Spring one2gx2010 spring-nonrelational_data


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Spring one2gx2010 spring-nonrelational_data

  1. 1. Chicago, October 19 - 22, 2010Using Spring with NoSQLdatabasesMark PollackChris Richardson
  2. 2. Goal of this talk• The benefits and drawbacks of NoSQL databases • How the Spring Data project simplifies the development of NoSQL applications
  3. 3. About Chris •Grew up in England and live in Oakland, CA •Over 25+ years of software development experience including 14 years of Java •Speaker at JavaOne, SpringOne, NFJS, JavaPolis, Spring Experience, etc. •Organize the Oakland JUG and the Groovy Grails meetup http://www.theregister.co.uk/2009/08/19/springsource_cloud_foundry/
  4. 4. About Mark• Spring committer since 2003• Founder of Spring.NET• Work on some other new open source projects – Spring Gemfire – Spring AMQP• Used to work on large scale data collection/analysis in High Energy Physics (~10yrs ago)
  5. 5. Agenda• Why NoSQL?• Some NoSQL Databases• Spring NoSQL projects• Demos/Code/Deep dive
  6. 6. Why NoSQL?
  7. 7. Relational databases are great• 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….
  8. 8. The trouble with relational database• Object/relational impedance mismatch – Complicated to map rich domain model to relational schema – Relational schema is rigid• Extremely difficult/impossible to scale writes: – Vertical scaling is limited/expensive – Horizontal scaling is limited or requires $$• Performance can be suboptimal
  9. 9. Why NoSQL? Internet scale is the primary driver in the use of non-relational DBs Large Data Size – TBs to PBs High Transaction rates Need for High Availability Pictures courtesy of Gilt GroupRelational DBs Harder to scale to larger size Transactions and joins reduce access rates
  10. 10. Different priorities• Relational Databases with ACID semantics – Atomic, Consistent, Isolated, Durable• Internet Systems with BASE semantics – Basically Available, Scalable, Eventually Consistent• NoSQL/BASE values remove standard DB behaviors – No Joins – Very limited "transactions" – Different Durability/consistency guarantees• NoSQL datastores emerge as “point solutions” – Amazon/Google papers – Facebook, LinkedIn …
  11. 11. The NoSQL Landscape There are a variety of non-relational datastores Many are Open Source Many with companies behind them Rapidly changing feature sets ScalarisNeo4J db4o Objectivity Amazon Dynamo Terrastore Gemfire Sones HBase CouchDBInfoGrid RavenDB Hadoop Scalaris Tokyo Cabinet MongoDB Hypertable Cassandra Azure Table CoherenceMnesia Voldemort Gigaspaces Riak Amazon SimpleDB Redis BerkeleyDB MemcacheDB Tamino InfiniteGraph Chordless Expect consolidation over time With so many choices, picking which one to use can be difficult
  12. 12. Stops on a tour through NoSQL-land What is the data model? How to I access my data? What are the primary use cases? What notable companies that are using it?
  13. 13. Data Model Key Value K1 V1 Key -> value K2 V2 Redis, Riak, Voldemort… K3 V2 “Amazon Dynamo inspired” Column Key -> ‘column families’ HBase, Cassandra “Google Bigtable inspired”
  14. 14. Data ModelDocument Collections containing semi- { id: ‘4b2b9f67a1f631733d917a7b"),’ author: ‘joe’, structured data tags : [‘example’, ‘db’], comments : [ { author: jim, comment: OK }, { author: ‘ida, comment: ‘Bad } key-values , JSON, XML, ] CouchDB, MongoDB { id: ‘4b2b9f67a1f631733d917a7c"), author: ‘ida’, ... { id: ‘4b2b9f67a1f631733d917a7d"), author: ‘jim’, ...Graph Graph: nodes and edges Edges can be directional Each can contain key-value attributes Neo4j, Sones, InfiniteGraph
  15. 15. How to I access my data?
  16. 16. How to I access my data? Query Mechanism Key lookup Map-Reduce Query by example Query language ‘out of box’ query power usually linked to data model Increasing power KV -> Column -> Document/Graph Counter examples Riak is key-value and supports Map-Reduce Spring Data Mapper… .
