OceanBase Bacchus: a High-Performance and Scalable Cloud-Native Shared Storage Architecture for Multi-Cloud

2026-02-27

AI Summary

This work addresses the challenge of balancing cost and performance in shared-storage databases under high concurrency, where traditional B+ trees suffer from inefficient in-place updates and existing LSM-tree designs are hindered by complex cross-node shared logging mechanisms. The authors propose a novel LSM-tree architecture tailored for cloud object storage, which innovatively offloads a Paxos-based append-only log file system (PALF) as a dedicated shared log service. By integrating stateless compute nodes, a shared block cache, and asynchronous background tasks, the design achieves elastic decoupling of compute, caching, and storage layers. This approach effectively resolves the log-sharing bottleneck in disaggregated architectures and supports multi-cloud deployment. Experimental results demonstrate that the system matches or exceeds HBase in OLTP performance while significantly outperforming StarRocks in OLAP workloads, simultaneously reducing storage costs by 59% for OLTP and 89% for OLAP.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Search and Optimization: Distributed Search
• Machine Learning: Scalability of ML Systems

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Data transparency and provenance

Abstract

Although an increasing number of databases now embrace shared-storage architectures, current storage-disaggregated systems have yet to strike an optimal balance between cost and performance. In high-concurrency read/write scenarios, B+-tree-based shared storage struggles to efficiently absorb frequent in-place updates. Existing LSM-tree-backed disaggregated storage designs are hindered by the intricate implementation of cross-node shared-log mechanisms, where no satisfactory solution yet exists. This paper presents OceanBase Bacchus, an LSM-tree architecture tailored for object storage provided by cloud vendors. The system sustains high-performance reads and writes while rendering compute nodes stateless through shared service-oriented PALF (Paxos-backed Append-only Log File system) logging and asynchronous background services. We employ a Shared Block Cache Service to flexibly utilize cache resources. Our design places log synchronization into a shared service, providing a novel solution for log sharing in storage-compute-separated databases. The architecture decouples functionality across modules, enabling elastic scaling where compute, cache, and storage resources can be resized rapidly and independently. Through experimental evaluation using multiple benchmark tests, including SysBench and TPC-H, we confirm that OceanBase Bacchus achieves performance comparable to or superior to that of HBase in OLTP scenarios and significantly outperforms StarRocks in OLAP workloads. Leveraging Bacchus's support for multi-cloud deployment and consistent performance, we not only retain high availability and competitive performance but also achieve substantial reductions in storage costs by 59% in OLTP scenarios and 89% in OLAP scenarios.

Problem

• shared-storage architecture
• LSM-tree
• log sharing
• storage-compute disaggregation
• high-concurrency workloads

Innovation

• shared-storage architecture
• LSM-tree
• cloud-native
• log disaggregation
• multi-cloud