Java Backend Development with Quarkus (Turnkey)

Many Java applications take 10 seconds to start and consume 500 MB of RAM. In Kubernetes, this is critical: slow startup kills auto-scaling, and excess resources increase cloud bills. We've rethought backend architecture—we use Quarkus, a framework optimized for cloud-native. Our engineers have 10+ years of Java experience and 50+ delivered projects. We guarantee 3–5× reduction in infrastructure costs through native compilation. For example, one retail client saved $4000 per month on cloud expenses after migrating from Spring Boot.

How Quarkus Saves Resources?

Quarkus compiles applications into native images via Quarkus. Cold start time—0.01–0.1 seconds vs. 2–10 seconds for Spring Boot. Memory consumption—20–60 MB vs. 200–500 MB. These aren't just numbers: fast startup enables real horizontal scaling in Kubernetes, and for Serverless, it's the difference between a working and non-working solution.

Quarkus starts 10× faster and uses 5× less memory. It uses familiar specifications: JAX-RS, CDI, JPA, MicroProfile. Migration from Spring takes a few days. Key differences: DI via CDI (@ApplicationScoped), REST via RESTEasy Reactive (JAX-RS), ORM via Hibernate with Panache (Active Record or Repository).

How Quarkus Development Works?

We start by configuring the project and Dev Services: PostgreSQL, Kafka, Redis launch automatically in Docker. Migrations via Flyway. Quarkus Dev Mode has one of the best hot-reload mechanisms in the Java ecosystem: changes apply without restart.

// Product entity with Panache Active Record
@Entity
@Table(name = "products")
public class Product extends PanacheEntityBase {

    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    public Long id;

    @Column(nullable = false, length = 255)
    public String name;

    @Column(unique = true)
    public String slug;

    @Column(precision = 10, scale = 2)
    public BigDecimal price;

    @ManyToOne(fetch = FetchType.LAZY)
    @JoinColumn(name = "category_id")
    public Category category;

    public boolean isActive = true;

    @Column(columnDefinition = "jsonb")
    @Type(JsonType.class)
    public Map<String, Object> attributes = new HashMap<>();

    // Static Panache methods
    public static List<Product> findActive() {
        return list("isActive", true);
    }

    public static Page<Product> findActiveByCategory(Long categoryId, int page, int size) {
        return find("category.id = ?1 and isActive = true", categoryId)
            .page(page, size);
    }
}

Resources (controllers) are implemented with RESTEasy Reactive, which natively supports non-blocking operations:

@Path("/api/v1/products")
@Produces(MediaType.APPLICATION_JSON)
@Consumes(MediaType.APPLICATION_JSON)
@ApplicationScoped
public class ProductResource {

    @Inject
    ProductService productService;

    @GET
    @Authenticated
    public Response list(
            @QueryParam("page") @DefaultValue("0") int page,
            @QueryParam("size") @DefaultValue("20") @Max(100) int size,
            @QueryParam("category_id") Long categoryId) {

        PanacheQuery<Product> query = categoryId != null
            ? Product.find("category.id = ?1 and isActive = true", categoryId)
            : Product.find("isActive", true);

        List<Product> products = query
            .page(page, size)
            .list();

        long total = query.count();

        return Response.ok(new PagedResponse<>(
            products.stream().map(ProductDto::from).toList(),
            page, size, total
        )).build();
    }

    @POST
    @RolesAllowed("admin")
    @Transactional
    public Response create(@Valid CreateProductRequest request) {
        Product product = productService.create(request);
        return Response.status(Response.Status.CREATED)
            .entity(ProductDto.from(product))
            .build();
    }

    @GET
    @Path("/{id}")
    public ProductDto get(@PathParam("id") Long id) {
        return Product.findByIdOptional(id)
            .map(ProductDto::from)
            .orElseThrow(NotFoundException::new);
    }

    @DELETE
    @Path("/{id}")
    @RolesAllowed("admin")
    @Transactional
    public Response delete(@PathParam("id") Long id) {
        boolean deleted = Product.deleteById(id);
        return deleted ? Response.noContent().build() : Response.status(404).build();
    }
}

How to Configure Security?

We configure security via SmallRye JWT. Configuration in application.properties:

mp.jwt.verify.publickey.location=META-INF/resources/publicKey.pem
mp.jwt.verify.issuer=https://myapp.com
quarkus.http.auth.permission.authenticated.paths=/api/v1/*
quarkus.http.auth.permission.authenticated.policy=authenticated
quarkus.http.auth.permission.public.paths=/api/v1/auth/*,/api/v1/products
quarkus.http.auth.permission.public.policy=permit

What Does Native Compilation Provide?

We configure native build once in CI/CD. Build command:

./mvnw package -Pnative -DskipTests
# or via Docker
./mvnw package -Pnative -Dquarkus.native.container-build=true

At production startup, you get a binary that launches in milliseconds. This is especially important for Serverless functions and ephemeral pods in Kubernetes.

Case Study: E-Commerce Backend on Quarkus

We implemented a catalog with filtering and a shopping cart for a major retailer. The original Spring Boot project started in 8 seconds, consumed 400 MB RAM. After migration to Quarkus native: cold start 0.05 s, memory 45 MB. A cheap 2-core, 4 GB RAM server handles a peak load of 1000 RPS. Cloud savings were 4× — over $4000 per month.

Work Process

1. Requirements analysis and technical specification. We capture API, entity, and integration requirements.
2. Project setup: Quarkus 3.x, Java 21, PostgreSQL, Redis, Docker Compose.
3. Entity and resource development using Panache and RESTEasy Reactive.
4. Authorization implementation (JWT, OAuth2, Keycloak).
5. Integration with external services via REST or message queues.
6. Writing tests (QuarkusTest + RestAssured).
7. CI/CD configuration with native build and Kubernetes deployment.
8. Delivery of documentation, repository, credentials, and team training.

What's Included

• REST API development with OpenAPI documentation
• Authorization setup (JWT, OAuth2, Keycloak)
• Database migrations (Flyway/Liquibase)
• Integration with message queues (Kafka, RabbitMQ) and cache (Redis)
• Test writing (QuarkusTest + RestAssured)
• CI/CD pipeline with native build and Kubernetes deployment
• Delivery of repository, documentation, credentials, and training for the client's team

Estimated Timelines

Contact us for a project assessment. Get a consultation on migrating from Spring Boot or starting a new project. We guarantee transparent timelines and results that meet Core Web Vitals.

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.