How would you architect a large-scale web application?

Long Answer

Designing a large-scale web application architecture is a leadership exercise as much as a technical one. You must enable many teams to ship safely and quickly, while keeping reliability, cost, and technical debt in check. The blueprint blends domain boundaries, paved roads, and measurable governance.

1) System shape: modular monolith → service extraction by evidence
Begin with a modular monolith organized by business domains (Accounts, Billing, Catalog, Checkout, Analytics). Enforce boundaries via package/module visibility, separate data access layers, and internal contracts. Extract a service only when a module’s scaling profile, deployment cadence, or team ownership demands it. When you split, preserve a stable public contract (gRPC/REST events), keep data ownership single-writer, and connect others through APIs or event streams (outbox pattern).

2) Contracts and data model
Define narrow, versioned API contracts (OpenAPI/Protobuf) and domain events (OrderPlaced, InvoicePaid). Treat the relational database as the system of record; normalize core entities, then add read projections (materialized views, cache) for hot paths. Avoid distributed joins across services—consume events or read models instead. Use schemas and consumer contract tests so clients fail fast in CI, not production.

3) Platform engineering and paved roads
Create golden paths: repo templates with linting, test harness, tracing, health checks, and deployment manifests. Provide infra as code (Terraform, Pulumi), a standardized CI/CD pipeline (build → test → scan → deploy), and preview environments per PR with seeded data. Include blue/green or canary rollout, automatic rollback on SLO violations, and feature flags to decouple deploy from release. Developers should rarely hand-craft YAML.

4) Runtime architecture for scalability and resilience
Front the app with a CDN and WAF; terminate TLS at an API gateway that centralizes auth, rate limits, and request logging. Run stateless app nodes on containers/functions with horizontal autoscaling. Use a managed RDBMS with read replicas and partitioning where data volume proves it; queues (Kafka/Rabbit/SQS) handle asynchronous workloads and backpressure. Cache aggressively (edge, application, query-level). For fault isolation, adopt circuit breakers, bulkheads, and timeouts; design graceful degradation paths (read-only mode, skeleton UIs).

5) Observability and SLOs
Bake in telemetry: structured logs with trace/correlation IDs, RED/USE metrics, and distributed tracing across app, jobs, and data layers. Define SLOs (e.g., checkout availability 99.9%, P95 < 300 ms) and alert on error budget burn, not single spikes. Make golden dashboards and run weekly reliability reviews that convert incidents into action items and, if needed, a temporary release throttle.

6) Collaboration model: ownership, RFCs, ADRs
Map code ownership to domains; each directory has OWNERS and on-call rotation. Cross-team changes follow a short RFC and are memorialized as Architecture Decision Records (ADRs). Establish a Technical Design Review Cadence (lightweight, time-boxed). Document public contracts and event catalogs so teams self-serve. This reduces meetings and prevents “tribal knowledge” lock-in.

7) Security and compliance
Centralize auth (OIDC), define scopes/claims-based authorization, and guard internal contracts with mTLS. Automate dependency and container scanning, SBOM generation, and secrets in a vault. For regulated flows, add audit logs, encryption at rest/in transit, and key rotation. Security becomes paved-road defaults, not ad-hoc heroics.

8) Testing strategy
Adopt a pyramid: fast unit tests for domain logic, integration tests for data/queues, and a few critical E2E smoke flows. Add contract tests at service boundaries. Use ephemeral test environments and seed data builders to avoid flake. Performance tests (load, soak) run against realistic datasets; regressions fail the pipeline.

9) Managing technical debt deliberately
Track debt in a visible debt register tagged by risk, blast radius, and eco-cost (latency, toil, dollars). Tie work to error budget burn or DORA regressions. Allocate a recurring “reliability & debt” budget (for example, 15–20% team capacity) and time-box spikes. Prefer refactors that increase leverage: deleting dead paths, simplifying contracts, or paving a new template everyone adopts. Debt without a plan is a risk; debt with a plan is an investment.

10) Evolution and migration playbooks
For schema or contract changes, use expand → migrate → contract. Run shadow reads or dual-writes, measure parity, then flip traffic. Keep rollback scripts as first-class citizens. When carving out a service, migrate the write path first, backfill state via CDC, and only then move reads. Publish an owner’s runbook: alerts, dashboards, SLOs, and how to operate the thing at 3 a.m.

This approach yields a large-scale web application architecture that scales horizontally, remains maintainable through clear seams and paved roads, and lets many teams collaborate productively while technical debt is managed with intent, not ignored.

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Table

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Common Mistakes

• Premature microservices that multiply latency, deployments, and on-call toil.
  
• Wide, unstable contracts that leak internals and freeze refactors.
  
• Skipping observability and SLOs, so incidents are invisible and opinions trump data.
  
• Treating CI/CD as bespoke per team; paved roads rot, drift increases.
  
• Distributed joins and cross-DB transactions that collapse under load.
  
