Disaggregated cloud databases: when to disaggregate vs monolithic stacks

Disaggregation is a hot topic in cloud databases ^[1]. The idea is to decouple storage, compute, and queries so components can scale independently. That tension—scale versus latency—drives the debate.

Disaggregation explained — The core idea is to split the traditional monolithic stack into independent layers, so teams can scale or upgrade parts without touching others ^[1]. This approach is described as a new architecture for cloud databases ^[1]. Proponents argue it enables modularity and targeted optimization across storage, compute, and query gear.

Cloudflare's R2 SQL — Cloudflare's R2 SQL dives into a distributed query engine that runs across storage and compute boundaries, showing how queries can be dispatched to multiple disaggregated components ^[2]. This work illustrates both the promise of cross-boundary querying and the practical questions such a design raises about latency and coordination.

PlanetScale deployments via Cloudflare Workers — PlanetScale deployments from Cloudflare Workers illustrate how a serverless frontend can coupled with a disaggregated backend to power full-stack apps ^[3]. The approach points to faster frontends without locking teams into a single monolithic stack.

• Scalability — disaggregation aims to scale layers independently ^[1].
• Management — modular components can simplify ops and upgrades ^[1].
• Tradeoffs — latency between layers and added complexity are real considerations ^[2][3].

These experiments hint at a future where modular, cross-boundary databases become common, even as teams tune latency budgets carefully.

References