One of the easiest ways to misunderstand decentralized storage is to assume that data must be delivered perfectly and immediately in order to be safe. Walrus is built on the opposite insight: permanence does not come from flawless delivery, it comes from guaranteed recoverability.
The recovery model in Walrus is formalized through two reconstruction lemmas one for primary slivers and one for secondary slivers. On paper they look mathematical, but in practice they explain why Walrus can keep data alive even when things go wrong.
Primary sliver reconstruction: durability first
Primary slivers form the core representation of stored data.
When a blob is written, it is erasure-coded and split into symbols distributed across storage nodes. Each primary sliver can be reconstructed as long as 2f + 1 valid symbols are available.
That threshold is crucial.
It means a node does not need to receive its full primary sliver during the write phase. Messages can be delayed. Nodes can go offline. Some participants can even act maliciously. None of that permanently threatens the data.
As long as enough symbols exist somewhere in the system, the full primary sliver can always be rebuilt later.
This allows Walrus to avoid the most dangerous assumption in distributed systems: synchrony. The network never waits for “perfect delivery.” It keeps moving forward, confident that missing pieces are mathematically recoverable.
In short:
delivery may be incomplete
availability proofs may be partial
but data is never lost
Because recovery is guaranteed, not hoped for.
Secondary sliver reconstruction: recovery efficiency
Primary reconstruction alone ensures durability, but Walrus adds a second dimension to make recovery practical and efficient.
Secondary slivers are encoded with a lower reconstruction threshold of f + 1 symbols.
This asymmetry is intentional.
Secondary slivers act as recovery helpers. If a node completely missed its primary sliver for example due to downtime or network churn it doesn’t need to request full retransmission. Instead, it can use secondary slivers gathered from other nodes to reconstruct the missing primary data.
This turns recovery into a local operation rather than a global one.
No full re-uploads.
No system-wide coordination.
No blocking of future epochs.
Just reconstruction using already-available encoded material.
Why the two dimensions matter together
Individually, each lemma provides resilience. Together, they create convergence.
Primary slivers guarantee correctness and long-term durability.
Secondary slivers guarantee that recovery remains feasible and lightweight.
This two-dimensional design is what allows Walrus to say something very strong:
Even if data is not fully delivered today, the system will eventually converge to a complete and correct state.
That’s a fundamentally different philosophy from most storage systems, which treat missing data as failure.
Walrus treats missing data as temporary incompleteness.
Safety without synchrony
The deeper insight here is that Walrus decouples safety from timing.
Many systems assume data must be written correctly at the moment of storage or it’s unsafe forever. Walrus proves that this assumption is unnecessary.
Safety comes from reconstruction thresholds, not from delivery guarantees.
As long as enough encoded symbols exist somewhere in the network, data can always be:
recovered
verified
redistributed
This is what allows Walrus to handle:
node churn
delayed messages
reconfiguration events
partial failures
without halting progress or risking permanence.
What this enables at the system level
These reconstruction guarantees are the reason Walrus can scale as real infrastructure:
Nodes can crash and rejoin without data loss
Epoch transitions don’t depend on perfect handoffs
Reconfiguration doesn’t stall waiting for offline nodes
Storage load can rebalance naturally over time
Instead of fragile assumptions, the protocol relies on math.
Instead of panic recovery, it relies on convergence.
Walrus doesn’t promise that data is always perfectly placed at every moment.
It promises something stronger:
that data can always be recovered.
And in decentralized storage, recoverability not perfection is what makes permanence real.
