Explore the shift from central databases to edge-based SQLite. Learn how Data Topology impacts latency and why libSQL and Cloudflare D1 outperform read replicas.

The central question is no longer just about which database engine is technically superior, but rather a more spatial inquiry: where should the data actually live relative to the request?
Central database vs per account (edge) database. What’s the difference? Are each of them possible? Are each of them scalable?






The primary difference lies in the spatial relationship between the data and the user request. Traditional central databases, even with regional read replicas, often result in significant lag, such as a 120-millisecond delay for global users. In contrast, edge databases like libSQL or Cloudflare D1 collapse the read path, bringing data closer to the request to achieve near-instantaneous response times of 1 to 3 milliseconds.
While spinning up a read replica in a local region was the standard playbook for a decade, it still involves network requests between application and database servers. As we move through 2026, this architecture is being replaced by edge-based implementations. Modern data topology focuses on where data lives relative to the request, moving beyond simple replication to eliminate the round-trip tensions inherent in centralized data center models.
Edge databases like libSQL and Cloudflare D1 improve performance by fundamentally changing the data topology. Instead of an application server making a network call to a separate database server, these modern implementations allow the data to live essentially where the request happens. This shift from a traditional Postgres setup to edge-based SQLite can reduce latency from over 100 milliseconds to just 1 to 3 milliseconds for read-heavy applications.
Создано выпускниками Колумбийского университета в Сан-Франциско
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Создано выпускниками Колумбийского университета в Сан-Франциско
