Python 3.12 is Slow as Hell

Yeah, Python is everywhere - about a quarter of all developers use it. But popular doesn't mean fast.

The GIL is a Pain in the Ass

Python's Global Interpreter Lock prevents real multithreading. You're stuck with multiprocessing, which is clunky as hell. I spent a weekend trying to make our Python API handle more than 100 concurrent requests. Gave up and rewrote it in Go in two days. Response times went from 800ms to 40ms.

The GIL exists because Python's memory management wasn't designed for concurrency. Every single thread has to take turns executing Python bytecode. On a 32-core server, Python still acts like it's 1995 with a single CPU. There are workarounds with asyncio, but they're messy and don't fix the fundamental problem.

Memory Hogging and Startup Hell

Every Python object has ridiculous overhead. A simple integer uses way more memory than C. Scale that across millions of objects and you're burning through RAM like it's free.

Python apps also take forever to start. Loading Django takes forever even for a hello world app. Try deploying that to AWS Lambda and watch your cold start times destroy user experience. Go binaries start instantly.

When You Actually Need Python

Don't get me wrong - Python has its place. Machine learning libraries like PyTorch and scikit-learn are unbeatable. Data analysis with pandas and numpy works great. Jupyter notebooks for prototyping? Perfect.

But if you're building anything that users actually interact with - APIs, web services, real-time systems - you'll hit Python's performance wall fast. The ecosystem is amazing until you need speed. Even Python's creator admits the GIL is a problem, but removing it would break too much legacy code.

Update: Python 3.13 finally introduced optional GIL removal, but it's experimental, has a ~40% performance hit, and breaks most C extensions. You need to compile Python with --disable-gil and most packages won't work. NumPy? Broken. Pandas? Broken. It's more of a research project than production-ready feature.

Go: Stop Fighting Concurrency

Go is what you switch to when Python's threading makes you want to quit programming. Goroutines actually work unlike Python's fake threading.

I rewrote our Python API in Go over a weekend. Python version: 800ms response times, constantly hitting memory limits, processes crashing randomly. Go version: 40ms responses, rock solid under load. Same algorithm, same database queries.

Go's not perfect - error handling is verbose as hell (if err != nil everywhere), and generics still feel bolted-on. But goroutines are magic. You can spawn millions of them without breaking a sweat. Each one uses 2KB of memory vs Python's 8MB threads that aren't even real threads thanks to the GIL.

Gotchas that took me way too long to figure out: Context cancellation doesn't kill child processes - you'll leak them. The zero value of a slice is nil, but a slice of length 0 is not nil (this will bite you). And don't use goroutines in init() functions, they'll deadlock randomly.

Rust: Prepare for Suffering

Rust is fast as hell but will make you question your sanity. The borrow checker is like having a really pedantic coworker who's always right but explains things in the most confusing way possible.

Spent 3 months porting a Python data processing pipeline to Rust. The result was 50x faster and used 90% less memory. But those 3 months included a lot of staring at compile errors that made no sense until suddenly they did.

Rust prevents segfaults by making everything a compile error instead. It's mathematically impossible to have data races or memory leaks, which is amazing for production systems. Just be prepared to spend your first month fighting the compiler over things that work fine in Python. The Rust book helps, but expect to read each chapter three times.

Pain points nobody warns you about: The compiler error messages are actually helpful after month 2. Install rust-analyzer or you'll go insane. cargo clippy is your friend, but it's also very judgmental. And async/await is great until you need to share state between tasks - then you're back to fighting the borrow checker.

Java: The Enterprise Survivor

Java runs fast but takes 30 seconds to start up. Perfect for long-running services, terrible for anything interactive. The JVM warmup time kills serverless deployments.

The JVM is actually incredible - decades of HotSpot optimization mean Java often runs faster than C++ for many workloads. But the ecosystem is enterprise hell. Spring Boot helps, but you'll still write 50 lines of boilerplate for what takes 5 in Python.

Java works great when you need reliability over developer happiness. Banks and insurance companies run their core systems on Java for a reason - it's boring and predictable. The OpenJDK ecosystem is solid, and tools like Maven actually work unlike npm.

Production gotchas: JVM warm-up means first 1000 requests are slow as hell. Spring Boot auto-configuration will surprise you at 3 AM when something stops working. Maven dependency resolution is basically gambling - same POM can pull different versions on different machines. G1GC pauses will spike your latency randomly under high load.