In software development, we usually think of the code as being the riskiest part of a system. It gets our attention because we debug, test, optimize, and deploy code. But on the day my application crashed in production I learned differently: Infrastructure failure in software development has the greatest impact when it fails, often quietly.
What happened wasn’t a logic bug or missing edge case. The application didn’t fail because of the backend, it failed because of how my system infrastructure was configured, monitored and scaled.
A Stable System… Until It Wasn’t
The application had been running smoothly. It had survived multiple deployments, user sessions, database operations, and heavy API interactions. But the moment traffic spiked beyond forecasted levels, the system slowed. Then it stopped responding. Then it crashed.
The incident wasn’t sudden – the symptoms were gradual, but the collapse was complete. In the postmortem analysis, every sign pointed to one thing: resource exhaustion.
The server began accumulating memory usage for a number of different reasons at an unexpected and unusual rate.
The multiple concurrent requests – particularly with a reverse proxy, invoked a lot more memory usage on the server than was expected.
The reverse proxy layer began buffering large payloads in-memory, when it shouldn’t have, simply because it had that option. When highly loaded, this creates a saturation scenario in memory.
Uncontrolled Logging Behavior:
The logging system, which had been set to verbose mode for debugging purposes during testing, was never rolled back to a production-appropriate level.
As a result, large amounts of structured data were being written to disk continuously. Over time, this behavior flooded the disk – silently and invisibly.
Disk Capacity Reached 100%:
Once the storage hit full capacity, everything else began to fail in sequence. Log files couldn’t rotate. Swap memory couldn’t write. Temp files couldn’t be generated.
The system monitoring tools themselves were unable to record metrics.
The environment began failing silently, and by the time the application errors appeared externally, the infrastructure was already paralyzed internally.
Monitoring Was Too Shallow:
There were basic availability checks (e.g., HTTP status monitoring) but there was no visibility of system-level metrics such as disk space utilization, memory thresholds, swap activity, or file descriptor limits.
This created unknowns around how the system operated under unaudited real use.
Improper Resource Isolation:
All application layers were competing for the same pool of system resources.
Logs, proxy buffers, runtime memory, and swap were all living on the same disk and memory allocations.
This made it impossible to prioritize essential services during emergency load — resulting in a full system-wide choke.
Too often, teams deploy into production thinking performance is a function of their codebase. But in reality, the true determinants of system resilience are infrastructure design, observability, and resource isolation. Infrastructure doesn’t shout. It whispers – until it crashes everything.
Software Development: Here are some core truths the incident taught me
Disk space is a critical resource – not just for file storage, but for memory overflow, log rotation, and application stability.
Memory pressure builds quietly – especially when multiple services operate without constraints or visibility.
Logging should be treated like an infrastructure service – unmonitored logging is one of the fastest ways to eat I/O and storage without warning.
Monitoring is only useful when it covers the full stack – not just uptime checks, but system health, resource saturation, and hardware thresholds.
Infrastructure isn’t passive – it’s an active part of your architecture. If you’re not managing it intentionally, it’s managing you — and not kindly.
Infrastructure Failure in Software Development: Post-Incident Actions
The first step after the crash was no longer just to restart the app, but instead to re-think how we planned and monitored the infrastructure for software development.
Resource limits were defined throughout the stack – not just application resource limits, but service limits as well, including proxies and loggers.
Storage was decoupled by purpose: separating application logs, static files, and system operation so that a failure in one would not choke off the other two.
Real-time infrastructure monitoring was deployed to ensure we had alert thresholds for disk usage, memory saturation, swap activity, and I/O performance.
Observability was extended to include monitoring the infrastructure layer – not just application failures, but signs of degradation in service.
Software Development: Code Rarely Fails Alone
Crashes like this one are reminders that great code in software development is only as good as the environment it runs in. You can build performant APIs, write perfect logic, and follow the cleanest architectural practices — and still fail spectacularly if your infrastructure is under-planned.
In production, infrastructure is not invisible. It’s the invisible boundary between “working” and “broken.” When that boundary is misjudged – by assumptions, ignored limits, or silent failures – you don’t just lose uptime. You lose trust, momentum, and stability.
Arunangshu Das Arunangshu Das is a passionate backend engineer at Mindfire Solutions, specializing in designing resilient, scalable systems and solving complex infrastructure challenges. With experience in production-level deployments, performance tuning, and incident post-mortems, he ensures that software not only functions—but survives unexpected load and failure. Arunangshu is deeply interested in system reliability, resource optimisation, and operational excellence, and often shares insights from real-world situations to help engineering teams build more robust architectures. Outside work, he keeps up with emerging backend technologies and enjoys mentoring peers in good engineering practices.
