HBase: The Definitive Guide
Random Access to Your Planet-Size Data
Samenvatting
If 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
Specificaties
Inhoudsopgave
Preface
1. Introduction
-The Dawn of Big Data
-The Problem with Relational Database Systems
-Nonrelational Database Systems, Not-Only SQL or NoSQL?
-Building Blocks
-HBase: The Hadoop Database
2. Installation
-Quick-Start Guide
-Requirements
-Filesystems for HBase
-Installation Choices
-Run Modes
-Configuration
-Deployment
-Operating a Cluster
3. Client API: The Basics
-General Notes
-CRUD Operations
-Batch Operations
-Row Locks
-Scans
-Miscellaneous Features
4. Client API: Advanced Features
-Filters
-Counters
-Coprocessors
-HTablePool
-Connection Handling
5. Client API: Administrative Features
-Schema Definition
-HBaseAdmin
6. Available Clients
-Introduction to REST, Thrift, and Avro
-Interactive Clients
-Batch Clients
-Shell
-Web-based UI
7. MapReduce Integration
-Framework
-MapReduce over HBase
8. Architecture
-Seek Versus Transfer
-Storage
-Write-Ahead Log
-Read Path
-Region Lookups
-The Region Life Cycle
-ZooKeeper
-Replication
9. Advanced Usage
-Key Design
-Advanced Schemas
-Secondary Indexes
-Search Integration
-Transactions
-Bloom Filters
-Versioning
10. Cluster Monitoring
-Introduction
-The Metrics Framework
-Ganglia
-JMX
-Nagios
11. Performance Tuning
-Garbage Collection Tuning
-Memstore-Local Allocation Buffer
-Compression
-Optimizing Splits and Compactions
-Load Balancing
-Merging Regions
-Client API: Best Practices
-Configuration
-Load Tests
12. Cluster Administration
-Operational Tasks
-Data Tasks
-Additional Tasks
-Changing Logging Levels
-Troubleshooting
Appendix A: HBase Configuration Properties
Appendix B: Road Map
Appendix C: Upgrade from Previous Releases
Appendix D: Distributions
Appendix E: Hush SQL Schema
Appendix F: HBase Versus Bigtable
Index
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