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Storage-compute disaggregation has recently emerged as a novel architecture in modern data centers, particularly in the cloud. By decoupling compute from storage, this new architecture enables independent and elastic scaling of compute and storage resources, potentially increasing resource utilization and reducing overall costs. To best leverage the disaggregated architecture, a new breed of database systems termed storage-disaggregated databases has recently been developed, such as Amazon Aurora, Microsoft Socrates, Google AlloyDB, Alibaba PolarDB, and Huawei Taurus. However, little is known about the effectiveness of the design principles in these databases since they are typically developed by industry giants, and only the overall performance results are presented without detailing the impact of individual design principles. As a result, many critical research questions remain unclear, such as the performance impact of storage-disaggregation, the log-as-the-database design, shared-storage, and various log-replay methods. In this paper, we investigate the performance implications of the design principles that are widely adopted in storage-disaggregated databases for the first time. As these databases were usually not open-sourced, we have made a significant effort to implement a storage-disaggregated database prototype based on PostgreSQL v13.0. By fully controlling and instrumenting the codebase, we are able to selectively enable and disable individual optimizations and techniques to evaluate their impact on performance in various scenarios. Furthermore, we open-source our storage-disaggregated database prototype for use by the broader database research community, fostering collaboration and innovation in this field.
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This content will become publicly available on February 1, 2027
Reducing Tail Latency in Storage-Disaggregated Database Systems
Storage-disaggregated databases have become the standard in the cloud due to many benefits, including improved resource utilization, reduced resource fragmentation, and the ability to independently and elastically scale compute and storage, ultimately leading to cost savings. This work focuses on OLTP databases. Examples include Amazon Aurora, Microsoft Socrates, and Neon. However, a significant limitation we have identified in storage-disaggregated databases is the long tail latency. This issue arises from the unique architecture of these databases, specifically the log-as-the-database design principle. Under this design, when a transaction is committed, only the logs are sent to the storage engine over the network to minimize data movement, while the actual pages are replayed on the storage side. Thus, certain page requests may encounter a lengthy log replay chain, which lead to long latency.
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- Award ID(s):
- 2337806
- PAR ID:
- 10657054
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Management of Data
- Volume:
- 4
- ISSN:
- 2836-6573
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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