6. Common Myths and Truths
Myth 1: "Language X has the best performance, so we should use it"
Truth: Performance is not the only criterion — often it's not even the most important one.
For most web applications, the bottlenecks are in:
- Database queries (accounting for 70%+ of time)
- Network I/O (calling external APIs)
- Caching strategy (Redis, Memcached)
The language's own performance difference accounts for only a small portion. Through architectural optimization (caching, async, horizontal scaling), Python can support millions of concurrent users.
Example: Instagram supports 500 million users with Python, compensating for language performance shortcomings through caching and async architecture.
Myth 2: "Once I learn language X, I don't need to learn others"
Truth: Modern systems are often multi-language hybrid architectures.
Typical microservices architecture:
- API gateway: Go (high performance)
- Business logic: Java or Python (high development efficiency)
- AI/ML services: Python (mature ecosystem)
- Real-time push: Node.js (good WebSocket support)
- High-performance computing: Rust or C++ (extreme performance)
Advice: Master one deeply, understand several broadly. Go deep on your primary language; for others, understand their design philosophy and suitable scenarios.
Myth 3: "Newer languages are always better than older ones"
Truth: Languages aren't good or bad — only suitable or not.
Python (1991): Older than Go (2009), but unchallenged in the AI/ML domain. Java (1995): Older than Go (2009), but still dominates enterprise applications. PHP (1994): Mocked for 20 years, but still powers half the internet.
The key is not a language's age, but ecosystem maturity and team familiarity.