An Introduction to Spring Data. Data Access 2.0? Thomas Risberg VMware twitter: trisberg. Thursday, May 26, 2011

Transcription

1 An Introduction to Spring Data OR Data Access 2.0? Thomas Risberg VMware twitter: trisberg

2 A couple of recent trends Heavyweight Databases Heavy handed DBAs Heavyweight App Servers Lightweight NOSQL Databases Lightweight App Servers Cloud Deployments

3 Data Access 1.0 A brief history of data access during the last quarter century The relational database has been extremely successful the last 25 years It was the Swiss Army knife of data access for many developers and it worked well...

4 New demands on data access... until we needed inexpensive horizontal scaling for some large web based applications and we needed to deploy our apps in the cloud...

5 NOSQL provides specialized tools Big Data/Data Warehousing - Hbase, Hadoop, Cassandra Web App Backend Caching/Persistence - Redis, Riak, MongoDB Social Networking Features - Neo4j Disconnected Data Replication - CouchDB

6 Welcome to Data Access 2.0

7 Spring Framework built-in data access support Data Access 1.0 Transaction abstractions, Data access exceptions JDBC - JdbcTemplate ORM - Hibernate, JPA support OXM - Object to XML mapping Cache support (Spring 3.1)

8 Spring Data Spring Data Redis/Riak Spring Data Mongo/Couch Spring Data Graph Neo4j Spring Data Column Spring Data Blob Spring Data JPA Repository Spring Data JDBC Extensions Spring Gemfire Spring Hadoop Grails inconseqential

9 What is Spring Data Bring classic Spring value propositions to NOSQL and extend the SQL based support Productivity Programming model consistency Support for a wide range of NOSQL databases

10 Spring Data Building Blocks Low level data access APIs MongoTemplate, RedisTemplate... Object Mapping (Java and GORM) Cross Store Persistence Programming model Generic Repository support (Hades) Productivity support in Roo and Grails

11 Finding Spring Data GitHub: Web page: Forum:

12 Three databases for today s talk Document database Relational database Graph database

13 Three persistence strategies for today s talk Lower-level Template approach Conventions based persistence -- Repositories (Hades) Cross-store persistence using JPA and a NOSQL database

14 JSON-style documents: Lists, Maps, Primitive values Documents organized into collections Full or partial document updates GridFS for efficiently storing large files Index support secondary and compound Rich query language for dynamic queries Map/Reduce Replication and Auto Sharding

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16 Spring Template Pattern Resource management Callback methods doinsomeresource Exception translation Simple Query API

17 Mongo DB using MongoTemplate CODE master/data-mongo-template 17

18 Also known as HSQLDB or Hypersonic SQL Relational Database Table oriented data model SQL used for queries... you know the rest...

19 Spring Data Repository Support Eliminate boilerplate code interface-only approach finder methods findbylastname Specifications for type safe queries JPA CriteriaBuilder integration QueryDsl integration

20 Type-safe queries for multiple backends including JPA, SQL and MongoDB in Java. Generates Query classes using Java APT Code completion in IDE Domain types and properties can be referenced safely Adopts better to refactoring changes in domain types

21 Repository Support Spring Data JPA Spring Data Mongo Spring Data JDBC Extensions

22 Relational database using JPA Repository CODE tree/master/spring-data-jpa-showcase 22

23 DB is a collection of graph nodes, relationships Nodes and relationships have properties Querying is done via a traversal API Indexes on node/relationship properties Written in Java, can be embedded Transactional (ACID) Master-Slave replication Standalone using REST API

24 Spring Data Graph Neo4j Using AspectJ support we are providing a new programming model: Use annotations to define POJO entities Constructor advice automatically handles entity creation Entity field state persisted to graph using aspects Leverage graph database APIs from POJO model Annotation-driven indexing of entities for search

25 Spring Data Graph Neo4j Cross-store JPA data and NOSQL data can share a data model Separate the persistence provider by using annotations could be the entire Entity or, some of the fields of an Entity We call this cross-store persistence One transaction manager to coordinate the NOSQL store with the JPA relational database AspectJ support to manage the NOSQL entities and fields holds on to changed values in change sets until the transaction commits for non-transactional data stores

26 A cross-store scenario... You have a traditional web app using JPA to persist data to a relational database...

27 JPA Data Model public class private Long id; private String name; private String city; private String state; private String = "user_account") public class private Long id; private String username; private String firstname; private String private Date = CascadeType.ALL) private Set<Restaurant> favorites;

28 Cross-store Data = true) public class private Long id; private String name; private String city; private String state; private String zipcode; public class Recommendation private UserAccount private Restaurant restaurant; private int stars; private String = = true) public class private Long id; private String username; private String firstname; private String private Date = CascadeType.ALL) private Set<Restaurant> String = "friends", elementclass = UserAccount.class) Set<UserAccount> = "recommends", elementclass = Recommendation.class) Iterable<Recommendation> recommendations;

29 Cross-store Data = true) public class private Long id; private String name; private String city; private String state; private String zipcode; public class Recommendation private UserAccount private Restaurant restaurant; private int stars; private String = = true) public class private Long id; private String username; private String firstname; private String private Date = CascadeType.ALL) private Set<Restaurant> String = "friends", elementclass = UserAccount.class) Set<UserAccount> = "recommends", elementclass = Recommendation.class) Iterable<Recommendation> recommendations;

30 Cross-store Data = true) public class private Long id; private String name; private String city; private String state; private String zipcode; public class Recommendation private UserAccount private Restaurant restaurant; private int stars; private String = = true) public class private Long id; private String username; private String firstname; private String private Date = CascadeType.ALL) private Set<Restaurant> String = "friends", elementclass = UserAccount.class) Set<UserAccount> = "recommends", elementclass = Recommendation.class) Iterable<Recommendation> recommendations;

31 Cross-store Data = true) public class private Long id; private String name; private String city; private String state; private String zipcode; public class Recommendation private UserAccount private Restaurant restaurant; private int stars; private String = = true) public class private Long id; private String username; private String firstname; private String private Date = CascadeType.ALL) private Set<Restaurant> String = "friends", elementclass = UserAccount.class) Set<UserAccount> = "recommends", elementclass = Recommendation.class) Iterable<Recommendation> recommendations;

32 Cross-store Data = true) public class private Long id; private String name; private String city; private String state; private String zipcode; public class Recommendation private UserAccount private Restaurant restaurant; private int stars; private String = = true) public class private Long id; private String username; private String firstname; private String private Date = CascadeType.ALL) private Set<Restaurant> String = "friends", elementclass = UserAccount.class) Set<UserAccount> = "recommends", elementclass = Recommendation.class) Iterable<Recommendation> recommendations;

33 Cross-store persistence with JPA/Neo4j CODE RELEASE/myrestaurants-social 29

34 Questions?