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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham, UK :
Packt Publishing,
2015.
|
| Edition: | Second edition. |
| Series: | Community experience distilled.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | 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 that have troubled technologists for years. |
| Item Description: | Includes index. |
| Physical Description: | 1 online resource (145 pages) : illustrations |
| ISBN: | 9781783554058 1783554053 |
Staff View
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| 246 | 3 | 0 | |a Get acquainted with the highly scalable data grid, Hazelcast, and learn how to bring its powerful in-memory features into your application |
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| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 � 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 | |
| 520 | |a 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 that have troubled technologists for years. | ||
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| contents | 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 � 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 |
| ctrlnum | (OCoLC)918862372 |
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| discipline | Informatik |
| edition | Second edition. |
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| series | Community experience distilled. |
| series2 | Community experience distilled |
| spelling | Johns, Mat, author. 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 / Mat Johns. Get acquainted with the highly scalable data grid, Hazelcast, and learn how to bring its powerful in-memory features into your application Second edition. Birmingham, UK : Packt Publishing, 2015. 1 online resource (145 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Community experience distilled Online resource; title from cover (Safari, viewed August 12, 2015). Includes index. Print version record. 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 � 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 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 that have troubled technologists for years. Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Information retrieval. http://id.loc.gov/authorities/subjects/sh85066148 Information Storage and Retrieval https://id.nlm.nih.gov/mesh/D016247 Recherche de l'information. information retrieval. aat COMPUTERS Programming Languages General. bisacsh Electronic data processing fast Information retrieval fast Print version: Johns, Mat. Getting started with Hazelcast. Second edition 9781783554058 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 |
| spellingShingle | Johns, Mat 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 / Community experience distilled. 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 � 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 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Information retrieval. http://id.loc.gov/authorities/subjects/sh85066148 Information Storage and Retrieval https://id.nlm.nih.gov/mesh/D016247 Recherche de l'information. information retrieval. aat COMPUTERS Programming Languages General. bisacsh Electronic data processing fast Information retrieval fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85042288 http://id.loc.gov/authorities/subjects/sh85066148 https://id.nlm.nih.gov/mesh/D016247 |
| title | 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 / |
| title_alt | Get acquainted with the highly scalable data grid, Hazelcast, and learn how to bring its powerful in-memory features into your application |
| title_auth | 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 / |
| title_exact_search | 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 / |
| title_full | 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 / Mat Johns. |
| title_fullStr | 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 / Mat Johns. |
| title_full_unstemmed | 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 / Mat Johns. |
| title_short | Getting started with Hazelcast : |
| title_sort | 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 |
| title_sub | get acquainted with the highly scalable data grid, Hazelcast, and learn how to bring its powerful in-memory features into your application / |
| topic | Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Information retrieval. http://id.loc.gov/authorities/subjects/sh85066148 Information Storage and Retrieval https://id.nlm.nih.gov/mesh/D016247 Recherche de l'information. information retrieval. aat COMPUTERS Programming Languages General. bisacsh Electronic data processing fast Information retrieval fast |
| topic_facet | Electronic data processing. Information retrieval. Information Storage and Retrieval Recherche de l'information. information retrieval. COMPUTERS Programming Languages General. Electronic data processing Information retrieval |
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