How to design a scalable, fault-tolerant back end for millions?

Long Answer

Designing a back end that survives massive concurrency is about eliminating single bottlenecks, bounding tail latency, and embracing failure as normal. The blueprint blends global routing, stateless compute, efficient data access, and resilience patterns—measured constantly against SLOs.

1) Global entry and traffic shaping
Place an anycast/geo load balancer or API gateway in front. Terminate TLS, enforce rate limits per API key/tenant, and normalize headers. Use edge/CDN caching for static and cacheable GETs with fine-grained TTLs and ETags. Add request classification (public, authenticated, high-cost) to steer heavy flows to specialized pools and protect the core.

2) Stateless, horizontally scalable services
Package services in containers; scale out, not up. Keep services stateless: session data lives in Redis or tokens (JWT). Enable autoscaling on CPU, latency, and queue depth. Favor async I/O and connection pooling to maximize concurrency. Partition services by capability (auth, catalog, checkout) so each can scale independently and fail without cascading.

3) Backpressure and asynchronous pipelines
During spikes, synchronous writes become chokepoints. Ingest requests, validate quickly, then enqueue to durable queues/streams (Kafka, RabbitMQ, SQS). Workers process idempotent jobs in parallel. Implement bulkheads—separate thread pools and queues—to prevent noisy neighbors. For user-visible flows, return 202 + operation IDs and stream progress; for critical flows, offer synchronous “fast path” with strict timeouts.

4) Data layer for scale
Reads: serve from distributed caches (Redis/Memcached) with cache-aside or write-through. Warm hot keys, compress payloads, and use key namespaces per tenant.
Writes: shard OLTP databases by key (user, tenant, region). Employ read replicas and connection limits to prevent stampedes. For NoSQL, model access patterns up-front; for SQL, lean on partitioning and covering indexes. Keep transactions short; prefer eventual consistency where UX allows. Move analytics to a separate store (OLAP) or CDC-driven lakehouse so reporting cannot throttle OLTP.

5) Resilience patterns
Bound every remote call with timeouts; retry idempotent operations with exponential backoff + jitter; never retry non-idempotent writes blindly. Apply circuit breakers to failing dependencies and return fallbacks (cached responses, default prices, degraded recommendations). Use hedged requests (duplicate a read after a short delay) for tail-latency control. Implement graceful degradation (serve cached inventory, queue writes) when dependencies wobble.

6) Multi-AZ / multi-region fault tolerance
Run services across multiple availability zones behind zone-aware balancers. Replicate data synchronously within a region for HA; replicate asynchronously cross-region based on RPO. Choose active-active for stateless tiers; pick active-passive or global databases for state depending on consistency needs. Health-checked DNS or a global accelerator shifts traffic during regional incidents.

7) Security, quotas, and cost control
Authenticate at the edge; authorize per route with scopes/ABAC. Enforce per-tenant quotas and token-bucket limits to prevent abuse. Compress and cache to cut egress. Prefer autoscaling + right-sizing over fixed fleets; consider spot capacity for noncritical workers.

8) Observability and SLOs
Instrument RED metrics (Rate, Errors, Duration) and USE (Utilization, Saturation, Errors) on infra. Correlate traces across services; attach request IDs everywhere. Log structured events with PII redaction. Define SLOs: availability, p95/p99 latency, durability, and error budgets; wire alerts on burn rate. Add synthetic canaries and chaos experiments (kill pods, inject latency, drop a zone) to verify reality.

9) Release safety
Use progressive delivery: canaries, blue/green, and gradual traffic shifting. Feature flags guard risky paths. Database changes go through expand-migrate-contract workflows. Roll back quickly on SLO breach via automation.

10) Validation plan
Load-test with realistic traffic mixes (reads/writes, cold vs warm cache) and failure injections. Watch queue depth, DB saturation, and p99 latency while scaling. Prove that losing a zone degrades gracefully within RTO/RPO, that queues drain after spikes, and that costs scale sublinearly via caching and autoscaling.

Together, these choices deliver a scalable backend architecture that handles millions of concurrent requests: horizontally elastic at the edge and compute layer, efficient and partitioned in data, and fault tolerant by design.

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Table

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Common Mistakes

Relying on vertical scaling and sticky sessions—single points of pain. Treating caches as magic and skipping cache invalidation, causing staleness and mystery bugs. Unbounded retries without jitter that amplify incidents. Missing timeouts so threads hang and exhaust pools. Putting OLTP and analytics on the same database, letting reports throttle hot paths. Global transactions across services instead of idempotent, decoupled steps. One-size-fits-all consistency—forcing strong consistency where eventual is fine. No backpressure: synchronous writes during spikes crush DBs. Ignoring p99 latency, optimizing only averages. Deploying without canaries or rollbacks. Finally, weak observability—no traces, no SLOs—so teams argue instead of act.

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Sample Answers (Junior / Mid / Senior)

Junior:
“I’d run stateless APIs behind a load balancer and use autoscaling. Reads go to a cache; writes hit the DB. I’d add retries and timeouts and monitor latency and error rates.”

