Question 1

Python vs Node.js for backend development — when should I choose Python?

Accepted Answer

Choose Python when: your project involves data processing, ML/AI integration, scientific computing, or complex business logic that benefits from Python's readability. Django provides a batteries-included framework with admin panel, ORM, authentication, and migrations out of the box. Choose Node.js when: you need maximum concurrent connections, real-time WebSocket performance, or your team is already JavaScript-heavy. Python's GIL limits true multi-threading for CPU work, but async frameworks (FastAPI, asyncio) handle I/O concurrency well.

Question 2

Django vs FastAPI — which framework do your Python developers use?

Accepted Answer

Django for: full-stack web applications with admin panels, content management, e-commerce, and projects where you need ORM, authentication, migrations, and admin UI out of the box. FastAPI for: high-performance APIs, microservices, ML model serving, and projects where async performance matters. FastAPI is 2-5x faster than Django REST Framework for pure API workloads. We often use both: FastAPI for the public API layer, Django for the internal admin and content management. The choice depends on your project's specific requirements.

Question 3

How do your Python developers handle asynchronous programming?

Accepted Answer

We use Python's asyncio ecosystem: FastAPI with Uvicorn for async HTTP, aiohttp/httpx for async HTTP clients, asyncpg for async PostgreSQL, and motor for async MongoDB. For background tasks, we use Celery with Redis/RabbitMQ broker or ARQ for lighter async task queues. We avoid mixing sync and async code in the same call chain — a common source of deadlocks. For CPU-bound work within async services, we use ProcessPoolExecutor or separate worker processes.

Question 4

Can Python handle high-traffic production systems?

Accepted Answer

Yes — Instagram (Django), Spotify (backend services), and Dropbox (originally Django) all run Python at massive scale. Key patterns: horizontal scaling behind load balancers, async frameworks for I/O concurrency, connection pooling for databases, caching layers (Redis, Memcached), and Celery for background job processing. Python's GIL is not a bottleneck for web workloads (which are I/O-bound). For CPU-intensive tasks, we use multiprocessing, NumPy (which releases the GIL), or separate Go/Rust services.

Question 5

How do you integrate machine learning models into Python backends?

Accepted Answer

Multiple patterns depending on latency requirements: in-process inference with PyTorch/TensorFlow loaded in the same process (lowest latency, highest memory), model serving with FastAPI + MLflow for versioned model deployment, or dedicated model serving infrastructure (TorchServe, TensorFlow Serving, Triton) for GPU-accelerated inference at scale. We implement request queuing for batch inference, model caching with Redis, and graceful degradation when model services are overloaded.

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