Staying in the marketplace for a long time, the most heard things are project teams bragging about performance, flaunting costs, and showcasing scale numbers. It seems that as long as these indicators are pushed higher, any difficulty can be solved. But in reality, these are at most superficial efforts; whether the system can truly survive depends fundamentally on something else.

The real core issue is surprisingly simple: in three years, will the data structures you are using now still be able to support the system?

Many people tend to selectively forget this question. Just pick a medium-scale application that updates its status four to eight times a day, each time 30 to 60KB. Over a year, the historical data alone will accumulate to 35 to 70GB. The key point is that this isn’t cold data stored in a corner gathering dust; it may need to be traced back or reused at any time. The reality? Many systems can’t even last two years before they start struggling with their own historical data.

Why is that? Ultimately, it’s the pitfalls—nightmare compatibility issues, data structures being tightly locked down, and any attempt to modify them could trigger a cascade of failures. So most teams’ solutions are: add caching layers, duplicate data, and patch fixes. The final result is that developers become more cautious, innovative ideas can’t be born, and the so-called long-term approach turns into empty talk.

Walrus’s protocol approach is different. It doesn’t intend to hide or wipe out historical data; instead, it treats history as an integral part of the system itself. In this design, objects are not replaced or disappeared due to updates; they continue to exist and evolve along with the system. What you write isn’t one-time data, but more like incubating a living entity that can grow gradually.

The power of this design philosophy lies in its ability to not only change the technical execution logic but also fundamentally alter developers’ mindset towards innovation.