  17. 17. Reality Check - Relational DBs are not going away NoSQL usage small by comparison… But growing…
  18. 18. Future = multi-paradigm data storage for enterprise applicationsIEEE Software Sept/October 2010 - Debasish Ghosh / Twitter @debasishg
  19. 19. Agenda• Why NoSQL?• Some NoSQL Databases• Spring NoSQL projects• Demos/Code/Deep dive
  20. 20. Redis• Advanced key-value store – Think memcached on steroids (the good kind) – Values can be binary strings, Lists, Sets, Ordered K1 V1 Sets, Hash maps, .. – Operations for each data type, e.g. appending K2 V2 to a list, adding to a set, retrieving a slice of a K3 V2 list, …• Very fast: ~100K operations/second on entry- level hardware• Persistent – Periodic snapshots of memory OR append commands to log file – Limits are size of keys retained in memory.• Has “transactions”• Libraries – Many languages, all separate open source projects
  21. 21. Redis CLIredis> sadd myset a(integer) 1redis> sadd myset b(integer) 1redis> smembers myset1. "a"2. "b"redis> srem myset a(integer) 1redis> smembers myset1. "b"
  22. 22. Scaling Redis• Master/slave replication – 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 – 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
  23. 23. 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, ….
  24. 24. Cassandra• Originally developed by Facebook for inbox search – Now an Apache open-source project• Extremely scalable• Data is replicated and sharded• The data model will hurt your brain • Column-oriented database • 4 or 5-dimensional hash map
  25. 25. Cassandra data model – insert/update My Column family (within a key space) Keys Columns a colA: value1 colB: value2 colC: value3 This can be data! b colA: value colD: value colE: value Insert(key=a, columName=colZ, value=foo) Keys Columns a colA: value1 colB: value2 colC: value3 colZ: foo b colA: value colD: value colE: value
  26. 26. Cassandra query – range slice Keys Columns a colA: value1 colB: value2 colC: value3 colZ: foo b colA: value colD: value colE: value rangeSlice(startKey=a, endKey=b, startColumn=colA, endColumnName=colB) Keys Columns a colA: value1 colB: value2 b colA: value
  27. 27. Cassandra CLI$ bin/cassandra-clicassandra> connect localhost/9160Connected to "Test Cluster" on localhost/9160cassandra> get Keyspace1.Standard1[foo]Returned 0 resultscassandra> set Keyspace1.Standard1[foo][a] = 2Value insertedcassandra> get Keyspace1.Standard1[foo]=> (Column=61, value=1, timestamp...)cassandra> set Keyspace1.Standard1[foo][bar] = abcValue insertedcassandra> get Keyspace1.Standard1[foo]=> (Column=626172, value=abc, timestamp...)=> (Column=61, value=1, timestamp...)cassandra> count Keyspace1.Standard1[foo]2 columnscassandra>
  28. 28. Scaling Cassandra through sharding/replication Keys = [D, A] •Client connects to any node •Dynamically add/remove nodes Node 1 •Configurable # replicas Token = A •Reads/Writes specify how many nodes Replicates to Replicates to Keys = [A, B] Node 4 Node 2 Token = D Token = BKeys = [C, D] Replicates to Replicates to Node 3 Token = C Keys = [B, C]
  29. 29. Cassandra use cases• Use cases • Big data • Persistent cache • (Write intensive) Logging• Who is using it – Digg, Facebook, Twitter, Reddit, Rackspace – Cloudkick, Cisco, SimpleGeo, Ooyala, OpenX – “The largest production cluster has over 100 TB of data in over 150 machines.“ – Casssandra web site
  30. 30. MongoDB• JSON-style documents – Lists, Maps, Primitive values• Documents organized into collections (~table)• Full or partial document updates – Transactional update in place on one document – Atomic Modifiers• GridFS for efficiently storing large files• Index support – secondary and compound• Rich query language for dynamic queries• Map/Reduce• Replication and Auto Sharding
