Python vs Java Threads for I/O-Bound Tasks

When I recently posted about the speed-up in Mark Burgoyne's pyResToolbox using Rust, there was some push back concerning the Rust benchmarks as I was showing that my Java implementation was faster. https://lnkd.in/dgk-WyZA The general comment was that Rust should be faster than Java. I'd agree. Rust is compiled ahead of time and can optimise the compiled code for the target CPU. Java is compiled using a [just-in-time compiler](https://lnkd.in/deEMcCnm) from bytecode that is generated to be run on any CPU. Since my previous intent was just to show Java versus Python, or Java versus Python with Rust acceleration in a casual way, I decided to undertake a more comprehensive comparison which would allow fairer comparison. Mark Burgoyne has supplied a [variety of code examples](https://lnkd.in/dm7HyCd9) for the BNS viscosity method, including a pure Rust implementation and VBA for Excel. The results for this more comprehensive comparison are shown in the image along with this post. Note the logarithmic scale for calculations per second! Note the difference between single pressure and multiple pressure, is that multiple pressure does calculations for an array of pressure points on the same composition, so the loop can be optimised. This shows that Rust with parallel threaded execution for the multiple pressure point solutions is by far the fastest implementation, reaching ~ 61 million calculations per second. In comparison VBA for Excel manages a paltry 772 calculations per second. Rust is about 80,000 times faster, or nearly 5 orders of magnitude. As a long time advocate that most speed comes from the algorithm design itself, it is still remarkable to see just what a difference the language and environment makes. Remember, these are benchmarks for an **identical** algorithm. The difference is purely the language implementation. This dramatically illustrates the difference between speed and convenience. Where Excel and Python are (arguably) more accessible, parallel algorithms in Java and Rust deliver better speed. I think the Rust-accelerated Python solution that Mark wrote is a good compromise between the two. As an aside, this is the first Rust code I've ever written / used. I'm pretty impressed.

This image provides a side-by-side comparison between Java and Python across several technical and practical categories. It is structured as a table with features listed down the center column, comparing Java on the left and Python on the right. Here is a breakdown of the key points mentioned in the image: Language Fundamentals Usability: Java is described as a fundamental language for multiple platforms, while Python is noted as being more high-level. Language Type: Both are identified as object-oriented, but the image highlights Python's advantage as a scripting language. Syntax & Readability: Python is characterized by easier syntax and shorter code, leading to better readability. Java is described as having more complex syntax and longer lines of code. Performance and Development Speed: The image labels Java as faster due to it being statically typed. Python is described as slower because it is interpreted (though the image contains a slight typo stating it is "manually typed"). Productivity: Python is credited with higher productivity and being "easier to use" because it requires less coding than Java. Legacy: Java legacy systems are typically larger and more numerous, whereas Python has fewer legacy issues. Practical Application Databases: Java Database Connectivity (JDBC) is noted as popular and widely used; the image suggests Python's access layers are "weaker" by comparison. Practical Agility: Java is noted for its popularity in mobile and web applications. Python is highlighted as the more popular choice for modern fields like Machine Learning (ML), AI, and Data Science. Search Growth: The image notes that Python is experiencing significant growth in search results compared to Java.

As a long-time Java engineer, I continue to be impressed by how much Python has evolved. What once felt like a simple scripting language has grown into a remarkably capable ecosystem: C-backed libraries like NumPy, performance-oriented tooling in Rust, native coroutine support with async and await, and multiple concurrency models for very different workloads. One thing I find especially interesting is Python’s concurrency toolbox. Choosing the right model usually comes down to one question: What is your code actually waiting on? If your program is mostly waiting on the network, a database, or disk, you are likely dealing with an I/O-bound problem. In that case, asyncio can be a strong fit when the surrounding stack is async-native. If your program spends most of its time computing, parsing, or transforming data, you are likely dealing with a CPU-bound problem. In standard CPython, threads usually do not speed up pure Python CPU work because of the GIL. For that, multiprocessing is often the better fit. A few practical rules I keep in mind: • asyncio for high-concurrency I/O with async-native libraries • threads for blocking libraries or simpler concurrency • multiprocessing for CPU-heavy pure Python workloads • threads with native libraries when heavy work runs in C or Rust A good example is PyArrow and PyIceberg. PyArrow’s Parquet reader supports multi-threaded reads. That means you can get parallelism without rewriting everything around asyncio, because the heavy work happens in native code rather than Python bytecode. PyIceberg builds on this ecosystem. From the Python caller’s point of view, the workflow is still synchronous, while file access and data processing can benefit from native parallelism underneath. The key lesson for me: Not every high-performance I/O workflow in Python needs asyncio. If the underlying engine is native and already parallelizes efficiently, threads can be the right tool. If the stack is async-native, asyncio becomes much more compelling. A simple mental model: Async-native I/O → asyncio Native libraries parallelizing outside Python → threads CPU-heavy pure Python → multiprocessing Not sure → profile first That mindset is often more useful than memorizing any specific framework. #Python #Java #Concurrency #AsyncIO #Threading #Multiprocessing #Performance #SoftwareEngineering #DataEngineering

Python vs Java – Choosing the Right Tool for the Job This visual highlights a quick comparison between two of the most popular programming languages: Python and Java. 💡 Key Differences: Typing: Python is dynamically typed, while Java is statically typed Code Length: Python is concise and readable; Java is more structured but verbose Frameworks: Python (Django, Flask) vs Java (Spring, Hibernate) Learning Curve: Python is beginner-friendly; Java requires more setup and understanding Industry Use: Both are widely used by top companies for scalable applications 🚀 Final Thought: There’s no “better” language — it depends on your goal. Choose Python for speed, simplicity, AI, and automation Choose Java for large-scale, enterprise-level applications

