Isbn: 9781970001594 - an architecture for fast and general data processing on large clusters (24 risultati)

hardcover. Condizione: Very Good. Fast Shipping - Safe and Secure 7 days a week.

Condizione: good. This is a pre-loved book that shows moderate signs of wear from previous reading. You may notice creases, edge wear, or a cracked spine, but it remains in solid, readable condition. Please note: -May include library or rental stickers, stamps, or markings. -Supplemental materials e.g., CDs, access codes, inserts are not guaranteed. -Box sets may not come with the original outer box. If it does, the box will not be in perfect condition. Your satisfaction is our top priority! If you have any questions or concerns about your order, please don't hesitate to reach out. Thank you for shopping with us and supporting small businessâ"happy reading.…

Hardcover/Hardback. Condizione: New. No Jacket. First edition, first printing. A digital text: this copy printed in the UK. Quarto-size, 144 pages (xii, 132), tabular data, charts, graphs with spot colour, references; Issued without jacket. New book. Pictures show this actual book. UK orders are post-free.…

Condizione: new.

Condizione: New.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: New. 2016. hardcover. . . . . .

Condizione: As New. Unread book in perfect condition.

Condizione: New. pp. 142.

Condizione: New.

Condizione: New. pp. 142.

Condizione: New. 2016. hardcover. . . . . . Books ship from the US and Ireland.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New. In English.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: Brand New. 141 pages. 9.25x7.50x0.50 inches. In Stock.

Hardcover. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Hardcover. Condizione: Brand New. 141 pages. 9.25x7.50x0.50 inches. In Stock. This item is printed on demand.

Hardback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condizione: New. PRINT ON DEMAND pp. 142.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and inte. …

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing.We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective.This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added. …

Buch. Condizione: Neu. An Architecture for Fast and General Data Processing on Large Clusters | Matei Zaharia | Buch | Einband - fest (Hardcover) | Englisch | 2016 | ACM Books | EAN 9781970001594 | Verantwortliche Person für die EU: Mare Nostrum Group B.V., Doelen 72, 4831 GR BREDA, NIEDERLANDE, gpsr[at]mare-nostrum[dot]co[dot]uk | Anbieter: preigu Print on Demand. …