An architecture for fast and general data processing on large clusters [electronic resources] / Matei Zaharia.
Material type: TextSeries: ACM books ; #11.Publication details: [New York] : Association for Computing Machinery ; [San Rafael] : Morgan & Claypool Publishers, c2016Description: 1 online resource (208 pages); 1 pdf (208 pages)ISBN: 9781970001587 (epub); 1970001585 (epub); 9781970001570 (pdf); 1970001577 (pdf); 9781970001594; 9781970001563Subject(s): Electronic data processing -- Distributed processing | Distributed databases | Big data | Big data | Distributed databases | Electronic data processing -- Distributed processingDDC classification: 004.36 LOC classification: QA76.9.D5 | Z34 2016ebOnline resources: Available in ACM Digital Library. Requires Log In to view full text.Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|
General Circulation | APU Library Online Database | E-Book | QA76.9.D5 Z34 2016eb (Browse shelf (Opens below)) | 1 | Available |
Browsing APU Library shelves, Shelving location: Online Database, Collection: E-Book Close shelf browser (Hides shelf browser)
QA76.9.A25 P27 2014eb Trust extension as a mechanism for secure code execution on commodity computers | QA76.9 .D26 B76 2019eb Making databases work : the pragmatic wisdom of Michael Stonebraker | QA76.9.D343 Z42 2016eb Text data management and analysis : a practical introduction to information retrieval and text mining | QA76.9.D5 Z34 2016eb An architecture for fast and general data processing on large clusters | QA76.9.H85 J47 2016eb The VR book: human-centered design for virtual reality | QA76.9.U83 O93 2019eb The handbook of multimodal-multisensor interfaces: Language processing, software, commercialization, and emerging directions | QA76.9.U83 O95 2017eb The handbook of multimodal-multisensor interfaces. Volume 1, Foundations, user modeling, and common modality combinations |
Includes bibliographical references (pages 119-128).
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 --
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 --
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 --
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 --
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 --
6. Conclusion -- 6.1 Lessons learned -- 6.2 Evolution of spark in industry -- 6.3 Future work -- References -- Author's biography.
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.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
There are no comments on this title.