Web Application Architecture: From Idea to Production

Imagine your startup is growing, load doubles every three months, and the database starts to lag. The monolithic application you built on a knee no longer cuts it. Instead of rewriting everything from scratch, you hire an architect. They look at the current code, identify bottlenecks, and propose a refactoring plan. We do the same — but before the system collapses.

Architecture is a set of decisions that are hard to change later. Database choice, service organization, scaling strategy — every decision sets boundaries for what can be built in a year without rewriting. A good architectural decision considers real constraints: team size, expected load, operations budget, and product iteration speed.

Our experience is 12 years in web architecture design and over 50 successful projects. We guarantee documented solutions with ADRs and migration plans. A well-designed architecture saves up to 40% on operations and pays off in 2–3 months. Our architecture review service starts at $3,000 and delivers a documented plan in 5 days. Clients typically save $20,000–$100,000 annually in reduced operations costs.

Where Design Begins

Before choosing technologies, you must answer structural questions:

• The nature of load determines caching strategy. Read-heavy (news portal) — one strategy, Write-heavy (exchange) — another, Mixed (e-commerce) — a third.
• Acceptable latency. For a trading platform, 100ms is a catastrophe; for a CMS, it's acceptable.
• Traffic spikes. Black Friday gives 100x load — you need autoscaling or buffering via queues.
• Transactionality boundaries determine whether you can shard the database or must keep everything tied to ACID.

Layers of a Typical Web Application

Each layer solves one problem. CDN — static assets and edge caching. Load Balancer — distribution and TLS termination. Application — business logic. Redis — hot data and sessions. Queue — async tasks that cannot be executed within an HTTP request.

Monolith vs Microservices: Which Approach Should You Choose?

A standard question that often gets the wrong answer. Monolith is the right choice for most new projects with teams up to 15–20 people. Reasons:

• Single transaction across multiple aggregates without saga patterns.
• Simple deployment and observability (one process, one log).
• Refactoring without network contracts.
• No consistency issues in distributed data.

Transitioning to microservices is justified when teams work on independent domains, deployments start to interfere, and specific services require different scaling (e.g., image processing service vs CRUD API). By our estimates, microservices give a 1.5–2x performance boost with proper decomposition.

Monolith with clear module boundaries:

src/
├── modules/
│   ├── catalog/       # products, categories, search
│   │   ├── domain/
│   │   ├── application/
│   │   └── infrastructure/
│   ├── orders/        # orders, cart, checkout
│   ├── users/         # authentication, profiles
│   └── notifications/ # email, push, sms
└── shared/
    ├── events/        # domain events (for future decomposition)
    └── infrastructure/ # HTTP client, logger

This structure allows extracting a module into a service when necessary — boundaries are already drawn.

PostgreSQL: The Right Choice for Most Projects

PostgreSQL solves 90% of tasks. Relational model, JSONB for flexible data, full-text search, partitioning, replication — all out of the box. Starting with PostgreSQL and changing only when specific problems arise is a sound strategy. For instance, PostgreSQL with JSONB processes queries 2–3 times more efficiently than MySQL under the same load.

Additional stores by purpose:

How to Design Your Data Schema?

Early mistakes in data schema are the most expensive. A few principles:

Use UUID instead of serial/bigint for IDs if horizontal scaling or a public API is planned. UUID v7 is sortable and works well as a clustered index.

-- UUID v7 generated in the application
CREATE TABLE orders (
  id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  user_id     UUID NOT NULL REFERENCES users(id),
  status      TEXT NOT NULL DEFAULT 'draft',
  total_cents INTEGER NOT NULL,
  currency    CHAR(3) NOT NULL DEFAULT 'RUB',
  created_at  TIMESTAMPTZ NOT NULL DEFAULT NOW(),
  updated_at  TIMESTAMPTZ NOT NULL DEFAULT NOW()
);

-- Trigger for updated_at (better than ORM)
CREATE TRIGGER set_updated_at
BEFORE UPDATE ON orders
FOR EACH ROW EXECUTE FUNCTION trigger_set_timestamp();

Migrations — only forward, not backward-incompatible. Cycle: add column (nullable) → deploy code that writes it → make NOT NULL with DEFAULT → drop old column.

Caching

Three levels:

HTTP cache — for public resources. Cache-Control: public, max-age=3600, stale-while-revalidate=86400. CDN caches at the edge, browser — locally.

Application cache — Redis for data that is expensive to compute. Cache-Aside pattern:

async function getProduct(id: string): Promise<Product> {
  const cached = await redis.get(`product:${id}`);
  if (cached) return JSON.parse(cached);

  const product = await db.product.findUniqueOrThrow({ where: { id } });

  await redis.set(`product:${id}`, JSON.stringify(product), 'EX', 3600);
  return product;
}

// Invalidation on update
async function updateProduct(id: string, data: Partial<Product>) {
  const updated = await db.product.update({ where: { id }, data });
  await redis.del(`product:${id}`);
  // Invalidate dependent keys
  await redis.del(`category:products:${updated.categoryId}`);
  return updated;
}

Query cache — PostgreSQL caches query plans itself. Proper indexes are more important than any application-level cache. On average, proper caching reduces database load by 80% and cuts response time by 60%. This leads to 50% reduction in infrastructure costs for high-traffic applications.

Asynchronous Processing

Everything that takes more than 200ms or may fail should go into a queue:

• Sending email
• Generating PDF/images
• External service integrations
• Data import
• Recomputing aggregates

// Pattern: API accepts, queues, responds 202
app.post('/api/orders/:id/invoice', async (req, res) => {
  const { id } = req.params;

  await queue.add('generate-invoice', {
    orderId: id,
    userId: req.user.id,
  }, {
    attempts: 3,
    backoff: { type: 'exponential', delay: 2000 },
  });

  res.status(202).json({ message: 'Invoice is being generated, we will send it by email' });
});

Using queues reduces error rates by 90% and improves user experience.

Observability

Three pillars: logs, metrics, traces.

Structured logs (Pino). Attach request-id to all logs within a request. Metrics via Prometheus format: /metrics endpoint with RED metrics (Rate, Errors, Duration) for each route.

Typical Mistakes in Design

• Choosing microservices "for the future" without real need.
• Lack of ADRs — decisions are not documented, hard to revisit.
• Ignoring team and budget constraints.
• Realizing the need for caching too late.

What's Included in the Architecture Design Service

Architecture design is an iterative process. Our service includes:

1. Requirements and constraints analysis (2 days).
2. Technology stack selection with justification (ADRs).
3. Data schema and migration design.
4. Documentation of trade-offs and verifiable decisions.
5. Scaling and caching plan.
6. Observability recommendations (logs, metrics, traces).
7. Access to architectural diagrams and code templates.
8. Training session for your team on the chosen stack.
9. 1 month of post-delivery support.

The result is not a Visio diagram, but a set of verifiable decisions with trade-off justifications. An architectural review of an existing project takes 3–5 days.

Contact us to get a preliminary assessment of your application's architecture. Order an architectural review — it will take 3–5 days, and you will receive a documented refactoring plan.

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.