How would you architect a large-scale Laravel application?

Long Answer

A production-grade Laravel application architecture must support large-scale workloads, keep the codebase maintainable, and enable modular feature separation. Laravel offers first-class primitives to achieve this: service providers for composition, events for decoupling, and jobs for asynchronous execution. The aim is a system that is fast, testable, and evolvable without fragile couplings.

1) Domain boundaries and folder structure
Organize by business capability, not by technical layers. Create top-level modules such as Accounts, Catalog, Orders, and Billing. Each module contains Http (controllers and requests), Domain (entities, value objects, policies), Application (use cases, services), and Infrastructure (repositories, mappers, external clients). Keep framework glue at the edges. Use PHP namespaces that reflect the module, and avoid cross-module imports that bypass contracts.

2) Service providers as composition roots
Every module registers a service provider that binds interfaces to implementations, publishes configuration, and wires observers, policies, and routes. The application boot process becomes predictable: core providers load first, then feature providers. Bind repositories, domain services, and client factories in the container. Expose only stable interfaces from providers so other modules depend on contracts, not concrete classes. This yields replaceable parts and clean seams for testing.

3) Controllers thin, application services rich
Controllers handle transport concerns: validation via form requests, authentication, and response shaping. Business rules live in application services that orchestrate domain objects and repositories. Return data transfer objects or view models rather than passing Eloquent models through the entire stack. This prevents an anemic domain and keeps logic testable without booting the framework.

4) Persistence and repositories
Use repositories to encapsulate Eloquent queries and to prepare for future storage changes. Add query objects for complex read paths and use keyset pagination for predictable performance. Enforce optimistic concurrency where required. For multi-tenant systems, scope queries by tenant in the repository layer to avoid leaks and to keep controller code simple.

5) Events for decoupling and integration
Model key business facts as events such as OrderPlaced, PaymentCaptured, or UserRegistered. Domain events fire from application services after successful transactions. Multiple listeners can react independently to the same event, updating read models, sending notifications, or scheduling jobs. Keep event payloads explicit and immutable, include correlation identifiers, and version them as the domain evolves. This pattern enables modular feature separation and avoids brittle direct calls.

6) Jobs and queues for throughput
Slow or bursty work belongs in jobs: emails, invoicing, exports, third-party webhooks, media processing. Make jobs idempotent with natural keys; handle retries with exponential backoff and dead-letter queues. Partition queues by domain and priority so one stream cannot starve another. Use batch jobs for bulk operations, and chain jobs for multi-step flows. For long tasks, report progress and store checkpoints to allow safe restarts during deployments.

7) Caching, read models, and performance
Adopt layered caching: response cache where safe, application cache for frequently used aggregates, and database query cache via tags. Store denormalized read models maintained by event listeners to offload expensive joins from hot paths. Cache keys should include tenant, locale, and version identifiers. Evict by tag or on relevant events rather than using large time-based expirations. Add rate limits, circuit breakers, and timeouts to all external calls.

8) Validation, policies, and invariants
Validate inputs with form requests and rules. Enforce authorization using policies bound in service providers. Keep domain invariants inside entities or services so they are enforced consistently from any entry point. Add guards around money, quantities, and states using value objects to avoid silent type errors.

9) Observability and operations
Emit structured logs with correlation identifiers that propagate through controllers, events, and jobs. Track RED metrics (rate, errors, duration), queue depth, retry rates, and cache hit ratios. Enable distributed tracing where possible. Health checks should distinguish liveness from readiness so rolling deployments and graceful shutdowns drain workers without losing jobs.

10) Configuration, environment parity, and delivery
Keep configuration in code and environment variables. Use per-environment service providers sparingly for feature flags or bindings that differ in production. Automate database migrations with zero-downtime patterns and backfills via jobs. Adopt a monorepo or a well-defined package structure for modules. Contracts change additively and are documented with change logs.

11) Testing and contract safety
Write unit tests for domain and application services, integration tests for repositories, and feature tests for critical routes. Test events and jobs with in-memory queues and fakes. Capture performance baselines and run smoke loads on canary releases. Add consumer-driven tests when modules depend on one another through contracts.

12) Runtime scaling and workers
Run multiple stateless PHP-FPM or Octane workers behind a load balancer. Scale queue workers horizontally by queue and priority. Use supervisor or containers with proper signal handling. Tune connection pools and set queue timeouts to avoid indefinite stalls. Warm caches and compile containers during deployment to reduce cold-start latency.

This Laravel application architecture leverages service providers for composition, events for decoupling, and jobs for throughput, producing a modular system that scales traffic and teams while remaining maintainable.

‍

Table

‍

Common Mistakes

• Placing business logic in controllers or models, producing tight coupling and poor testability.
  
• Skipping service providers, binding concrete classes directly and making modules hard to replace.
  
• Emitting unversioned events with ambiguous payloads that break listeners during changes.
  
• Using a single global queue where heavy jobs starve critical notifications or payments.
  
• Writing non-idempotent jobs that double charge or duplicate emails under retries.
  
