High Performance in-memory computing with Apache Ignite von Bhuiyan, Shamim; Zheludkov, Michael; Isachenko, Timur: New (2017)

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pp. 360.

Bestandsnummer des Verk�ufers 26385568874

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Bibliografische Details

Titel: High Performance in-memory computing with Apache Ignite
Verlag: lulu.com
Erscheinungsjahr: 2017
Sprache: Englisch
ISBN-10: 1365732355
ISBN-13: 9781365732355
Einband: Softcover
Zustand: New

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Inhaltsangabe

This book covers a verity of topics, including in-memory data grid, highly available service grid, streaming (event processing for IoT and fast data) and in-memory computing use cases from high-performance computing to get performance gains. The book will be particularly useful for those, who have the following use cases: 1) You have a high volume of ACID transactions in your system. 2) You have database bottleneck in your application and want to solve the problem. 3) You want to develop and deploy Microservices in a distributed fashion. 4) You have an existing Hadoop ecosystem (OLAP) and want to improve the performance of map/reduce jobs without making any changes in your existing map/reduce jobs. 5) You want to share Spark RDD directly in-memory (without storing the state into the disk) 7) You are planning to process continuous never-ending streams and complex events of data. 8) You want to use distributed computations in parallel fashion to gain high performance.

Rese�a del editor

This book covers a verity of topics, including in-memory data grid, highly available service grid, streaming (event processing for IoT and fast data) and in-memory computing use cases from high-performance computing to get performance gains. The book will be particularly useful for those, who have the following use cases: 1) You have a high volume of ACID transactions in your system. 2) You have database bottleneck in your application and want to solve the problem. 3) You want to develop and deploy Microservices in a distributed fashion. 4) You have an existing Hadoop ecosystem (OLAP) and want to improve the performance of map/reduce jobs without making any changes in your existing map/reduce jobs. 5) You want to share Spark RDD directly in-memory (without storing the state into the disk) 7) You are planning to process continuous never-ending streams and complex events of data. 8) You want to use distributed computations in parallel fashion to gain high performance.

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