EN
Distributed scraping involves running multiple instances of a web scraper across various machines, such as Digital Ocean droplets, to enhance the speed and efficiency of data collection. By utilizing a central queue managed by a Redis instance, this method allows for horizontal scaling, which can potentially improve performance compared to a standard Scrapy project.
In a recent experiment, a Scrapy project configured with 32 concurrent requests completed approximately 1,400 requests in just over 160 seconds. However, the goal was to determine if distributed scraping could outperform this setup. After pushing all URLs into the queue, the distributed approach managed to scrape the remaining URLs in about 176 seconds, which was slightly slower than the single-node setup.
When scraping from multiple nodes, the use of proxies becomes essential to avoid being blocked and to ensure smooth data retrieval. High-quality, ethically sourced residential proxies are particularly effective in bypassing anti-bot protections. These proxies can be easily integrated into the scraping process, allowing for seamless operation and access to data without expiration.
In the distributed scraping setup, 10 separate nodes were connected to the Redis queue, each scraping a new URL and returning the data. Despite the increase in nodes, the performance did not significantly surpass the single-node configuration. Theoretically, if each of the 1,400 URLs had a dedicated node, the scraping could be completed in mere seconds. However, this raises concerns about the potential for misuse, such as DDoS attacks.
After running the distributed scraping script, the total time taken to complete the scraping task was approximately 91 seconds with 19 nodes. This was a marked improvement over the initial 161 seconds but still highlighted the limitations of distributed scraping for simple tasks. The experiment served as a proof of concept, demonstrating that while distributed scraping can be faster, it may not always justify the added complexity and cost.
Several challenges arose during the distributed scraping process, including the need for custom tools to manage virtual private servers (VPS), distribute code, and handle node failures. Additionally, geographical latency issues were encountered when the Redis instance was located in the US while the servers were in the UK. The cost of running multiple servers and a Redis instance also adds to the complexity of the setup.
While distributed scraping can offer speed benefits, especially for resource-intensive tasks like browser automation, it may not be the best approach for simpler scraping projects. The network speed often becomes the limiting factor rather than the computational power of individual machines. For future projects, it may be more efficient to utilize a single machine with asynchronous capabilities rather than managing a complex distributed system.
Q: What is distributed scraping?
A: Distributed scraping involves running multiple instances of a web scraper across various machines to enhance the speed and efficiency of data collection.
Q: How does distributed scraping improve performance?
A: By utilizing a central queue managed by a Redis instance, distributed scraping allows for horizontal scaling, which can improve performance compared to a standard Scrapy project.
Q: What were the performance metrics of the Scrapy project?
A: A Scrapy project configured with 32 concurrent requests completed approximately 1,400 requests in just over 160 seconds.
Q: How did the distributed scraping approach perform compared to the single-node setup?
A: The distributed approach managed to scrape the remaining URLs in about 176 seconds, which was slightly slower than the single-node setup.
Q: Why are proxies important in distributed scraping?
A: Proxies are essential to avoid being blocked and to ensure smooth data retrieval when scraping from multiple nodes.
Q: What type of proxies are recommended for distributed scraping?
A: High-quality, ethically sourced residential proxies are particularly effective in bypassing anti-bot protections.
Q: What challenges were faced during the distributed scraping process?
A: Challenges included the need for custom tools to manage VPS, distribute code, handle node failures, and geographical latency issues.
Q: What was the total time taken to complete the scraping task in the distributed scraping experiment?
A: The total time taken was approximately 91 seconds with 19 nodes, which was an improvement over the initial 161 seconds.
Q: Is distributed scraping worth it for all projects?
A: While distributed scraping can offer speed benefits, it may not be the best approach for simpler scraping projects due to added complexity and cost.
Q: What is the conclusion regarding the use of distributed scraping?
A: For future projects, it may be more efficient to utilize a single machine with asynchronous capabilities rather than managing a complex distributed system.