In the Web3 technology stack, Sui is typically understood as the processor, while Walrus is the storage hard drive. This analogy sounds good, but it also highlights a key architectural trap — you can never violate the fundamental properties of hardware.

Hard drives are inherently like this: large storage capacity, high throughput, but slow random read/write speeds and long latency. This is not a flaw; it is an inevitable choice of physical design. Where is the problem? Many developers migrating from Web2, accustomed to millisecond-level responses from Redis or the quick feedback of high-performance databases, naturally think of storing high-frequency interactive dynamic data in Walrus. For example, storing real-time state synchronization for multiplayer online games in Walrus blobs. This is not innovation; it’s a disaster.

Walrus reads must go through network addressing, chunk downloading, erasure coding recovery — the entire physical process. So, latency can’t be millisecond-level; seconds are an optimistic estimate. What happens if hot data is forcibly stored here? User experience becomes painfully slow, that’s one issue. Even more heartbreaking is that frequent rewrites of these small files will generate astronomical Gas fees — because each write is essentially a transaction on the Sui chain.

What is the correct approach? Strict separation of hot and cold data. Any data requiring sub-second response, or data that changes more than once a day, should be prohibited from being directly written into Walrus. These should stay on the Sui chain objects (as RAM), or be managed with traditional indexers. Walrus’s role is very simple: to serve as an archiving layer. Store static snapshots that, once generated, are no longer modified.

No matter how powerful the protocol is, it cannot withstand misuse. Respecting the physical differences of each layer is essential for a truly robust architecture.