Mid:
“I’d add a CDN and API gateway with rate limits. Services are stateless containers; spikes are absorbed with queues and workers. Data is sharded with read replicas, cache-aside via Redis. Resilience patterns—timeouts, retries with jitter, and circuit breakers—bound tail latency. Multi-AZ plus health-checked failover handles faults.”

Senior:
“Global routing + edge caching protect origin. Each capability runs as a stateless service with autoscale; writes flow through idempotent queues (backpressure). The data tier mixes sharding and replicas; analytics is decoupled via CDC. Fault tolerance: circuit breakers, hedging, bulkheads, graceful degradation. SLO-driven ops with RED/USE, canaries, chaos, and automated rollback. Cost is managed via quotas, right-sizing, and spot for noncritical workers.”

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Evaluation Criteria

Interviewers look for:

• Clear horizontal scalability story (stateless services, autoscale, edge/cache).
  
• Sound data design (sharding, replicas, cache-aside, short transactions).
  
• Robust fault tolerance (timeouts, retries + jitter, circuit breakers, bulkheads, hedging).
  
• Concrete backpressure (queues, idempotency, async pipelines).
  
• HA/DR across AZs/regions with explicit RTO/RPO.
  
• Observability with SLOs, tracing, and chaos validation.
  
• Release safety (canary, blue/green, feature flags).
  
• Security/quotas and cost control under scale.
  Vague answers that say “add servers” or “use microservices” score low; detailed trade-offs with measurable SLOs score high.

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Preparation Tips

Build a small system: edge → gateway → stateless API → Redis → sharded DB → queue + workers. Add CDN caching for GETs. Implement token-bucket rate limits and per-tenant quotas. Add cache-aside reads with TTLs and invalidation on writes. Make writes idempotent (keys, dedupe tables). Wrap every call with timeouts, retries + jitter, and circuit breakers. Add a queue for heavy tasks; expose 202 + operation IDs. Instrument RED/USE metrics and distributed tracing; define SLOs and alerts. Create a load test mixing hot-key reads and bursty writes; inject failures (drop DB node, add 500ms latency, kill a zone). Practice a 60–90s pitch that ties results to SLOs and explains how backpressure and graceful degradation protected users.

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Real-world Context

A ticketing platform’s flash sales produced 50× spikes. Moving to edge caching + cache-aside Redis dropped origin QPS by 70%. Async order writes via queues kept p99 under 400 ms during launches. A fintech separated OLTP from analytics with CDC to a warehouse; checkout p95 stabilized. Another team’s outage was traced to retries without jitter; adding circuit breakers and backoff eliminated retry storms. A marketplace cut error rates by routing hot product reads through hedged requests. Regular chaos drills—killing a zone, slowing the DB—proved multi-AZ resilience and validated automated rollback. The pattern repeats: caching and queues absorb heat, partitioned data scales, and resilience patterns bound the tail.

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Key Takeaways

• Scale stateless services horizontally; protect with edge cache and quotas.
  
• Read fast via Redis; write safely via sharding/replicas and queues.
  
• Bound tail latency with timeouts, retries + jitter, circuit breakers, hedging.
  
• Design for failure: multi-AZ/region, graceful degradation, canaries.
  
• Prove it with SLOs, tracing, load tests, and chaos drills.

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Practice Exercise

Scenario: You must serve 2M concurrent clients with mixed traffic: 85% cacheable reads, 15% writes that trigger downstream workflows. Availability target: 99.95%. p95 latency: ≤200 ms for reads, ≤600 ms for writes. You’ve had incidents with DB saturation and retry storms.

Tasks:

1. Edge & limits: Put a CDN/API gateway in front; enable ETag/TTL for GETs and token-bucket rate limits per tenant.
   
2. Compute: Containerize services; enable autoscaling on CPU, p95, and queue depth. Make all services stateless; sessions live in Redis/JWT.
   
3. Backpressure: Route writes to a durable queue; make operations idempotent (request IDs). Workers use bulkheads and fixed concurrency.
   
4. Data: Implement cache-aside Redis for hot keys; shard OLTP by tenant; add read replicas. Keep transactions short; move analytics via CDC.
   
5. Resilience: Add timeouts, retries with jitter, circuit breakers, and hedged reads. Define graceful fallbacks (serve cached catalog, defer recompute).
   
6. HA/DR: Deploy across 3 AZs; async replicate to a second region; health-checked DNS for failover.
   
7. Observability: Define SLOs; instrument RED/USE; add tracing, canaries, and dashboards.
   
8. Validation: Run a load test that spikes 20× while injecting 300 ms DB latency and dropping one AZ. Track p95/p99, error budget burn, queue depth, and cost.
   
   

Deliverable: A 2-minute narrative + dashboard screenshots showing pre/post metrics, how backpressure bounded latency, how fault tolerance patterns avoided a meltdown, and which levers you’d tune (limits, cache TTLs, worker concurrency) during an incident.