Building A Fast and Efficient LSM-tree Store by Integrating Local Storage with Cloud Storage

Abstract

The explosive growth of modern web-scale applications has made cost-effectiveness a primary design goal for their underlying databases. As a backbone of modern databases, LSM-tree based key-value stores (LSM store) face limited storage options. They are either designed for local storage that is relatively small, expensive, and fast or for cloud storage that offers larger capacities at reduced costs but slower. Designing an LSM store by integrating local storage with cloud storage services is a promising way to balance the cost and performance. However, such design faces challenges such as data reorganization, metadata overhead, and reliability issues. In this article, we propose RocksMash, a fast and efficient LSM store that uses local storage to store frequently accessed data and metadata while using cloud to hold the rest of the data to achieve Extension of Conference Paper. An earlier version of this article was presented at 2021 IEEE International Conference on Cluster Computing (CLUSTER), 7-10 September, 2021 [58]. In this article, we broaden the design to allow for faster recovery. RocksMash proposes the extended WAL to trade faster recovery time for slightly more storage space. RocksMash searches WAL files in reverse chronological order to reduce data to be processed, logically reducing the size of WAL files to be processed, and thus improving scan efficiency. Evaluation results show that the proposed approach improves the recovery performance by up to 10×.