Librería Portfolio

Imagine what you could do if scalability wasn´t a problem. With this hands-on guide, you'll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This expanded second edition-updated for Cassandra 3.0-provides the technical details and practical examples you need to put this database to work in a production environment.

Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra's non-relational design, with special attention to data modeling. If you're a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra's speed and flexibility.

Understand Cassandra's distributed and decentralized structure
Use the Cassandra Query Language (CQL) and cqlsh-the CQL shell
Create a working data model and compare it with an equivalent relational model
Develop sample applications using client drivers for languages including Java, Python, and Node.js
Explore cluster topology and learn how nodes exchange data
Maintain a high level of performance in your cluster
Deploy Cassandra on site, in the Cloud, or with Docker
Integrate Cassandra with Spark, Hadoop, Elasticsearch, Solr, and Lucene



Chapter 1Beyond Relational Databases
What's Wrong with Relational Databases?
A Quick Review of Relational Databases
Web Scale
The Rise of NoSQL
Summary
Chapter 2Introducing Cassandra
The Cassandra Elevator Pitch
Where Did Cassandra Come From?
Is Cassandra a Good Fit for My Project?
Getting Involved
Summary
Chapter 3Installing Cassandra
Installing the Apache Distribution
Building from Source
Running Cassandra
Other Cassandra Distributions
Running the CQL Shell
Basic cqlsh Commands
Summary
Chapter 4The Cassandra Query Language
The Relational Data Model
Cassandra's Data Model
CQL Types
Secondary Indexes
Summary
Chapter 5Data Modeling
Conceptual Data Modeling
RDBMS Design
Defining Application Queries
Logical Data Modeling
Physical Data Modeling
Evaluating and Refining
Defining Database Schema
Summary
Chapter 6The Cassandra Architecture
Data Centers and Racks
Gossip and Failure Detection
Snitches
Rings and Tokens
Virtual Nodes
Partitioners
Replication Strategies
Consistency Levels
Queries and Coordinator Nodes
Memtables, SSTables, and Commit Logs
Caching
Hinted Handoff
Lightweight Transactions and Paxos
Tombstones
Bloom Filters
Compaction
Anti-Entropy, Repair, and Merkle Trees
Staged Event-Driven Architecture (SEDA)
Managers and Services
System Keyspaces
Summary
Chapter 7Configuring Cassandra
Cassandra Cluster Manager
Creating a Cluster
Seed Nodes
Partitioners
Snitches
Node Configuration
Adding Nodes to a Cluster
Dynamic Ring Participation
Replication Strategies
Summary
Chapter 8Clients
Hector, Astyanax, and Other Legacy Clients
DataStax Java Driver
DataStax Python Driver
DataStax Node.js Driver
DataStax Ruby Driver
DataStax C# Driver
DataStax C/C++ Driver
DataStax PHP Driver
Summary
Chapter 9Reading and Writing Data
Writing
Reading
Deleting
Summary
Chapter 10Monitoring
Logging
Monitoring Cassandra with JMX
Cassandra's MBeans
Monitoring with nodetool
Summary
Chapter 11Maintenance
Health Check
Basic Maintenance
Adding Nodes
Handling Node Failure
Upgrading Cassandra
Backup and Recovery
SSTable Utilities
Maintenance Tools
Summary
Chapter 12Performance Tuning
Managing Performance
Caching
Memtables
Commit Logs
SSTables
Hinted Handoff
Compaction
Concurrency and Threading
Networking and Timeouts
JVM Settings
Using cassandra-stress
Summary
Chapter 13Security
Authentication and Authorization
Encryption
JMX Security
Summary
Chapter 14Deploying and Integrating
Planning a Cluster Deployment
Cloud Deployment
Integrations
Summary