Optimizing Search Layouts in Packed Memory Arrays

Brian Wheatman, Randal C. Burns, Aydin Buluç, Helen Xu

Abstract: This paper introduces Search-optimized Packed Memory Arrays (SPMAs), a collection of data structures based on Packed Memory Arrays (PMAs) that address suboptimal search via cache-optimized search layouts. Traditionally, PMAs and B-trees have tradeoffs between searches/inserts and scans: B-trees were faster for searches and inserts, while PMAs were faster for scans. Our empirical evaluation shows that SPMAs overcome this tradeoff for unsorted input distributions: on average, SPMAs are faster than B+-trees (a variant of B-trees optimized for scans) on all major operations. We generated datasets and search/insert workloads from the Yahoo! Cloud Serving Benchmark (YCSB) and found that SPMAs are about 2× faster than B+-trees regardless of the ratio of searches to inserts. On uniform random inputs, SPMAs are on average between 1.3× −2.3× faster than B+-trees on all operations. Finally, we vary the amount of sortedness in the inputs to stress the worst-case insert distribution in the PMA. We find that the worst-case B+-tree insertion throughput is about 1.5× faster than the worst-case PMA insertion throughput. However, the worst-case input for the PMA is sorted and highly unlikely to appear naturally in practice. The SPMAs maintain higher insertion throughput than the B+-tree when the input is up to 25% sorted.