NoSQL Distilled A Brief Guide to the Emerging World of Polyglot Persistence

ISBN-10: 0321826620
ISBN-13: 9780321826626
Edition: 2013 (Revised)
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Description: NoSQL technologies are well-suited for many new applications where traditional RDBMSes often don't perform or scale well - including systems requiring large-scale indexing, serving pages on high-traffic websites, and heavy-duty streaming media  More...

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Book details

Copyright year: 2013
Publisher: Pearson Education, Limited
Publication date: 8/8/2012
Binding: Paperback
Pages: 192
Size: 7.00" wide x 9.25" long x 0.50" tall
Weight: 0.682
Language: English

NoSQL technologies are well-suited for many new applications where traditional RDBMSes often don't perform or scale well - including systems requiring large-scale indexing, serving pages on high-traffic websites, and heavy-duty streaming media delivery. In NoSQL Distilled, renowned software expert Martin Fowler and Thoughtworks database consultant Pramod J. Sadalage demystify NoSQL, succinctly explore the architectural and design issues associated with implementing it, and offer realistic use cases. Modeled after UML Distilled, Fowler's international best-seller, NoSQL Distilled covers all this and more: * Evaluating which enterprise applications NoSQL is (and isn't) appropriate for * Understanding the architectural tradeoffs associated with deploying NoSQL * Comparing leading NoSQL offerings such as MongoDB, CouchDB, Cassandra, Riak, and Neo4J * Integrating NoSQL with legacy systems * Managing performance, reliability, availability, and recoverability * Overcoming widespread misconceptions about NoSQL and its tradeoffs * Understanding how NoSQL can support the transition to cloud-based systems * Using NoSQL in agile development environments * Applying NoSQL in search/retrieval, metadata management, text analysis, social networking, business intelligence, and financial services applications * Understanding NoSQL query languages, including Object Query MDX, and XQuery and more

Preface
Understand
Why NoSQL?
The Value of Relational Databases
Getting at Persistent Data
Concurrency
Integration
A (Mostly) Standard Model
Impedance Mismatch
Application and Integration Databases
Attack of the Clusters
The Emergence of NoSQL
Key Points
Aggregate Data Models
Aggregates
Example of Relations and Aggregates
Consequences of Aggregate Orientation
Key-Value and Document Data Models
Column-Family Stores
Summarizing Aggregate-Oriented Databases
Further Reading
Key Points
More Details on Data Models
Relationships
Graph Databases
Schemaless Databases
Materialized Views
Modeling for Data Access
Key Points
Distribution Models
Single Server
Sharding
Master-Slave Replication
Peer-to-Peer Replication
Combining Sharding and Replication
Key Points
Consistency
Update Consistency
Read Consistency
Relaxing Consistency
The CAP Theorem
Relaxing Durability
Quorums
Further Reading
Key Points
Version Stamps
Business and System Transactions
Version Stamps on Multiple Nodes
Key Points
Map-Reduce
Basic Map-Reduce
Partitioning and Combining
Composing Map-Reduce Calculations
A Two Stage Map-Reduce Example
Incremental Map-Reduce
Further Reading
Key Points
Implement
Key-Value Databases
What Is a Key-Value Store
Key-Value Store Features
Consistency
Transactions
Query Features
Structure of Data
Scaling
Suitable Use Cases
Storing Session Information
User Profiles, Preferences
Shopping Cart Data
When Not to Use
Relationships among Data
Multioperation Transactions
Query by Data
Operations by Sets
Document Databases
What Is a Document Database?
Features
Consistency
Transactions
Availability
Query Features
Scaling
Suitable Use Cases
Event Logging
Content Management Systems, Blogging Platforms
Web Analytics or Real-Time Analytics
E-Commerce Applications
When Not to Use
Complex Transactions Spanning Different Operations
Queries against Varying Aggregate Structure
Column-Family Stores
What Is a Column-Family Data Store?
Features
Consistency
Transactions
Availability
Query Features
Scaling
Suitable Use Cases
Event Logging
Content Management Systems, Blogging Platforms
Counters
Expiring Usage
When Not to Use
Graph Databases
What Is a Graph Database?
Features
Consistency
Transactions
Availability
Query Features
Scaling
Suitable Use Cases
Connected Data
Routing, Dispatch, and Location-Based Services
Recommendation Engines
When Not to Use
Schema Migrations
Schema Changes
Schema Changes in RDBMS
Migrations for Green Field Projects
Migrations in Legacy Projects
Schema Changes in a NoSQL Data Store
Incremental Migration
Migrations in Graph Databases
Changing Aggregate Structure
Further Reading
Key Points
Polyglot Persistence
Disparate Data Storage Needs
Polyglot Data Store Usage
Service Usage over Direct Data Store Usage
Expanding for Better Functionality
Choosing the Right Technology
Enterprise Concerns with Polyglot Persistence
Deployment Complexity
Key Points
Beyond NoSQL
File Systems
Event Sourcing
Memory Image
Version Control
XML Databases
Object Databases
Key Points
Choosing Your Database
Programmer Productivity
Data-Access Performance
Sticking with the Default
Hedging Your Bets
Key Points
Final Thoughts
Bibliography
Index

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