• Caching without keys or tags that include tenant and version, causing data leaks or stale reads.
  
• Overusing observers for business logic that belongs in application services.
  
• Running migrations that lock tables during peak hours without backfills or phased rollouts.
  
• Lacking correlation identifiers and queue lag dashboards, making incidents slow to diagnose.
  
  

Sample Answers (Junior / Mid / Senior)

Junior:
“I would keep controllers thin and move logic to services. Each module would have a service provider that binds interfaces and registers policies. Slow work such as emails would run as jobs on a queue, and I would use events like UserRegistered to trigger them.”

Mid:
“My Laravel application architecture is modular: domains packaged with their own service providers and repositories. Controllers validate and call use cases. Domain events publish changes, and listeners update read models or dispatch jobs. Queues are partitioned by priority, jobs are idempotent, and cache keys include tenant and locale. Metrics track queue depth, p95 latency, and error rates.”

Senior:
“I compose the app through module service providers that expose contracts and policies. Business facts are expressed as versioned events with correlation identifiers. Slow operations are jobs with retries, dead-letter queues, and progress checkpoints. Read models and tagged caches keep hot paths fast. Deployments drain workers gracefully, and observability covers RED metrics, traces, and consumer lag. Contracts evolve additively.”

‍

Evaluation Criteria

A strong answer defines a Laravel application architecture with domain modules, service providers for binding and bootstrapping, thin controllers, repositories, and application services. It should use events for decoupling and jobs for asynchronous work with idempotency, retries, and dead-letter queues. It should describe caching, read models, and tenancy scoping, and include observability with metrics, tracing, and health probes. It should mention safe migrations and graceful shutdowns. Red flags include logic in controllers, a single global queue, unversioned events, missing idempotency, and no instrumentation.

‍

Preparation Tips

• Create a small domain module with its own service provider, interfaces, and bindings.
  
• Refactor a controller to call an application service and return a DTO.
  
• Emit a domain event and attach two listeners: one updates a read model, one dispatches a job.
  
• Implement an idempotent job with retries and a dead-letter mechanism; prove safe re-execution.
  
• Add tagged caches and invalidate them on events.
  
• Introduce correlation identifiers and structured logs; wire queue depth and latency dashboards.
  
• Practice zero-downtime migrations: expand, backfill via jobs, switch reads, then contract.
  
• Partition queues by domain or priority; demonstrate that a heavy export does not block notifications.
  
• Document contracts and version event payloads; add change logs and deprecation timelines.
  
  

Real-world Context

A marketplace split a monolith into Laravel modules aligned with domains. Each module registered a service provider that bound repositories and policies. Controllers became thin, while application services enforced rules. When OrderPlaced fired, listeners updated a denormalized order summary and dispatched invoice jobs. Partitioned queues ensured exports never delayed payment captures. Read models and tagged caches reduced hot query latency. Migrations followed expand, backfill with jobs, switch reads, and contract, enabling zero downtime. With correlation identifiers and queue lag dashboards, incidents were triaged quickly. The Laravel application architecture scaled traffic and team size without sacrificing clarity.

‍

Key Takeaways

• Compose modules through service providers that bind interfaces and register policies.
  
• Keep controllers thin; put business logic in application services and repositories.
  
• Use events for decoupling and jobs for throughput with idempotency and retries.
  
• Maintain read models and tagged caches to keep hot paths fast.
  
• Instrument metrics and tracing; run zero-downtime migrations and drain workers gracefully.
  
  

Practice Exercise

Scenario:
You are building a multi-tenant billing and orders platform on Laravel. The system must process spikes in checkout, issue invoices, and notify customers without slowing the frontend. Teams will ship features independently and must avoid tight coupling.

Tasks:

1. Create two modules, Orders and Billing, each with a service provider that binds repositories, policies, and routes.
   
2. Implement PlaceOrder as an application service. The controller validates with a form request, calls the service, and returns a DTO.
   
3. After a successful place, emit a versioned OrderPlaced event with correlation identifiers and tenant information.
   
4. Add two listeners: one updates a denormalized OrderSummary read model; the other dispatches a GenerateInvoice job.
   
5. Make GenerateInvoice idempotent, with retries and a dead-letter queue. Store a natural idempotency key and write progress checkpoints.
   
6. Partition queues into high, default, and low. Map invoice jobs to high, exports to low. Demonstrate that exports cannot starve invoices.
   
7. Add tagged caching for order summaries keyed by tenant and version. Invalidate on OrderUpdated events.
   
8. Instrument metrics: rate, errors, latency, queue depth, retry rates. Add health probes and graceful shutdown for workers.
   
9. Perform a zero-downtime change adding tax_amount: expand schema, backfill with a job, switch reads, and remove legacy code.
   
10. Document the module contracts, event versions, and queue priorities in a short architecture note.
    
    

Deliverable:
A concise implementation plan and code skeleton that demonstrates a scalable, modular Laravel application architecture using service providers, events, and jobs to handle large-scale workloads safely.