Overview

With tired storage, Fluss allows you to scale compute and storage resources independently, provides better client isolation, and allow faster maintenance.

Fluss organize data into different storage layers based on its access patterns, performance requirements, and cost considerations.

Fluss ensures the recent data is stored in local for higher write/read performance and the historically data is stored in remote storage for lower cost.

What's more, since the native format of Fluss's data is optimized for real-time write/read which is inevitable unfriendly to batch analytics, Fluss also introduces a lakehouse storage which stores the data in the well-known open data lake format for better analytics performance. Currently, only Paimon is supported, but more kinds of data lake support are on the ways. Keep eyes on us!

The over tired storage architecture is shown in the following diagram: