基于列的区块链系统学习存储
Authors:
Ce Zhang and Cheng Xu, Hong Kong Baptist University; Haibo Hu, Hong Kong Polytechnic University; Jianliang Xu, Hong Kong Baptist University
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×.
摘要中文：
区块链系统面临着高昂的存储成本，因为每个节点都需要存储和维护整个区块链数据。在调查了以太坊的存储后，我们发现存储成本主要来自索引，即Merkle Patricia Trie（MPT）。为了支持来源查询，MPT在数据更新期间持久化索引节点，这增加了太多的存储开销。为了减小存储大小，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更小的索引大小和更高效的查询性能。然而，由于需要持久化索引节点和学习索引的大节点大小，将其直接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COLE，这是一种用于区块链系统的新型基于列的学习存储。我们遵循基于列的数据库设计，连续存储每个州的历史值，这些值由学习模型索引，以促进高效的数据检索和来源查询。我们开发了一系列写优化策略来实现磁盘环境中的COLE。进行了广泛的实验来验证所提出的COLE系统的性能。与MPT相比，COLE将存储大小减少了94%，同时将系统吞吐量提高了1.4×-5.4×。
演示视频
论文pdf 链接:https://www.usenix.org/system/files/fast24-zhang_ce.pdf
幻灯片链接:https://www.usenix.org/system/files/fast24_slides-zhang_ce.pdf
