Spring Data
Published on 12. October 2012
316 pages
978-1-4493-3189-4 (ISBN)
Description
You can choose several data access frameworks when building Java enterprise applications that work with relational databases. But what about big data? This hands-on introduction shows you how Spring Data makes it relatively easy to build applications across a wide range of new data access technologies such as NoSQL and Hadoop.Through several sample projects, youll learn how Spring Data provides a consistent programming model that retains NoSQL-specific features and capabilities, and helps you develop Hadoop applications across a wide range of use-cases such as data analysis, event stream processing, and workflow. Youll also discover the features Spring Data adds to Springs existing JPA and JDBC support for writing RDBMS-based data access layers.Learn about Springs template helper classes to simplify the use ofdatabase-specific functionalityExplore Spring Datas repository abstraction and advanced query functionalityUse Spring Data with Redis (key/value store), HBase(column-family), MongoDB (document database), and Neo4j (graph database)Discover the GemFire distributed data grid solutionExport Spring Data JPA-managed entities to the Web as RESTful web servicesSimplify the development of HBase applications, using a lightweight object-mapping frameworkBuild example big-data pipelines with Spring Batch and Spring Integration
More details
Other editions
Additional editions
Book
11/2012
1st Edition
O'Reilly
€37.50
Shipment within 10-20 days
Content
- Intro
- Table of Contents
- Foreword
- Preface
- Overview of the New Data Access Landscape
- How to Read This Book
- Conventions Used in This Book
- Using Code Examples
- Safari® Books Online
- How to Contact Us
- Acknowledgments
- Part I. Background
- Chapter 1. The Spring Data Project
- NoSQL Data Access for Spring Developers
- General Themes
- The Domain
- The Sample Code
- Importing the Source Code into Your IDE
- STS/Eclipse
- IntelliJ IDEA
- Chapter 2. Repositories: Convenient Data Access Layers
- Quick Start
- Defining Query Methods
- Query Lookup Strategies
- Query Derivation
- Property expressions
- Pagination and Sorting
- Defining Repositories
- Fine-Tuning Repository Interfaces
- Manually Implementing Repository Methods
- IDE Integration
- IntelliJ IDEA
- Chapter 3. Type-Safe Querying Using Querydsl
- Introduction to Querydsl
- Generating the Query Metamodel
- Build System Integration
- Supported Annotation Processors
- Querying Stores Using Querydsl
- Integration with Spring Data Repositories
- Executing Predicates
- Manually Implementing Repositories
- Part II. Relational Databases
- Chapter 4. JPA Repositories
- The Sample Project
- The Traditional Approach
- Bootstrapping the Sample Code
- Using Spring Data Repositories
- Transactionality
- Repository Querydsl Integration
- Chapter 5. Type-Safe JDBC Programming with Querydsl SQL
- The Sample Project and Setup
- The HyperSQL Database
- The SQL Module of Querydsl
- Build System Integration
- The Database Schema
- The Domain Implementation of the Sample Project
- The QueryDslJdbcTemplate
- Executing Queries
- The Beginning of the Repository Implementation
- Querying for a Single Object
- The OneToManyResultSetExtractor Abstract Class
- The CustomerListExtractor Implementation
- The Implementations for the RowMappers
- Querying for a List of Objects
- Insert, Update, and Delete Operations
- Inserting with the SQLInsertClause
- Updating with the SQLUpdateClause
- Deleting Rows with the SQLDeleteClause
- Part III. NoSQL
- Chapter 6. MongoDB: A Document Store
- MongoDB in a Nutshell
- Setting Up MongoDB
- Using the MongoDB Shell
- The MongoDB Java Driver
- Setting Up the Infrastructure Using the Spring Namespace
- The Mapping Subsystem
- The Domain Model
- Addresses and email addresses
- Customers
- Products
- Orders and line items
