What is Concurrency?

How Concurrency Works

A sequential client sends one request, waits for the response, and only then sends the next — spending nearly all its time waiting on the network. A concurrent client keeps many requests in flight at once using threads, worker processes, or asynchronous I/O, so the waiting overlaps instead of accumulating. Throughput follows a simple relationship: at an average request time of 500 ms, 100 concurrent requests sustain roughly 200 completions per second, where a single connection would manage two.

Every layer imposes its own ceiling. The operating system caps open sockets, HTTP client libraries cap connection-pool sizes, proxies cap simultaneous connections per user or port, and target servers cap what they will serve one visitor. When offered load exceeds any of these, requests queue, response times stretch, and timeouts begin to fire — the classic signature of over-concurrency.

Concurrency is not the same as parallelism: a single-threaded asynchronous scraper can hold thousands of requests in flight, because the work is waiting on networks rather than computing.

Why It Matters for Proxies and Scraping

Concurrency is the standard answer to latency: individual proxied requests may take hundreds of milliseconds, so parallelism of work, not per-request speed, determines how fast a crawl finishes. But concentrated concurrency from a single IP address is also one of the most conspicuous automation signals there is — rate limiting and bot detection count requests per IP per unit of time, and a hundred simultaneous connections from one address look nothing like a human visitor.

The fix is distribution. Spreading concurrent requests across many exit IPs keeps each address's rate modest while total throughput stays high — this is the core reason rotating pools exist, and why ProxyOmega's Budget Unlimited draws from a 1.5M+ residential pool. Scale concurrency against your success rate rather than raw throughput: rising errors mean the target, the pool, or your own stack has hit a ceiling.

Practical Notes and Common Misconceptions

More concurrency is not automatically more speed. Past the bottleneck, added requests only queue: throughput flattens while response times and timeout rates climb, and effective throughput can actually fall. Ramp up gradually, watch p95 response times and error rates, and back off at the knee of the curve.

Keep per-target politeness separate from global scale: a scraper can safely run thousands of requests in flight across many domains while holding each individual domain to a modest handful at a time.