This repository is a benchmark suite for comparing the performance of different programming languages in solving the 'related posts' problem: given a list of posts with tags, compute the top 5 related posts for each post based on shared tags. The architecture consists of language-specific implementations (over 50 languages) that all follow the same algorithm: build a tag-to-post-index map, then for each post count shared tags via that map, and select the top 5 posts using a custom priority queue. The repository includes Dockerfiles for each language, helper scripts for running benchmarks, and a workflow that automates execution across multiple post counts (5k, 20k, 60k) and updates a README with results. The code demonstrates strong engineering practices: consistent algorithm across implementations, use of language-specific optimizations (e.g., SIMD, custom hash maps, parallelization), and careful attention to performance measurement (excluding I/O).
This repository provides reference implementations of common system design patterns in Python and Java. It is primarily an educational resource containing standalone algorithm implementations for consistent hashing, rate limiting (token bucket, leaky bucket, sliding window variants), and load balancing algorithms (round robin, weighted round robin, least connections, least response time, IP hash). The code serves as a learning tool for system design interviews and concepts rather than a production-ready application. Each implementation is self-contained with usage examples embedded in the source files. The repository has no external dependencies beyond standard libraries, making it easy to extract and study individual patterns.