Search the practice set
126 questions, 454 terms and 400 topics in 20 areas.
60 results for “Service Mesh”
Service Mesh
An infrastructure layer of sidecar proxies that handles service-to-service networking — mTLS, retries, timeouts, routing, telemetry — outside the application.
Airbnb's Service-Oriented Migration
Airbnb decomposed a large Rails monolith by first extracting a unified data-access layer, so that services were built on owned data rather than on shared database tables.
Data Mesh
An organisational approach that gives domain teams ownership of their analytical data as a product, with a self-serve platform and federated governance.
Database per Service
Each service owning its own datastore, with no other service reading or writing it directly.
Managed Service
A capability the provider operates — provisioning, patching, backup, scaling and failover — leaving you the configuration and the data.
Managed Service Upgrade Window
The period during which a provider may apply patches or version upgrades to a managed service, usually involving a failover or brief unavailability.
Service Boundary
The line separating what one service owns and is accountable for from what it must ask another service about.
Service Control Policy
An organisation-level guardrail that limits what any identity in an account may do, regardless of the permissions granted within that account.
Service Discovery
The mechanism by which a caller finds a currently healthy network address for a service whose instances are ephemeral.
Service Level Agreement
A contractual commitment about service level, with a defined remedy — usually a service credit — when it is missed.
Service Level Indicator
The actual measurement of a service's behaviour that an objective is set against — a ratio of good events to valid events.
Service Level Objective
An internal target for a service level indicator, set below the level at which users notice, and used to decide whether to ship or to stabilise.
Service Quota
A per-account, per-region cap on how much of a resource may be used — a common and easily-avoided cause of scaling failures and DR failures.
Service Registry
The database of currently available service instances and their addresses, maintained by registration and pruned by health checking.
Cell-Based Architecture
Partitioning a service into complete, independent copies of itself, each serving a subset of customers, so a failure is bounded to one cell.
Client-Side Discovery
The caller queries the registry itself and chooses an instance, rather than sending to a stable address that something else resolves.
Cloud Exit Plan
A documented, costed assessment of what leaving a provider or service would require — increasingly a regulatory expectation and a better lock-in control than portability itself.
DORA Metrics
Four measures of software delivery performance — deployment frequency, lead time for change, change failure rate, and time to restore service.
Disaster Recovery
The plan and capability for restoring service after an event that takes out a whole site, region or system.
Distributed Tracing
Following one logical request across every service it touches by propagating a shared trace identifier and recording timed spans.
Envelope Encryption
Encrypting data with a locally-generated data key, then encrypting that key with a master key held in a key management service, and storing the wrapped key alongside the ciphertext.
Golden Signals
The four measurements that cover most of what matters for a request-driven service: latency, traffic, errors and saturation.
JSON Web Token
A signed, self-contained token carrying claims, which a service can validate locally without calling the issuer.
Paved Road
A supported, opinionated default way to build and run a service, made easy enough that teams choose it rather than being required to.
Pinterest's MySQL Sharding
Pinterest sharded MySQL by embedding the shard ID inside every primary key, making any object's location computable from its ID alone with no lookup service.
Prime Video's Move Back to a Monolith
Amazon Prime Video consolidated a serverless, distributed audio/video monitoring service into a single process and reported a 90% cost reduction — the most-cited example of micros…
RED Method
A minimal per-service dashboard: Rate, Errors, Duration — the request-centric view of whether users are being served.
You are reviewing a design for an internal tool with 200 users. It proposes Kubernetes, microservices, Kafka, a service mesh and CQRS. How do you handle the review?
What the interviewer is testing Judgement, and whether you can push back without alienating a team. Anyone can spot over engineering; the question is what you d
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 card payment authorisation service runs active-active across two regions. A network partition splits them. Do you keep accepting authorisations, and what breaks either way?
What the interviewer is testing Whether you can apply CAP to a domain where the cost of each choice is concrete, and whether you recognise that "it depends" has
A downstream service slows from 50 ms to 3 s. Within two minutes every service in the request path is down, including ones that do not call it. Explain the mechanism and how you would have prevented it.
What the interviewer is testing Whether you understand that most outages are amplification, not failure — and whether you can name the specific mechanism rather
A downstream team needs to react to order changes. The order service can publish events, or they can consume CDC from its database. Which, and why?
