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RT Book, Whole SR Electronic DC OPAC T1 An architecture for fast and general data processing on large clusters / Matei Zaharia T2 ACM books. ISSN:23746777 A1 Zaharia, Matei. YR 2016 FD 2016 SP 1 PDF (xii, 128 pages) K1 Electronic data processing -- Distributed processing K1 Distributed databases K1 Big data ED First edition. PB Association for Computing Machinery : Morgan & Claypool PP [New York] ; [San Rafael, California] SN 9781970001570 LA English (英語) CL LCC:QA76.9.D5 CL DC23:004.36 NO Includes bibliographical references (pages 119-128) NO Abstract freely available; full-text restricted to subscribers or individual document purchasers NO Title from PDF title page (viewed on May 11, 2016) NO Mode of access: World Wide Web NO System requirements: Adobe Acrobat Reader NO 1. Introduction -- 1.1 Problems with specialized systems -- 1.2 Resilient distributed datasets (RDDs) -- 1.3 Models implemented over RDDs -- 1.4 Summary of results -- 1.5 Book overview -- NO 2. Resilient distributed datasets -- 2.1 Introduction -- 2.2 RDD abstraction -- 2.3 Spark programming interface -- 2.4 Representing RDDs -- 2.5 Implementation -- 2.6 Evaluation -- 2.7 Discussion -- 2.8 Related work -- 2.9 Summary -- NO 3. Models built over RDDs -- 3.1 Introduction -- 3.2 Techniques for implementing other models on RDDs -- 3.3 Shark: SQL on RDDs -- 3.4 Implementation -- 3.5 Performance -- 3.6 Combining SQL with complex analytics -- 3.7 Summary -- NO 4. Discretized streams -- 4.1 Introduction -- 4.2 Goals and background -- 4.3 Discretized streams (D-streams) -- 4.4 System architecture -- 4.5 Fault and straggler recovery -- 4.6 Evaluation -- 4.7 Discussion -- 4.8 Related work -- 4.9 Summary -- NO 5. Generality of RDDs -- 5.1 Introduction -- 5.2 Expressiveness perspective -- 5.3 Systems perspective -- 5.4 Limitations and extensions -- 5.5 Related work -- 5.6 Summary -- NO 6. Conclusion -- 6.1 Lessons learned -- 6.2 Evolution of spark in industry -- 6.3 Future work -- References -- Author's biography NO The past few years have seen a major change in computing systems, as growing data volumes and stalling processor speeds require more and more applications to scale out to clusters. Today, a myriad data sources, from the Internet to business operations to scientific instruments, produce large and valuable data streams. However, the processing capabilities of single machines have not kept up with the size of data. As a result, organizations increasingly need to scale out their computations over clusters. At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too NO Also available in print NO HTTP:URL=http://dx.doi.org/10.1145/2886107 Information=Abstract with links to full text NO 書誌ID=EB00004414; LK [E Book]http://dx.doi.org/10.1145/2886107 OL 30