Distributed Data Insights Notes on distributed data systems

About

·2 min read

About Dikang Gu and Distributed Data Insights — a blog on distributed data systems.

Contents

About me

I’m Dikang Gu, a distributed systems engineer who has spent his career building the data infrastructure behind some of the largest consumer platforms in the world.

I’m currently a Technical Director at Roblox, where I lead the OLTP database and storage platform that provides data storage and serving for Roblox’s hundreds of millions of monthly users and millions of creators. The work centers on fault-tolerant distributed storage, smart partitioning, rock-solid replication, and squeezing every drop of performance out of planet-scale systems.

Before Roblox, I spent many years at Meta (Facebook / Instagram) on the infrastructure teams. As tech lead of Instagram’s storage team, I helped scale Apache Cassandra to thousands of nodes and led the development of Rocksandra — a pluggable RocksDB storage engine for Cassandra — which we later open-sourced. I’ve also worked on optimal data placement at global scale (Akkio) and presented this work at venues including Cassandra Summit, OSCON, F8, and DataStax Accelerate.

Earlier in my career I worked on virtualization at VMware, storage at EMC, and did an internship at Google. I hold an M.S. in Computer Science from Shanghai Jiao Tong University and a B.S. in Computer Science from Nanjing University of Aeronautics and Astronautics. I’m based in the San Francisco Bay Area.

About this blog

Distributed Data Insights is where I write down the patterns, failure modes, and trade-offs that only really become clear once you’ve operated these systems in production:

  • Storage & indexing — LSM-trees, B-trees, write/read amplification, compaction.
  • Replication & consistency — quorums, leader election, linearizability vs. eventual consistency.
  • Streaming & pipelines — exactly-once semantics, backpressure, watermarks.
  • Partitioning & scale — sharding strategies, hot keys, rebalancing.
  • Operations — observability, capacity planning, and post-incident lessons.

The goal is practical depth: enough theory to reason from first principles, and enough real-world detail to actually be useful on Monday morning.

Get in touch

Find me on LinkedIn or GitHub. Want to suggest a topic or point out something I got wrong? Open an issue or PR on the site repository.

Keep reading