Goroutines vs asyncio: memory under stress in Go and Python

The question#

Go and Python approach concurrency with different models. Goroutines distribute work through the Go runtime; asyncio coordinates coroutines inside an event loop. The useful question is not which language “wins,” but how operational cost changes as concurrency grows.

The design compares two equivalent services, not two complete ecosystems.

The test scenario#

Each service would expose an HTTP endpoint that waits for a simulated database response and returns the same JSON. Load would increase from 100 to 1,000 concurrent connections while recording resident memory, throughput and p95 latency.

wrk -t8 -c1000 -d60s http://service:8080/resource

The machine, duration, payload, CPU limits and runtime version must remain fixed for the comparison to be useful.

What to measure#

• Memory at rest and during the peak.
• p50, p95 and p99 latency.
• Sustained throughput, not just an instantaneous maximum.
• Errors and timeouts during saturation.

Reading the result#

A memory difference does not automatically make one implementation the better choice. Team speed, available libraries and production observability also matter.

The result should inform a concrete decision: capacity per instance, infrastructure cost or available headroom before scaling.

Conclusion#

Go and Python may behave differently under load, but the decision also depends on the ecosystem and team. Running the benchmark with a published environment would reduce uncertainty; this guide defines how to do that without presenting results that have not yet been measured.