From ARIES to MARS: transaction support for next-generation, solid-state drives
From ARIES to MARS: transaction support for next-generation, solid-state drives
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DOI：
10.1145/2517349.2522724
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发表时间：
2013-11
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影响因子：
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通讯作者：
Joel Coburn;Trevor Bunker;Meir Schwarz;Rajesh K. Gupta;S. Swanson
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作者：
Joel Coburn;Trevor Bunker;Meir Schwarz;Rajesh K. Gupta;S. Swanson
基于事务的系统通常依赖于预写日志（WAL）算法，该算法旨在最大限度地提高基于磁盘的存储的性能。然而，新兴的快速、字节可寻址的非易失性存储器（NVM）技术（例如，相变存储器、自旋转移矩MRAM和忆阻器）呈现非常不同的性能特性，因此轻率地应用现有算法可能导致令人失望的性能。本文提出了一种新的存储原语，称为可编辑原子写入（EAW），使复杂的，高度优化的WAL计划在快速的基于NVM的存储系统。EAWs允许应用程序安全地访问和修改日志内容，而不是将日志作为一个只附加，只写的数据结构，我们证明，这可以使实现复杂的事务更简单，更有效。我们使用EAW来构建MARS，这是一种WAL方案，它提供了与ARIES [26]（一种广泛使用的数据库WAL系统）相同的功能，但避免了以磁盘为中心的实现决策。我们已经在下一代SSD中实现了EAW和MARS，以证明与正常写入相比，EAW的开销最小，并且它们为哈希表、B+树和大型图的事务更新提供了大幅加速。此外，MARS的性能比ARIES高出3.7倍，同时降低了软件复杂性。
Transaction-based systems often rely on write-ahead logging (WAL) algorithms designed to maximize performance on disk-based storage. However, emerging fast, byte-addressable, non-volatile memory (NVM) technologies (e.g., phase-change memories, spin-transfer torque MRAMs, and the memristor) present very different performance characteristics, so blithely applying existing algorithms can lead to disappointing performance. This paper presents a novel storage primitive, called editable atomic writes (EAW), that enables sophisticated, highly-optimized WAL schemes in fast NVM-based storage systems. EAWs allow applications to safely access and modify log contents rather than treating the log as an append-only, write-only data structure, and we demonstrate that this can make implementating complex transactions simpler and more efficient. We use EAWs to build MARS, a WAL scheme that provides the same as features ARIES [26] (a widely-used WAL system for databases) but avoids making disk-centric implementation decisions. We have implemented EAWs and MARS in a next-generation SSD to demonstrate that the overhead of EAWs is minimal compared to normal writes, and that they provide large speedups for transactional updates to hash tables, B+trees, and large graphs. In addition, MARS outperforms ARIES by up to 3.7 x while reducing software complexity.