Getting started with Hazelcast : get acquainted with the highly scalable data grid, Hazelcast, and learn how to bring its powerful in-memory features into your application /

This book is a great introduction for Java developers, software architects, or DevOps looking to enable scalable and agile data within their applications. Providing in-memory object storage, cluster-wide state and messaging, or even scalable task execution, Hazelcast helps solve a number of issues t...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Johns, Mat (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2015.
Edición:Second edition.
Colección:Community experience distilled.
Temas:
Electronic data processing.
Information retrieval.
Information Storage and Retrieval
Recherche de l'information.
information retrieval.
COMPUTERS > Programming Languages > General.
Electronic data processing
Information retrieval
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Copyright
  • Credits
  • About the Author
  • About the Reviewers
  • www.PacktPub.com
  • Table of Contents
  • Preface
  • Chapter 1: What is Hazelcast?
  • Starting out as usual
  • Data deciding to hang around
  • Therein lies the problem
  • Breaking the mould
  • Moving to new ground
  • Playing around with our data
  • Summary
  • Chapter 2: Getting off the Ground
  • Let's get started
  • Showing off straightaway
  • Mapping back to the real world
  • Sets, lists, and queues
  • Many things at a time
  • Searching and indexing
  • What happens when we reach our limits?Summary
  • Chapter 3: Going Concurrent
  • Atomic control
  • Distributed locking
  • Tactical locking
  • Transactionally rolling on
  • Differences when queuing
  • Enterprising onwards
  • Collectively counting up
  • Spreading the word
  • Summary
  • Chapter 4: Divide and Conquer
  • Divvying up the data
  • Backups everywhere and nowhere
  • Scaling up the cluster
  • Having some of our data everywhere
  • Grouping and separating nodes
  • Network partitioning
  • Maintaining quorum
  • Summary
  • Chapter 5: Listening OutListening to the goings-on
  • The sound of our own data
  • Continuously querying
  • Listeners racing into action
  • Keyless collections
  • Programmatic configuration ahead of time
  • Events unfolding in the wider world
  • Moving data around the place
  • Extending quorum
  • Summary
  • Chapter 6: Spreading the Load
  • All power to the compute
  • Giving up when tasks take too long
  • Running once, running everywhere
  • Placing tasks next to the data
  • Partitioning control by name
  • Self-updating results
  • In-place entry processingSummary
  • Chapter 7: Gathering Results
  • What is this big data hype all about?
  • Trying to make sense of it all
  • Combining data where possible
  • Putting theory into practice
  • Combining results as we go
  • Simplifying just aggregating up
  • Summary
  • Chapter 8: Typical Deployments
  • All heap and nowhere to go
  • Stepping back from the cluster
  • Serialization and classes
  • Getting straight to the point
  • Architectural overview
  • Peer-to-peer clusters
  • Smart clients and server clusters
  • Dummy client proxying through a single nodeSummary
  • Chapter 9: From the Outside Looking In
  • What about the rest of us?
  • Memcache
  • Going RESTful
  • Cluster status via REST
  • REST resilience
  • Summary
  • Chapter 10: Going Global
  • Getting set up in the cloud
  • Under manual control
  • Discovery â€? the Amazonian way
  • Filtering the possibilities
  • Spreading out around the globe
  • Summary
  • Chapter 11: Playing Well with Others
  • Don't pass what you need, depend on it
  • Simplifying collection access
  • Transparently caching others' data

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