Distributed Systems Lab
Experiments exploring reliability, consistency, failure recovery, and scalable service design.
Contents
A personal sandbox for building small, deliberately breakable systems to study the behaviours that are hard to observe in production: partitions, retries, backpressure, and recovery.
Overview
The lab is a collection of focused experiments rather than a single product. Each one isolates a distributed-systems concept, implements it minimally, and then breaks it on purpose to see what happens.
Problem statement
Many distributed-systems concepts are easy to describe and surprisingly hard to internalize. Building and breaking small systems is the most reliable way to turn textbook knowledge into intuition.
Constraints
- Each experiment should run locally on a laptop or a small cluster.
- Failure injection must be deterministic enough to reproduce.
- Results must be measurable, not anecdotal.
Architecture and approach
Planned experiments:
- Leader election — compare lease-based and consensus-based approaches under partition.
- Retry storms — model how naive retries amplify load, then add jitter, budgets, and circuit breakers.
- Backpressure — observe queue behaviour under sustained overload with and without admission control.
- Replication & consistency — trace read-your-writes guarantees across replicas with injected lag.
Technical decisions and tradeoffs
Decisions will be recorded per experiment as they are built.
Implementation details
Code, harnesses, and failure-injection tooling will be documented here.
Results and impact
Measurements and write-ups will be added as experiments complete.
Lessons learned
Related technologies
Go · C# / .NET · Kubernetes · Load generation and tracing tools