| 000 | 03974nam a22005297a 4500 | ||
|---|---|---|---|
| 001 | 20437654 | ||
| 003 | APU | ||
| 005 | 20220824171944.0 | ||
| 008 | 210803t20162016nyu b 000 0 eng d | ||
| 010 | _a 2017471066 | ||
| 020 | _a9781970001587 (epub) | ||
| 020 | _a1970001585 (epub) | ||
| 020 | _a9781970001570 (pdf) | ||
| 020 | _a1970001577 (pdf) | ||
| 020 | _a9781970001594 | ||
| 020 | _a9781970001563 | ||
| 035 | _a(OCoLC)ocn953497712 | ||
| 040 |
_aYDXCP _beng _cAPU _dSF |
||
| 042 | _alccopycat | ||
| 050 | 0 | 0 |
_aQA76.9.D5 _bZ34 2016eb |
| 082 | 0 | 4 |
_a004.36 _223 |
| 100 | 1 |
_aZaharia, Matei, _947413 |
|
| 245 | 1 | 3 |
_aAn architecture for fast and general data processing on large clusters _h[electronic resources] / _cMatei Zaharia. |
| 260 |
_a[New York] : Association for Computing Machinery ; [San Rafael] : _bMorgan & Claypool Publishers, _cc2016. |
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| 300 | _a1 online resource (208 pages) ; | ||
| 300 | _a1 pdf (208 pages) ; | ||
| 490 | 1 |
_aACM books ; _v#11 _x2374-6777 ; |
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| 504 | _aIncludes bibliographical references (pages 119-128). | ||
| 505 | 0 | _a1. 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 -- | |
| 505 | 8 | _a2. 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 -- | |
| 505 | 8 | _a3. 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 -- | |
| 505 | 8 | _a4. 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 -- | |
| 505 | 8 | _a5. 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 -- | |
| 505 | 8 | _a6. Conclusion -- 6.1 Lessons learned -- 6.2 Evolution of spark in industry -- 6.3 Future work -- References -- Author's biography. | |
| 520 | _aThe 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. | ||
| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 650 | 0 |
_aElectronic data processing _xDistributed processing. |
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| 650 | 0 |
_aDistributed databases. _911734 |
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| 650 | 0 | _aBig data. | |
| 650 | 7 |
_aBig data. _2fast _947414 |
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| 650 | 7 |
_aDistributed databases. _2fast _911734 |
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| 650 | 7 |
_aElectronic data processing _xDistributed processing. _2fast _947415 |
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| 830 | 0 |
_aACM books ; _v#11. _947379 |
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| 856 |
_uhttps://dl-acm-org.ezproxy.apu.edu.my/doi/book/10.1145/2886107 _zAvailable in ACM Digital Library. Requires Log In to view full text. |
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| 942 |
_2lcc _cE-Book |
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| 999 |
_c383489 _d383489 |
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