How Distributed Scraping Helped Me Scale a Project by 10x

When I first started scraping data, like many web scrapers, I began with simple scripts running on my local machine. These small-scale projects served their purpose—scraping a few hundred URLs, collecting basic data for personal projects or early freelance jobs. But as my work evolved, so did the scale of the projects I took on. Soon enough, I found myself needing to scrape thousands, and eventually tens of thousands, of URLs—across multiple websites—daily.

At this point, I realized that my current approach wouldn’t cut it anymore. The bottlenecks were obvious: long delays due to rate limits, scrapers failing due to IP bans, and inconsistent performance from my single-machine setup. I needed a way to scale up quickly and efficiently. Enter distributed scraping.

In this article, I’ll share how I used distributed scraping to take a project that was struggling to handle 1,000 pages a day and scale it to handle over 10,000 pages daily, with more reliability and speed.

The Project: Scraping E-commerce Data at Scale

The client I was working with was in the e-commerce space. Their goal was to gather product data from a wide range of online retailers to build competitive market insights. The scope of the project included scraping prices, product descriptions, availability, and customer reviews from over 200 e-commerce websites, each containing thousands of product pages. The challenge? Scraping 10,000+ pages daily across multiple sites without running into IP bans, rate limiting, or scraping failure due to downtime.

Initially, I approached the project by scaling my scraper scripts on a single machine, using multi-threading. While this worked for a small subset of the websites, it was clear that a single machine simply didn’t have the capacity to handle this amount of data efficiently. I needed more machines and a better system to manage the distribution of tasks.

Why Distributed Scraping?

Distributed scraping is the process of splitting the scraping workload across multiple machines or instances (typically cloud-based) to increase performance, reliability, and scalability. By spreading the load, you can not only scrape faster but also reduce the chances of hitting rate limits or getting IP blocked.

There were three main reasons I opted for a distributed approach:

1. Concurrency: Scraping from a single machine, even using multiple threads, meant I could only hit so many pages per minute. Distributing across multiple instances would allow me to run scrapers in parallel, increasing my throughput significantly.
2. IP Rotation: Many websites monitor incoming traffic for high request rates from the same IP. Distributing the workload across multiple machines, each with different IP addresses, would allow me to sidestep this issue.
3. Reliability and Redundancy: In distributed systems, failure on one machine doesn’t mean the entire scraping operation stops. This added reliability was crucial in ensuring the project ran smoothly over the long term.

The Setup: Distributed Scraping Architecture

To scale this project, I needed to overhaul my infrastructure. Here's how I set up distributed scraping using cloud-based instances, proxies, and task queues.

1. Cloud-Based Instances

I decided to host multiple scraping instances in the cloud using DigitalOcean droplets and AWS EC2 instances. Each instance ran a Dockerized version of the scraping code, allowing me to easily replicate the setup across multiple machines. Using cloud-based instances meant that I could spin up new scrapers on-demand and scale horizontally when needed.

By deploying scrapers on these machines, I could distribute the workload across multiple regions. This not only increased speed but also reduced the risk of IP blocking since each machine had a different public IP.

2. Task Queues

To manage the distribution of scraping tasks, I implemented a task queue system using Celery (for Python) and Redis as the message broker. Each scraping job (a URL or set of URLs) was added to the queue, and the distributed scrapers would pick up these tasks asynchronously.

The task queue approach allowed for automatic load balancing, ensuring that each machine handled only a fraction of the workload at any given time. If one machine went down, the task would be redistributed to other machines, making the system more robust.

3. Rotating Proxies

Even with distributed instances, I still had to deal with the potential for IP blocking if any machine made too many requests too quickly. To avoid this, I integrated rotating proxies from ScraperAPI. Each scraper instance would route its requests through a different proxy on every request, further masking the origin of the traffic.

This setup significantly reduced the likelihood of rate limiting or IP bans, ensuring that I could hit each website more frequently without causing issues.

4. Rate Limiting and Throttling

While proxies helped avoid IP blocks, I still had to respect the rate limits of the websites I was scraping. To manage this, I implemented request throttling—introducing small delays between requests to avoid overwhelming the servers.

With distributed scraping, rate limiting became easier to manage. Each machine could make requests at a slow pace, but because the workload was spread across multiple machines, the overall throughput remained high. In one day, we were able to scrape nearly 12,000 product pages without triggering any rate limits.

Scaling Results: From 1,000 to 10,000+ Pages Daily

After implementing the distributed scraping system, the results were immediate and impressive. Here’s what changed:

• Throughput: Initially, the project was scraping around 1,000 pages per day. With distributed scraping, we scaled that number to 10,000+ pages daily. This was a tenfold increase in speed and efficiency.
• Reliability: Distributed scraping added redundancy to the system, meaning that if one machine failed or went down, the rest of the scrapers could continue without disruption. Downtime and failures were reduced significantly.
• Scalability: The architecture was designed to be easily scalable. As the project grew, we could spin up more instances and increase the number of proxies, allowing us to handle even larger datasets without sacrificing speed.
• Cost Efficiency: Cloud instances and proxies added some cost to the project, but the increase in efficiency outweighed the expenses. We were able to complete the project faster, which saved the client money in the long run.

Key Lessons Learned

Distributed scraping was a game-changer for this project, but it also came with its own challenges. Here are some of the key lessons I learned along the way:

1. Monitoring Is Crucial: With distributed systems, keeping track of what’s happening across multiple machines is critical. I set up logging and monitoring systems to track scraper performance, task completion, and failure rates. This allowed me to quickly identify and fix issues.
2. Error Handling Pays Off: Even with a robust system, errors are inevitable. By implementing retries and error handling early on, I avoided major issues later. When a scraper failed to load a page, it automatically retried the task with a different proxy, reducing failure rates.
3. Don’t Overload Sites: Scaling up scraping doesn’t mean bombarding websites with requests. Rate limiting, throttling, and respecting robots.txt files helped maintain ethical scraping practices, ensuring the project ran smoothly without legal or ethical concerns.

Conclusion

Scaling web scraping projects requires more than just throwing more machines at the problem. Distributed scraping, when done correctly, can dramatically increase the speed, reliability, and efficiency of your scrapers. By leveraging task queues, rotating proxies, cloud-based instances, and rate limiting, I was able to scale a project from scraping 1,000 pages daily to over 10,000 pages, all while maintaining reliability and ethical practices.

For anyone looking to take their web scraping projects to the next level, distributed scraping offers a scalable, cost-effective solution that can handle even the largest datasets.