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Scaling Hibernate with Terracotta
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Scaling Hibernate with Terracotta


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Terracotta (an open source technology) provides a clustered, durable virtual heap. Terracotta's goal is to make Java apps scale with as little effort as possible. If you are using Hibernate, there …

Terracotta (an open source technology) provides a clustered, durable virtual heap. Terracotta's goal is to make Java apps scale with as little effort as possible. If you are using Hibernate, there are several patterns that can be used to leverage Terracotta and reduce the load on your database so your app can scale.

First, you can use the Terracotta clustered Hibernate cache. This is a high-performance clustered cache and allows you to avoid hitting the database on all nodes in your cluster. It's suitable, not just for read-only, but also for read-mostly and read-write use cases, which traditionally have not been viewed as good use cases for Hibernate second level cache.

Another high performance option is to disconnect your POJOs from their Hibernate session and manage them entirely in Terracotta shared heap instead. This is a great option for conversational data where the conversational data is not of long-term interest but must be persistent and highly-available. This pattern can significantly reduce your database load but does require more changes to your application than using second-level cache.

This talk will examine the basics of what Terracotta provides and examples of how you can scale your Hibernate application with both clustered second level cache and detached clustered state. Also, we'll take a look at Terracotta's Hibernate-specific monitoring tools.

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  • 1. Scaling Hibernate with Terracotta Alex Miller (@puredanger)
  • 2. Architecture and Scale
  • 3. Applications in Three Tiers Client Server Database
  • 4. How do we scale? Client Client Client Server Server Server Database
  • 5. But what about failover?
  • 6. Option 1: Servers share state Client Client Client Server Server Server Database
  • 7. How? • RMI • JMS • Custom (JGroups, etc)
  • 8. Option 2: Database (“stateless”) Client Client Client Server Server Server Database
  • 9. Stateless == “State in the database” • Database load • Serialization to and from the database • Network bandwidth • Object / relational mapping due to impedance mismatch
  • 10. Option 3: Terracotta! Client Client Client Server Server Server Terracotta Terracotta Terracotta Terracotta Terracotta Database Server Server Instance Instance
  • 11. Why is this better? • Hub and spoke vs peer to peer • Avoid object / relational translation • Avoid serialization • Reduce database overload • Programming model you already know • Focus on your app, not scalability and high availability
  • 12. What state should go in Terracotta? Appropriateness Memory Terracotta Database Data Lifetime
  • 13. How does Terracotta work?
  • 14. Core Concepts • Roots • Instrumented Classes • Locks • Integration Modules
  • 15. Clustered Heap App App app = new App();;
  • 16. Clustered Heap App EmployeeManager employeeMgr = new EmployeeManager(); Employee Manager Object lock Object lock = new Object(); Map<String,Employee> = new HashMap<String,Employee>(); HashMap employees
  • 17. Clustered Heap App Employee " U9 9 4 1 1 " M anag er Ob ject lock Employee.AC T IV E = = 1 Employee " Gandalf" HashM ap employees Employee employee = new Employee( “U99411”, “Gandalf”, Employee.ACTIVE);
  • 18. Clustered Heap App employeeMgr.addEmployee(employee); Employee "U99411" Manager Object lock Employee.ACT IVE == 1 "U99411" Employee "Gandalf" HashMap employees synchronized(lock) { employees.put(e.getId(), e); }
  • 19. Clustered Heap App Employee "U99411" Manager Object lock Employee.ACT IVE == 1 "U99411" Employee "Gandalf" HashMap employees "U23526" Employee "U23526" Employee.ACT IVE = 1 "U99411"
  • 20. Configuration • Roots • app.App.employeeMgr - a root of the clustered heap • Instrumented classes • app.* - instrument app classes at load time • Locks • employeeMgr.addEmployee() -> cluster synchronized blocks
  • 21. Integration Modules • Pre-built configuration • Integration with specific 3rd-party libraries • Hibernate • Spring • Ehcache • Quartz • Lucene • many more...
  • 22. Scaling Hibernate • Hibernate Second Level Cache • Clustered detached entities • Close session, detach entities • Cluster those entities in Terracotta • Merge with new session later
  • 23. Hibernate Caching Application Thread Application Thread Session Session 1st Level Cache Cache Cache Cache Concurrency Concurrency Concurrency Hibernate Strategy Strategy Strategy CacheProvider 2nd Level Cache Cache Cache Cache Region Region Region Database
  • 24. Entity and collection caches • Entity and collection cache regions • Mark a Hibernate entity or a collection in an entity as @Cacheable • Specify a cache concurrency strategy • ReadOnly, ReadWrite, NonstrictReadWrite, Transactional • Turn on second level caching in the Hibernate config
  • 25. Query Cache • Query cache regions • Mark HQL, Criteria, Query as cacheable • Store result set id values • Timestamp cache region - last update time for each entity type • Useful for caching natural key lookups (non-primary key) • ...but lots of hidden issues
  • 26. Terracotta Hibernate Second Level Cache • Easy integration and configuration • Supports entity, collection, and query cache regions • Supports read-only, read-write, and nonstrict-read-write cache concurrency strategies • Hibernate-specific tooling • High performance with cache coherency
  • 27. Enabling Second Level Cache • Mark your entities with a cache concurrency strategy • In hibernate.cfg.xml: <cache usage="read-write"/> • With annotations: @Cache(usage=CacheConcurrencyStrategy.READ_WRITE) • hibernate.cfg.xml • <property name="cache.use_second_level_cache">true</property> • <property name="cache.provider_class"> org.terracotta.hibernate.TerracottaHibernateCacheProvider</property>
  • 28. Enabling Second Level Cache • Define the tc-hibernate-cache.xml in your classpath <?xml version=”1.0” encoding=”UTF-8”?> <terracotta-hibernate-cache-configuration> <default-configuration> <time-to-idle-seconds>7200</time-to-idle-seconds> <time-to-live-seconds>7200</time-to-live-seconds> </default-configuration> <cache> <region-name>org.terracotta.authinator.domain.Account</region-name> <!-- as many region-names here as you want --> <configuration> <time-to-idle-seconds>600</time-to-idle-seconds> <time-to-live-seconds>600</time-to-live-seconds> </configuration> </cache> </terracotta-hibernate-cache-configuration> • Add the Terracotta Hibernate cache provider jar to your classpath • -cp terracotta-hibernate-cache-1.0.0.jar • Add the Terracotta Hibernate cache agent jar to your command line • -javaagent:terracotta-hibernate-agent-1.0.0.jar
  • 29. Tooling Demo
  • 30. Performance - Read-Only Comparison Throughput Latency 200K 100 Transactions per second 80 150K Avg Latency (ms) 60 100K 40 50K 20 0K 0 Database IMDG EhcacheTerracotta Database IMDG EhcacheTerracotta
  • 31. How else is Terracotta used? • Distributed cache • Clustered HTTP sessions • Batch processing • Grid • Messaging and events
  • 32. Who Uses It? • e-Commerce • Online gaming • Financial services • Travel & leisure • Social networking
  • 33. Thanks! • Terracotta Open Source JVM clustering: • • Apress: “The Definitive Guide to Terracotta” • by Ari Zilka, Alex Miller, Geert Bevin, Jonas Boner, Orion Letizi, Taylor Gautier • Alex Miller • @puredanger •