  31. 31. MongoDB Data Model { "name" : "Ajanta",• BSON data types "type" : "Indian", "serviceArea" : [• Documents types "94619", – Data "94618" ], – Query "openingHours" : [ – Modifier for updates {• Query and Modify "dayOfWeek" : Monday, "open" : 1730, operators "close" : 2130 – $tl, $gt, $ne, $in, $or, } $elemMatch, $orderBy, ], $group, "_id" : ObjectId("4bddc2f49d1505567c – $where + javascript 6220a0") – $inc, $set, $push, $pull }
  32. 32. MongoDB query by example• 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": {$gte: 1800} } } } DBCursor cursor = collection.find(qbeObject); while (cursor.hasNext()) { DBObject o = cursor.next(); … }
  33. 33. MongoDB CLI$ bin/mongo> use mydb> r1 = {name: Ajanta}{name: Ajanta}> r2 = {name: Montclair Egg Shop}{name: Montclair Egg Shop}> db.restaurants.save(r1)> r1{ _id: ObjectId("98…"), name: "Ajanta"}> db.restaurants.save(r2)> r2{ _id: ObjectId("66…"), name: "Montclair Egg Shop"}> db.restaurants.find(){ _id: ObjectId("98…"), name: "Ajanta"}{ _id: ObjectId("66…"), name: "Montclair Egg Shop"}> db.restaurants.find(name: /^A/){ _id: ObjectId("98…"), name: "Ajanta"}
  34. 34. Scaling MongoDB Shard 1 Shard 2 mongod mongod mongod mongod mongod mongod A shard consists of aConfig Server replica set = generalization mongod mongos mongos of master slave mongod Collections mongod spread over client multiple shards
  35. 35. 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
  36. 36. CouchDB• JSON documents• Database is flat collection of documents• Queries are implemented as views using Map/Reduce in Javascript – Queries typically defined up front – Views are indexed and incrementally updated• MVCC based – Need to deal with reconciliation• Multi-Master (bi) replication• Scale out with replication• Mobile version - Android• Use as a Server-Side JavaScript application server – “CouchApps”• Cloudant provides Java VM based views
  37. 37. CouchDB with curl$ curl -X PUT http://localhost:5984/contacts{"ok":true}$ curl -X PUT http://localhost:5984/contacts/markpollack -d{"firstName":"Mark", "lastName":"Pollack", "email":"mpollack@vmware.com"}‘{"ok":true,"id":"markpollack","rev":"1-7bb43f219a8d4306614dbd526ed577fe"}$curl -X GET http://localhost:5984/contacts/markpollack{"_id":"markpollack", "_rev":"1-7bb43f219a8d4306614dbd526ed577fe","firstName":"Mark","lastName":"Pollack","email":"mpollack@vmware.com"}
  38. 38. Futon
  39. 39. Futon
  40. 40. CouchDB use cases• Use cases – Synchronization • master-master replication • Offline replication for mobile devices: “Ground Computing” – Web apps that consume JSON • Server side JavaScript using CouchApps – Content management systems – Caching• Who is using it? – Mozilla, BBC, Palm, Apple, – IBM, Ubuntu, meebo, Engine Yard – Cloudant, UNICEF, Credit Suisse
  41. 41. Neo4j• Graph Database• 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 – Also a REST API• Transactional (ACID)• Master-Slave replication in next release
  42. 42. Neo4j Data Model
  43. 43. Neo4j Use Cases• Use Cases – Social networking – Bioinformatics – Fraud detection• Who is using it? – Fanbox, Jayway – Large companies that wish to remain anonymous
  44. 44. Agenda• Why NoSQL?• Some NoSQL Databases• Spring NoSQL projects• Demos/Code/Deep dive
  45. 45. Spring Data Project Goals• Bring classic Spring value propositions to NoSQL – Productivity – Programming model consistency• Support for a wide range of NoSQL databases• Many entry points to use – Low level data access APIs – Opinionated APIs (Think JdbcTemplate) – Object Mapping (Java and GORM) – Cross Store Persistence Programming model – Productivity support in Roo and Grails – Vertical Applications – Guidance
  46. 46. Spring Data• Working together with various projects/companies – Redis / VMware – Riak / Basho – CouchDB / CouchOne – Mongo / 10gen – Neo4j / Neo Technologies
