{"id":164265,"date":"2011-01-01T00:00:00","date_gmt":"2011-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/optimistic-concurrency-control-by-melding-trees\/"},"modified":"2018-10-16T20:12:39","modified_gmt":"2018-10-17T03:12:39","slug":"optimistic-concurrency-control-by-melding-trees","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/optimistic-concurrency-control-by-melding-trees\/","title":{"rendered":"Optimistic Concurrency Control by Melding Trees"},"content":{"rendered":"
This paper describes a new optimistic concurrency control algorithm for tree-structured data called meld. Each transaction executes on a snapshot of a multiversion database and logs a record with its intended updates. Meld processes log records in log order on a cached partial-copy of the last committed state to determine whether each transaction commits. If so, it merges the transaction\u2019s updates into that state. Meld is used in the Hyder transaction system and enables Hyder to scale out without partitioning. Since meld is on the critical path of transaction execution, it must be very fast. The paper describes the meld algorithm in detail and reports on an evaluation of an implementation. It can perform over 400K update transactions per second for transactions with two operations, and 130K for transactions with eight operations.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper describes a new optimistic concurrency control algorithm for tree-structured data called meld. Each transaction executes on a snapshot of a multiversion database and logs a record with its intended updates. Meld processes log records in log order on a cached partial-copy of the last committed state to determine whether each transaction commits. If 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