Reliability, High Availability, and Tradeoffs Questions

Design patterns and decision making for ensuring availability correctness and graceful behavior under failure while balancing technical trade offs. Topics include redundancy and failover strategies active passive and active active deployments; fault isolation using bulkheads and circuit breaker patterns; graceful degradation and feature gating strategies; defining and mapping service level objectives and service level agreements to recovery point and recovery time objectives; multi region and multi availability zone deployment considerations; testing for reliability including chaos engineering and fault injection; and reasoning about consistency versus availability trade offs and the operational cost of stronger guarantees. Candidates should be able to choose reliability patterns to meet business objectives and to explain their implications for cost performance and maintainability.

EasyTechnical

27 practiced

Describe the bulkhead pattern and how it isolates failures in a distributed system. Provide at least three concrete implementation techniques (for example: per-service thread pools, process/container isolation, per-tenant connection pools or quotas) and a short example of an outage that bulkheads would mitigate. Explain any downsides of overly-aggressive bulkheading.

EasyTechnical

25 practiced

Explain the difference between eventual consistency and strong consistency in distributed systems. Provide one concrete example where eventual consistency is acceptable (and why) and one where strong consistency is required. Also discuss operational costs (latency, complexity, monitoring) of providing stronger consistency guarantees.

EasyTechnical

36 practiced

List and explain common load-balancing algorithms such as round-robin, least-connections, weighted-round-robin, and ip-hash. For each algorithm provide an example scenario where it performs well and one where it performs poorly (for example: long-lived connections, heterogeneous server capacities, session affinity needs).

HardSystem Design

25 practiced

Architect a multi-tenant SaaS platform that must offer per-customer SLA guarantees via isolation mechanisms such as capacity reservations, burst borrowing, noisy-neighbor mitigation, and per-tenant billing/observability. Discuss implementation options (namespace-level quotas, dedicated clusters, logical partitioning), cost/operational trade-offs, and how to migrate tenants between tiers without downtime.

Sample Answer

Requirements & constraints:- Functional: per-tenant SLA on latency, throughput, availability; per-tenant billing & observability.- Non‑functional: scale to thousands of tenants, cost-efficiency, minimal downtime for tier changes, noisy‑neighbor isolation.

High-level architecture:- Control plane (tenant metadata, billing, provisioning, orchestration)- Data plane (shared compute/storage where workloads run)- Multi-tenant observability & policy engine (metrics, alerts, chargeback)- Resource manager (enforces quotas, reservations, burst, throttling)

Isolation implementation options (pros/cons):1. Namespace-level quotas (Kubernetes namespaces + ResourceQuota/LimitRange + QoS classes) - Pros: low cost, easy orchestration, fast provisioning - Cons: weaker isolation (noisy neighbors still possible), best-effort SLAs - Use when tenants are small or SLAs are modest.

2. Logical partitioning with reserved node pools (shared cluster with tainted/node-pools per tier) - Pros: stronger isolation (dedicated nodes per tier), more predictable performance, cost amortized - Cons: moderately higher cost and operational complexity

3. Dedicated clusters per tenant/tier - Pros: maximal isolation and easy per-tenant SLA guarantees - Cons: highest cost & operational overhead; use for high-paying customers requiring hard SLAs.

Isolation mechanisms to implement:- Capacity reservations: allocate guaranteed CPU/memory using guaranteed QoS pods or cgroups on nodes; reserve node-pool capacity per tenant/tier.- Burst borrowing: implement burst pools and token-bucket admission—tenants can borrow unused capacity up to limits; tracked and charged.- Noisy‑neighbor mitigation: cgroup CPU shares + CPU manager for pinned CPUs, IO throttling (blkio/IOPS limits), pod eviction policies and circuit breakers; autoscaling isolation by scaling dedicated pools rather than stealing resources.- Network isolation: network policies, per-tenant egress bandwidth policing, rate limiters.- Storage QoS: provisioned IOPS volumes, per-tenant storage classes.

Billing & observability:- Tag all resources with tenant IDs; collect fine-grained metrics (CPU, memory, network, IOPS, P95 latency) via Prometheus/metrics pipeline.- Charge by reserved capacity, burst usage, and SLA level; generate usage reports and alerts.- SLA monitoring: continuous SLO evaluation (error budget), notify control plane to trigger throttling or scale-up when approaching breach.

