There is a phenomenon worth noting—applications that are truly close to real-world business often see data scales spiral out of control.

Initially, it might just be a few KB configuration files, then evolve into tens of MB of user behavior records, and later into continuous streams of state data, logs, and derivative content. Everyone has experienced difficulties during this process.

Where is the core issue? In most decentralized storage solutions, the design logic assumes you won't frequently modify or adjust data. But once the data volume grows, the costs of updates and management complexity explode simultaneously. This is a well-known pain point.

Walrus chooses to intervene at this point, with a clear approach—its goal is not to let you "store more," but to keep the system organized as data continues to grow. Through an object-level storage model, data can expand while maintaining consistent identity identifiers. Currently, in the testing environment, it supports MB-level objects and ensures read stability through distributed node redundancy.

The fundamental change brought by this design is at the behavior layer. Developers no longer need to repeatedly perform tedious operations like data splitting, merging, or migration, allowing data structures to operate stably over the long term.

Honestly, the true value of such solutions often isn't apparent at small scales. The real test comes when data approaches the actual business volume. Risks are also very real—when the number of objects and node scale grow in tandem, network scheduling and incentive mechanisms still need more time to be validated. But if you've already started pondering questions like "how to manage data after a few months," then this direction isn't unfamiliar.