Distributed Rate Limiter
Low-latency distributed rate limiting service supporting token bucket and sliding-window algorithms across horizontally scaled applications.
250k checks/sec · <1ms p99
- GO
- REDIS
- gRPC
- PROMETHEUS
// system_manifest_v2.0
I design, build, and scale distributed systems that handle millions of requests. Focused on reliability, observability, and clean abstractions.
{
"role": "Backend Engineer",
"stack": [
"Go",
"Rust",
"Node.js",
"Python"
],
"infra": [
"AWS",
"K8s",
"Docker",
"Terraform"
],
"data": [
"PostgreSQL",
"Redis",
"Kafka",
"gRPC"
],
"status": "open_to_work"
}Low-latency distributed rate limiting service supporting token bucket and sliding-window algorithms across horizontally scaled applications.
250k checks/sec · <1ms p99
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5+
Years Experience
production systems
20+
Systems Scaled
microservices
1M+
Peak Traffic
req/sec handled
99.99%
Uptime Record
SLA maintained
Empreinte.com
Architected event-driven platform processing 1M+ events/min. Reduced infrastructure costs by 40%.
Notes on the persistence layer, dead-letter routing, and backpressure decisions behind a Go message broker that stays up at 99.99%.
read ▸How JWT validation, per-tenant rate limiting, and connection pooling fit into a single-digit-millisecond budget for 10M users.
read ▸Trace sampling that keeps the interesting requests, cardinality budgets for metrics, and why every service ships the same three RED dashboards.
read ▸© 2026 · Built with Astro · All systems nominal