FeatureGate

About this project

• •A feature flag check sits in the hot path of every request, so it cannot be a network call. FeatureGate evaluates locally and pushes changes out over SSE.
• •Segment-based targeting on top of a rule evaluation engine that uses consistent hashing for percentage rollouts, so a user who is in the 10% stays in the 10% as the rollout grows. Flag changes propagate to connected clients over SSE in under 500ms.
• •The Node.js SDK on npm caches flags in memory and evaluates locally, putting a check under a millisecond and removing the per-request network call entirely. The developer experience is deliberately LaunchDarkly-compatible so migrating costs nothing.
• •Multi-stage Docker builds deployed to GCP Cloud Run through a four-stage GitHub Actions pipeline — lint, test, build, deploy — against Terraform-provisioned infrastructure including Memorystore for Redis and Cloud Run auto-scaling.

Key Features

• •Consistent hashing so percentage rollouts stay stable
• •Flag changes propagate over SSE in under 500ms
• •Node SDK evaluates in memory, under a millisecond
• •LaunchDarkly-compatible developer experience
• •Terraform-provisioned GCP infrastructure
• •Four-stage GitHub Actions pipeline

Technologies Used