Concurrent requests let your web scraper send multiple HTTP requests at the same time instead of waiting for each one to finish before starting the next. This simple change can turn a scraping job that takes hours into one that finishes in minutes.
What are concurrent requests?
When you scrape websites sequentially, your scraper sends one request, waits for the response, processes the data, then moves to the next URL. During all that waiting, your scraper sits idle. Concurrent requests flip this approach by keeping multiple requests active at once. While one request travels across the internet, others are already sending, receiving, or being processed.
How concurrent requests work
There are three main ways to handle concurrent requests in web scraping:
Why concurrent requests matter for web scraping
Network latency is the biggest time sink in web scraping. Each request might take 200 to 500 milliseconds just traveling to the server and back. If you need to scrape 10,000 pages sequentially at 300ms each, that is over 50 minutes of waiting. With 10 concurrent requests, you could finish the same job in about 5 minutes.
Concurrent requests also make better use of your available bandwidth and computing power. Instead of your CPU sitting idle during network waits, it stays busy parsing and processing data from completed requests.
Risks and limitations
More is not always better with concurrent requests. Pushing too many requests at once creates real problems:
Best practices for concurrent requests
Start conservative and scale up. Begin with 5 to 10 concurrent requests per domain and monitor your success rate. If responses stay fast and errors stay low, you can gradually increase.
Set hard caps on concurrency both globally and per domain. Even if you can handle 100 total concurrent requests, limit each individual website to a reasonable number like 5 to 10.
Build in backoff logic. When you start getting 429 (too many requests) or 5xx errors, slow down automatically. Exponential backoff with random delays prevents your scraper from hammering a struggling server.
Track your metrics. Watch your success rate, average response time, and error distribution. These numbers tell you when to push harder or pull back.
Concurrent requests vs rate limiting
Concurrency and rate limiting are related but different. Concurrency controls how many requests are active at any moment. Rate limiting controls how many requests you send per second or minute. You can have high concurrency with low rates (many slow connections) or low concurrency with high rates (fast bursts). Smart scrapers use both controls together to stay efficient without getting blocked.
How Browse AI handles concurrent requests
Managing concurrent requests manually requires significant technical work and constant monitoring. Browse AI handles this complexity for you with its no-code web scraping platform. It automatically manages request timing, implements smart delays, and scales concurrency based on what each website can handle. You get fast, reliable data extraction without worrying about getting blocked or overwhelming target servers.
