Distributed Systemsedge ingestion, Redis & backend scaling

I'm Dibbayajyoti Roy — a backend and distributed systems engineer. This is the work that lives between the request and the database: edge ingestion, queue-backed pipelines, multi-tenant data isolation, Redis optimization, and the unglamorous distributed systems debugging that keeps production healthy. This page collects it.

Klinder-OSS ingests analytics events at the edge on Cloudflare Workers and Cloudflare Queues, then persists to Neon Postgres with Row-Level Security for multi-tenant isolation. The ingestion path is being ported to Rust via workers-rs to push p95 latency under 10ms — see Rust engineering. Compared against a hosted analytics product on Klinder-OSS vs PostHog.

A real production incident: a naive SCAN over a remote Redis was timing out at 100 seconds. Moving to batched MGET with client-side filtering produced a ~90% efficiency improvement and zero timeouts. It is a small case study in distributed systems debugging — the fix was the access pattern, not the hardware. The full write-up is on the writing page.

On HR SaaS at Yupcha Softwares I designed 12+ REST endpoints in Node.js and Express on PostgreSQL, deployed to Ubuntu VMs on a Proxmox cluster behind nginx reverse proxies with Let's Encrypt TLS and systemd supervision, and cut page load from 3.4s to 1.9s by eliminating N+1 queries, adding composite indexes, and splitting Next.js bundle routes — everyday backend scaling work.

• What distributed systems work has Dibbayajyoti Roy done?

  He built the Klinder-OSS analytics pipeline — edge ingestion on Cloudflare Workers and Queues with Neon Postgres and Row-Level Security for multi-tenant isolation — and runs production backend services on a Proxmox cluster.

• What was the Redis polling bottleneck?

  A naive SCAN over a remote Redis was timing out at 100 seconds in production. Switching to batched MGET with client-side filtering gave a ~90% efficiency improvement and eliminated the timeouts. The full incident write-up is on the writing page.

• How does he handle multi-tenant SaaS isolation?

  Neon Postgres with Row-Level Security, so tenant isolation is enforced at the database layer rather than trusted to application code.

• What backend scaling work has he shipped?

  On production HR SaaS he cut page load from 3.4s to 1.9s by eliminating N+1 queries, adding composite indexes, and splitting Next.js bundle routes, and designed 12+ REST endpoints in Node.js and Express.

Keep reading: Rust engineering · Klinder-OSS vs PostHog · projects & experience · get in touch