Relational Databases But scaling is hard! -Replication -Multiple instances w/ shared disk -Sharding
Relational Databases on a cloudMaster/replicas: which master?A single master? I was promised elasticityLess reliable “disks”IP in configuration files? DNS update times?Who coordinates this? How does that failover?
¬SQLmore meaning NotOnlySQL ¬SQL U SQL = anything
No-SQL goalsVery heterogeneus• Large datasets• High availability• Low latency / higher throughput• Specific data access pattern• Specific data structures• ...
NotOnlySQL• Document based stores• Column based• Graph oriented databases• Key / value stores• Full-Text Search
NotOnlySQLChoose one. Before starting. Stick to it.
Flexibility at a cost• Programming model • one per product :-( • Often very thight code coupling • No standard drivers / stable APIs• no schema => app driven schema• query (Map Reduce, specific DSL, ...)• data structure transpires• Transactions ?• durability / consistency puzzles
Where does Infinispan fit?Distributed Key/Value store • (or Replicated, local only efficient cache, invalidating cache) Each node is equal • Just start more nodes, or kill some No bottlenecks • by design Cloud-network friendly • JGroups • And “cloud storage” friendly too!
But how to use it?map.put( “user-34”, userInstance );map.get( “user-34” );map.remove( “user-34” );
Its a ConcurrentMap !map.put( “user-34”, userInstance );map.get( “user-34” );map.remove( “user-34” );map.putIfAbsent( “user-38”, another );
Other Hibernate/Infinispan collaborations● Second level cache for Hibernate ORM● Hibernate Search indexing backend● Infinispan Query
Cloud-hack experimentsLets play with Infinispans integration forHibernates second level cache design: - usually configured in clustering mode INVALIDATION. •Lets use DIST or REPL instead. - Disable expiry/timeouts. Whats the effect on your cloud-deployed database?
Cloud-hack experimentsNow introduce Hibernate Search: - full-text queries should be handled byLucene, NOT by the database.Hibernate Search identifies hits from theLucene index, but loads them by PK. *by default
These tools are very appropriate for the job:Load by PK -> second level cache -> Key/Value storeFullText query -> Hibernate Search -> Lucene Indexes
These tools are very appropriate for the job:Load by PK -> second level cache -> Key/Value storeFullText query -> Hibernate Search -> Lucene Indexes What if we now shut down the database?
Goals• Encourage new data usage patterns• Familiar environment• Ease of use• Easy to jump in• Easy to jump out• Push NoSQL exploration in enterprises• “PaaS for existing API” initiative
What it does• JPA front end to key/value stores • Object CRUD (incl polymorphism and associations) • OO queries (JP-QL)• Reuses • Hibernate Core • Hibernate Search (and Lucene) • Infinispan• Is not a silver bullet • not for all NoSQL use cases
Schema or no schema?• Schema-less • move to new schema very easy • app deal with old and new structure or migrate all data • need strict development guidelines• Schema • reduce likelihood of rogue developer corruption • share with other apps • “didn’t think about that” bugs reduced
Entities as serialized blobs?• Serialize objects into the (key) value • store the whole graph?• maintain consistency with duplicated objects • guaranteed identity a == b • concurrency / latency • structure change and (de)serialization, class definition changes
OGM’s approach to schema• Keep what’s best from relational model • as much as possible • tables / columns / pks• Decorrelate object structure from data structure• Data stored as (self-described) tuples• Core types limited • portability
OGM’s approach to schema• Store metadata for queries • Lucene index• CRUD operations are key lookups
How does it work?• Entities are stored as tuples (Map<String,Object>) • Or Documents?• The key is composed of • table name • entity id• Collections are represented as a list of tuples- The key is composed of: • table name hosting the collection information • column names representing the FK • column values representing the FK
Queries / Infinispan• Hibernate Search indexes entities• Store Lucene indexes in Infinispan• JP-QL to Lucene query transformation• Works for simple queries • Lucene is not a relational SQL engine
select a from Animal a where a.size > 20> animalQueryBuilder.range().onField(“size”).above(20).excludeLimit().createQuery();select u from Order o join o.user u where o.price > 100 and u.city =“Paris”> orderQB.bool() .must( orderQB.range() .onField(“price”).above(100).excludeLimit().createQuery() ) .must( orderQB.keyword(“user.city”).matching(“Paris”) .createQuery()).createQuery();
Why Infinispan?• We know it well• Supports transactions• Supports distribution of Lucene indexes• Designed for clouds• Its a key/value store with support for Map/Reduce • Simple • Likely a common point for many other “databases”
Why Infinispan?•Map/Reduce as an alternative to indexed queries •Might be chosen by a clever JP-QL engine•Potential for additional query types
Why ?Nothing new to learn for most common operations:• JPA models• JP-QL queriesEverything else is performance tuning, including:• Move to/from different NoSQL implementations• Move to/from a SQL implementation• Move to/from clouds/laptops• JPA is a well known standard: move to/from Hibernate :-)
Development state: • Query via Hibernate Search • Smart JP-QL parser is on github • Available in master: • EHCache • Infinispan • In development branches: • MongoDB • Voldemort
Summary• Performance / scalability is different• Isolation is different