- Setting Up the Mapping Infrastructure
- Using the Spring namespace
- In Spring JavaConfig
- Indexing
- Customizing Conversion
- Implementing custom converters
- Registering custom converters
- MongoTemplate
- Mongo Repositories
- Infrastructure Setup
- Repositories in Detail
- Mongo Querydsl Integration
- Chapter 7. Neo4j: A Graph Database
- Graph Databases
- Neo4j
- Spring Data Neo4j Overview
- Modeling the Domain as a Graph
- Persisting Domain Objects with Spring Data Neo4j
- Neo4jTemplate
- Combining Graph and Repository Power
- Basic Graph Repository Operations
- Derived and Annotated Finder Methods
- Annotated finder methods
- Result handling
- Derived finder methods
- Advanced Graph Use Cases in the Example Domain
- Multiple Roles for a Single Node
- Product Categories and Tags as Examples for In-Graph Indexes
- Leverage Similar Interests (Collaborative Filtering)
- Recommendations
- Transactions, Entity Life Cycle, and Fetch Strategies
- Advanced Mapping Mode
- Working with Neo4j Server
- Continuing From Here
- Chapter 8. Redis: A Key/Value Store
- Redis in a Nutshell
- Setting Up Redis
- Using the Redis Shell
- Connecting to Redis
- Object Conversion
- Object Mapping
- Atomic Counters
- Pub/Sub Functionality
- Listening and Responding to Messages
- Using Spring's Cache Abstraction with Redis
- Part IV. Rapid Application Development
- Chapter 9. Persistence Layers with Spring Roo
- A Brief Introduction to Roo
- Roo's Persistence Layers
- Quick Start
- Using Roo from the Command Line
- Using Roo with Spring Tool Suite
- A Spring Roo JPA Repository Example
- Creating the Project
- Setting Up JPA Persistence
- Creating the Entities
- Defining the Repositories
- Creating the Web Layer
- Running the Example
- A Spring Roo MongoDB Repository Example
- Creating the Project
- Setting Up MongoDB Persistence
- Creating the Entities
- Defining the Repositories
- Creating the Web Layer
- Running the Example
- Chapter 10. REST Repository Exporter
- The Sample Project
- Interacting with the REST Exporter
- Accessing Products
- Accessing Customers
- Accessing Orders
- Part V. Big Data
- Chapter 11. Spring for Apache Hadoop
- Challenges Developing with Hadoop
- Hello World
- Hello World Revealed
- Hello World Using Spring for Apache Hadoop
- Scripting HDFS on the JVM
- Combining HDFS Scripting and Job Submission
- Job Scheduling
- Scheduling MapReduce Jobs with a TaskScheduler
- Scheduling MapReduce Jobs with Quartz
- Chapter 12. Analyzing Data with Hadoop
- Using Hive
- Hello World
- Running a Hive Server
- Using the Hive Thrift Client
- Using the Hive JDBC Client
- Apache Logfile Analysis Using Hive
- Using Pig
- Hello World
- Running a PigServer
- Controlling Runtime Script Execution
- Calling Pig Scripts Inside Spring Integration Data Pipelines
- Apache Logfile Analysis Using Pig
- Using HBase
- Hello World
- Using the HBase Java Client
- Chapter 13. Integrating Big Data Pipelines with Spring Batch and Spring Integration
- Collecting and Loading Data into HDFS
- An Introduction to Spring Integration
- Copying Logfiles
- Event Streams
- Event Forwarding
- Management
- An Introduction to Spring Batch
- Processing and Loading Data from a Database
- Hadoop Workflows
- Spring Batch Support for Hadoop
- Wordcount as a Spring Batch Application
- Hive and Pig Steps
- Exporting Data from HDFS
- From HDFS to JDBC
- From HDFS to MongoDB
- Collecting and Loading Data into Splunk
- Part VI. Data Grids
- Chapter 14. GemFire: A Distributed Data Grid
- GemFire in a Nutshell
- Caches and Regions
- How to Get GemFire
- Configuring GemFire with the Spring XML Namespace
- Cache Configuration
- Region Configuration
- Cache Client Configuration
- Cache Server Configuration
- WAN Configuration
- Disk Store Configuration
- Data Access with GemfireTemplate
- Repository Usage
- POJO Mapping
- Creating a Repository
- PDX Serialization
- Continuous Query Support
- Bibliography
- Index