  47. 47. Spring Data Projects Data Commons Planned Polyglot persistence Guidance Docs Datastore Key-Value The big picture Redis Datastore Column Cassandra, HBase Datastore Document Hadoop MongoDB, CouchDB Datastore Key-Value Datastore Graph Riak Neo4j Datastore Mapping Datastore Mapping Additional DB Map Redis, Gemfire Blob storage Amazon, Atmos, Azure Reaching M1 release in Q4. SQL - Generic DAOs Grails/Roo support
  48. 48. Spring Datastore Key-Value (Redis) Interface based low-level Redis Client API Method names are Redis command names Pluggable implementations (Jedis, JRedis) Translation to Spring DAO exception hierarchy Connection Pooling RedisTemplate Descriptive method names, grouped into natural categories ServerOps, SetOps Convenience methods – ‘redis recipies’ getAndConvert, convertAndSet for payload/value serialization Java, JSON, XML Protocol Buffers, Avro, to come… Redis backed java.util.Set, List, Map, Capped Collections RedisPlatformTransactionManager Redis Messaging RedisMessageTemplate, RedisMessageListenerContainer
  49. 49. Spring Redis ExamplesRedisClientFactory clientFactory = new JedisClientFactory();RedisClient client = clientFactory.createClient();client.sadd("s1", "1"); client.sadd("s1", "2");client.sadd("s1", "3");client.sadd("s2", "2"); client.sadd("s2", "3");Set<String> intersection = client.sinter("s1", "s2");System.out.println(intersection); // [3,2]RedisTemplate redisTemplate = new RedisTemplate(client)Person p = new Person("Joe", "Trader", 33);redisTemplate.convertAndSet("trader:1", p);p = redisTemplate.getAndConvert("trader:1", Person.class);intersection = redisTemplate.getSetOps().getIntersectionOfSets(“s1”,”s2”)RedisSet s1 = new RedisSet(template, "s1");s1.add("1"); s1.add("2"); s1.add("3");RedisSet s2 = new RedisSet(template, "s2");s2.add("2"); s2.add("3");Set s3 = s1.intersection("s3", s1, s2); // [3,2]
  50. 50. Spring MongoDB• MongoTemplate – ‘RowMapper’ like functionality for mapping Mongo query results. – Basic POJO mapping support for simple CRUD • Leverage Spring 3.0 TypeConverters and SpEL – Connection affinity callback• Support common map-reduce operations from Mongo Cookbook• Higher level integration – File Upload – Spring MVC activity monitoring and analysis
  51. 51. Spring MongoDB ExampleMongo mongo = new Mongo();DB db = mongo.getDB("mvc");MongoTemplate mongoTemplate = new MongoTemplate(db, "people");Person p = new Person("Joe", "Trader", 33);mongoTemplate.save(p);List<People> people = mongoTemplate.queryForCollection(Person.class);
  52. 52. Spring CouchDB CouchTemplate RestTemplate based API for CouchDB Basic JSON/POJO mapping support CouchDbMappingJacksonHttpMessageConverter Android implementation .NET implementation planned Repository base classes Out of the box CRUD Embedded View Definitions Automatic view generation Plans to work with ektrop OS project http://code.google.com/p/ektorp/
  53. 53. Spring CouchDB ExampleRestTemplate restTemplate = new RestTemplate();restTemplate.setMessageConverters(couchDbConverter);List<Restaurants> rests = (List<Restaurant>) restTemplate.getForObject(getDbURL() + "_design/demo/_view/all", Restaurant.class);UserAccount userAccount = new UserAccount("u1");restTemplate.put(getDbURL() + userAccount.getUserName(), userAccount);//Note, not handling response to get new revision string…
  54. 54. Ektrop CouchDB Example
  55. 55. Spring Neo4j• Easy POJO based programming model• Support for graph Entities, Relationships, and Properties – Properties leverage Spring 3.0 converters• DAO finder support – findAll, count, findById, findByPropertyValue, findAllByTraversalSee Graph Database Persistence Using Neo4j with Spring and RooTomorrow @ 10:15am in Lilac C
  56. 56. Spring Cross Store• Use Case – An existing application want to use NOSQL database as a part of its existing SQL-based application • Pick a problem space for which NOSQL is well suited• How to link together the two worlds? – Developer application code task – No longer a single domain entity• Spring Cross store allows for a single entity to be persisted across multiple datastores – AspectJ manages NOSQL entities/fields in change sets – Change sets are flushed when JPA commits – Interception to rehydrate when performing JPA queries
