{"id":334958,"date":"2016-12-11T19:35:58","date_gmt":"2016-12-12T03:35:58","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=334958"},"modified":"2018-10-16T20:06:42","modified_gmt":"2018-10-17T03:06:42","slug":"multi-version-range-concurrency-control-deuteronomy","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/multi-version-range-concurrency-control-deuteronomy\/","title":{"rendered":"Multi-Version Range Concurrency Control in Deuteronomy"},"content":{"rendered":"<p>The Deuteronomy transactional key value store executes millions of serializable transactions\/second by exploiting multi-version timestamp order concurrency control. However, it has not supported range operations, only individual record operations (e.g., create, read, update, delete). In this paper, we enhance our multiversion timestamp order technique to handle range concurrency and prevent phantoms. Importantly, we maintain high performance while respecting the clean separation of duties required by Deuteronomy, where a transaction component performs purely logical concurrency control (including range support), while a data component performs data storage and management duties. Like the rest of the Deuteronomy stack, our range technique manages concurrency information in a latch-free manner. With our range enhancement, Deuteronomy can reach scan speeds of nearly 250 million records\/s (more than 27 GB\/s) on modern hardware, while providing serializable isolation complete with phantom prevention<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Deuteronomy transactional key value store executes millions of serializable transactions\/second by exploiting multi-version timestamp order concurrency control. However, it has not supported range operations, only individual record operations (e.g., create, read, update, delete). In this paper, we enhance our multiversion timestamp order technique to handle range concurrency and prevent phantoms. Importantly, we maintain high [&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":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"42nd International Conference on Very Large Data Bases (VLDB), New Delhi, India","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":"42nd International Conference on Very Large Data Bases (VLDB), New Delhi, 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