{"id":166494,"date":"2014-06-01T00:00:00","date_gmt":"2014-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/indexing-on-modern-hardware-hekaton-and-beyond\/"},"modified":"2018-10-16T20:19:19","modified_gmt":"2018-10-17T03:19:19","slug":"indexing-on-modern-hardware-hekaton-and-beyond","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/indexing-on-modern-hardware-hekaton-and-beyond\/","title":{"rendered":"Indexing on Modern Hardware: Hekaton and Beyond"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Recent OLTP support exploits new techniques, running on modern hardware, to achieve unprecedented performance compared with prior approaches. In SQL Server, the Hekaton main-memory database engine embodies this new OLTP support. Hekaton uses the Bw-tree to achieve its great indexing performance. The Bw-Tree is a latch-free B-tree index that also exploits log-structured storage when used \u201cbeyond\u201d Hekaton as a separate key value store. It is designed from the ground up to address two hardware trends: (1) Multi-core and main memory hierarchy: the Bw-tree is completely latch-free, using an atomic compare-and-swap instruction to install state changes on a \u201cpage address\u201d mapping table; it performs updates as \u201cdeltas\u201d to avoid update-in-place. These improve performance by eliminating thread blocking while improving cache hit ratios. (2) flash storage: the Bw-tree organizes secondary storage in a log-structured manner, using large sequential writes to avoid the adverse performance impact of random writes. We demontrate the architectural versatility and performance of the Bw-tree in two scenarios: (a) running live within Hekaton and (2) running as a standalone key value store compared to both BerkeleyDB and a state-of-the-art in-memory range index (latch-free skiplists). Using workloads from real-world applications (Microsoft XBox Live Primetime and enterprise deduplication), we show the Bw-tree is 19x faster than BerkeleyDB and 3x faster than skiplists.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recent OLTP support exploits new techniques, running on modern hardware, to achieve unprecedented performance compared with prior approaches. In SQL Server, the Hekaton main-memory database engine embodies this new OLTP support. Hekaton uses the Bw-tree to achieve its great indexing performance. The Bw-Tree is a latch-free B-tree index that also exploits log-structured storage when used [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2014","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 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