PPT-Distributed query optimization

Data Management for Big Data 20182019 s pring semester Dario Della Monica These slides are a modified version of the slides provided with the book Özsu and Valduriez Principles of . In addition to extensibility dynamic pro gramming and memorization based on and extended from the EXODUS and Volcano prototypes this new optimizer provides i manipulation of operator arguments using rules or functions ii operators that are both logi Group 4: . Nipun . Garg, Surabhi . Mithal. http://www-users.cs.umn.edu/~smithal/. Chapter Organization. 2. OLD Organization. 5.1 . Evaluation of Spatial Operations . 5.2 . Query Optimization . 5.3 . CSE 8330. Instructor: . Dr.Margaret. H. Dunham. Presenter: . Akshaya. . Aradhya. Introduction. Query optimization in XML databases. Query optimization in Parallel databases. Comparison. Conclusion and Future work. Query Optimization Process. (simplified a bit). Parse the SQL query into a logical tree:. identify distinct blocks (corresponding to nested sub-queries or views). . Query rewrite phase:. apply . algebraic transformations. Data Analytics. Qifan Pu, Ganesh . Ananthanarayanan. , Peter . Bodik. , . Srikanth. . Kandula. , . Aditya. . Akella. , . Paramvir. . Bahl. , Ion . Stoica. WAN. Geo-distributed Data. . Analytics. Seattle. Approximate Algorithms. Alessandro Farinelli. Approximate Algorithms: outline. No guarantees. DSA-1, MGM-1 (exchange individual assignments). Max-Sum (exchange functions). Off-Line guarantees. K-optimality and extensions. Processing . on OpenCL-based FPGAs. Zeke Wang. , Johns Paul, Hui Yan Cheah . (. NTU,. . Singapore), . Bingsheng He (. NUS,. Singapore), . Wei Zhang (HKUST, Hong Kong). 1. Outline. Background and Problem. Processing. Carsten. Binnig, Donald . Kossmann. and Eric Lo. ICDE . 2007. Presented . by . Ankit. Shah . Bikash. Chandra. Motivation. Testing database applications requires generating test . databases. built . on Virtual Private . Servers. . e-Science for . cheminformatics. and drug discovery. 4th IEEE International Conference on e-Science 2008. Kangseok. Kim, Marlon E. Pierce. Community Grids Laboratory, Indiana University. on OpenCL-based FPGAs. Zeke Wang. , Johns Paul, Hui Yan Cheah . (. NTU,. . Singapore), . Bingsheng He (. NUS,. Singapore), . Wei Zhang (HKUST, Hong Kong). 1. Outline. Background and Problem. Challenges. Query optimization. :. The process of choosing a suitable execution strategy for processing a query.. Two internal representations of a query:. Query Tree. Query Graph. Introduction to Query Processing (2). Qifan Pu, Ganesh . Ananthanarayanan. , Peter . Bodik. , . Srikanth. . Kandula. , . Aditya. . Akella. , . Paramvir. . Bahl. , Ion . Stoica. Global DC Footprints. Apps are deployed in many DCs and edges. Chapters 12.4-12.6 and 15.2-15.3 of Cow Book. Arun Kumar. CSE 132C. Database System Implementation. Slide ACKs: Jignesh Patel. Query. Query Result. Database Server. Select R.text from . Report R, Weather W. Query Processing. Document-at-a-time. Calculates complete scores for documents by processing all term lists, one document at a time. Term-at-a-time. Accumulates scores for documents by processing term lists one at a time.