When studying distributed storage protocols, there's a commonly overlooked phenomenon worth noting: write capacity is often exaggerated, while the importance of read performance is easily underestimated.

A successful write only indicates that the system's capacity to accommodate data is sufficient; what truly determines whether an application can survive is the speed at which data can be read out. Imagine a website that loads so slowly it feels like a slideshow, AI agents waiting to access data, or images in an app constantly loading—these seemingly "small" user experience issues can destroy even the most advanced underlying technology.

The reason why the Walrus protocol deserves special attention is that it complicates the read process. When data is written, it is encoded into multiple shards(slivers) and hashed across various storage nodes; on-chain, only metadata and verifiable references are recorded. During reading, enough shards must be collected from different nodes, then the original data is reconstructed.

The core role in this process is the aggregator(. It’s like a puzzle studio coordinator—doesn't need to permanently hold all the puzzle pieces, but when a user wants to see the complete image, it is responsible for collecting the necessary pieces from everywhere and assembling them. Once reconstruction is complete, the content can be further accelerated through CDN or caching, enabling truly fast access.

This design approach indicates that the bottleneck in distributed storage is not in writing, but in reading. No matter how clever the system design is, if the last mile user experience can't keep up, everything is in vain.