Is Java Still Used and Relevant in 2024?
In 2024, Java (together with its associated framework ecosystem) has been experiencing a steady loss of its “cool factor” in the web development community of late. By contrast, dynamically-typed languages such as Python, why are trending upward in popularity.
In this article, I’ll examine if this loss of interest in Java is justified by breaking down the technical and business perspectives of the language with comparison to Python.
To get to the bottom of this, we’ll analyze how Java is architecturally inclined towards scalability and how it outperforms Python in benchmarks. We’ll also explore what made Twitter decide to switch from Ruby to Java and look into a market segment where it has a strong presence.
Then we’ll look at the factors that can cause differences in time-to-market when using Java versus dynamic languages. The final aspects we’ll assess are hiring, from a remote-working perspective, and financial considerations. With this information, you should be well equipped to make a savvy decision whether to consider using Java for your software project in 2024.
Table Of Contents
The Technical Perspective: Java vs Other Languages (Speed & Multithreading)
Python vs Java: Architectural Differences
Python is an interpreted, dynamic language and Java is a compiled, static language. Throughout this article, we’ll be referring to the default (reference) implementation of both Python and Java. While Java’s JVM takes a bit longer to get going due to its warm-up phase, the Python runtime is faster at starting up. But this is where Python’s advantages in terms of pure execution performance stop.
Python interprets all code at runtime, and, even though it does translate code to bytecode, the fact that it does so at runtime means that there’s not much optimization going on. This is in contrast with Java, where code is translated to bytecode at compilation time and then executed inside a JVM. Yes, there are Python implementations that can compile bytecode before runtime, but this negates the whole point of the reference implementation of Python: using this means that you start going towards Java and introduce a slower startup time, which essentially negates one of the advantages of using Python in the first place.
At an abstract level, there are multiple ways to achieve parallelism, and multithreading is one of the most popular choices. Python lacks true multithreading support because of its Global Interpreter Lock: this prevents multiple threads from executing in the same interpreter. Java, on the other hand, offers out-of-the-box support for multithreading via its standard API, which can be further enhanced by its many existing frameworks.