The recommendation: published events, with CDC as the mechanism if needed The distinction that matters is what the consumer becomes coupled to . CDC consumed di
A service autoscales on CPU. During incidents it never scales out, even as latency triples. Why, and what would you scale on instead?
Why CPU never triggers The service is latency bound, not compute bound. Its threads are waiting — on a database, a downstream API, a lock, or a connection pool
A service becomes unresponsive during an incident in a dependency it barely uses. Investigation finds the client had no read timeout. Explain the full mechanism.
The mechanism, step by step 1. The dependency slows. It has not failed — it accepts connections and eventually responds, just very slowly. This is the important
A service calls four dependencies. How do you size its thread pools, and why is the total often smaller than people expect?
The sizing rule Little's Law, per dependency: concurrency = throughput to that dependency × its latency. Dependency Calls/s Latency Concurrency Pool Auth 500 10
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 is launching a new service and asks what its SLO should be. How do you help them decide, and why is "99.99%" usually the wrong first answer?
What the interviewer is testing Whether you treat reliability as a cost benefit decision with a budget, or as a virtue to maximise. Why not four nines It costs
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
A team wants to self-manage Kafka on Kubernetes to save money against the managed service. Evaluate.
The comparison they have probably made Instance cost versus managed service list price. That comparison usually favours self managing by a wide margin, and it o
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
In an order saga, the shipment service permanently rejects an order after the card has been captured. What now?
What this actually is A step after the pivot has failed permanently , which is the case saga design is supposed to make rare and cannot make impossible. Everyth
One customer's batch job saturates a shared service and degrades everyone. Rate limiting them fixes it, until the next customer does the same. What is the structural answer?
Why per customer rate limits keep failing A static limit is set from what that customer was doing, not from what the service can serve. It is reactive — you dis
One service restarts unpredictably; another is slow with low CPU. Both run on Kubernetes. What are you looking at?
The two symptoms map to the two resource behaviours Unpredictable restarts → OOM kill. Memory is incompressible: a container exceeding its memory limit is kille
Prime Video reported a 90% cost cut by consolidating a serverless distributed service into one process. Does that mean microservices were the wrong choice, and what is the actual decision rule?
What actually happened The Prime Video Video Quality Analysis team's 2023 post describes an audio/video monitoring service built as Step Functions orchestrating
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
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.
You are designing APIs for a platform with a web app, a mobile app, internal service-to-service traffic and third-party partners. What do you expose, and where?
What the interviewer is testing Whether you choose per constraint or adopt one technology as an identity. The wrong answers here are all defensible sounding and
You are designing a new service. What must be in place before it goes live so that whoever is paged at 3 AM can diagnose it without you?
What the interviewer is testing Whether observability is designed in or bolted on, and whether you think about the person on call rather than about the tooling.
You move a user profile service to eventual consistency and support tickets start arriving: users update their name and the old one is still shown. Fix it without abandoning the architecture.
What the interviewer is testing Whether you know that consistency is per operation rather than per system, and whether you reach for a targeted guarantee instea
You must roll out MFA to 40,000 employees. Security wants hardware keys; the service desk fears the call volume. Design the rollout.
The framing that resolves the argument Not every identity carries the same risk, so not every identity needs the same factor. A uniform mandate is what creates
Your service registry becomes unavailable. Every service is healthy. What happens, and what should happen?
What usually happens A total outage , despite every service being fine — because resolution is on the request path and a failed resolution fails the request. Th
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
p99 latency on checkout tripled overnight. Dashboards look normal, no deployment went out, and every service reports healthy. How do you find it?
What the interviewer is testing Systematic diagnosis in a distributed system when the obvious signals are clean — which is the realistic version of this problem
Service Mesh
Sidecars applied estate-wide, and the scale at which that pays.
Service Mesh Networking
Sidecar proxies supplying mTLS, retries and traffic policy.
Service Boundaries
Drawing lines along change patterns rather than technical layers.
Service Discovery
Finding a healthy address for something whose instances are ephemeral.
Debugging Distributed Systems
Localising a regression when every service reports healthy.
Distributed Tracing
Reconstructing one request's path across every service it touched.