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Design a simple chaos experiment for a cache dependency?
Hypothesis that the service degrades gracefully when Redis is unavailable, monitor error rate, latency, DB load, and cache hit rate.
What is blast radius and how do you limit it?
Blast radius is the scope of users or systems an experiment can harm; limit it by targeting a small traffic percentage and by having an automated abort.
How does chaos engineering differ from other testing?
It experiments on real systems by injecting faults to test a steady-state hypothesis, versus verifying known behaviors like integration or load tests.
Reliability paved roads for an internal PaaS?
Built-in observability, safe deploys with health checks and rollback, sane timeouts/retries/limits, and SLO tooling.
Design a chaos experiment for a payment dependency?
Define a measurable steady state, hypothesize it holds when payments fail, limit blast radius to a small traffic slice, and auto-abort on SLO breach.
Replication and consistency for active-active regions?
Choose per data class between synchronous (low RPO, higher latency) and async replication, address write conflicts, and reason via CAP and PACELC.
Zero-downtime index migration on a hot table?
Build the index concurrently to avoid table locks, run off-peak with monitoring, and keep it reversible since dropping an index is cheap.
Mitigating risk from an unproven external dependency?
Timeouts and circuit breakers to fail fast, bulkheads to isolate resources, fallbacks or cached/degraded responses.
Reliability patterns for queue-based job processing?
Retries with backoff and jitter for transient faults, dead-letter queues plus a poison-message limit, idempotent handlers and visibility timeouts.
What is an SLO and how do you define API availability?
SLO is a target on an SLI, availability SLI is good requests over valid requests, you need labeled request counts over a window.
What telemetry must a new microservice ship with?
Metrics for trends like p99 latency and error rate, structured logs for per-request detail, traces for cross-service causality.
When horizontal scaling is the wrong fix
Adding instances fails when the bottleneck is a shared resource like one database, a lock, or a queue, so more instances just add contention; investigate where time is actually spent.
Why tail latency (p99) matters
Averages hide the worst experiences, and fan-out amplifies tails so most requests touch a slow path; causes include GC pauses, queueing, contention, and noisy neighbors.
Front-end performance budgets
A performance budget is an enforced limit on metrics like Core Web Vitals and bundle size, checked in CI to fail builds that regress.
Little's Law for capacity planning
L equals lambda times W, concurrency equals arrival rate times time in system; rearrange to size threads or concurrency for a target throughput and latency.
Capacity planning for annual traffic spikes
Forecast peak from history, load-test to find per-unit capacity, use autoscaling with pre-warming and headroom, and protect with caching and graceful degradation.
Diagnose database CPU saturation under load
Find the expensive queries via the database's stats, check for missing indexes and full scans, then fix with indexing, query rewrites, caching, or read replicas.
Load vs stress vs soak testing
Load tests expected traffic, stress pushes past limits to find the breaking point, soak runs sustained load for hours to expose leaks.
Golden signals for capacity planning
Monitor the four golden signals, latency, traffic, errors, and saturation, from day one, watching percentiles and saturation to forecast scaling.
Canary a shared downstream microservice
Route a slice of traffic to the canary via mesh rules, propagate context, and use distributed tracing to measure impact on upstream callers across the full path.