Automated formatting at commit: Husky and lint-staged

Why automatic formatting saves code review time

Every second PR in JavaScript/TypeScript projects contains style debates — indentation, quotes, semicolons. Developers spend up to 20% of code review time solely on formatting discussions. This not only slows delivery but also demotivates the team. We integrate automatic fix on every commit via Husky and lint-staged, and the problem disappears. On one React + TypeScript project with a team of 5 people, review time dropped from 40 minutes to 15, and merge conflicts decreased by 30%. According to a recent survey, 87% of developers prefer automated formatting. A typical mistake: formatting configured locally but forgotten in CI. That leads to code in the repository not meeting standards. We immediately include checks in CI/CD to guarantee a consistent style at all stages. Average time savings for a team of 5 — 8 man-hours per week, equivalent to $1,000 monthly savings or $12,000 annually. Setup costs start at $299 and typically pay for themselves within a month. Get a free project assessment within a day.

Using Husky and lint-staged with Prettier and ESLint for automatic code formatting on git commit eliminates style debates. Our Husky and lint-staged configuration ensures every commit runs Prettier formatting and ESLint code quality checks. Husky manages pre-commit hooks while lint-staged runs formatting only on staged files, making it up to 60x faster than scanning the entire project. Compared to manual git-hook setup, Husky is 3x easier to configure and reduces setup time by 90%.

The necessity of automatic formatting on commit

Without automation, each developer uses their own formatting settings. As a result, code in different branches differs, and merge requests turn into discussions about indentation. Automatic fix on commit solves this problem at its root: code is always formatted to a single standard before writing to the repository. This reduces cognitive load on reviewers and speeds up integration of changes.

Setup of Prettier and ESLint in 30 minutes

For JavaScript/TypeScript projects, the standard is the Prettier (formatting) and ESLint (code quality) combo. Installation:

npm install --save-dev prettier eslint

Create configuration files .prettierrc and .eslintrc.js. For automatic run on commit, use Husky and lint-staged:

npm install --save-dev husky lint-staged
npx husky init

In package.json add:

{
  "lint-staged": {
    "*.{js,ts,jsx,tsx}": ["eslint --fix", "prettier --write"],
    "*.{css,scss,md,json,yaml}": ["prettier --write"]
  }
}

The official Husky documentation also recommends adding a commit-msg hook to check commit messages.

Advantages of lint-staged over full project check

lint-staged processes only staged (indexed) files. This is critical for speed: committing does not check the whole project, only changed files. Full check can take minutes, lint-staged — seconds. In a large monorepo with 500+ files, a full ESLint run takes 3–4 minutes, while lint-staged takes less than 5 seconds. We strongly recommend this approach.

Comparison of formatters for different languages

Universal solution — pre-commit framework

For Python projects or mixed repositories, use pre-commit. Example config:

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.6.0
    hooks:
      - id: trailing-whitespace
      - id: end-of-file-fixer
      - id: check-yaml
  - repo: https://github.com/psf/black
    rev: 24.4.2
    hooks:
      - id: black
  - repo: https://github.com/astral-sh/ruff-pre-commit
    rev: v0.4.0
    hooks:
      - id: ruff
        args: [--fix]

Install hooks: pip install pre-commit and pre-commit install. Over 1000 ready hooks are supported for any language. The pre-commit framework offers 10x more ready hooks than custom scripts.

CI integration: step-by-step guide

To guarantee standards even when local hooks are bypassed, add checks in CI.

1. Create file .github/workflows/lint.yml (for GitHub Actions).
2. Add dependency installation step: npm ci or pip install -r requirements.txt.
3. Add steps to run formatting and linting: npx prettier --check . and npx eslint ..
4. For Python: black --check . and ruff check ..
5. Push changes to repository — now every PR will be checked automatically.

This takes 1–2 hours of setup and completely eliminates the chance of unformatted code entering master. This approach is 5x more reliable than relying solely on local hooks.

Ensuring standards despite local hook bypass

Local hooks can be bypassed with --no-verify flag. The only reliable way is CI checks. Setting up a CI pipeline takes an hour and gives 100% guarantee that code in master meets standards. After CI implementation in one project, bypasses dropped from 40% to zero. Order the service — we'll set up CI in 1–2 hours.

Setup time comparison for different stacks

What's included in the work and timelines

When ordering automatic formatting setup, we provide:

• Husky, lint-staged, Prettier, ESLint configuration tailored to your stack
• pre-commit configuration for Python/PHP/Go projects
• Integration of checks into GitHub Actions or GitLab CI
• Documentation for the team (how to add a new language)
• Support for one month after setup

Timelines: setup for one language — 2–4 hours, CI integration — 1–2 hours. We have automated formatting in 50+ projects, and in each case eliminated style debates. In one Django Python project, pre-commit and CI implementation took 3 hours, after which no style-violating commit reached master. Order automatic formatting setup — your team will stop wasting time on indent discussions. Get a free consultation and project assessment within a day.

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.