000			04112nam a2200553 i 4500
001			011452886107
005			20260609132937.0
006			m eo d
007			cr cn |||m|||a
008			160511s2016 nyua fob 000 0deng d
020			_a9781970001570 _qebook
020			_z9781970001587 _qePub
020			_z9781970001563 _qpaperback
020			_z9781970001594 _qhardcover
024	7		_a10.1145/2886107 _2doi
035			_a(OCoLC)949774958
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA76.9.D5 _bZ242 2016
082	0	4	_a004.36 _223
035			_a(CaBNVSL)swl00406471
100	1		_aZaharia, Matei., _eauthor.
245	1	3	_aAn architecture for fast and general data processing on large clusters / _cMatei Zaharia.
250			_aFirst edition.
264		1	_a[New York] : _bAssociation for Computing Machinery ; _a[San Rafael, California] : _bMorgan & Claypool, _c2016.
300			_a1 PDF (xii, 128 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aACM books, _x2374-6777 ; _v#11
504			_aIncludes bibliographical references (pages 119-128).
506			_aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
588			_aTitle from PDF title page (viewed on May 11, 2016).
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader.
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	3		_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.
530			_aAlso available in print.
650		0	_aElectronic data processing _xDistributed processing.
650		0	_aDistributed databases.
650		0	_aBig data.
776	0	8	_iPrint version: _z9781970001594 _z9781970001563
830		0	_aACM books ; _v#11. _x2374-6777
856	4	8	_3Abstract with links to full text _uhttp://dx.doi.org/10.1145/2886107
999			_c876 _d876