Building a Scalable Web Scraper: From Basic to Distributed Architecture

Web scraping is a critical component of data acquisition for many online businesses, but building a system that can handle large volumes efficiently requires careful architectural planning.

Starting with a simple web scraper on a local machine might seem sufficient initially, but limitations quickly become apparent. In a basic implementation, a scraper might only process around 1.5 listings per minute—meaning 10,000 listings would take approximately 15,000 minutes (over 10 days) to complete.

The Basic Web Scraper Architecture

A fundamental web scraping workflow typically involves:

1. Scraping metadata from target websites
2. Inserting this data into a listings database
3. Sending content for translation if needed
4. Storing translated content in a separate database table

For image processing, a parallel workflow includes:

1. Downloading images from scraped listings
2. Uploading these images to cloud storage (like S3)
3. Storing image metadata and location keys in an images table

Scaling with Message Queues

To dramatically improve throughput, implementing a distributed architecture with message queues transforms the process. This approach offers several key advantages:

1. Parallelism

With queues, you can spawn multiple worker processes that simultaneously handle different listings. Instead of processing sequentially, multiple workers pull from a main queue, allowing for significantly higher throughput. While you could theoretically spawn a worker for each listing, it’s better to incrementally scale to avoid overwhelming target sites with API calls.

2. Rate Limiting

Many API providers impose rate limits on requests. For example, some banking APIs might restrict calls to 30 per minute. Queue-based architectures allow for implementing controlled processing rates, such as processing only 10 transactions every 20 seconds with exponential backoff and retry mechanisms.

3. Failure Tracking

Queues provide visibility into processing status, making it easier to handle failures. If your process fails due to exhausted API credits or other issues, listings remain in the processing queue. This allows you to pause, address the problem (such as refilling credits), and resume processing without losing track of what needs to be completed.

Implementation Results

After implementing a queue-based distributed scraper with multiple workers:

• Two parallel listing workers process approximately 31 listings per minute (about 15 listings per worker)
• Ten image workers can process 30-35 images per minute
• System resource usage remains manageable with CPU usage at 10-20% and memory consumption between 1-2 GB
• Network usage becomes the primary resource constraint due to multiple simultaneous API calls

This distributed approach provides a 20x improvement in processing speed compared to the basic implementation, making large-scale web scraping projects feasible and efficient.

When building your own web scraper, consider starting with a simple architecture to understand the workflow, then incrementally add queue-based distribution to scale as your data needs grow.