  57. 57. Spring Cross Store Example
  58. 58. Spring Cross Store Example
  59. 59. Spring Cross Store Example
  60. 60. Spring Mapping• GORM for Redis, Gemfire…. – Built on pure Java• Mapping metadata is not tied to GORM – Can use annotations, XML etc.• Will be working on making this mapping framework available to Java developers
  61. 61. Spring Mapping
  62. 62. Spring Mapping
  63. 63. Agenda• Why NoSQL?• Some NoSQL Databases• Spring NoSQL projects• Demos/Code/Deep dive
  64. 64. Redis meets POJOs in Action• Finding restaurants that are open at the delivery time (e.g. 94619) and serve the delivery zip code (e.g. 6pm)select r.*from FTGO_RESTAURANT_ZIPCODE zip, FTGO_RESTAURANT_TIME_RANGE tr, FTGO_RESTAURANT rwhere r.RESTAURANT_ID = zip.RESTAURANT_ID and tr.RESTAURANT_ID = r.RESTAURANT_ID; and zip.ZIPCODE = 94619 and tr.day_Of_week = Monday Joins and and tr.open_time <= 1800 complex and 1800<= tr.close_time where clause! No way Redis can handle this! Right?
  65. 65. Redis – denormalizing the data is the key Tr_id Day of Open_time Close_tim Zip code Week e Ajanta10 Monday 1130 1430 94707 Ajanta10 Monday 1130 1430 94619 Ajanta11 Monday 1730 2130 94707 Ajanta11 Monday 1730 2130 94619 Egg30 Monday 0700 1430 94707 … Two simpler queries(SELECT tr_id FROM time_range_zip_code where day_of_week = ? and zip_code = ? open_time < ?)INTERSECTION(SELECT tr_id FROM time_range_zip_code where day_of_week = ? and zip_code = ? and ? < close_time)
  66. 66. Redis – storing the opening hours using sorted setsSorted Set Keys Members = TimeRangeId with opening time as score open:94707:Monday Ajanta10:1130 Ajanta11:1730 open:94619:Monday Egg30:0700 Ajanta10:1130 Ajanta11:1730 ZRANGEBYSCORE open:94619:Monday 0 1800 {Egg30, Ajanta10, Ajanta11, …} Finds all time ranges that open before 6pm
  67. 67. Redis – storing closing times with sorted setsOrdered Set Keys Members = TimeRangeId with closing time as score close:94707:Monday Ajanta10:1430 Ajanta11:2130 close:94619:Monday Egg30:1430 Ajanta10:1430 Ajanta11:2130 ZRANGEBYSCORE close:94619:Monday 1800 2400 Finds all time ranges that close {Ajanta11, …} after 6pm
  68. 68. Redis – computing the intersection {Egg30, Ajanta10, Ajanta11, …} Intersection {Ajanta11, …} = {Ajanta11, …}• This is super-cool!• Illustrates the feasibility of using Redis as a queryable datastore to improve scalability
  69. 69. Cassandra – Food to Go AvailableRestaurants Column Family Keys Columns 1430 94619_ Super Monday_ Egg30: {…} column 0700 1430 94619_ Monday_ Ajanta10: {…} 1130 2130 94619_ Monday_ Ajanta11: {…} 1730
  70. 70. Cassandra – Food to Go rangeSlice(startKey=94619_Monday_0000, endKey=94619_Monday_1800, startColumn=1800, endColumnName=2359) Keys Columns 2130 94619_ Monday_ Ajanta11: {…} 1730
  71. 71. Food to Go - MongoDB{ { "name" : "Ajanta", serviceArea:"94619", "type" : "Indian", openingHours: { "serviceArea" : [ $elemMatch : { "94619", "dayOfWeek" : Monday, "94618" "open": {$lte: 1800}, ], "close": {$gte: 1800} "openingHours" : [ } { } "dayOfWeek" : Monday, } "open" : 1730, "close" : 2130 } ], Straightforward to model a "_id" : restaurant aggregate as a MongoDBObjectId("4bddc2f49d1505567c document and execute queries6220a0")}
  72. 72. MongoDB – MVC Analytics• Monitoring page view and web application activity is critical to analyzing effectiveness of – Marketing campaigns – Site design / new features• Standard solutions are problematic aside from scaling – Google Analytics • not real time, hard to setup – MySQL • Downtime for schema changes• Solutions needs to handle high write volume, handle large amounts of data, allow for flexible data exploration.
