AppSignal’s Post

Excited to share a new article I wrote for AppSignal. I wrote about a problem many Python teams run into with Celery: ✅ Retries can make failed tasks look less serious than they really are. ✅ The real exception path is often harder to follow than it should be. ✅ Debugging usually means going through worker logs to piece together what happened. ✅ That delay makes background job failures harder to catch early. In the article, I break down: ✅ Where Celery’s defaults create visibility gaps. ✅ Why retries can make failure tracking messy. ✅ What this looks like in real Python applications. ✅ How AppSignal improves visibility by surfacing failures, retries, and task context more clearly. Here’s the article: https://lnkd.in/d_XgE3g9 #Python #Celery #BackendEngineering #Observability #DevOps #TechnicalWriting

yield is one of those Python keywords that looks simple until someone asks you to explain it. Most developers can tell you a function with yield in it produces values and works in for loops. Fewer can explain why that same function doesn't actually run when you call it. Turns out, that's the whole point. Generators (functions with yield) are functions that pause mid-execution and resume exactly where they left off: local variables, loop counters, everything intact. In my Python Context Managers series, I'm covering generators as a dedicated article because they are not a standalone concept. They are the engine behind @contextmanager, a cleaner way to build context managers in Python. You can't fully understand one without understanding the other. This article is a deep dive into generator functions: https://lnkd.in/dSNegaWK A function that remembers where it left off changes everything. #Python #SoftwareEngineering #Backend #Programming #WebDevelopment #BuildBreakLearn

✍️ New post introducing profiling-explorer, a tool for exploring Python profiling data (pstats files). Use it with the classic cProfile (now called profiling.tracing) or Python 3.15’s new sampling profiler, Tachyon (profiling.sampling). https://lnkd.in/eZ6D8ZMD #Python

A common mistake in async Python is using async and await without actually designing for concurrency. The code looks asynchronous, but if execution is still mostly sequential, latency will remain poor. To get real value from async Python, you need to understand when to use direct await, when to schedule work with create_task(), and when to use TaskGroup for structured concurrency. The core distinction is simple: → direct await is sequential → create_task() lets independent work start earlier → TaskGroup gives you structured concurrency with better failure handling A common issue in real code is hidden serialization: multiple I/O operations are written as separate awaits, so each one waits for the previous one to finish. In the first version, latency accumulates across each network call. In the second, those operations can overlap, so total latency is closer to the slowest individual call rather than the sum of all of them. That is the difference between writing asynchronous code and designing a concurrent system. One more important distinction: → concurrency is not the same as parallelism → asyncio is mainly useful for I/O-bound workloads → CPU-bound work still runs into the GIL → for CPU-heavy workloads, you usually need multiprocessing or another form of offloading Async Python does not improve performance automatically. It gives you tools to avoid wasting time on idle waiting. If concurrency is not structured intentionally, the code may be asynchronous in syntax while still behaving too much like synchronous code.

🐍📰 Variables in Python: Usage and Best Practices Explore Python variables from creation to best practices, covering naming conventions, dynamic typing, variable scope, and type hints with examples https://lnkd.in/dUFf5QGE

🐍📰 Python mmap: Improved File I/O With Memory Mapping In this tutorial, you'll get a quick overview of the different types of memory before diving into how and why memory mapping with mmap can make your file I/O operations faster. #python

🐍📰 Python eval(): Evaluate Expressions Dynamically In this tutorial, you'll learn how eval() works and how to use it effectively in your programs. Additionally, you'll learn how to minimize the security risks associated to the use of eval() #python

Python: 06 🐍 Python Tip: Master the input() Function! Ever wondered how to make your Python programs interactive? It all starts with taking input from the user! ⌨️ 1) How to capture input? -To get data from a user, we have to use the input() function. To see it in action, you need to write in the terminal using: '$ python3 app.py' 2) The "Type" Trap 🔍 -By default, Python is a bit picky. If you want to know the type of our functions, You can verify this using the type() function: Python code: x = input("x: ") print(type(x)) Output: <class 'str'> — This means 'x' is a string! 3) Converting Types (Type Casting) 🛠️ If you want to do math, you have to convert that string into an integer. Let's take a look at this example- Python code: x = input("x: ") y = int(x) + 4 # Converting x to an integer so we can add 4! [Why do this? Without int(), here we called int() function to detect the input from the user, otherwise Python tries to do "x" + 4. Since you can't add text to a number, your code would crash! 💥] print(f"x is: {x}, y is {y}") The Result 🚀: If you input 4, the output will be: ✅ x is: 4, y is: 8 Happy coding! 💻✨ #Python #CodingTips #Programming101 #LearnPython #SoftwareDevelopment

Most Python code works. That’s not the problem. The problem is - most of it doesn’t scale past the person who wrote it. You’ve probably seen code like this: • full of comments explaining what’s happening • try/finally blocks everywhere • repeated logic for caching, logging, auth • functions doing 5 things at once It works. Until it doesn’t. The shift that changed how I think about Python: 👉 Stop writing logic 👉 Start using language-level patterns Once you start seeing it this way: • with replaces cleanup logic • decorators replace repeated behavior • generators replace unnecessary data structures • dunder methods make your objects feel native The result? Code that explains itself without comments, removes entire classes of bugs, and actually scales across teams. I wrote a deep dive on this - not surface-level tips, but how these patterns actually work, when to use them, and how they reshape your code. 👉 Read the full article: https://lnkd.in/g_9GZDRk Curious — what’s one Python concept that only clicked after real-world experience? For me, it was realizing generators aren’t about syntax - they’re about thinking in streams instead of collections. #Python #CleanCode #SoftwareEngineering #ScalableSystems #DesignPatterns #AdvancedPython #BackendDevelopment