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What could you do with data if scalability wasn't a problem? With this hands-on guide, you'll learn how Apache Cassandra handles hundreds of terabytes of data while remaining highly available across multiple data centers -- capabilities that have attracted Facebook, Twitter, and other data-intensive companies. Cassandra: The Definitive Guide provides the technical details and practical examples you need to assess this database management system and put it to work in a production...
What could you do with data if scalability wasn't a problem? With this hands-on guide, you'll learn how Apache Cassandra handles hundreds of terabytes of data while remaining highly available across multiple data centers -- capabilities that have attracted Facebook, Twitter, and other data-intensive companies. Cassandra: The Definitive Guide provides the technical details and practical examples you need to assess this database management system and put it to work in a production environment.
Author Eben Hewitt demonstrates the advantages of Cassandra's nonrelational design, and pays special attention to data modeling. If you're a developer, DBA, application architect, or manager looking to solve a database scaling issue or future-proof your application, this guide shows you how to harness Cassandra's speed and flexibility.
Foreword; Preface; Why Apache Cassandra?; Is This Book for You?; What’s in This Book?; Finding Out More; Conventions Used in This Book; Using Code Examples; Safari® Enabled; How to Contact Us; Acknowledgments; Chapter 1: Introducing Cassandra; 1.1 What’s Wrong with Relational Databases?; 1.2 A Quick Review of Relational Databases; 1.3 The Cassandra Elevator Pitch; 1.4 Where Did Cassandra Come From?; 1.5 Use Cases for Cassandra; 1.6 Who Is Using Cassandra?; 1.7 Summary; Chapter 2: Installing Cassandra; 2.1 Installing the Binary; 2.2 Building from Source; 2.3 Running Cassandra; 2.4 Running the Command-Line Client Interface; 2.5 Basic CLI Commands; 2.6 Summary; Chapter 3: The Cassandra Data Model; 3.1 The Relational Data Model; 3.2 A Simple Introduction; 3.3 Clusters; 3.4 Keyspaces; 3.5 Column Families; 3.6 Columns; 3.7 Super Columns; 3.8 Design Differences Between RDBMS and Cassandra; 3.9 Design Patterns; 3.10 Some Things to Keep in Mind; 3.11 Summary; Chapter 4: Sample Application; 4.1 Data Design; 4.2 Hotel App RDBMS Design; 4.3 Hotel App Cassandra Design; 4.4 Hotel Application Code; 4.5 Twissandra; 4.6 Summary; Chapter 5: The Cassandra Architecture; 5.1 System Keyspace; 5.2 Peer-to-Peer; 5.3 Gossip and Failure Detection; 5.4 Anti-Entropy and Read Repair; 5.5 Memtables, SSTables, and Commit Logs; 5.6 Hinted Handoff; 5.7 Compaction; 5.8 Bloom Filters; 5.9 Tombstones; 5.10 Staged Event-Driven Architecture (SEDA); 5.11 Managers and Services; 5.12 Summary; Chapter 6: Configuring Cassandra; 6.1 Keyspaces; 6.2 Replicas; 6.3 Replica Placement Strategies; 6.4 Replication Factor; 6.5 Partitioners; 6.6 Snitches; 6.7 Creating a Cluster; 6.8 Dynamic Ring Participation; 6.9 Security; 6.10 Miscellaneous Settings; 6.11 Additional Tools; 6.12 Summary; Chapter 7: Reading and Writing Data; 7.1 Query Differences Between RDBMS and Cassandra; 7.2 Basic Write Properties; 7.3 Consistency Levels; 7.4 Basic Read Properties; 7.5 The API; 7.6 Setup and Inserting Data; 7.7 Using a Simple Get; 7.8 Seeding Some Values; 7.9 Slice Predicate; 7.10 Get Range Slices; 7.11 Multiget Slice; 7.12 Deleting; 7.13 Batch Mutates; 7.14 Programmatically Defining Keyspaces and Column Families; 7.15 Summary; Chapter 8: Clients; 8.1 Basic Client API; 8.2 Thrift; 8.3 Avro; 8.4 A Bit of Git; 8.5 Connecting Client Nodes; 8.6 Cassandra Web Console; 8.7 Hector (Java); 8.8 HectorSharp (C#); 8.9 Chirper; 8.10 Chiton (Python); 8.11 Pelops (Java); 8.12 Kundera (Java ORM); 8.13 Fauna (Ruby); 8.14 Summary; Chapter 9: Monitoring; 9.1 Logging; 9.2 Overview of JMX and MBeans; 9.3 Interacting with Cassandra via JMX; 9.4 Cassandra’s MBeans; 9.5 Custom Cassandra MBeans; 9.6 Runtime Analysis Tools; 9.7 Health Check; 9.8 Summary; Chapter 10: Maintenance; 10.1 Getting Ring Information; 10.2 Getting Statistics; 10.3 Basic Maintenance; 10.4 Snapshots; 10.5 Load-Balancing the Cluster; 10.6 Decommissioning a Node; 10.7 Updating Nodes; 10.8 Summary; Chapter 11: Performance Tuning; 11.1 Data Storage; 11.2 Reply Timeout; 11.3 Commit Logs; 11.4 Memtables; 11.5 Concurrency; 11.6 Caching; 11.7 Buffer Sizes; 11.8 Using the Python Stress Test; 11.9 Startup and JVM Settings; 11.10 Summary; Chapter 12: Integrating Hadoop; 12.1 What Is Hadoop?; 12.2 Working with MapReduce; 12.3 Running the Word Count Example; 12.4 Tools Above MapReduce; 12.5 Cluster Configuration; 12.6 Use Cases; 12.7 Summary; The Nonrelational Landscape; Nonrelational Databases; Object Databases; XML Databases; Document-Oriented Databases; Graph Databases; Key-Value Stores and Distributed Hashtables; Columnar Databases; Summary; Glossary; Colophon;
Eben Hewitt is Director of Application Architecture at a publicly traded company where he is responsible for the design of their mission-critical, global-scale web, mobile and SOA integration projects. He has written several programming books, including Java SOA Cookbook (O'Reilly).
Posted July 24, 2014
Posted July 24, 2014