Scalability & Capacity Planning Questions
Analyzing how a system's resource needs change as demand grows, and deciding what scaling strategy and headroom to plan for ahead of that growth. Covers translating a stated growth trajectory, for example doubling in six months, a 10x seasonal spike, or a step-change in active users, into a headroom and saturation forecast: how much spare capacity exists today, when it runs out, and what safety margin to hold under forecast uncertainty. Covers the horizontal-versus-vertical scaling decision, including why horizontal scaling itself has diminishing returns as coordination overhead grows with node count, and identifying which resource becomes the binding constraint first as demand increases. Also covers weighing that plan against cost and communicating the trade-off to non-technical stakeholders such as finance or product. This is forward-looking planning, not execution: it does not cover provisioning or autoscaling mechanics, live incident triage, profiling an already-slow code path for hotspots, tail-latency measurement and tracing, or caching and concurrency design patterns, each its own discipline.
Quantify cost versus performance when choosing between larger instance types (vertical scaling) with high single-instance throughput and horizontal scaling across many smaller instances. Outline a benchmark plan: what workloads you'd run, what metrics you'd collect (cost per request, latency percentiles, ops per second), how you'd compute amortized cost, and what business or technical thresholds would tip your recommendation toward one approach.
Your service just had an outage caused by running out of capacity. Walk through how you would build a capacity planning practice to prevent this from happening again: how you'd estimate headroom, which metrics you'd monitor to catch the trend early, how you'd set safety margins, and how you'd communicate capacity risk to product teams before it becomes an incident.
You need to build a capacity plan for a data-serving fleet that sees seasonal spikes up to 10x its baseline load. Walk through which metrics you'd track to forecast the spikes (traffic, CPU, latency, queue lengths), how you'd decide between pre-warming capacity ahead of time versus reserving it on standby, and how you'd weigh the cost of holding that headroom against the availability risk of not having it.
Describe how you would perform capacity planning for a service projected to double its traffic in the next 6 months. Include how you'd calculate headroom, what scaling strategy you'd pick, what workloads you'd benchmark to validate your numbers, and how you'd weigh cost against performance.
A service's request rate spikes to roughly 10x its normal load for one hour every day. Walk through how you'd plan for this: would you keep dedicated headroom on standby for that hour, scale into it dynamically, or reshape the workload itself so it doesn't need the peak capacity at all, and what would drive that choice? How would you keep cost under control for the other 23 hours a day?
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