Most public chains and application protocols are designed with the assumption that they will remain in their initial state forever. But in reality? Truly surviving systems are never like that.

What happens when a system ages? Its architecture becomes less perfect, business logic grows more complex, requirements are continuously iterated, and historical data piles up more and more. Once an application runs for over a year, the development team faces a dilemma—the core data structures are almost off-limits for changes, because even a slight mistake in modifying a field could make historical data irretrievable, and user trust could collapse as a result.

Walrus aims to solve this problem.

It’s not about making writes faster, but about giving you the courage to modify data structures. The key point is that, within Walrus’s object model, object identities remain stable at all times, states can evolve continuously, and historical data is not overwritten but fully preserved.

According to public test data, even under high concurrency read scenarios, Walrus can maintain millisecond-level latency. Its multi-node collaboration mechanism ensures that data is not lost or corrupted. As a result, historical data is no longer just cold storage; it becomes an active resource that can be genuinely utilized by the business.

In simple terms, Walrus’s greatest competitive advantage is not cost, but confidence—you can truly dare to entrust your core business data to it for the long term.