Sui, a Layer 1 blockchain network, has introduced Tidehunter, a new storage engine engineered to align with the performance demands, data access characteristics, and operational constraints commonly found in contemporary blockchain infrastructures.
The system is positioned as a potential successor to the existing database layer used by both validators and full nodes, reflecting a broader effort to modernize core infrastructure in response to the evolving scale and workload profiles of production blockchain environments.
Sui originally relied on RocksDB as its primary key–value storage layer, a widely adopted and mature solution that enabled rapid protocol development. As the platform expanded and operational demands increased, fundamental limitations of general-purpose LSM-tree databases became increasingly apparent in production-like environments.
Extensive tuning and deep internal expertise could not fully address structural inefficiencies that conflicted with the access patterns typical of blockchain systems. This led to a strategic shift toward designing a storage engine optimized specifically for blockchain workloads, resulting in the development of Tidehunter.
A central factor behind this decision was persistent write amplification. Measurements under realistic Sui workloads showed amplification levels of roughly ten to twelve times, meaning that relatively small volumes of application data generated disproportionately large amounts of disk traffic. While such behavior is common in LSM-based systems, it reduces effective storage bandwidth and intensifies contention between background compaction and read operations. In write-intensive or balanced read-write environments, this overhead becomes increasingly restrictive as throughput scales.
Load testing on high-performance clusters confirmed the impact, with disk utilization nearing saturation despite moderate application write rates, highlighting the growing mismatch between conventional storage architectures and modern blockchain performance requirements.
Storage behavior in Sui and comparable blockchain platforms is dominated by a small set of recurring data access patterns, and Tidehunter is architected specifically around these characteristics. A large portion of state is addressed using cryptographic hash keys that are evenly distributed and typically map to relatively large records, which removes locality but simplifies consistency and correctness.
At the same time, blockchains rely heavily on append-oriented structures, such as consensus logs and checkpoints, where data is written in order and later retrieved using monotonically increasing identifiers. These environments are also inherently write-heavy, while still requiring fast access on latency-critical read paths, making excessive write amplification a direct threat to both throughput and responsiveness.
At the center of Tidehunter is a high-concurrency write pipeline built to exploit the parallel capabilities of modern solid-state storage. Incoming writes are funneled through a lock-free write-ahead log capable of sustaining extremely high operation rates, with contention limited to a minimal allocation step.
Data copying proceeds in parallel, and the system avoids per-operation system calls by using writable memory-mapped files, while durability is handled asynchronously by background services. This design produces a predictable and highly parallel write path that can saturate disk bandwidth without becoming constrained by CPU overhead.
Reducing write amplification is treated as a primary architectural objective rather than an optimization step. Instead of using the log as a temporary staging area, Tidehunter stores data permanently in log segments and builds indexes that reference offsets directly, eliminating repeated rewrites of values.
Indexes are heavily sharded to keep write amplification low and to increase parallelism, removing the need for traditional LSM-tree structures. For append-dominated datasets, such as checkpoints and consensus records, specialized sharding strategies keep recent data tightly grouped so that write overhead remains stable even as historical data grows.
For tables addressed by uniformly distributed hash keys, Tidehunter introduces a uniform lookup index optimized for predictable, low-latency access. Rather than issuing multiple small and random reads, the index reads a slightly larger contiguous region that statistically contains the desired entry, allowing most lookups to complete in a single disk round trip.
This approach deliberately trades some read throughput for lower and more stable latency, a tradeoff that becomes practical because reduced write amplification frees substantial disk bandwidth for read traffic. The result is more consistent performance on latency-sensitive operations such as transaction execution and state validation.
To further control tail latency at scale, Tidehunter combines direct I/O with application-managed caching. Large historical reads bypass the operating system’s page cache to prevent cache pollution, while recent and frequently accessed data is retained in user-space caches informed by application-level access patterns. In combination with its indexing layout, this reduces unnecessary disk round trips and improves predictability under sustained load.
Data lifecycle management is also simplified. Because records are stored directly in log segments, removing obsolete historical data can be performed by deleting entire log files once they fall outside the retention window. This avoids the complex and I/O-intensive compaction mechanisms required by LSM-based databases and enables faster, more predictable pruning even as datasets expand.
Across workloads designed to reflect real Sui usage, Tidehunter demonstrates higher throughput and lower latency than RocksDB while consuming significantly less disk write bandwidth. The most visible improvement comes from the near elimination of write amplification, which allows disk activity to more closely match application-level writes and preserves I/O capacity for reads. These effects are observed both in controlled benchmarks and in full validator deployments, indicating that the gains extend beyond synthetic testing.
Evaluation is performed using a database-agnostic benchmark framework that models realistic mixes of inserts, deletions, point lookups, and iteration workloads. Tests are parameterized to reflect Sui-like key distributions, value sizes, and read-write ratios, and are executed on hardware aligned with recommended validator specifications. Under these conditions, Tidehunter consistently sustains higher throughput and lower latency than RocksDB, with the largest advantages appearing in write-heavy and balanced scenarios.
Validator-level benchmarks further confirm the results. When integrated directly into Sui and subjected to sustained transaction load, systems using Tidehunter maintain stable throughput and lower latency at operating points where RocksDB-backed deployments begin to suffer from rising disk utilization and performance degradation. Measurements show reduced disk pressure, steadier CPU usage, and improved finality latency, highlighting a clear divergence in behavior under comparable load.
Tidehunter represents a practical response to the operational demands of long-running, high-throughput blockchain systems. As blockchains move toward sustained rather than burst-driven workloads, storage efficiency becomes a foundational requirement for protocol performance. The design of Tidehunter reflects a shift toward infrastructure built explicitly for that next stage of scale, with further technical detail and deployment plans expected to follow.
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