Fault-tolerant precise data access on distributed log-structured merge-tree
Fault-tolerant precise data access on distributed log-structured merge-tree
复制标题
分布式日志结构合并树上的容错精确数据访问
DOI：
10.1007/s11704-018-7198-6
复制
发表时间：
2019
影响因子：
4.2
通讯作者：
Zhou Aoying
中科院分区：
文献类型：
--
作者：
Zhu Tao;Hu Huiqi;Qian Weining;Zhou Huan;Zhou Aoying
日志结构的合并树被许多分布式存储系统所采用。它将一个大型数据库分解为多个部分：一个写入部分和几个只读部分。记录首先写入内存优化结构，然后定期压缩到磁盘结构中。它实现了高写吞吐量。然而，它带来的副作用是读请求必须经过多个结构才能找到所需的记录。在分布式数据库系统中，lsm树的不同部分以分布式方式存储。为此，查询层中的服务器必须发出多个网络通信以从底层存储层提取数据项。为了解决这个问题，本文提出了一种精确的数据访问策略，该策略包括：设计一个具有低维护开销的高效结构，用于测试记录是否存在于lsm树的写入部分；提出了一种基于租约的同步策略，用于在远程查询服务器上维护结构的一致副本。我们进一步证明，当LSM-Tree在后端重新组织多个结构时，该技术能够鲁棒地工作。它还具有容错性，能够在节点发生故障后恢复数据访问中使用的结构。使用YCSB基准测试的实验表明，该解决方案比现有方法的吞吐量提高了6倍。
Log-structured merge tree has been adopted by many distributed storage systems. It decomposes a large database into multiple parts: an in-writing part and several read-only ones. Records are firstly written into a memory-optimized structure and then compacted into in-disk structures periodically. It achieves high write throughput. However, it brings side effect that read requests have to go through multiple structures to find the required record. In a distributed database system, different parts of the LSM-tree are stored in distributed fashion. To this end, a server in the query layer has to issues multiple network communications to pull data items from the underlying storage layer. Coming to its rescue, this work proposes a precise data access strategy which includes: an efficient structure with low maintaining overhead designed to test whether a record exists in the in-writing part of the LSM-tree; a lease-based synchronization strategy proposed to maintain consistent copies of the structure on remote query servers.We further prove the technique is capable of working robustly when the LSM-Tree is re-organizing multiple structures in the backend. It is also fault-tolerant, which is able to recover the structures used in data access after node failures happen. Experiments using the YCSB benchmark show that the solution has 6x throughput improvement over existing methods.