Walrus is built so that strong read and write performance is the norm, not an afterthought. For clients, using Walrus feels similar to interacting with modern cloud storage—fast, parallel, and reliable—while still maintaining decentralization and robust security.
High write throughput is achieved through efficient data encoding and distribution. When a client uploads a blob, the data is split and erasure-coded into many smaller pieces, which are sent in parallel to multiple storage nodes instead of being written sequentially to a few replicas. Since each node handles only a portion of the data, bottlenecks are avoided. Writes complete once a quorum of nodes responds, allowing progress even if some nodes are slow or temporarily offline.
Read performance follows the same parallel model. Clients fetch multiple data pieces simultaneously from different nodes and reconstruct the original blob as soon as enough pieces arrive. Because any sufficient subset can restore the data, slow or unresponsive nodes can be skipped, making reads resilient to network variability, churn, and partial failures.
Walrus also decouples data transfer from availability verification. While storage nodes continuously prove data possession, clients are not forced to wait on heavy verification steps for every operation. This avoids the latency overhead common in systems where reads and writes are tightly bound to on-chain checks or expensive processing.
By combining erasure coding, quorum-based completion, and extensive parallelism, Walrus delivers consistently high throughput for both reads and writes. The result is a decentralized storage network that scales with its node count while remaining fast enough for real-world use cases, from data-heavy Web3 applications to long-term public datasets.
