Visible to the public Search Lookaside Buffer: Efficient Caching for Index Data Structures

TitleSearch Lookaside Buffer: Efficient Caching for Index Data Structures
Publication TypeConference Paper
Year of Publication2017
AuthorsWu, Xingbo, Ni, Fan, Jiang, Song
Conference NameProceedings of the 2017 Symposium on Cloud Computing
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-5028-0
Keywordscaching, Human Behavior, index data structure, Key Management, Key-value Store, Metrics, pubcrawl, resilience, Resiliency, Scalability
AbstractWith 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.
URLhttp://doi.acm.org/10.1145/3127479.3127483
DOI10.1145/3127479.3127483
Citation Keywu_search_2017