Everything in DevOps & Cloud, page 2
A team keeps blowing its error budget. First steps?
Analyze where the budget is burning via SLIs and postmortems, validate the SLO and SLIs are sound, then partner blamelessly on the top fixes.
What is an error budget and how is it used?
The budget is the allowed unreliability (100 percent minus the SLO); track its burn, ship freely when budget remains, and freeze risky changes to focus on reliability when exhausted.
Defining SLIs and an SLO for an auth service?
Pick user-centric SLIs like login availability and latency, measure good over valid events at the right boundary, then set an achievable SLO with a window.
Client-side chaos for an uncontrollable third party?
Inject faults at your client boundary via a proxy or fault-injecting wrapper, simulate timeouts, errors, and latency, then verify timeouts, retries, breakers, and fallbacks.
Chaos test for gray-failure cascades in shared services?
Inject partial latency into a shared service, hypothesize tenants stay isolated within steady state, and monitor cross-system queue depth, pool saturation, retries, and per-tenant SLIs.
Automating chaos in CI/CD for continuous verification?
Run codified experiments against staging or canary with pass/fail on steady-state SLIs; prerequisites are observability, automated abort, and isolation.
Resource faults versus network faults: when each matters?
Resource faults probe local saturation and autoscaling; network faults probe distributed-call resilience like timeouts and retries.
Why does 200ms latency drop requests? Diagnose it.
Little's Law shows added latency raises in-flight requests, exhausting the thread or connection pool; check pool saturation, timeouts, and retries.
How do you run your first production chaos experiment?
Pick a low-risk known weakness, define a measurable hypothesis, brief stakeholders and on-call, run small with an abort, then analyze and fix.
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.
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