Downloaded 27 times
![Data Model = Binary JSON documents
{
"name" : "Ajanta",
One document
"type" : "Indian",
=
"serviceArea" : [
"94619", one DDD aggregate
"94618"
],
"openingHours" : [
{
• Sequence of bytes on disk = fast I/O
- No joins/seeks
"dayOfWeek" : Monday,
"open" : 1730,
- In-place updates when possible => no index updates
"close" : 2130 • Transaction = update of single document
}
],
"_id" : ObjectId("4bddc2f49d1505567c6220a0")
}
21](https://image.slidesharecdn.com/vmwarepronamc-121230155617-phpapp01/75/Wmware-NoSQL-19-2048.jpg)
Uploaded byMurat Çakal
Wmware NoSQL
This document provides an introduction to using Spring Data to simplify development of NoSQL applications. It discusses why NoSQL databases emerged as alternatives to relational databases, gives an overview of popular NoSQL databases like Redis, MongoDB, Neo4j and their features. It then introduces Spring Data and how it provides common APIs and conventions to work with various NoSQL databases. Specific database APIs for MongoDB, HyperSQL and Neo4j are also covered along with how Spring Data supports cross-store persistence across SQL and NoSQL databases in a single transaction.
More Related Content
Wmware NoSQL
- 1. NoSQL / SpringData Polyglot Persistence – An introduction to Spring Data Pronam Chatterjee pronamc@vmware.com © 2011 VMware Inc. All rights reserved
- 2. Presentation goal How Spring Data simplifies the development of NoSQL applications 2
- 3. Agenda • Why NoSQL? • Overview of NoSQL databases • Introduction to Spring Data • Database APIs - MongoDB - HyperSQL - Neo4J 3
- 4. Relational databases aregreat • SQL = Rich, declarative query language • Database enforces referential integrity • ACID semantics • Well understood by developers • Well supported by frameworks and tools, e.g. Spring JDBC, Hibernate, JPA • Well understood by operations • Configuration • Care and feeding • Backups • Tuning • Failure and recovery • Performance characteristics • But…. 4
- 5. The trouble withrelational databases • Object/relational impedance mismatch - Complicated to map rich domain model to relational schema • Relational schema is rigid - Difficult to handle semi-structured data, e.g. varying attributes - Schema changes = downtime or $$ • Extremely difficult/impossible to scale writes: - Vertical scaling is limited/requires $$ - Horizontal scaling is limited or requires $$ • Performance can be suboptimal for some use cases 5
- 6. NoSQL databases haveemerged… Each one offers some combination of: • High performance • High scalability • Rich data-model • Schema less In return for: • Limited transactions • Relaxed consistency •… 6
- 7. … but thereare few commonalities • Everyone and their dog has written one • Different data models - Key-value - Column - Document - Graph • Different APIs – No JDBC, Hibernate, JPA (generally) • “Same sorry state as the database market in the 1970s before SQL was invented” http://queue.acm.org/detail.cfm?id=1961297 7
- 8. NoSQL databases haveemerged… • NoSQL usage small by comparison… • But growing… 8
- 9. Agenda • Why NoSQL? •Overview of NoSQL databases • Introduction to Spring Data • Database APIs - MongoDB - HyperSQL - Neo4J 10
- 10. Redis • Advanced key-valuestore - Think memcached on steroids (the good kind) - Values can be binary strings, Lists, Sets, Ordered Sets, Hash maps, .. - Operations for each data type, e.g. appending to a list, adding to a set, retrieving a slice of a list, … - Provides pub/sub-based messaging K1 V1 • Very fast: K2 V2 - In-memory operations - ~100K operations/second on entry-level hardware K3 V2 • Persistent - Periodic snapshots of memory OR append commands to log file - Limits are size of keys retained in memory. • Has “transactions” - Commands can be batched and executed atomically 11
- 11. Scaling Redis • Master/slavereplication - Tree of Redis servers - Non-persistent master can replicate to a persistent slave - Use slaves for read-only queries • Sharding - Client-side only – consistent hashing based on key - Server-side sharding – coming one day • Run multiple servers per physical host - Server is single threaded => Leverage multiple CPUs - 32 bit more efficient than 64 bit • Optional "virtual memory" - Ideally data should fit in RAM - Values (not keys) written to disc 13
- 12. Redis use cases •Use in conjunction with another database as the SOR • Drop-in replacement for Memcached - Session state - Cache of data retrieved from SOR - Denormalized datastore for high-performance queries • Hit counts using INCR command • Randomly selecting an item – SRANDMEMBER • Queuing – Lists with LPOP, RPUSH, …. • High score tables – Sorted sets Notable users: github, guardian.co.uk, …. 14
- 13. vFabric Gemfire -Elastic data fabric • High performance data grid • Enhanced parallel disk persistence • Non Disruptive up/down scalability • Session state - Cache of data retrieved from SOR - Denormalized datastore for high-performance queries • Heterogenous data sharing • Java • .net • C++ • Co-located Transactions 14
- 14. Gemfire - UseCases • Ultra low latency high throughput application • As an L2 cache in hibernate • Distributed Batch process • Session state - Tomcat - tcServer • Wide Area replication 14
- 15. Neo4j •Graph datamodel - Collection of graph nodes - Typed relationships between nodes - Nodes and relationships have properties •High performance traversal API from roots - Breadth first/depth first •Query to find root nodes - Indexes on node/relationship properties - Pluggable - Lucene is the default •Graph algorithms: shortest path, … •Transactional (ACID) including 2PC •Deployment modes - Embedded – written in Java - Server with REST API 15
- 16.
