Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer
The D-Wave adiabatic quantum annealer solves hard combinatorial optimization problems leveraging quantum physics. The newest version features over 1000 qubits and was released in August 2015. We were given access to such a machine, currently hosted at NASA Ames Research Center in California, to explore the potential for hard optimization problems that arise in the context of databases. In this pap
War of the Hadoop SQL engines. And the winner is ...? - Sonra
War of the Hadoop SQL engines. And the winner is …? You may have wondered why we were quiet over the last couple of weeks? Well, we locked ourselves into the basement and did some research and a couple of projects and PoCs on Hadoop, Big Data, and distributed processing frameworks in general. We were also looking at Clickstream data and Web Analytics solutions. Over the next couple of weeks we wil
Sargable - Wikipedia
This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these messages) This article possibly contains original research. Please improve it by verifying the claims made and adding inline citations. Statements consisting only of original research should be removed. (August 2015) (Learn how and when to remove this message) This
FilterPushdownDev - Apache Hive - Apache Software Foundation
IntroductionThis document explains how we are planning to add support in Hive's optimizer for pushing filters down into physical access methods. This is an important optimization for minimizing the amount of data scanned and processed by an access method (e.g. for an indexed key lookup), as well as reducing the amount of data passed into Hive for further query evaluation. Use CasesBelow are the ma
Prestoã®codegen - myui's memo
以å‰ã€Prestoã®ãƒã‚¤ãƒˆã‚³ãƒ¼ãƒ‰ç”Ÿæˆéƒ¨åˆ†ã®ã‚½ãƒ¼ã‚¹ã‚³ãƒ¼ãƒ‰ã‚’èªã‚“ã ã®ã§ã€hackå†é–‹æ™‚ã®ãŸã‚ã«ãƒ¡ãƒ¢ã—ã¦ãŠã。 コード生æˆã«ã¯objectwebã®ASMを利用ã—ã¦ã„る。Parser generatorã¯ANTLR。 ExpressionCompiler#internalCompileFilterAndProjectOperator codegenã—ã¦ã„ã‚‹ã®ã¯filterå¥ã¨projectionå¥ã®ã¿ã€‚Joinã¯æ®‹å¿µãªãŒã‚‰codegenã•ã‚Œã¦ã„ãªã„。 SqlStageExecution#startTasks evaluate planã®ä¸èº«ã¯remote taskã¨ã„ã†ã®ãŒsubStages（StageExecutionPlan）ãŒã‚ã‚‹ã¨ä½œã‚‰ã‚Œã‚‹ join関係ã®rewriteã¯PredicatePushDown。Volcanoã®exchange抽象operatorã§remoteã®å®Ÿè¡Œã‚’抽象化。Lo
並列データベースシステムã®æ¦‚念ã¨åŽŸç†
3. 講義内容 ï® åºè«– - 並列データベースã®å‰ã« ï® ä¸¦åˆ—å‡¦ç†ã®åŸºç¤Ž ï® ï® ä¸¦åˆ—å‡¦ç†ã®Terminology 並列計算機アーã‚テクãƒãƒ£ ï® ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®ã‚¢ãƒ¼ã‚テクãƒãƒ£ ï® ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹å‡¦ç†ã®ä¸¦åˆ—化 ï® çµåˆå‡¦ç†ã®é«˜é€ŸåŒ– ï® ï® ï® ï® ä¸¦åˆ—ãƒãƒƒã‚·ãƒ¥çµåˆ 並列ソート パーティショニング手法 多é‡çµåˆã‚„計算機間ã®ãƒ‡ãƒ¼ã‚¿äº¤æ›ã§ç™ºç”Ÿã™ã‚‹å•é¡Œ ï® MapReduceã«ã‚ˆã‚‹é–¢ä¿‚演算ã®ä¸¦åˆ—å‡¦ç† 3 4. データベース開発ã®æµã‚Œ ï® Coddã®è«–æ–‡: 1970å¹´ ï® ï® ï® ï® System Rã‚„Ingres: 70年代ä¸ç›¤ Oracle, IBM DB2, Ingres: 80年代åºç›¤ 並列データベースã®éš†ç››: 80å¹´ä»£å¾ŒåŠ ï® ï® A Relational Model of Data for Large Shared Data Banks, Communications of ACM 商用
Stratis D. Viglas - Home
I am a Professor in the School of Informatics at the University of Edinburgh, where I am the Chair of Data Management on New Hardware. I am a member of the Institute for Computing Systems Architecture, the Database Group, and an associate member of the Laboratory for Foundations of Computer Science. Current research interests Just-in-time SQL compilation One of the traditional ways of evaluating S
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Non-Invarsive Progressive Optimization for In-Memory Databases
Impala BE Note
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