Conference：USENIX Conference on File and Storage Technologies
(FAST)
CCF level：CCF A
Categories：Computer Architecture/Parallel and Distributed Computing/Storage Systems
Year：2024
1
Title:
COLE: A Column-based Learned Storage for Blockchain Systems
COLE：一种用于区块链系统的基于列的学习存储
Authors:
Abstract:
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). To support provenance queries, MPT persists the index nodes during the data update, which adds too much storage overhead. To reduce the storage size, 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 requirement of persisting index nodes and the learned index's large node size. To tackle this, 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 1.4×-5.4×.
区块链系统的存储成本很高，因为每个节点都需要存储和维护整个区块链数据。在研究了以太坊的存储后，我们发现存储成本主要来自索引，即 Merkle Patricia Trie (MPT)。为了支持出处查询，MPT 在数据更新期间会保留索引节点，这会增加太多的存储开销。为了减少存储大小，最初的想法是利用新兴的学习索引技术，该技术已被证明具有较小的索引大小和更高效的查询性能。然而，由于需要保留索引节点和学习索引的节点大小较大，直接将其应用于区块链存储会导致更高的开销。为了解决这个问题，我们提出了一种用于区块链系统的新型基于列的学习存储 COLE。我们遵循基于列的数据库设计来连续存储每个状态的历史值，这些历史值由学习模型索引，以促进高效的数据检索和出处查询。我们开发了一系列写入优化策略来在磁盘环境中实现 COLE。进行了大量的实验来验证所提出的 COLE 系统的性能。与MPT相比，COLE在提高系统吞吐量1.4倍~5.4倍的同时，存储规模减少了94%。
