Search the practice set
126 questions, 454 terms and 400 topics in 20 areas.
57 results for “Spot & Interruptible Capacity”
Capacity Planning
Deciding in advance how much capacity will be needed, given growth, seasonality and failure scenarios, and ensuring it can be there in time.
Provisioned vs Serverless Capacity
Paying for a fixed database size continuously, versus paying for capacity consumed with automatic scaling — a crossover decision driven by duty cycle.
Admission Control
Deciding at the edge whether to accept a request at all, based on current capacity, before any work is done on it.
Autoscaling
Adding and removing capacity automatically in response to a demand signal, to track load without paying for peak all the time.
Caching Strategy
The chosen pattern for how a cache is populated, read and invalidated — cache-aside, read-through, write-through or write-behind.
Cloud Pricing Models
The purchase options for cloud compute — on-demand, committed use, and spot — which differ by up to 90% for identical hardware.
Concurrency
The number of operations in progress at once — distinct from parallelism, which is how many are literally executing simultaneously.
Connection Pool
A fixed set of reusable database connections shared by an application's requests, and one of the most common hidden capacity ceilings.
Google Maps and Planetary-Scale Spatial Serving
Map serving is fast because almost nothing is computed on request — the world is precomputed into a pyramid of tiles, and space is indexed onto a one-dimensional curve.
Hedged Request
Sending a duplicate of a request to a second replica after a short delay and using whichever response returns first, to cut tail latency.
Horizontal vs Vertical Scaling
Adding more machines versus making one machine bigger — and the fact that vertical is underrated for stateful tiers.
Hot Partition
One partition receiving disproportionate traffic, so the system saturates at a fraction of its aggregate capacity.
Little's Law
In a stable system, the average number of items in it equals the arrival rate times the average time each spends in it — L = λW.
Little's Law Applied to Pools
Using L = λW to size connection and thread pools from measured throughput and latency rather than from a default.
Load Testing
Driving a system with realistic traffic at a target volume to verify it meets its performance targets before real users do.
Rate Limiting
Bounding how many requests a caller may make in a window, to protect capacity and enforce fair use.
Redundancy
Having more instances of a component than the load requires, so that failures can be absorbed without loss of service.
Scalability
The ability to handle growing load by adding resources, ideally with cost rising no faster than the load.
Serverless
A model where the provider allocates and scales compute per request, and you are billed for execution rather than for provisioned capacity.
Tail Latency
The latency experienced by the slowest small percentage of requests, which is what users and dependent services actually feel.
Target Tracking Scaling
An autoscaling policy that adds or removes capacity to hold a chosen metric near a target value, like a thermostat, rather than reacting to threshold breaches.
Thread Pool Isolation
Giving each downstream dependency its own pool of threads or permits, so one slow dependency cannot consume the capacity needed to serve everything else.
Throughput
The rate of work a system completes per unit of time — and a quantity that trades against latency rather than tracking it.
Twitter's Timeline Fan-Out
Twitter precomputes each user's timeline at write time but handles very-high-follower accounts at read time, because neither strategy alone survives both ends of the distribution.
WhatsApp's Small-Team Scale
WhatsApp served hundreds of millions of users with a few dozen engineers by matching one technology choice precisely to the workload and refusing to add anything else.
Zoom's Pandemic Scale-Up
Zoom grew from around 10 million to over 300 million daily meeting participants in roughly three months, absorbed by a hybrid architecture and a distributed media routing design.
Your service will exceed capacity by 30% during a known peak. Do you shed load or brown out, and how do you decide what goes first?
The decision Brown out first; shed only if that is not enough. They are complementary rather than alternatives, and brownout is strictly less harmful when it is
A worker's queue depth grows steadily through the day and never recovers. Adding workers helps for an hour, then it resumes. What is happening?
The diagnosis Arrival rate exceeds service rate. A queue that grows monotonically is not a queueing problem, it is a capacity problem, and no amount of bufferin
Peak trading day is six weeks away and expected to be four times normal traffic. What do you do in those six weeks?
What the interviewer is testing Whether you can run a readiness programme rather than just "add servers", and whether you know what fails at peak that does not
Precompute every user's timeline at write time, or assemble it at read time? Explain why the answer for a social feed is neither.
Why each pure strategy fails Fan out on read. Store each post once; on timeline load, query the posts of everyone the user follows and merge. Writes are trivial
Your system handles 1,000 requests per second today. Marketing says a campaign will bring 10,000 next month. What breaks first, and how do you find out?
What the interviewer is testing Whether you reason about bottlenecks systematically or start adding servers. The shape of the answer Scaling is not uniform. Som
Spot & Interruptible Capacity
Deep discounts for work that can be interrupted and resumed.
Capacity Modelling
Arithmetic before load tests, and headroom for failure as well as peak.
Capacity Planning
What does not autoscale, and the lead-time items that need a date.
Performance & Capacity
General material on performance and capacity engineering.
Reserved & Committed Capacity
Committing the baseline, laddering terms, and expiry as a silent failure.
Bottleneck Analysis
Finding the constraint, and expecting a second one behind it.
Caching for Performance
Layer choice, hit ratio as a first-class metric, and cold-cache recovery.
Cloud Pricing Models
On-demand, committed and spot, and the crossover arithmetic.
Concurrency
Operations in flight, and the limits that are the real capacity ceiling.
Connection Pooling
The most common hidden ceiling, and the metric nobody collects.
Database Performance
Plans, indexes, contention and the pool in front of the database.
Horizontal vs Vertical Scaling
Scale out for stateless, scale up first for stateful.
Latency
Distributions rather than averages, and the floors physics imposes.
Little's Law
L = λW, and the pool sizes it computes directly.
Load Testing
Realistic data, realistic mix, and a ramp rather than a step.
Network Performance Tuning
Keep-alive, compression, payload size and round-trip elimination.
Peak Event Readiness
Freeze, pre-scale, shed order, warm caches and rehearse.
Performance Budgets
Targets enforced in CI so regressions fail the build.
Profiling & Optimisation
Measuring before optimising, and optimising the dominant term.
Queueing Theory
Why latency explodes as utilisation approaches capacity.
Soak Testing
Long runs that surface leaks and slow degradation.
Stress Testing
Pushing past target to learn what breaks first and how it fails.
Tail Latency
p99 behaviour, amplification across fan-out, and hedged requests.
Throughput
Work completed per unit time, and why it trades against latency.
WebSockets & Realtime
Persistent bidirectional connections and the capacity model they impose.