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Hibernate ORM: Tips, Tricks, and Performance Techniques
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Hibernate ORM: Tips, Tricks, and Performance Techniques

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DevNexus 2014 …

DevNexus 2014

Out-of-the-box, Hibernate ORM offers limited overhead and decent throughput. Early-stage applications enjoy the convenience of ORM/JPA with great performance. However, scaling your application into an enterprise-level system introduces more demanding needs.

This talk will describe numerous tips and techniques to both increase Hibernate ORM performance, as well as decrease overhead. These include some basic tricks, such as mapping and fetching strategies. Entity enhancement instrumentation, third-party second level caching, Hibernate Search, and more complex considerations will also be discussed. The talk will include live demonstrations techniques and their before-and-after results.

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  • 1. Hibernate ORM: Tips, Tricks, and Performance Techniques Brett Meyer Senior Software Engineer Hibernate ORM, Red Hat
  • 2. Brett Meyer • Hibernate ORM – ORM 4 & 5 development – Hibernate OSGi – Developer community engagement – Red Hat enterprise support, Hibernate engineering lead • Other contributions – Apache Camel – Infinispan • Contact me – @brettemeyer or +brettmeyer – Freenode #hibernate or #hibernate-dev (brmeyer)
  • 3. github.com/brmeyer /HibernateDemos slideshare.net/brmeyer
  • 4. ORM? JPA? • ORM: Object/Relational Mapping – Persistence: Data objects outlive the JVM app – Maps Java POJOs to relational databases – Supports OO concepts: inheritance, object identity, etc. – Navigate data by walking the object graph, not the explicit relational model • JPA: Java Persistence API • Hibernate ORM provides its own native API, in addition to full JPA support • Annotations and XML
  • 5. Overview • • • • • • • • Fetching Strategies 2nd Level Entity Cache Query Cache Cache Management Bytecode Enhancement Hibernate Search Misc. Tips Q&A
  • 6. Caveats • No “one strategy to rule them all” • Varies greatly between apps • Important to understand concepts, then apply as necessary • Does not replace database tuning!
  • 7. First, the antithesis: public User get(String username) { final Session session = openSession(); session.getTransaction().begin(); final User user = (User) session.get( User.class, username ); session.getTransaction().commit(); return user; } public boolean login(String username) { return get(username) != null; } Clean? Yes. But...
  • 8. EAGER Demo • Prototypes start “simple” – EAGER – No caching – Overuse of the kitchen sink • DAO looks clean • Then you see the SQL logs
  • 9. Fetching Strategies
  • 10. Fetching Strategies • By far, the most frequent mistake • Also the most costly • 2 concepts: – WHEN (fetch timing) – HOW (fetch style)
  • 11. WHEN (fetch timing)
  • 12. Fetch Timing: EAGER • • • • Immediate Can be convenient (in some ways) Significantly increases payload Enormous amount of unnecessary fetches for deep association tree
  • 13. Fetch Timing: LAZY • Fetched when first accessed • Collections – LAZY by default – Utilizes Hibernate's internal concept of “persistent collections” – DEMO • Single attribute (basic) – Requires bytecode enhancement – Not typically used nor beneficial
  • 14. Fetch Timing: LAZY (cont'd) • Single (ToOne) associations – Fetched when accessed – Proxy • Default • Fetched when accessed (except ID) • Handled internally by Hibernate – No-proxy • Fetched when accessed (including ID) • No visible proxy • Requires buildtime bytecode enhancement
  • 15. Fetch Timing: EXTRA LAZY • • • • Collections only Fetch individual elements as accessed Does not fetch entire collection DEMO
  • 16. HOW (fetch style)
  • 17. Fetch Style: JOIN • Join fetch (left/outer join) • Great for ToOne associations • Multiple collection joins – Possible for non-bags – Warning: Cartesian product! SELECT is normally faster • DEMO
  • 18. Fetch Style: SELECT • Follow-up selects • Default style for collections (avoids cartesian products) • Can result in 1+N selects (fetch per collection entry) • DEMO
  • 19. Fetch Style: BATCH • Fetches multiple entries when one is accessed • Configurable on both class and collection levels (“batch-size”) • Simple select and list of keys • Multiple algorithms (new as of 4.2) • Determines # of entries to fetch, based on # of provided keys
  • 20. Fetch Style: BATCH (cont'd) • Legacy: pre-determined sizes, rounded down – batch-size==25, 24 elements in collection – Fetches -> 12, 10, 2 • Padded: same as Legacy, size rounded up • Dynamic: builds SQL for the # of keys, limited by “batch-size” • DEMO
  • 21. Fetch Style: SUBSELECT • Follow-up select • Fetches all collection entries when accessed for the 1st time • Original root entry select used as subselect • Performance depends on the DB itself • DEMO
  • 22. Fetch Style: PROFILE • named profile defining fetch styles • “activated” through the Session
