Search the practice set
126 questions, 454 terms and 400 topics in 20 areas.
45 results for “Peak Event Readiness”
Event Sourcing
Storing the full sequence of state-changing events as the system of record, and deriving current state by replaying them.
Event Store
An append-only store of domain events organised into per-entity streams, serving as the system of record rather than as a log beside it.
Event Stream
An append-only, retained log of events that many independent consumers read at their own position, and can re-read.
Event Upcasting
Transforming an old event's stored form into the current shape as it is read, so historical events remain replayable after the schema changes.
Event-Driven Architecture
A style in which components communicate by emitting and reacting to facts about what happened, rather than by calling each other.
Event-Driven Integration
Systems integrating by publishing and consuming events rather than by calling each other's APIs.
Architecture Cost Model
A calculation, made during design, of what an architecture will cost to run at expected and at peak volume.
Autoscaling
Adding and removing capacity automatically in response to a demand signal, to track load without paying for peak all the time.
Consumer Group
A set of consumers that cooperatively read one stream, with each partition assigned to exactly one member, so the group collectively processes every message once.
Disaster Recovery
The plan and capability for restoring service after an event that takes out a whole site, region or system.
Game Day
A scheduled exercise in which a failure is deliberately introduced and the team responds as though it were real, to test the system and the response together.
Health Check
An endpoint the platform polls to decide whether an instance should be restarted or should receive traffic — two different questions needing two different checks.
Image Registry
The store from which container images are pulled, and an under-appreciated availability and security dependency of every deployment and every autoscale event.
LinkedIn and the Origin of Kafka
Kafka was built to replace point-to-point data integration between many systems with a single durable log that any system could publish to and any number could read.
Log Compaction
A retention policy that keeps only the most recent value for each key rather than deleting by age, so the log becomes a durable snapshot of current state.
Offset Management
How a consumer records its position in a stream, and the decision that determines whether processing is at-least-once or at-most-once.
Saga Orchestrator
A component that explicitly drives a saga's steps and compensations, holding the flow in one place rather than distributing it across event subscriptions.
Shopify's Pods and Modular Monolith
Shopify handles Black Friday scale with isolated pods — complete stacks each serving a subset of merchants — while keeping the application itself a deliberately modular monolith.
Snapshotting
Periodically storing an aggregate's computed state so it can be loaded without replaying its entire event history.
Thundering Herd
A large number of clients acting simultaneously because they were synchronised by a shared event, producing a spike that the steady-state design never sized for.
Watermark
A moving assertion that no events older than a given event-time will arrive, which is what allows an event-time window to be closed and emitted.
Webhook
An HTTP callback from a provider to a consumer-supplied URL when an event occurs, replacing polling with push.
Windowing
Grouping an unbounded stream into finite chunks so aggregation can produce results, defined over event time rather than arrival time.
Consumer lag on a Kafka topic grows during peak and does not recover overnight. You add consumers and nothing improves. Why?
The mechanism Each partition is assigned to exactly one consumer within a group. With ten partitions and ten consumers, an eleventh consumer joins the group, is
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
A 43-second network partition caused GitHub over 24 hours of degraded service in 2018. How does a 43-second event become a day-long incident?
The case, as publicly reported On 21 October 2018, routine maintenance replacing failing optical equipment caused a 43 second loss of connectivity between GitHu
A design review presents a new event-driven platform. What cost questions do you ask before approving it?
What the interviewer is testing Whether cost is part of your architecture review or an afterthought handled by finance later, and whether you know the specific
A service writes to its database and then publishes an event to Kafka. Sometimes consumers see an event for a record that does not exist, and sometimes a record exists with no event. Why, and how do you fix it?
What the interviewer is testing Recognition of the dual write problem — one of the most common defects in event driven systems and one that testing rarely catch
A team wants event sourcing for a new order service, citing audit requirements. What do you recommend?
The recommendation: probably an audit log, not event sourcing If the requirement is audit , event sourcing is a very expensive way to obtain it. An append only
When should a service call another synchronously, and when should it publish an event instead? Give me the deciding test, not a preference.
The deciding test Does this user action succeed or fail based on this callee's response? If yes, the call is synchronous, because you need the answer to decide.
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 streaming aggregation reports lower totals than the batch job it replaced. Both read the same source. What is likely happening?
The likely cause: late events dropped past the watermark The batch job reads a completed day and sees everything, including records that arrived hours after the
An order service must notify inventory, billing, shipping and analytics when an order is placed. Synchronous calls or events? Justify your choice per consumer.
What the interviewer is testing Whether you apply the decision per interaction rather than adopting one style globally. The framing that matters Synchronous cal
Orders must trigger inventory reservation, a confirmation email, an analytics record and a fraud check. Queue, topic, or both — and what breaks if you choose wrong?
What the interviewer is testing Whether you know the difference between work distribution and notification — a distinction that produces one of the most confusi
Peak Event Readiness
Freeze, pre-scale, shed order, warm caches and rehearse.
Event Sourcing
Storing the change log as the system of record, and what that costs forever.
Event Sourcing
The event log as the system of record, with state as a projection.
Event Streaming
Retained ordered logs, consumer offsets, partitions and replay.
Event-Driven Architecture
Publishing facts, and trading comprehensibility for decoupling.
Event-Driven Integration
Publishing facts rather than commands, and versioning event schemas.
Capacity Modelling
Arithmetic before load tests, and headroom for failure as well as peak.
Health Checks
Liveness versus readiness, and the check that causes the outage.
Migration Risk
Bounding blast radius, staging by cohort, and honest readiness reporting.
Outbox
Making the event atomic with the business write it describes.
Serverless vs Containers
Spiky and event-driven versus sustained throughput.