FeatureBase

Real-time feature store serving low-latency features to models online.

📖 Overview

FeatureBase computes and caches features in Redis so models get consistent, low-latency feature values at inference time, with the same definitions used for training and serving.

Part of my . Built on the "" model.

🚀 Quick Start

# 1. Clone
git clone https://github.com/Kimosabey/feature-base.git
cd feature-base

# 2. Install
# (see docs/GETTING_STARTED.md for the full setup)

# 3. Run
docker compose up

✨ Key Features

• Online feature serving from Redis
• Consistent train/serve feature definitions
• TTL and freshness controls
• Simple get/set feature API

🏗️ Architecture

%%{init: {'theme':'base','themeVariables':{'primaryColor':'#ffffff','lineColor':'#2563eb','mainBkg':'#ffffff'}}}%%
graph LR
    A([Event])
    B([Feature Compute])
    C([Redis Store])
    D([Serve to Model])
    A --> B
    B --> C
    C --> D
    style A fill:#eff6ff,stroke:#2563eb,stroke-width:2px,color:#1e40af
    style B fill:#eff6ff,stroke:#2563eb,stroke-width:2px,color:#1e40af
    style C fill:#eff6ff,stroke:#2563eb,stroke-width:2px,color:#1e40af
    style D fill:#eff6ff,stroke:#2563eb,stroke-width:2px,color:#1e40af

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Train/serve consistency — making sure the feature a model sees online matches how it was trained.

See docs/ARCHITECTURE.md for the full HLD/LLD and design decisions.

🧰 Tech Stack

Layer	Technology	Role
Redis	Redis	In-memory store / cache / queue
Node.js	Node.js	Application runtime / service layer

📚 Documentation

• Architecture — high- and low-level design, decision log
• Getting Started — prerequisites, setup, environment
• Failure Scenarios — fault analysis and recovery
• Interview Q&A — deep-dive walkthrough

🔭 Future Enhancements

• Point-in-time correctness
• Feature versioning
• Batch backfill

📄 License

Released under the MIT License.

👤 Author

Harshan Aiyappa Senior Full-Stack Hybrid AI Engineer Voice AI • Distributed Systems • Infrastructure