Search the practice set
126 questions, 454 terms and 400 topics in 20 areas.
39 results for “Time to Market”
Time to Market
How long it takes to get a capability in front of customers — often the constraint that dominates every other architectural quality.
Dwell Time
The period between an attacker gaining access and being detected — the metric that determines how much damage an intrusion can do.
Point-in-Time Recovery
Restoring a database to any moment within a retention window by replaying transaction logs onto a base backup, rather than only to a snapshot boundary.
Time Travel
Querying a table as it existed at a previous version or timestamp, made possible by keeping the metadata and files of prior commits.
Autoscaling
Adding and removing capacity automatically in response to a demand signal, to track load without paying for peak all the time.
Block Storage
A virtual disk attached to one instance at a time, presented as raw blocks and formatted with a filesystem — the storage databases and stateful workloads run on.
Blue-Green Database Schema
The constraint that makes fast rollback actually work — both application versions must be able to run against one schema at the same time.
Cache Penetration
Repeated lookups for keys that do not exist, which miss the cache every time by definition and pass straight through to the store.
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.
Cardinality
The number of distinct time series produced by a metric, which is the product of the distinct values of all its labels — and the main driver of monitoring cost.
DORA Metrics
Four measures of software delivery performance — deployment frequency, lead time for change, change failure rate, and time to restore service.
Denormalisation
Deliberately duplicating data across records to make reads cheap, accepting the write-time cost of keeping copies in step.
Discord's Message Store Migrations
Discord moved from MongoDB to Cassandra to ScyllaDB as message volume grew from millions to trillions, each time for a specific and different reason.
Lease
Time-bounded leadership or ownership that must be renewed before it expires, so a leader that becomes unreachable automatically relinquishes its role.
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.
Netflix's Recommendation Architecture
Netflix splits personalisation into offline, nearline and online layers so that expensive computation happens ahead of time and the request path stays fast.
Open Table Format
A metadata layer over files in object storage that supplies ACID transactions, schema evolution and time travel — the thing that turns a data lake into a lakehouse.
Optimistic Concurrency Control
Allowing concurrent work without locks and detecting conflict at write time by checking that the underlying version has not changed.
RTO and RPO
How long recovery may take (RTO) and how much data may be lost (RPO), the two numbers that determine the cost of a resilience design.
Replication Lag
How far behind a replica is, measured in time or in log position — the quantity that determines how stale a replica read can be.
Retrieval-Augmented Generation
Retrieving relevant documents at query time and putting them in the model's context, so answers are grounded in your data rather than in training data.
Schema Evolution
Changing a table's structure over time while keeping existing data readable and existing consumers working.
Sequence Diagram
A diagram showing the ordered exchange of messages between participants over time, used to make an interaction's control flow and failure points explicit.
Slowly Changing Dimension
A strategy for handling attributes that change over time, deciding whether history is preserved and how facts attach to the correct version.
Throughput
The rate of work a system completes per unit of time — and a quantity that trades against latency rather than tracking it.
Timeout Budget
Assigning a request an overall deadline at the edge and passing the remaining time down each hop, so no service works on something already out of time.
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.
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.
Windowing
Grouping an unbounded stream into finite chunks so aggregation can produce results, defined over event time rather than arrival time.
eBay's Architectural Generations
eBay rewrote its core platform several times across its first decade, each time because the previous generation had hit a limit that could not be tuned away.
A startup asks you to architect their product "to handle millions of users". They currently have none. What do you actually build?
What the interviewer is testing Whether you can identify the real constraint. The stated requirement is scale; the actual constraint is survival, and an archite
Netflix personalises an entire home page in the time a TV takes to draw a screen. How, given that scoring every title for every member on request is impossible?
What the interviewer is testing Whether you reach for the offline/online split — the single most reusable idea in latency critical personalisation — rather than
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
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
Time to Market
The constraint that dominates most products, and how to trade against it.
CAP & PACELC
What you must give up during a partition, and the latency choice the rest of the time.
Capacity Planning
What does not autoscale, and the lead-time items that need a date.
Strangler Fig
Routing capabilities to new implementations behind a facade, one at a time.
Throughput
Work completed per unit time, and why it trades against latency.