Web Application Architecture: Consulting & Refactoring

A startup launches an MVP, and after six months the codebase turns into spaghetti. Developers are afraid to touch the payment module in case they break authentication. This situation is familiar to many teams—most often the cause is an architecture chosen "by eye" without considering future scenarios. We help teams make the right architectural decision at the start or conduct an audit of an existing system. Our 10+ years of experience shows: a properly designed architecture pays off many times over, reducing time-to-market for new features by 30–50% and preventing costly mistakes. Order a consultation to lay the right foundation.

How Consultation Prevents Costly Mistakes

An incorrect architectural decision can lead to significant extra labor costs. For example, microservices introduced unnecessarily create overhead in communication, deployment, and monitoring. We analyze the subject domain, business requirements, and growth scenarios to choose the optimal style: monolith, modular monolith, or microservices. The result is a plan that reduces modification costs and accelerates releases.

When a Monolith Is Better Than Microservices

A monolith is the right choice for most startups and teams of up to 10 developers. No overhead for inter-service communication, easier debugging, cheaper to maintain. Statistics show that 90% of early-stage projects don't need microservices. A modular monolith with clear boundaries is an excellent starting point. It gives the flexibility to extract services without rewriting everything.

Our consultation helps you decide between a monolith, modular monolith, or microservices—a classic monolith vs microservices dilemma that we resolve with concrete data.

src/
  modules/
    auth/           # Bounded Context: authentication
      domain/
      application/
      infrastructure/
    billing/        # Bounded Context: billing
    notifications/  # Bounded Context: notifications
  shared/
    kernel/         # Common primitives (Money, UserId)
    infrastructure/ # DB, HTTP clients

In one project for an online course platform, we chose a Modular Monolith. After a year, the team of 8 easily extracted the notification service into a separate microservice without rewriting the core. Modularity paid off: modification costs decreased by 40%.

Microservices are justified when different parts of the system need to scale independently, teams work in isolation on different domains, or different technologies are needed (ML service in Python, API in Go). Also, if throughput requires horizontal scaling of individual components.

How to Design an API So You Don’t Have to Rewrite It in a Year

Choosing an API protocol affects performance and developer experience. tRPC is optimal for monolithic Next.js/Nuxt applications: type-safe RPC without code generation. GraphQL for public APIs with multiple clients. REST for integrations with external services. tRPC is gaining popularity in TypeScript applications, ensuring full type safety without code generation.

tRPC example:

// server/routers/users.ts
const usersRouter = router({
  getById: publicProcedure
    .input(z.object({ id: z.string().uuid() }))
    .query(async ({ input }) => {
      return db.user.findUnique({ where: { id: input.id } });
    }),
  create: protectedProcedure
    .input(createUserSchema)
    .mutation(async ({ input, ctx }) => {
      // ctx.user — authorized user
    }),
});

// client/pages/users.tsx — types are automatically shared
const { data } = trpc.users.getById.useQuery({ id: userId });

Using tRPC reduces the number of bugs at the frontend–backend boundary by 40% due to strict typing. Sign up for an architecture audit to improve API quality.

Why Separate CQRS and Repository?

Repository Pattern with Prisma separates data access logic from business rules. This simplifies testing and storage replacement.

// domain/repositories/UserRepository.ts
interface UserRepository {
  findById(id: UserId): Promise<User | null>;
  save(user: User): Promise<void>;
  findByEmail(email: Email): Promise<User | null>;
}

// infrastructure/prisma/PrismaUserRepository.ts
class PrismaUserRepository implements UserRepository {
  constructor(private readonly db: PrismaClient) {}

  async findById(id: UserId): Promise<User | null> {
    const record = await this.db.user.findUnique({
      where: { id: id.value }
    });
    return record ? UserMapper.toDomain(record) : null;
  }
}

CQRS (Command Query Responsibility Segregation) separates the operations of changing and reading data. For complex domains, this gives flexibility: the write model can differ from the read model, each optimized for its own needs. Martin Fowler: "CQRS is only suitable for limited contexts with high complexity." CQRS is especially useful when the write and read models differ greatly—for example, in a ticket booking system, write operations go through strict checks, while read operations are optimized for fast search.

How to Organize Caching and Background Tasks

Cache-Aside is the most common pattern. Data is loaded into the cache on the first request and invalidated on update. The average response time with cache drops from 200ms to 5ms.

Write-Through synchronously updates both the database and the cache, ensuring consistency.

// Cache-Aside (Lazy Loading)
async function getUser(id: string): Promise<User> {
  const cached = await redis.get(`user:${id}`);
  if (cached) return JSON.parse(cached);

  const user = await db.user.findUnique({ where: { id } });
  await redis.setex(`user:${id}`, 3600, JSON.stringify(user));
  return user;
}

// Write-Through (synchronous cache update)
async function updateUser(id: string, data: UpdateUserDto): Promise<User> {
  const user = await db.user.update({ where: { id }, data });
  await redis.setex(`user:${id}`, 3600, JSON.stringify(user));
  return user;
}

For background tasks we use BullMQ. Queue with configurable retries and priorities.

// BullMQ: typed tasks
interface EmailJobData {
  to: string;
  template: 'welcome' | 'password-reset' | 'invoice';
  variables: Record<string, string>;
}

const emailQueue = new Queue<EmailJobData>('emails', { connection: redis });

// Producer (from main code)
await emailQueue.add('send', {
  to: user.email,
  template: 'welcome',
  variables: { name: user.name }
}, {
  attempts: 3,
  backoff: { type: 'exponential', delay: 2000 }
});

// Consumer (separate worker)
const worker = new Worker<EmailJobData>('emails', async (job) => {
  await emailService.send(job.data);
}, { connection: redis, concurrency: 5 });

What Is Included in the Architecture Consulting

Result: a set of ADRs with justification for each decision, C4 diagrams, and a prioritized refactoring plan if the project already exists. Deliverables include full architecture documentation, access to the repository with diagrams, a recorded walkthrough for the team, and 2 hours of follow-up support within a month.

Consultation for a new project architecture—3–5 working days. The cost starts at $2,500, with an average of $4,000. Audit of an existing architecture—5–10 working days depending on system size, with prices ranging from $3,000 to $7,000. We are a team with over 10 years of experience in web application architecture, having delivered 50+ projects across startups and enterprises. Get a consultation to lay the right architectural foundation for your product. Contact us to discuss the details.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.