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arXiv 1109.6885cs.DB
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使用多核CPU在读取优化的数据库上快速更新

Fast Updates on Read-Optimized Databases Using Multi-Core CPUs

Jens Krueger, Changkyu Kim, Martin Grund, Nadathur Satish, David Schwalb, Jatin Chhugani, Hasso Plattner, Pradeep Dubey, Alexander Zeier

摘要:

读取优化的列式数据库使用差异更新来处理写入,方法是维护一个单独的写入优化的增量分区,该分区定期与读取优化和压缩的主分区合并。这种合并过程在更新密集型系统中引入了显著的开销和不可接受的停机时间,期望将事务和分析工作负载组合到一个系统中。在本文的第一部分中,我们报告了12个SAP Business Suite客户系统的数据分析。在后半部分,我们提出了一个优化的合并过程,减少了30倍的当前系统的合并开销。我们的线性时间合并算法利用现代多核CPU的底层高计算和带宽资源,具有架构感知优化和高效并行化。这使得压缩内存中的列存储能够处理企业应用程序所需的事务更新速率,同时保持读取优化数据库的属性以用于分析式查询。

英文摘要:

Read-optimized columnar databases use differential updates to handle writes by maintaining a separate write-optimized delta partition which is periodically merged with the read-optimized and compressed main partition. This merge process introduces significant overheads and unacceptable downtimes in update intensive systems, aspiring to combine transactional and analytical workloads into one system. In the first part of the paper, we report data analyses of 12 SAP Business Suite customer systems. In the second half, we present an optimized merge process reducing the merge overhead of current systems by a factor of 30. Our linear-time merge algorithm exploits the underlying high compute and bandwidth resources of modern multi-core CPUs with architecture-aware optimizations and efficient parallelization. This enables compressed in-memory column stores to handle the transactional update rate required by enterprise applications, while keeping properties of read-optimized databases for analytic-style queries.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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