How do you design a web ops workflow for high availability?

Long Answer

A robust web operations workflow blends people, process, and platform so incidents are rare, short, and well-understood. It turns availability, performance, and reliability into daily habits: define what “good” means, instrument it, automate safe change, and rehearse failure.

1. Objectives, SLOs, and capacity
   Start with business-level objectives translated into SLOs per critical user journey (for example, p95 ≤ 250 ms; availability ≥ 99.95%). Publish SLIs for request rate, latency, errors, and saturation.
   
2. Architecture for resilience
   Front services with a CDN/WAF, then an API gateway. Keep services stateless; externalize sessions and files. Partition by tenant or region to contain blast radius. Use leader/replica databases, read pools, and a search index; isolate OLTP from analytics.
   
3. Change management and delivery
   Ship small, reversible changes via continuous delivery. Enforce automated tests, security scans, and policy checks in CI. Deploy with canary or blue-green; bake time plus automatic rollback on SLO regression. Maintain feature flags for dark launches and kill-switches.
   
4. Traffic shaping and back-pressure
   Rate-limit at the edge and per tenant. Use token buckets, timeouts, and retries with jitter. Insert queues between the synchronous API and slow work to smooth spikes. Adopt bulkheads to isolate noisy neighbors and shed load intentionally when overwhelmed. Prefer idempotent endpoints and request IDs for safe retries.
   
5. Observability and incident response
   Collect logs, metrics, and traces with consistent correlation IDs. Expose golden signals on dashboards per service and per region. Alert on error-budget burn, not just CPU. Codify incident roles (commander, scribe, liaison), Slack channels, severity levels, and status-page cadence. Use runbooks with checklists, decision trees, and one-click operations (cache purge, failover, feature-flag off).
   
6. Data, caching, and performance
   Cache public pages at the edge and private reads in Redis with scoped keys. Profile hot paths; remove N+1 queries; add pagination and projection. Adopt asynchronous write-behind where acceptable; batch external calls; prefer streaming for large responses.
   
7. Multi-region and disaster recovery
   Run active-active where latency matters; otherwise warm-standby with tested RTO/RPO. Automate DNS or global traffic steering with health checks. Replicate data with clear ownership for writes; document consistency and client caching rules (ETag, If-None-Match).
   
8. Security and compliance in ops
   Use least-privilege IAM; short-lived credentials; and secrets in KMS or vaults. Sign artifacts and verify at deploy. Throttle abusive traffic at the edge.
   
9. Knowledge management and continuous improvement
   Keep a living service catalog with owners, dashboards, SLOs, runbooks, and escalation paths. After every P1/P2, publish a blameless postmortem with concrete follow-ups tied to error-budget policy. Teams practice failure drills quarterly and validate paging, dashboards, and runbooks against real failure modes to keep response sharp.
   
   

Executed daily, this workflow produces resilient systems: fast under load, transparent under failure, and safe to change. It scales across many websites and services because practices are standardized, signals are comparable, and ownership is unmistakable.

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Table

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Common Mistakes

Treating availability as a server count instead of an SLO; no error budgets. Letting OLTP handle search and analytics, causing locks and slow queries. Caching without keys that encode tenant/auth, leaking data. CPU-only autoscaling while p95 latency and queue depth burn. Single region and single database role; failover never rehearsed. No back-pressure: synchronous fan-out to slow dependencies during spikes. Deploying big batches without canaries or rollback. Thin observability: no traces across edge→service→DB, noisy alerts, missing runbooks. Incidents with unclear roles and no status updates. Secrets in code, long-lived credentials, and ad hoc access; audits impossible. Ignoring synthetic probes and real user monitoring so regressions ship unnoticed. Using retries without jitter, amplifying downstream failures. Mixing tenant data on shared caches and queues with no isolation. Closing tickets after a hotfix without postmortems or follow-up tasks, so the issue returns.

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Sample Answers (Junior / Mid / Senior)

Junior: I would front everything with a CDN and an API gateway, track latency and errors on dashboards, and use canary releases. I would cache public pages and keep private reads in Redis. For spikes, I would place slow jobs on a queue so the API stays responsive.

Mid: I separate read and write paths (CQRS), back search with an index, and publish changes via an outbox with change-data-capture. Services are stateless and autoscale on p95 latency and queue depth. I set SLOs per journey and alert on error-budget burn. Incidents follow a playbook with clear roles and status-page updates.

Senior: I design per-tenant sharding and regional write ownership, enforce token buckets and circuit breakers around dependencies, and plan active-active where latency matters. Releases ship via canary/blue-green with one-click rollback. Security uses least-privilege IAM, short-lived creds, and signed artifacts. We run game days, measure MTTR, and fund fixes through the error-budget policy. Observability ties logs, metrics, and traces together with a common request ID.