- 17. Neo4j Use Cases • Use Cases - Anything social - Cloud/Network management, i.e. tracking/managing physical/virtual resources - Any kind of geospatial data - Master data management - Bioinformatics - Fraud detection - Metadata management • Who is using it? - StudiVZ (the largest social network in Europe) - Fanbox - The Swedish military - And big organizations in datacom, intelligence, and finance that wish to remain anonymous 19
- 18. MongoDB • Document-oriented database - JSON-style documents: Lists, Maps, primitives - Documents organized into collections (~table) • Full or partial document updates - Transactional update in place on one document - Atomic Modifiers • Rich query language for dynamic queries • Index support – secondary and compound • GridFS for efficiently storing large files • Map/Reduce 20
- 19. Data Model =Binary JSON documents { "name" : "Ajanta", One document "type" : "Indian", = "serviceArea" : [ "94619", one DDD aggregate "94618" ], "openingHours" : [ { • Sequence of bytes on disk = fast I/O - No joins/seeks "dayOfWeek" : Monday, "open" : 1730, - In-place updates when possible => no index updates "close" : 2130 • Transaction = update of single document } ], "_id" : ObjectId("4bddc2f49d1505567c6220a0") } 21
- 20. MongoDB query byexample • Find a restaurant that serves the 94619 zip code and is open at 6pm on a Monday { serviceArea:"94619", openingHours: { $elemMatch : { "dayOfWeek" : "Monday", "open": {$lte: 1800}, "close": {$gte: 1800} } } } DBCursor cursor = collection.find(qbeObject); while (cursor.hasNext()) { DBObject o = cursor.next(); … } 23
- 21. MongoDB use cases • Use cases - Real-time analytics - Content management systems - Single document partial update - Caching - High volume writes • Who is using it? - Shutterfly, Foursquare - Bit.ly Intuit - SourceForge, NY Times - GILT Groupe, Evite, - SugarCRM Copyright (c) 2011 Chris Richardson. All rights reserved. 25
- 22. Other NoSQL databases •SimpleDB – “key-value” • Cassandra – column oriented database • CouchDB – document-oriented • Membase – key-value • Riak – key-value + links • Hbase – column-oriented… http://nosql-database.org/ has a list of 122 NoSQL databases 26
- 23. Agenda • WhyNoSQL? • Overview of NoSQL databases • Introduction to Spring Data • Database APIs - MongoDB - HyperSQL - Neo4J 27
- 24. NoSQL Java APIs Database Libraries Redis Jedis, JRedis, JDBC-Redis, RJC Neo4j Vendor-provided MongoDB Vendor-provided Java driver Gemfire Pure Java map API, Spring-Gemfire templates But • Usage patterns • Tedious configuration • Repetitive code • Error prone code •… 28
- 25. Spring Data ProjectGoals • Bring classic Spring value propositions to a wide range of NoSQL databases: - Productivity - Programming model consistency: E.g. <NoSQL>Template classes - “Portability” 30
- 26. Spring Data sub-projects • Commons: Polyglot persistence • Key-Value: Redis, Riak • Document: MongoDB, CouchDB • Graph: Neo4j • GORM for NoSQL http://www.springsource.org/spring-data 31
- 27. Many entry pointsto use • Auto-generated repository implementations • Opinionated APIs (Think JdbcTemplate) • Object Mapping (Java and GORM) • Cross Store Persistence Programming model • Productivity support in Roo and Grails 32
- 28. Cloud Foundry supportsNoSQL MongoDB and Redis are provided as services è Deploy your MongoDB and Redis applications in seconds 33
- 29. Agenda • Why NoSQL? •Overview of NoSQL databases • Introduction to Spring Data • Database APIs - MongoDB - HyperSQL - Neo4J 34
- 30. Three databases fortoday’s talk Document database Relational database Graph database 35
- 31. Three persistence strategiesfor today’s talk • Lower level template approach • Conventions based persistence (Hades) • Cross-Store persistence using JPA and a NoSQL datastore 36
- 32. Spring Template Patterns •Resource Management • Callback methods • Exception Translation • Simple Query API 37
- 33.
- 34. • Also knownas HSQLDB or Hypersonic SQL • Relational Database • Table oriented data model • SQL used for for queries • … you know the rest… 39
- 35. Spring Data RepositorySupport • Eliminate bolierplate code – only finder methods • findByLastName – Specifications for type safe queries • JPA CrietriaBuilder integration QueryDSL 40
- 36. • Type safequeries for multiple backends including JPA, SQL and MongoDB in Java • Generate 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 http://www.querydsl.com 41
- 37. QueryDSL • RepositorySupport • Spring Data JPA • Spring data Mongo • Spring Data JDBC extensions • QueryDslJdbcTemplate 42
- 38. Spring Data Neo4J • Using AspectJ support 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 43
- 39. Spring Data GraphNeo4J 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 44
- 40. A cross-store scenario... You have a traditional web app using JPA to persist data to a relational database ... 45
- 41. JPA Data Model 46 8/3/11 Slide 46
- 42.