搜索缓存:索引数据结构的高效缓存

Xingbo Wu, Fan Ni, Song Jiang
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引用次数: 4

摘要

随着商品计算机中DRAM容量的不断增加,应用程序倾向于在主存储器中存储大量数据以实现快速访问。因此,有效地遍历索引结构以定位所请求的数据对它们的性能至关重要。索引数据结构增长得如此之大,以至于只有一小部分可以缓存在CPU缓存中。CPU缓存可以利用访问局部性来保留索引中最常用的部分,以便快速访问。但是,在搜索数据项期间对目标数据的索引进行遍历可能会导致大量错误的时间和空间位置,从而导致CPU缓存空间严重未得到充分利用。在本文中,我们表明,即使对于高度倾斜的访问,索引遍历也会导致过多的缓存丢失,从而导致次优的数据访问性能。为了解决这个问题,我们引入了Search Lookaside Buffer (SLB)来选择性地只缓存搜索结果,而不是索引本身。SLB可以轻松地与任何索引数据结构集成,以提高有限的CPU缓存资源的利用率,并提高大型数据集上搜索请求的吞吐量。我们将SLB集成到各种索引数据结构和应用程序中。实验表明,SLB可以将索引数据结构的吞吐量提高一个数量级。对真实世界的键值跟踪的实验也显示哈希表的吞吐量提高了73%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Search lookaside buffer: efficient caching for index data structures
With the ever increasing DRAM capacity in commodity computers, applications tend to store large amount of data in main memory for fast access. Accordingly, efficient traversal of index structures to locate requested data becomes crucial to their performance. The index data structures grow so large that only a fraction of them can be cached in the CPU cache. The CPU cache can leverage access locality to keep the most frequently used part of an index in it for fast access. However, the traversal on the index to a target data during a search for a data item can result in significant false temporal and spatial localities, which make CPU cache space substantially underutilized. In this paper we show that even for highly skewed accesses the index traversal incurs excessive cache misses leading to suboptimal data access performance. To address the issue, we introduce Search Lookaside Buffer (SLB) to selectively cache only the search results, instead of the index itself. SLB can be easily integrated with any index data structure to increase utilization of the limited CPU cache resource and improve throughput of search requests on a large data set. We integrate SLB with various index data structures and applications. Experiments show that SLB can improve throughput of the index data structures by up to an order of magnitude. Experiments with real-world key-value traces also show up to 73% throughput improvement on a hash table.
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