Backend on Go Fiber: Performance and JWT Authorization

Our company is engaged in the development, support and maintenance of sites of any complexity. From simple one-page sites to large-scale cluster systems built on micro services. Experience of developers is confirmed by certificates from vendors.

Development and maintenance of all types of websites:

Informational websites or web applications
Business card websites, landing pages, corporate websites, online catalogs, quizzes, promo websites, blogs, news resources, informational portals, forums, aggregators
E-commerce websites or web applications
Online stores, B2B portals, marketplaces, online exchanges, cashback websites, exchanges, dropshipping platforms, product parsers
Business process management web applications
CRM systems, ERP systems, corporate portals, production management systems, information parsers
Electronic service websites or web applications
Classified ads platforms, online schools, online cinemas, website builders, portals for electronic services, video hosting platforms, thematic portals

These are just some of the technical types of websites we work with, and each of them can have its own specific features and functionality, as well as be customized to meet the specific needs and goals of the client.

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Backend on Go Fiber: Performance and JWT Authorization
Medium
from 1 week to 3 months
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Backend on Go Fiber with JWT authorization — a typical stack when a Node.js service hits limits, and migration to Go seems complex. Fiber provides a bridge: a familiar Express API but with fasthttp performance. We have been using Fiber for high-load projects for 5+ years and are ready to share experience from 20+ live projects.

One case — an e-commerce store with peak load of 10,000 RPS. Switching from Express to Fiber reduced response time by 40% and memory consumption by 30%. The Node.js development team mastered Fiber within a week thanks to the familiar syntax. Cloud cost savings amounted to approximately $300–500 per month due to lower memory usage, and the full migration paid off in 3 months with a budget of $12,000.

Why Fiber is faster than net/http

Fiber is a Go framework inspired by Express.js. If a developer comes from Node.js, the API will feel familiar. Under the hood, it uses fasthttp instead of the standard net/http, providing ~2x improvement in synthetic HTTP benchmarks. In real-world projects with PostgreSQL and Redis, the difference is smaller, but Fiber remains one of the fastest Go frameworks. According to official Fiber benchmarks, it handles ~400,000 requests/sec vs ~200,000 for Gin. We have implemented it in 10+ commercial projects and observed memory consumption reduction of up to 30%.

An important nuance: fasthttp is incompatible with net/http middleware. This means some of the Go ecosystem (e.g., standard OpenTelemetry middleware for net/http) does not work directly — adapters or Fiber-specific packages are needed.

Implementing JWT Authorization in Fiber

Step 1: Application Initialization

package main

import (
    "log"
    "os"

    "github.com/gofiber/fiber/v2"
    "github.com/gofiber/fiber/v2/middleware/compress"
    "github.com/gofiber/fiber/v2/middleware/cors"
    "github.com/gofiber/fiber/v2/middleware/helmet"
    "github.com/gofiber/fiber/v2/middleware/logger"
    "github.com/gofiber/fiber/v2/middleware/recover"
    "github.com/gofiber/fiber/v2/middleware/limiter"
)

func main() {
    app := fiber.New(fiber.Config{
        AppName:               "MyAPI v1.0",
        ReadTimeout:           10 * time.Second,
        WriteTimeout:          10 * time.Second,
        IdleTimeout:           120 * time.Second,
        BodyLimit:             4 * 1024 * 1024, // 4MB
        ErrorHandler:          customErrorHandler,
        DisableStartupMessage: true,
    })

    app.Use(recover.New())
    app.Use(helmet.New())
    app.Use(compress.New(compress.Config{Level: compress.LevelBestSpeed}))
    app.Use(cors.New(cors.Config{
        AllowOrigins:     os.Getenv("ALLOWED_ORIGINS"),
        AllowCredentials: true,
        AllowHeaders:     "Origin, Content-Type, Authorization",
    }))
    app.Use(logger.New(logger.Config{
        Format: "${time} | ${status} | ${latency} | ${method} ${path}\n",
    }))
    app.Use(limiter.New(limiter.Config{Max: 100, Expiration: 60 * time.Second}))

    setupRoutes(app)

    log.Fatal(app.Listen(":8080"))
}

Step 2: Routing and Grouping

func setupRoutes(app *fiber.App) {
    api := app.Group("/api/v1")

    // Public
    api.Post("/auth/login", authHandler.Login)
    api.Post("/auth/refresh", authHandler.Refresh)

    // With JWT middleware
    api.Get("/products", productHandler.List)
    api.Get("/products/:id", productHandler.Get)

    protected := api.Group("/", jwtMiddleware)
    protected.Get("/profile", authHandler.Profile)

    admin := api.Group("/admin", jwtMiddleware, roleMiddleware("admin"))
    admin.Post("/products", productHandler.Create)
    admin.Put("/products/:id", productHandler.Update)
    admin.Delete("/products/:id", productHandler.Delete)
}

Step 3: Handlers

We keep handlers thin: BodyParser into an input struct with validate tags, validator.Struct for validation, service call, mapping domain errors to HTTP statuses. Pagination — via c.QueryInt("page", 1) and c.QueryInt("limit", 20) with an upper limit of 100 records per page. Response is formed using fiber.Map with keys data and pagination so the frontend receives a uniform format. Validation errors return with status 422 and an errors field — an array of 3–5 fields per request. Each handler is about 30–40 lines of code, which is convenient for code review and typical testing.

