Project Snorlax
An automated detective that filters out downstream log noise in microservices to isolate "Patient Zero" crash errors.
Why it exists
Built for a national hackathon in Mumbai. The premise was simple: reading server logs at 2 AM is miserable, and microservice cascades make it worse. We wanted to build something that hands developers the smoking gun, not a stack of haystacks.
How it works
- Ingests high-velocity JSON logs using Redis Streams as a high-speed buffer.
- Employs local ML embeddings (Sentence-Transformers) to vectorize log texts.
- Groups vectorized anomalies with DBSCAN clustering to isolate the root failure.
- Sends the isolated root cause to an LLM for a clean, one-sentence diagnosis and suggested fix.
What happened
We took this to Mumbai, pitch-ready. The tech judges looked at it and declared: "This won't work in the real world." We didn't win, and the critique humbled us. But instead of throwing the code away, I went back home, rewrote the ingestion workers, patched the clustering bottlenecks, and cleared the edge cases that would have broken it in production. It works now.
What I'd change
If I did this again, I would swap DBSCAN for an incremental clustering algorithm to handle real-time streaming logs without periodically rebuilding the full vector database space.