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HBase: the Definitive Guide : Random Access to Your Planet-Size Data by Lars George (2011, Trade Paperback)

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About this product

Product Identifiers

PublisherO'reilly Media, Incorporated

ISBN-101449396100

ISBN-139781449396107

eBay Product ID (ePID)99547873

Product Key Features

Number of Pages554 Pages

Publication NameHbase: the Definitive Guide : Random Access to Your Planet-Size Data

LanguageEnglish

SubjectData Modeling & Design, Databases / Data Warehousing, Databases / Data Mining, Databases / General

Publication Year2011

TypeTextbook

AuthorLars George

Subject AreaComputers

FormatTrade Paperback

Dimensions

Item Height1.2 in

Item Weight33.1 Oz

Item Length9.3 in

Item Width7.7 in

Additional Product Features

Intended AudienceScholarly & Professional

IllustratedYes

Table Of ContentForeword;Preface; General Information; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments;Chapter 1: Introduction; 1.1 The Dawn of Big Data; 1.2 The Problem with Relational Database Systems; 1.3 Nonrelational Database Systems, Not-Only SQL or NoSQL?; 1.4 Building Blocks; 1.5 HBase: The Hadoop Database;Chapter 2: Installation; 2.1 Quick-Start Guide; 2.2 Requirements; 2.3 Filesystems for HBase; 2.4 Installation Choices; 2.5 Run Modes; 2.6 Configuration; 2.7 Deployment; 2.8 Operating a Cluster;Chapter 3: Client API: The Basics; 3.1 General Notes; 3.2 CRUD Operations; 3.3 Batch Operations; 3.4 Row Locks; 3.5 Scans; 3.6 Miscellaneous Features;Chapter 4: Client API: Advanced Features; 4.1 Filters; 4.2 Counters; 4.3 Coprocessors; 4.4 HTablePool; 4.5 Connection Handling;Chapter 5: Client API: Administrative Features; 5.1 Schema Definition; 5.2 HBaseAdmin;Chapter 6: Available Clients; 6.1 Introduction to REST, Thrift, and Avro; 6.2 Interactive Clients; 6.3 Batch Clients; 6.4 Shell; 6.5 Web-based UI;Chapter 7: MapReduce Integration; 7.1 Framework; 7.2 MapReduce over HBase;Chapter 8: Architecture; 8.1 Seek Versus Transfer; 8.2 Storage; 8.3 Write-Ahead Log; 8.4 Read Path; 8.5 Region Lookups; 8.6 The Region Life Cycle; 8.7 ZooKeeper; 8.8 Replication;Chapter 9: Advanced Usage; 9.1 Key Design; 9.2 Advanced Schemas; 9.3 Secondary Indexes; 9.4 Search Integration; 9.5 Transactions; 9.6 Bloom Filters; 9.7 Versioning;Chapter 10: Cluster Monitoring; 10.1 Introduction; 10.2 The Metrics Framework; 10.3 Ganglia; 10.4 JMX; 10.5 Nagios;Chapter 11: Performance Tuning; 11.1 Garbage Collection Tuning; 11.2 Memstore-Local Allocation Buffer; 11.3 Compression; 11.4 Optimizing Splits and Compactions; 11.5 Load Balancing; 11.6 Merging Regions; 11.7 Client API: Best Practices; 11.8 Configuration; 11.9 Load Tests;Chapter 12: Cluster Administration; 12.1 Operational Tasks; 12.2 Data Tasks; 12.3 Additional Tasks; 12.4 Changing Logging Levels; 12.5 Troubleshooting;HBase Configuration Properties;Road Map; HBase 0.92.0; HBase 0.94.0;Upgrade from Previous Releases; Upgrading to HBase 0.90.x; Upgrading to HBase 0.92.0;Distributions; Cloudera's Distribution Including Apache Hadoop;Hush SQL Schema;HBase Versus Bigtable;Colophon;

SynopsisIf you're looking for a scalable storage solution to accommodate a virtually endless amount of data, this book shows you how Apache HBase can fulfill your needs. As the open source implementation of Google's BigTable architecture, HBase scales to billions of rows and millions of columns, while ensuring that write and read performance remain constant. Many IT executives are asking pointed questions about HBase. This book provides meaningful answers, whether you're evaluating this non-relational database or planning to put it into practice right away. Discover how tight integration with Hadoop makes scalability with HBase easier Distribute large datasets across an inexpensive cluster of commodity servers Access HBase with native Java clients, or with gateway servers providing REST, Avro, or Thrift APIs Get details on HBase's architecture, including the storage format, write-ahead log, background processes, and more Integrate HBase with Hadoop's MapReduce framework for massively parallelized data processing jobs Learn how to tune clusters, design schemas, copy tables, import bulk data, decommission nodes, and many other tasks, HBase is the open-source implementation of Google's BigTablearchitecture. It strives to scale to billions of rows andmillions of columns, all distributed across an inexpensivecluster of commodity servers. HBase's primary storage systemis HDFS, Hadoop's distributed and replicated file system. Inaddition HBase has a native interface to Hadoop's Map Reduceframework which allows for easy development and subsequentexecution of batch jobs that can scan entire tables. HBasefits into a new category of database systems oftensummarized under the term "NoSQL" - or non-relationaldatabases. While there are quite a few system that comprisethe term they often only overlap marginally or have nothingin common but the fact that they abandon traditional modelsfound in relational database system (RDBMS) for specializedarchitectures that are built to solve a specific problem.For HBase this is storing a virtually endless amount of datawhile ensuring that write and read performance staysconstant, no matter how large the dataset is., HBase is the open-source implementation of Google's BigTablearchitecture. It strives to scale to billions of rows andmillions of columns, all distributed across an inexpensivecluster of commodity servers. HBase's primary storage systemis HDFS, Hadoop's distributed and replicated file system. Inaddition HBase has a native interface to ......

LC Classification NumberQA76.9.D3