Search the practice set
126 questions, 454 terms and 400 topics in 20 areas.
60 results for “Capacity Modelling”
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.
Threat Modelling
A structured exercise that identifies what can go wrong with a design, before it is built, by walking the system's trust boundaries.
Admission Control
Deciding at the edge whether to accept a request at all, based on current capacity, before any work is done on it.
Attack Surface
The complete set of points where an untrusted actor can interact with a system — and the quantity that reduction genuinely reduces risk.
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.
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.
Data-Flow Diagram
A diagram of how data moves between processes, stores and external entities, with trust boundaries drawn on it.
Denormalisation
Deliberately duplicating data across records to make reads cheap, accepting the write-time cost of keeping copies in step.
Document Store
A store that keeps semi-structured documents — typically JSON — retrievable by key and queryable by their contents.
Domain-Driven Design
Modelling software around the business domain, with boundaries drawn where the language of the business changes.
Foreign Key Constraint
A database-enforced rule that a referencing value must exist in the referenced table — referential integrity that no application bug can violate.
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.
Normalisation
Organising a schema so each fact is stored exactly once, removing the update anomalies that duplication creates.
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.
SQL vs NoSQL
A choice driven by access patterns, consistency requirements and query flexibility — not by data volume, which is the reason usually given.
STRIDE
A mnemonic for six threat categories — spoofing, tampering, repudiation, information disclosure, denial of service, elevation of privilege — walked across each component and data flow.
Scalability
The ability to handle growing load by adding resources, ideally with cost rising no faster than the load.
A finance report double-counts revenue after a new fact table is added. What is the likely modelling error?
The likely error: a fan out join between fact tables at different grains The classic mechanism. You have an order lines fact at line grain and a shipments fact
A team proposes storing the customer's address on every order row "so order history is accurate". Is that denormalisation or a modelling error?
The distinction that matters It is neither, quite — it is a temporal modelling requirement being solved by accident. Denormalisation duplicates a fact for perfo
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 document collaboration product needs sharing with individuals, teams, and inherited folder permissions. Which authorization model?
The requirement is relationship shaped The questions this product must answer are: is this user a member of a team that has access to a folder that contains thi
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
Discord stores trillions of messages. What is their partition key, and what problem does the second half of it solve?
The key (channel id, bucket) — where bucket is a fixed time window. What each half does channel id matches the read pattern. Clients read messages within a chan
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
Run a threat model on a new payment integration: our service calls a third-party payment provider and receives webhooks. Where are the interesting threats?
Draw the boundaries first Three trust boundaries, and nearly every interesting threat lives on one of them: 1. User → our service (untrusted input, authenticate
Capacity Modelling
Arithmetic before load tests, and headroom for failure as well as peak.
Architecture Cost Modelling
Pricing a design before building it, at expected and at ten times volume.
Capacity Planning
What does not autoscale, and the lead-time items that need a date.
Performance & Capacity
General material on performance and capacity engineering.
Relational Modelling
Normalisation, keys, constraints and the invariants a schema enforces.
Reserved & Committed Capacity
Committing the baseline, laddering terms, and expiry as a silent failure.
Spot & Interruptible Capacity
Deep discounts for work that can be interrupted and resumed.
Threat Modelling
Walking trust boundaries with STRIDE before anything is built.
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.
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.
Data Warehousing
Dimensional modelling, star schemas and analytical workloads.
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.