  73. 73. Web Analytics - SQL Still difficult to scale writes Application MySQL Server Master Reporting MySQL Server MySQL Slave MySQL Slave Slave
  74. 74. Web Analytics - MongoDB Application Server MongoD Reporting Server
  75. 75. Web Analytics - MongoDB Application MongoD Server MongoS Reporting Server MongoD
  76. 76. Spring MVC based solution• Custom Handler Adapter with a ActionInterceptor• ActionInterceptor has full context of web action – Controller class, method name, and parameters – WebRequest – ModelAndView – Exception• Similar to Ruby on Rails/ASP.NET MVC Action Filters• Collect data in MongoDB – MvcEvent – Increment counters per controller and action method• AnalyticsController to display results – Action method parameter information is critical for reporting• Also a good fit for an Insight plugin!
  77. 77. Collected Sample Data - Counters{ "_id" : ObjectId("4cba8e0a5a49000000004961"), "name" : "SignUpController", "count" : 8, "methods" : { "create" : 2, "createForm" : 2, "show" : 4 }}{ "_id" : ObjectId("4cba8ea85a49000000004962"), "name" : "RestaurantController", "count" : 9, "methods" : { "addFavoriteRestaurant" : 2, "list" : 5, "show" : 2 }}
  78. 78. Collected Sample Data{ "_id" : ObjectId("4cba929e4b445bc779b65c5f"), "action" : "addFavoriteRestaurant", "controller" : "RestaurantController", "date" : "Sun Oct 17 2010 02:07:26 GMT-0400 (Eastern Daylight Time)", "parameters" : { "p1" : "5", "p2" : "1", "p3" : "{currentUserAccountId= 1, userAccount_birthdate_date_format=M/d/yy,useraccount=Id: 1, Version: 1, UserName: mpollack, FirstName: Mark, LastName:Pollack, BirthDate: 2010-10-27 00:00:00.0, Favorites: 1, itemId=5}" }, "remoteUser" : "mpollack", "requestAddress" : "", "requestUri" : "/myrestaurants-analytics/restaurants/5/1"}
  79. 79. Restaurants Web Application
  80. 80. Favorite Restaurants Analysis
  81. 81. Controller Invocation
  82. 82. CouchDB – Mobile offline access and sync• Smartphones have huge data capacity• Can carry large data sets with you.• Mobile devices are not always connected• Data sync is a difficult problem to solve – CouchOne Mobile handles the dirty bits of sync – Available for Android• Same programming model as on server side• Spring RestTemplate based CouchDB API – On Server and Phone• Can also be used a full-stack environment on mobile – Database, JavaScript, Web Server
  83. 83. CouchDB – Offline data access and sync Continuous bi-directional replication Application Server
  84. 84. Sync Favorite Restaurants
  85. 85. Summary• Many new options for persistence – A particular use case in your application might be a good fit – Polyglot persistence will be increasingly common• Trade-offs with – Normalization – Transactions – Familiarity• The Spring Data project – Make it easier to build Spring-powered applications that use these new technologies• More info at http://www.springframework.org/spring-data – Welcome contributors, comments…
  86. 86. Q&AChris Richardson Mark Pollackcrichardson@vmware.com mpollack@vmware.com@crichardson @markpollack