Performance

Python LLM applications often start small: a FastAPI route, a call to an LLM provider, some prompt engineering, and you’re done. Then traffic grows, latencies spike, and your CPUs sit mostly idle while users wait seconds—or tens of seconds—for responses. What went wrong? One of the most common and least understood culprits is thread pool starvation. This article explains what thread pool starvation is, why it’s especially dangerous in Python LLM apps, how to detect it, and concrete patterns to avoid or fix it. ...

Python gives you a lot of flexibility with objects—but that flexibility comes at a cost. Instances normally carry a per-object dictionary to store attributes, which is powerful but memory‑hungry and a bit slower than it could be. __slots__ is a mechanism that lets you trade some of that flexibility for: Lower memory usage per instance Slightly faster attribute access A fixed, enforced set of attributes This article is a detailed, practical guide to __slots__: how it works, when it helps, when it hurts, and how to use it correctly in modern Python. ...

Python’s garbage collector (GC) automatically manages memory by reclaiming space from objects no longer in use, combining reference counting for immediate cleanup with a generational garbage collector to handle cyclic references efficiently.[1][2][6] This dual mechanism ensures reliable memory management without manual intervention, making Python suitable for large-scale applications. The Fundamentals: Reference Counting At its core, CPython—the standard Python implementation—uses reference counting. Every object maintains an internal count of references pointing to it.[1][5] ...

Table of contents Introduction What is MutationObserver? Why MutationObserver replaced Mutation Events Core concepts and API surface Creating an observer The observe() options The MutationRecord object Controlling the observer (disconnect, takeRecords) Common use cases Performance considerations and best practices Practical examples Basic example: logging DOM changes Waiting for elements that don’t exist yet Observing attribute and text changes with oldValue Integration with frameworks / polyfills Pitfalls and gotchas When not to use MutationObserver Summary / Conclusion Introduction MutationObserver is the standardized, efficient browser API for watching changes in the DOM and reacting to them programmatically. It enables reliable detection of node additions/removals, attribute updates, and text changes without costly polling or deprecated Mutation Events. ...

Introduction Lazy initialization is a technique where the creation or loading of a resource is deferred until it is actually needed. It’s a simple idea with far-reaching implications: faster startup times, reduced memory footprint, and the ability to postpone costly I/O or network calls. But laziness comes with trade-offs—especially around concurrency, error handling, and observability. When implemented thoughtfully, lazy initialization can significantly improve user experience and system efficiency; when done hastily, it can introduce deadlocks, latency spikes, and subtle bugs. ...