Data Availability (DA) layer design directly impacts the performance ceiling of the entire blockchain. Walrus Protocol has done some interesting work in this area—breaking the DA layer into three independent modules: storage, verification, and retrieval, each of which can be upgraded and iterated separately.

For storage, multi-level redundancy is used. The underlying layer employs erasure coding techniques to ensure data is not lost, while the upper layer dynamically adjusts the number of replicas based on data importance, thus avoiding resource waste and the risk of data loss. The verification process is even more interesting—combining data sampling and zero-knowledge proofs, allowing application providers to choose the verification strength based on their security requirements. Lightweight verification or full cryptographic guarantees are all at your discretion. The retrieval layer uses a distributed content-addressed network combined with edge caching and intelligent prefetching to significantly reduce data access latency.

In terms of performance metrics, Walrus is designed with parallel processing pipelines for high-throughput scenarios. Data sharding, encoding, and verification—these compute-intensive tasks—are broken into parallelizable subtasks to fully leverage multi-core processors and GPUs. Actual testing results show a 60% increase in data processing throughput and a 45% reduction in verification latency. Especially for the ZK-Rollup ecosystem, they optimized the encoding formats of polynomial commitments, cutting data preprocessing time before zero-knowledge proof generation by 35%.

The network layer also features adaptive design. Connection relationships between nodes are adjusted in real-time based on network status and load conditions. The system continuously monitors each node’s processing capacity, bandwidth usage, and geographic location to automatically optimize data transmission paths—ensuring low latency while enhancing network fault tolerance. The load balancing algorithm considers node processing power, remaining storage space, and response speed to intelligently allocate storage tasks, ensuring no single point becomes a bottleneck.