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Optimized Transactional Data Structure Approach to Concurrency Control for In-Memory Databases

机译:内存数据库并发控制的优化事务数据结构方法

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The optimistic concurrency control (OCC) utilized by in-memory databases performs writes on thread-local copies and makes the writes visible upon passing validation. However, high contention workloads suffer from failure of the validation step due to non-semantic memory access conflicts, leading to frequent transaction aborts. In this work, we improve the commit rate of in-memory databases by replacing OCC and the underlying indexing of key-value entries in the Silo database with a lock-free transactional dictionary. To further optimize the transactional commit rate, we present transactional merging, a technique that relaxes the semantic conflict resolution of transactional data structures by merging conflicting operations to reduce aborts. Transactional merging guarantees strict serializability through a strategy that recovers the correct abstract state given that a transaction attempting to merge operations aborts. The experimental evaluation demonstrates that the lock-free transactional dictionary with transactional merging achieves an average speedup of 175% over OCC and the Masstree indexing used in the Silo database for write-dominated workloads on a non-uniform memory access system.
机译:内存数据库利用的乐观并发控制(OCC)对线程本地副本执行写操作,并在通过验证后使写操作可见。但是,由于非语义内存访问冲突,高争用工作负载遭受验证步骤失败,从而导致频繁的事务中止。在这项工作中,我们通过使用无锁事务字典替换OCC和在Silo数据库中对键值条目的基础索引来提高内存数据库的提交率。为了进一步优化事务提交率,我们提出了事务合并,该技术通过合并冲突操作以减少异常中止,从而放松了事务数据结构的语义冲突解决方案。事务合并通过一种策略来保证严格的可序列化性,该策略可在尝试合并操作的事务中止的情况下恢复正确的抽象状态。实验评估表明,具有事务合并功能的无锁事务字典比OCC和Silo数据库中用于非均匀内存访问系统上写为主的工作负载的Masstree索引的平均速度提高了175%。

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