I created a Java binding for Google's Magika ONNX file-type detector. Maintains byte-for-byte parity with the upstream Python binding on every fixture. ~2 ms steady-state inference per file on M-series Mac. (cold first-call ~100s of ms) https://lnkd.in/eTjACKzf #java #magika #opensource #onnx

today I had such an awesome conversation with a "powerful" model. I asked it what was better java or python. at first it wouldnt answer so I pressed it. of course it answered python :) funny anecdotal arguments. " java has a compiler step" I say, "Eveyone uses an ide. They click green play button. no one really has a compile step." it says "Your right. I meant python repl." I said, "java has lots of repls .. beanshell, jbang groovy..." answer. Your right i was speaking anecdotally they both have repl." Man this thing was struggling. I moved onto the favorite python argument. "the consise code and easy language. " Then I hit it with, "Pythons packaging is a mess. you have to handcraft __init__.py to bandaid a broken language." "Pythons packaging isnt a mess, it is ' consistently inconsistent' " quote the powerful model. People talk about amazing "summarization" and "thinking" skills. its generally just feeding you hollow arguments. "consistently inconsistent"

Stop writing Python like Java/C++! Building scalable applications in Python means embracing its unique strengths, not fighting them. A truly "clean" API in Python isn't just about naming conventions; it's about thinking in terms of Python's object model, its dynamic nature, and its emphasis on readability. Let's look at how we handle optional parameters. Okay: class Service: def process(self, data, config=None): if config is None: config = {} # Boilerplate to handle None # ... process with data and config Best (Pythonic): class Service: def process(self, data, config=None): config = config or {} # Concise and idiomatic # ... process with data and config The "Best" version uses Python's truthiness. None evaluates to False, so config or {} will assign an empty dictionary if config is None, otherwise it uses the provided config. It's shorter, clearer, and less prone to errors. Takeaway: Design APIs that leverage Python's expressiveness for clarity and conciseness. #Python #CodingTips

Python vs Java: A Senior Engineer’s Perspective Python and Java dominate the programming world but they serve very different purposes. Understanding their trade-offs is key for designing robust, maintainable systems. 1. Syntax & Readability Python: Clean, concise, readable. Perfect for rapid prototyping. # Square numbers squared = [x**2 for x in range(5)] Java: Verbose, explicit, type-safe. Great for large-scale systems. int[] squared = new int[5]; for(int i=0;i<5;i++){ squared[i]=i*i; } 2. Performance Java: Compiled to bytecode → faster execution, better memory management. Python: Interpreted → slower raw speed, but NumPy, Cython, or PyPy can accelerate. 3. Typing & Errors Python: Dynamic typing → flexible, but runtime errors possible. Java: Static typing → upfront safety, fewer surprises in production. 4. Ecosystem Python: Data science, ML, scripting, automation. Libraries: pandas, TensorFlow, requests. Java: Enterprise backends, Android, high-performance systems. Libraries: Spring, Hibernate. 5. Deployment & Portability Java: JVM → “Write once, run anywhere”. Python: Lightweight, virtual environments needed; cross-platform dependencies can be tricky. Verdict Python: Rapid development, scripting, AI/ML, startups. Fast, readable, flexible. Java: Enterprise apps, Android, high-performance backend. Robust, maintainable, scalable. Rule of thumb: Python is a Swiss Army knife, Java is industrial grade machinery. Use the right tool at the right scale. Python might be more beneficial but Java still has its charm.

🚀 Mastering Prefix Sum & Suffix Sum in Java (DSA) Understanding Prefix Sum and Suffix Sum is a game-changer in Data Structures & Algorithms. These concepts help optimize problems that involve range sums and reduce time complexity significantly. 🔹 What is Prefix Sum? Prefix Sum is an array where each element at index `i` stores the sum of elements from index `0` to `i`. 👉 Formula: prefix[i] = prefix[i-1] + arr[i] 👉 Java Example: int[] arr = {1, 2, 3, 4, 5}; int n = arr.length; int[] prefix = new int[n]; prefix[0] = arr[0]; for(int i = 1; i < n; i++) { prefix[i] = prefix[i - 1] + arr[i]; } // Output: [1, 3, 6, 10, 15] 🔹 What is Suffix Sum ? Suffix Sum is an array where each element at index `i` stores the sum from index `i` to the end of the array. 👉 Formula: suffix[i] = suffix[i+1] + arr[i] 👉 Java Example: int[] arr = {1, 2, 3, 4, 5}; int n = arr.length; int[] suffix = new int[n]; suffix[n - 1] = arr[n - 1]; for(int i = n - 2; i >= 0; i--) { suffix[i] = suffix[i + 1] + arr[i]; } // Output: [15, 14, 12, 9, 5] 💡 Why is this important? ✔ Reduces time complexity from O(n²) → O(n) ✔ Used in range sum queries ✔ Helps in solving problems like equilibrium index, subarray sums, etc. Pro Tip: Once you understand prefix sums, try solving problems like: Subarray Sum Equals K Pivot Index Range Sum Query ✨ Consistency in DSA is the key. Small concepts like these build strong problem-solving foundations. #DSA #Java #Programming #Coding #SoftwareEngineering #SDET #Learning

Embabel treats LLMs as participants in strongly typed workflows — not black boxes — and the Spring creator Rod Johnson spring says that gives Java developers an edge Python can't match. By Darryl Taft