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Evaluation Criteria

Strong answers translate business goals into SLOs with explicit SLIs, then show how architecture, delivery, and operations uphold them. Look for an edge gateway with auth and rate limits; cache strategy that distinguishes public vs private; CQRS with search off OLTP; queues, timeouts, and circuit breakers for back-pressure; and stateless services that autoscale on p95 and queue depth. Expect incident mechanics (roles, status page), observability (logs/metrics/traces), and a tested rollback path. Security should include least-privilege IAM and managed secrets. Red flags: CPU-only scaling, single region, no cache keys, OLTP scans for search, no canary plan, or alerts unlinked to SLOs. Senior candidates quantify targets, explain active-active vs warm-standby trade-offs, and describe consistency contracts. Governance includes SBOMs and signed images in the pipeline, audited access, and postmortems with tracked actions.

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Preparation Tips

Build a small service behind an API gateway; publish OpenAPI and add OAuth2 and per-client limits. Create SLOs for a critical endpoint and wire SLIs (rate, errors, latency) to a dashboard. Implement CQRS: Redis for reads, relational leader for writes, and an outbox feeding CDC to a search index. Insert a queue between the API and a slow job; add retries with jitter, timeouts, and circuit breakers. Add tracing with a request ID across edge→service→DB; alert on error-budget burn. Practice canary and blue-green, plus one-click rollback. Simulate a region failure with traffic steering and verify RTO/RPO. Run a game day that breaks a dependency, saturates a queue, and exhausts cache, then tune autoscaling on p95 latency and queue depth. Document incident roles and write two runbooks (cache purge, feature-flag kill-switch). Add synthetic probes from three regions and compare to RUM data. Rotate secrets via a vault, sign images, and produce an SBOM in CI. Finally, record a five-minute demo that shows dashboards before/after a canary, your rollback, and the resulting postmortem tasks.

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Real-world Context

A marketplace split reads from writes and cached public endpoints at the edge; p95 dropped 40% and stayed stable during launches. A fintech adopted outbox + CDC and moved search to a dedicated index; downstream views were consistent within seconds and double-writes ceased. A media site enforced canary + rollback and token buckets around third-party APIs; spikes no longer cascaded into outages. A global SaaS added active-active regions with locality routing; users saw sub-300 ms p95 worldwide and failovers rehearsed. An education platform unified logs, metrics, and traces and staffed clear incident roles; MTTR fell 50% and on-call noise dropped after pruning alerts via postmortems. A retailer pre-warmed caches ahead of campaigns and published SLOs to stakeholders; confidence rose as error-budget burn guided priority. Another team shifted autoscaling triggers from CPU to p95 latency and queue depth; cost flattened while reliability improved. Security posture improved when secrets moved to vault with short-lived creds and signed releases; audits sped up and exposure windows shrank.

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Key Takeaways

• Define SLOs, measure SLIs, and let error budgets drive priorities.
  
• Front with CDN/WAF and an API gateway; cache public and private correctly.
  
• Use queues, timeouts, retries with jitter, and circuit breakers for spikes.
  
• Ship canaries, keep services stateless, and autoscale on p95 and queue depth.
  
• Standardize observability, incident roles, and blameless postmortems.
  
  

Practice Exercise

Scenario:
You operate eight public websites and three internal web services with global traffic, frequent campaigns, and third-party dependencies. Leadership requires p95 ≤ 250 ms, availability ≥ 99.95%, and safe weekly releases.

Tasks:

1. Define SLOs and SLIs for two critical journeys; publish dashboards and error-budget policy.
   
2. Design ingress: CDN/WAF + API gateway with OAuth2, mTLS, and per-client limits.
   
3. Separate reads/writes (CQRS); cache public at edge and private in Redis with scoped keys; move search to its own index.
   
4. Insert queues for slow work; add timeouts, retries with jitter, token buckets, and circuit breakers.
   
5. Plan multi-region: choose active-active or warm-standby, document RTO/RPO, automate DNS steering, and specify data ownership and consistency.
   
6. Build CI/CD with tests, security scans, canary/blue-green, automatic rollback on SLO regression, and feature-flag kill-switches.
   
7. Implement observability: request IDs, traces across edge→service→DB, alerts on budget burn, and synthetic probes from three regions.
   
8. Create runbooks for cache purge, failover, and dependency brownout; assign incident roles and status-page cadence.
   
9. Add security controls: least-privilege IAM, short-lived creds, vault-managed secrets, and signed artifacts.
   
   

Deliverable:
A one-page diagram of data flow and failover, the SLO table, the deployment plan with rollback steps, and screenshots of dashboards during a canary and a simulated dependency outage. Include a brief postmortem listing causes, fixes, and owners with due dates.