Step 4: JWT Middleware

package middleware

import (
    "strings"

    "github.com/gofiber/fiber/v2"
    "github.com/golang-jwt/jwt/v5"
)

func JWTMiddleware(secret string) fiber.Handler {
    return func(c *fiber.Ctx) error {
        auth := c.Get("Authorization")
        if !strings.HasPrefix(auth, "Bearer ") {
            return fiber.ErrUnauthorized
        }

        token, err := jwt.Parse(auth[7:], func(t *jwt.Token) (interface{}, error) {
            if _, ok := t.Method.(*jwt.SigningMethodHMAC); !ok {
                return nil, fiber.ErrUnauthorized
            }
            return []byte(secret), nil
        })

        if err != nil || !token.Valid {
            return fiber.ErrUnauthorized
        }

        claims := token.Claims.(jwt.MapClaims)
        c.Locals("userID", int(claims["sub"].(float64)))
        c.Locals("role", claims["role"])
        return c.Next()
    }
}

What are the limitations of fasthttp?

fasthttp reuses request/context objects to reduce GC pressure. This requires caution: you must not capture *fiber.Ctx in goroutines without c.Copy(). When passing context to async operations:

func (h *Handler) AsyncProcess(c *fiber.Ctx) error {
    // Do not do this — ctx will be reused before goroutine completes
    // go func() { h.svc.Process(c) }()

    // Make a copy
    cc := c.Copy()
    go func() {
        h.svc.ProcessAsync(context.Background(), cc.Body())
    }()

    return c.SendStatus(fiber.StatusAccepted)
}
Production configuration for HTTPS

For production, we recommend using TLS via a reverse proxy (Nginx) or built-in listener:

app.ListenTLS(":443", "/path/to/cert.pem", "/path/to/key.pem")

What's included in development

We document the work order in a SoW with a checklist of deliverables:

  1. Architecture design: database schema in dbdiagram, OpenAPI 3.1 specification, service diagram.
  2. Fiber application setup: middleware stack (CORS, logger, recover, limiter), timeout configuration.
  3. Domain implementation: handlers, services, repositories with pgx and transactions.
  4. Authorization: JWT access + refresh, role model, blacklist via Redis.
  5. Testing and deployment: unit + integration (100+ cases), Dockerfile, CI/CD in GitHub Actions.
Stage Result
Architecture Database schema, API documentation (OpenAPI)
Middleware CORS, logging, rate limits, security
Business logic Handlers, services, repositories
Authorization JWT with roles, refresh tokens
File upload Validation, storage (S3/local)
Testing Unit + integration tests (100+ cases)
Deployment Dockerfile, CI/CD, monitoring

Development timelines

Work Time
Setup + middleware + routes 3–5 days
Handlers + service layer 1–2 weeks
Repository + pgx 3–5 days
Auth + cache 3–5 days
Tests 1 week

API for a website: 4–8 weeks. Fiber is well-suited for teams with Node.js background transitioning to Go, and for projects with extreme RPS requirements. We have 5+ years of experience with Go and guarantee code quality.

Get a consultation and estimate in 1 day. Contact us to discuss your project.

Additionally, we cover the observability layer: connect OpenTelemetry via Fiber adapters, send traces to Jaeger or Grafana Tempo, export metrics to Prometheus via /metrics. For high-load APIs, we add a pgxpool connection pool with 25–50 connections and pgbouncer in transaction mode before PostgreSQL — this handles peaks up to 15,000 RPS without p95 degradation. We structure logs using zerolog in JSON so Loki and ClickHouse can index them by 8–10 fields. We also include graceful shutdown with a 30-second drain to prevent Kubernetes rollout from breaking open connections. All these details are documented in a 15–20 page runbook that remains with the client's team and is updated during the 3-month warranty support period.

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:

Role Details
Cache Caching results of heavy queries, HTML fragments
Queues Backend for Laravel Queue / Celery
Session store Distributed sessions in multi-instance environment
Pub/Sub Realtime events between services
Rate limiting Sliding window counters for API throttling
Leaderboards Sorted Sets for rankings

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

Task Timeline
REST API for mobile/SPA (medium complexity) 6–12 weeks
Backend with complex business logic + integrations 12–20 weeks
High-load service on Go 8–16 weeks
Migration from legacy PHP to Laravel 16–32 weeks

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.