  • 23. @Entity @FetchProfile(name = "customer-with-orders", fetchOverrides = { @FetchProfile.FetchOverride(entity = Customer.class, association = "orders", mode = FetchMode.JOIN) }) public class Customer { ... @OneToMany private Set<Order> orders; ... } Session session = ...; session.enableFetchProfile( "customer-with-orders" ); Customer customer = (Customer) session.get( Customer.class, customerId );
  • 24. Fetch Style Tips • Best to leave defaults in mappings • Define the style at the query level • Granular control
  • 25. nd 2 Level Entity Cache (2LC)
  • 26. 2LC • differs from the 1st level (Session) • SessionFactory level (JVM) • can scale horizontally on commodity hardware (clustered) • multiple providers • currently focus on Infinispan (JBoss) & EHCache (Terracotta)
  • 27. 2LC (cont'd) • disabled by default – can enable globally (not recommended) – enable for individual entities and collections – configurable at the Session level w/ CacheMode • cache all properties (default) or only non-lazy
  • 28. 2LC (cont'd) • Since JVM-level, concurrency is an issue • Concurrency strategies – Read only: simplest and optimal – Read-write • Supports updates • Should not use for serializable transaction isolation – Nonstrict-read-write • If updates are occasional and unlikely to collide • No strict transaction isolation – Transactional: • Fully transactional cache providers (ie, Infinispan and EHCache) • JTA required
  • 29. 2LC (cont'd) • DEMO
  • 30. Query Cache
  • 31. Query Cache • Caches query result sets • Repetitive queries with identical params • Different from an entity cache – Does not maintain entity state – Holds on to sets of identifiers • If used, best in conjunction with 2LC
  • 32. Query Cache (cont'd) • Disabled by default • Many applications do not gain anything • Queries invalidated upon relevant updates • Increases overhead by some: tracks when to invalidate based on commits • DEMO
  • 33. Cache Management
  • 34. Cache Management • Entities stored in Session cache when saved, updated, or retrieved • Session#flush syncs all cached with DB – Minimize usage • Manage memory: – Session#evict(entity) when no longer needed – Session#clear for all – Important for large dataset handling • Same for 2LC, but methods on SessionFactory#getCache
  • 35. Bytecode Enhancement
  • 36. Bytecode Enhancement • Not just for no-proxy, ToOne laziness • Reduced load on PersistenceContext – EntityEntry • Internally provides state & Session association • “Heavy” operation and typically a hotspot (multiple Maps) – ManagedEntity • Reduced memory and CPU loads • Entities maintain their own state with bytecode enhancement • (ManagedEntity)entity.$$_hibernate_getEntityEntry(); – 3 ways: • Entity implements ManagedEntity and maintains the association • Buildtime instrumentation (Ant and recently Gradle/Maven) • Runtime instrumentation
  • 37. Bytecode (cont'd) • dirtiness checking – Legacy: “dirtiness” determined by deep comparison of entity state – Enhancement: entity tracks its own dirtiness – Simply check the flag (no deep comparison) – Already mentioned… • Minimize amount of flushes to begin with • Minimize the # of entities in the cache -- evict when possible • Many additional bytecode improvements planned
  • 38. Hibernate Search
  • 39. Hibernate Search • Full-text search on the DB – Bad performance – CPU/IO overhead • Offload full-text queries to Hibernate Search engine – Fully indexed – Horizontally scalable • Based on Apache Lucene
  • 40. Hibernate Search (cont'd) • Annotate entities with @Indexed • Annotate properties with @Field – Index the text: index=Index.YES – “Analyze” the text: analyze=Analyze.YES • • • • Lucene analyzer Chunks sentences into words Lowercase all of them Exclude common words (“a”, “the”) • Combo of indexing and analysis == performant full-text searches!
  • 41. Misc. Tips
  • 42. Misc. Tips • Easy to overlook unintended fetches – Ex: Fully implemented toString with all associations (fetches everything simply for logging) • Use @Immutable when possible – Excludes entity from dirtiness checks – Other internal optimizations
  • 43. Misc. Tips (cont'd) • Use Bag or List for inverse collections, not Set – Collection#add & #addAll always return true (don't need to check for pre-existing values) – I.e., can add a value without fetching the collection Parent p = (Parent) session.load(Parent.class, id); Child c = new Child(); c.setParent(p); p.addChild(c); ... // does *not* fetch the collection p.getChildren().add(c);
  • 44. Misc. Tips (cont'd) • One-shot delete (non-inverse collections) – Scenario: 20 elements, need to delete 18 & add 3 – Default: Hibernate would delete the 18 one-byone, then add the 3 – Instead: • Discard the entire collection (deletes all elements) • Save a new collection with 5 elements • 1 bulk delete SQL and 5 inserts – Important concept for large amounts of data
  • 45. How to Help: hibernate.org /orm/contribute
  • 46. QUESTIONS? • • • • Q&A #hibernate or #hibernate-dev (brmeyer) @brettemeyer +brettmeyer