Reading data from a distributed network sounds simple, but in practice it's full of pitfalls. Walrus's read protocol doesn't rely on overly idealistic assumptions; it faces reality head-on — nodes are not always cooperative, and speeds are not always reliable. What to do? Multi-step handshake mechanisms to make reads both stable and verifiable.

Here's how it works. First, prioritize metadata. The client first collects signed metadata fragments, which record the location and mapping of data chunks. What's the benefit? It directly filters out garbage responses, avoiding wasting bandwidth on useless data.

Next, WAL tends to use secondary slivers (replica shards). This design is very deliberate — it doesn't depend on a single node, but ensures data integrity through redundancy and verification mechanisms. So even if some nodes fail, the system can still operate smoothly.

Essentially, this approach turns the uncertainties of distributed systems into controllable, verifiable elements. Compared to traditional assumptions (all nodes behave), WAL chooses a more realistic path.