• “Refactor Fridays” with no budget or criteria; debt grows silently until a rewrite is demanded.
  
• One-size-fits-all caching or autoscaling that ignores workload shapes.
  
• Big-bang schema changes without expand → migrate → contract and rollbacks.
  
  

Sample Answers (Junior / Mid / Senior)

Junior:
“I would start with a modular monolith for speed. I would add typed REST endpoints, keep business logic in services, and create a CI pipeline that runs tests and deploys with rollback. I would add tracing and dashboards, and use a CDN and cache to handle load.”

Mid:
“My large-scale web application architecture uses domain packages, contract-first APIs, and read projections. Platform engineering provides templates and IaC; CI/CD ships canaries with feature flags. We define SLOs and error budgets, and use queues for async work. When data shows a hotspot, we extract that module into a service with a stable contract and outbox events.”

Senior:
“I lead with paved roads, ownership, and SLOs. Code is organized by domain; contracts are versioned and tested. Runtime scales via CDN/gateway, autoscaling containers, RDBMS + replicas, and queues. Observability and DORA metrics drive decisions. Technical debt is tracked, budgeted, and paid via time-boxed refactors aligned to error budget burn. Migrations follow expand → migrate → contract with shadow reads and rollbacks.”

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Evaluation Criteria

A strong answer frames a large-scale web application architecture that optimizes team throughput and system reliability. Look for domain boundaries, monolith-first with evidence-driven extraction, contract-first APIs/events, and read projections. Platform engineering should provide golden paths (templates, IaC, CI/CD, observability). Scalability patterns include CDN/gateway, autoscaling, queues, caching, and graceful degradation. Governance shows up as ownership, RFCs/ADRs, SLOs, and a visible technical debt program. Red flags: early microservice sprawl, distributed joins, bespoke pipelines, missing telemetry, and unmanaged debt or big-bang rewrites.

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Preparation Tips

• Build a two-domain modular monolith; add typed contracts and a small event stream.
  
• Stand up a golden-path template with tests, tracing, health checks, and deploy manifests.
  
• Wire CI/CD with canary + auto-rollback on SLO breach; add preview environments.
  
• Add dashboards for RED/USE and define two SLOs; create one alert on error budget burn.
  
• Implement a read projection (materialized view or cache) for a hot endpoint; measure impact.
  
• Run a load test; add circuit breakers and graceful degradation; document results in an ADR.
  
• Create a debt register with risk scores; allocate a recurring capacity slice and close two items.
  
• Practice an expand → migrate → contract schema change with shadow reads and rollback.
  
  

Real-world Context

A commerce platform stayed monolithic for the first year, using domain modules and paved CI/CD. When checkout latency and ownership friction rose, they extracted Checkout as a service with a stable contract and outbox events; incidents dropped and deploy cadence increased. A collaboration SaaS adopted SLOs and error budgets; reliability work reclaimed 15% capacity but cut MTTR in half. Another team introduced a golden-path repo template; new services launched in hours, not weeks. Finally, a data-heavy product replaced distributed joins with read projections and queues, reducing P95 by 35% while keeping the technical debt register visible and steadily shrinking.

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Key Takeaways

• Monolith first, modular always; extract services when data proves it.
  
• Contract-first APIs/events; single-writer data ownership with read projections.
  
• Paved roads (IaC, CI/CD, observability) unlock team velocity and consistency.
  
• SLOs, error budgets, and DORA metrics steer reliability and roadmap trade-offs.
  
• Manage technical debt with a register, budget, and time-boxed, high-leverage refactors.
  
  

Practice Exercise

Scenario:
You are the Technical Lead for a fast-growing marketplace. Engineering has four squads (Search, Catalog, Checkout, Growth). Traffic spikes during promotions; leadership wants weekly releases, strict reliability, and a plan to keep technical debt under control.

Tasks:

1. Propose the initial large-scale web application architecture: modular monolith domains, public contracts, and data ownership for each squad.
   
2. Define the platform golden path: repo template, CI/CD (tests, scans, canary, rollback), preview environments, and IaC.
   
3. Specify runtime scaling: CDN/WAF, API gateway, autoscaling containers, queue for async tasks, and read projections for Search and Catalog.
   
4. Write two SLOs (availability and latency) for Checkout; describe error budget policy and how it throttles releases.
   
5. Design an extraction plan for Checkout when metrics show sustained hotspots: which contract, which DB tables move, and how outbox + CDC backfill work.
   
6. Create an observability plan: logs with correlation IDs, RED/USE dashboards, traces across services, and alert thresholds.
   
7. Draft a technical debt program: a register with risk scores, capacity allocation per sprint, and a quarterly review; include three example items with “delete-to-win” impact.
   
8. Provide migration playbooks for schema and API changes using expand → migrate → contract, shadow reads, canary traffic, and rollback scripts.
   
   

Deliverable:
A concise architecture and runbook showing how your large-scale web application architecture scales, remains maintainable, fosters cross-team collaboration, and manages technical debt proactively.