Distributed-Systems

Illustration of distributed workers processing tasks in a cloud environment.

Mastering Celery: Scaling Distributed Task Queues for Production-Ready Python Application Architecture

A deep dive into Celery architecture, real‑world scaling patterns, and ops best practices for reliable, high‑throughput Python applications.

A graph showing a long tail of latency distribution.

Deep Dive into Tail Latency: Avoiding the Little's Law Trap in High-Throughput Systems

A practical guide for engineers to recognize and mitigate tail‑latency pitfalls that break Little’s Law assumptions, using concrete Kafka and GCP examples.

Diagram of two buckets—one with tokens spilling out, the other leaking water—illustrating rate‑limiting concepts.

Architecting Production Rate Limiters: A Deep Dive into Token Bucket vs. Leaky Bucket Algorithms

A production‑focused guide that compares token bucket and leaky bucket rate limiters, showing how to choose, implement, and observe them at scale.

Diagram of a Celery worker pool processing tasks from a broker.

Architecting Scalable Python Applications: Using Celery as a Distributed Task Queue for Production Pipelines

A deep dive into using Celery as a distributed task queue for scalable Python applications, with concrete architecture diagrams, code samples, and operational best practices.

Graph of latency distribution with a long tail.

Deep Dive into Tail Latency: Avoiding the Little's Law Trap in High-Throughput Systems

A practical guide for engineers to recognize the limits of Little’s Law, measure tail latency, and apply proven techniques in high‑throughput services.