Migrating tenants between tiers without downtime:- Preconditions: immutable tenant ID, replicated configuration, blue/green node-pools.- Steps: 1. Provision target resources (new namespace/node-pool/cluster) and pre-warm (load images, mount volumes). 2. Start dual-write/replication for stateful data (DB replication, object storage cross-replication). 3. Deploy traffic shadowing: replicate live traffic to target to verify behavior; run canary instances. 4. Gradually cutover: update routing (service mesh, DNS, edge) to shift small % of traffic; monitor SLA metrics and error budget. 5. If healthy, increase traffic until 100%; decommission source resources after data drift window.- For stateless services use rolling update leveraging session stickiness or session replication; for stateful, prefer storage-level replication and promote replica.

Operational trade-offs:- Cost vs isolation: dedicated clusters maximize SLA but increase cost; node-pool partitioning hits balance for medium SLAs.- Complexity vs agility: namespace quotas are simple but require stronger runtime mitigation tools.- Autoscaling strategy: prefer horizontal scaling of dedicated pools to reduce noisy-neighbor risk rather than overcommit in shared pools.

Key engineering safeguards:- Automated runbooks for SLA breaches (scale, throttle, failover).- Billing reconciliation and dispute logs.- Chaos testing for noisy neighbor and migration flows.- RBAC and tenant-aware auditing.

Example tech stack suggestions:- Kubernetes, Karpenter/GKE/AKS node pools, Prometheus+Thanos, Istio/Envoy for routing and rate-limiting, Ceph/Rook or cloud-managed block storage with QoS, Kafka for control-plane events, Terraform/ArgoCD for infra-as-code.

This design provides graded isolation (namespace → node-pool → cluster), clear policies for reservations and burst borrowing, instrumentation for billing/SLA, and a migration path that ensures zero-downtime cutovers.

MediumTechnical

22 practiced

List practical techniques to gracefully degrade a user-facing service when backend dependencies are slow or failing. Cover options such as serving cached data, returning partial results, reducing feature richness (e.g., low-fidelity images), using approximate algorithms, or placing a read-only mode. For each technique describe implementation concerns and user experience trade-offs.

Sample Answer

Here are practical graceful-degradation techniques, with implementation concerns and UX trade-offs:

1) Serve cached data (full-page or fragments)- Implementation: layered cache (CDN, app cache, local/browser), TTL policies, stale-while-revalidate, cache keys per user/locale, fallbacks for auth-sensitive content.- Trade-offs: fast and reliable but may be stale; must surface timestamp/“last updated” and invalidate correctly to avoid showing wrong info.

2) Return partial results / progressive rendering- Implementation: APIs that return partial payloads, streaming (HTTP/2, SSE), client merges partials, contract versioning.- Trade-offs: keeps UI responsive but incomplete; need clear UI placeholders and progressive disclosure so users know data is loading.

3) Reduce feature richness (low-fidelity assets)- Implementation: serve low-res images, simplify CSS/JS, strip nonessential widgets based on network/service health flags.- Trade-offs: faster load, lower backend load, but reduced visual quality or interactions; communicate via microcopy (e.g., “low-quality view”).

4) Use approximate / cheaper algorithms- Implementation: fallback to sampling, heuristics, precomputed summaries, or ML models with lower latency; feature flags to switch modes.- Trade-offs: faster and cheaper but less precise; surface confidence/accuracy when relevant.

5) Read-only / degraded mode- Implementation: toggle writes at API/gateway, show UI in read-only, queue writes locally (optimistic) or in background.- Trade-offs: preserves data integrity and availability for reads but blocks user actions; offer “notify me” or queue UX to reduce frustration.

6) Circuit breakers & graceful error pages- Implementation: detect failures, open circuit, route to cached/fallback services, collect metrics and alarms.- Trade-offs: avoids cascading failures but may reduce functionality; provide clear messaging and ETA if possible.

7) Rate-limit or feature-throttle nonessential services- Implementation: prioritize critical APIs, degrade less critical microservices, use quotas.- Trade-offs: maintains core experience at cost of advanced features.

Cross-cutting concerns: observability (latency/error dashboards), feature flags, automated failover tests, security/privacy for cached data, and clear user messaging. Choose techniques based on SLA priorities: freshness vs availability, and design UX to set expectations.

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