COLE: A Column-based Learned Storage for Blockchain Systems

Blockchain systems suffer from high storage costs as every node needs to store and maintain the entire blockchain data. After investigating Ethereum's storage, we find that the storage cost mostly comes from the index, i.e., Merkle Patricia Trie (MPT), that is used to guarantee data integrity and support provenance queries. To reduce the index storage overhead, an initial idea is to leverage the emerging learned index technique, which has been shown to have a smaller index size and more efficient query performance. However, directly applying it to the blockchain storage results in even higher overhead owing to the blockchain's persistence requirement and the learned index's large node size. Meanwhile, existing learned indexes are designed for in-memory databases, whereas blockchain systems require disk-based storage and feature frequent data updates. To address these challenges, we propose COLE, a novel column-based learned storage for blockchain systems. We follow the column-based database design to contiguously store each state's historical values, which are indexed by learned models to facilitate efficient data retrieval and provenance queries. We develop a series of write-optimized strategies to realize COLE in disk environments. Extensive experiments are conducted to validate the performance of the proposed COLE system. Compared with MPT, COLE reduces the storage size by up to 94% while improving the system throughput by 1.4X-5.4X.