scrapy crawlers to multiple nodes for distributed crawling. Scrapyd provides a web interface that can easily manage and monitor the running status of distributed crawlers.
4. Celery: Celery is a distributed task queue framework that can be used for distributed crawling. Celery can distribute crawler tasks to multiple nodes for parallel processing, thereby improving crawling efficiency.
5. Apache Nutch: Apache Nutch is an open source distributed web crawler framework that supports multi-theme crawling and distributed crawling. Nutch uses the Hadoop distributed computing framework, which can handle large-scale crawling tasks.
The above are the commonly used distributed multi-theme web crawler frameworks in Python, and you can choose the appropriate framework according to your specific needs.
Python distributed web crawler implementation method:
1. Define the task queue
In a distributed crawler system, URLs to be processed need to be put into a task queue. Task allocation and scheduling can be implemented using message queues such as Redis or RabbitMQ.
2. Use multiple worker nodes
In a distributed crawler, the task queue will be acquired and assigned by multiple worker nodes at the same time. Each working node is responsible for obtaining a certain number of URLs from the task queue, and crawling the corresponding page content through the crawler program.
3. Add deduplication and persistent data storage functions
In order to avoid repeated acquisition of the same page as much as possible, it is necessary to deduplicate URLs. A cache library or database such as Redis can be used to store visited URLs and related information.
In addition, the collected data needs to be stored persistently. Relational or non-relational databases such as MySQL and MongoDB can be used for storage.
4. Use multithreading or coroutines to improve efficiency
Multithreading or coroutines can be used in each worker node to improve crawling efficiency and reduce crawling time.
5. Formulate specifications to control crawling depth and frequency
Distributed crawlers often access thousands of pages, so some rules need to be set, such as how many URLs each node can only access per second, and what constraints need to be observed when accessing.
6. Use a distributed crawler scheduler
In order to better manage and control the distributed crawler system, a dedicated distributed crawler scheduler can be used. The scheduler usually integrates components such as task queues, work nodes, and data storage, which can provide a more convenient and efficient crawler construction and management method.
To sum up, the construction of a distributed multi-topic web crawler may have certain complexity, but through reasonable design and implementation, it can quickly collect a large amount of useful information in a short period of time, and can adapt to the needs of various application scenarios.
The following is a simple Python distributed crawler code example:
1. Parent process
import multiprocessing
import queue
from crawlers import Crawler
from links_extractor import extract_links
def save_page(page_data):
# TODO: 将页面数据存入数据库或文件等
pass
def main():
start_url = 'http://www.example.com'
visited_urls = set()
num_processes = 4 # 这里使用 4 个进程进行爬取
# 创建任务队列和结果队列，用于任务和爬取结果的交互
task_queue = multiprocessing.Queue()
result_queue = multiprocessing.Queue()
# 向任务队列中添加初始 URL
task_queue.put(start_url)
# 创建爬虫进程
crawler_processes = [
multiprocessing.Process(
target=Crawler,
args=(task_queue, result_queue, visited_urls)
)
for _ in range(num_processes)
]
# 开始运行爬虫进程
# 添加代理ip（http://jshk.com.cn）
for process in crawler_processes:
process.start()
while True:
try:
# 不断从结果队列中获取爬取到的页面信息并保存到本地
page_data = result_queue.get(block=False)
save_page(page_data)
# 提取出页面中的所有链接，放入到任务队列中进行下一轮爬取
urls = extract_links(page_data['content'])
for url in urls:
if url not in visited_urls:
task_queue.put(url)
except queue.Empty:
# 如果任务队列为空且所有爬虫进程也已经处理完任务意味着爬虫任务已经完成
if all(not process.is_alive() for process in crawler_processes):
break
# 等待所有爬虫进程结束后再退出主进程
for process in crawler_processes:
process.join()
if __name__ == '__main__':
main()
2. The crawler process
import requests
def Crawler(task_queue, result_queue, visited_urls):
while True:
try:
url = task_queue.get(block=False)
if url not in visited_urls:
# 如果URL还没有被访问，则发送HTTP GET请求获取相应页面的内容
response = requests.get(url)
if response.status_code == 200:
page_data = {
'url': url,
'content': response.content
}
# 将成功访问到的页面内容放入到结果队列中
result_queue.put(page_data)
# 标记URL为已访问
visited_urls.add(url)
except queue.Empty:
# 如果队列为空，则表示当前进程已经完成了它的任务，可以结束当前进程
break
In the above code example, the parent process is responsible for putting the initial URL into the task queue, continuously obtaining the crawled page information from the result queue and extracting the links, and then putting these links into the task queue to wait for the next round of crawling . The crawler process obtains the URL to be crawled next in the task queue, and then sends an HTTP GET request to the URL. If the request successfully obtains the corresponding page content, it puts the page content into the result queue and marks the URL as already been visited. In this process, the parent process and the crawler process realize the interaction of tasks and results through queues, and each crawler process runs and can handle multiple tasks, thereby realizing the distributed crawler function.