Presentation prepared for JavaBreese in Odessa, Ukraine (18 May 2013)

1. CLUSTERING JAVA APPLICATIONS
WITH TERRACOTTA AND HAZELCAST
Boris Trofimov @ Sigma Ukraine

2. Why clasterization
Terracotta
Hazelcast
Summary
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AGENDA

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WHY CLASTERIZATION?
Moore's law is broken
Era of Big Data

4. Application clusterization
 Horizontal scaling, vertical scaling, MapReduce, MPI,...
Data Claterization
 Distributed Data Exchanging
 Distributed Coordination
 Distributed Heap
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KINDS OF CLASTERIZATION

5. SOMETIMES IT'S HARD TO CHOOSE...
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7. Maintained by private company
Supports Quartz and Ehcache
Terracota modes
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ABOUT TERRACOTTA

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9. BigMemory uses Ehcache as its main programming interface
Off-Heap without Garbage Collection Pauses
3 major bricks: CacheManager, Cache and Element
Serializable data classes
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DISTRIBUTED CACHE MODE

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EXAMPLE — CONFIGURATION

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EXAMPLE CRUD

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EXAMPLE — IN-MEMORY SEARCH

13. BigMemory GO vs BigMemory MAX
Might use Server Array
Respecting CA (CAP theorem)
Linear grouth
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CLUSTERIZATION

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HOW IT WORKS
Specify root objects
Launch terracotta server
Launch app instances

16. Terracotta uses aspect-oriented technologies to adapt the
application at class load time.
Terracotta does not use serialization.
DSO uses object identity, instrumented classes (byte-code
instrumentation), object-graph roots, and cluster-wide locks to
maintain data coherence
Distributed GC
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DSO INTERNALS

17. All Standard non-value types like maps, lists
POJO Objects
Synchronization Java objects like locks, semaphores
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OBJECTS TO SYNC

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EXAMPLE — DEFINE CONFIGURATION

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EXAMPLE — CODE

20. Start Terracotta Server (local or cluster)
Launch two Java application instances through terracotta
script launcher.
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EXAMPLE — LAUNCH

21. Sharing ordinary objects
Legacy clastering
Distributed Cache
Hibernate 2nd Cache (through DSO)
Web Session clustering (through DSO)
Clastering read/write data with fast update
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USE CASES

22. Internal ACID Transactions
Only data changes are broadcast
Local caches
DSO- POJO transparent clasterizations
Monitoring Control tools
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BENEFITS

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24. InMemory Distributed Cache
Open Source solution
Supports own special distributed data structures
Supports locks and events
Might be slcaled out
Easy to use and integrate
All objects must be serializable
Supports standard collection interfaces
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WHAT IS HAZELCAST

25. Distributed Queue
Distributed Topic
Distributed Map
Distributed MultiMap
Distributed Set
Distributed List
Distributed Locks
Distribured Event (Listeners)
Distributed Topic (Hello JMS)
Distributed Semaphore
Distributed Executor Service
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SUPPORTED DATA

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EXAMPLE — DISTRIBUTED MAP

27. Partitioning with replicas
Scalable Peer-toPeer architecture
Availability over Consistency
Failover cluster
Native Client support
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CLUSTERIZATION

28. User ASID Transactions over several operations
It is free, unlimited community license *
Persistence
Search enhanced by user-defined indexes
Spring support (beans, cachable)
Simple interfaces in comparison to Terracotta BigMemory
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BENEFITS

29. Share data across cluster
Send/receive messages
Distributed Coordination (locks)
Hibernate 2nd Cache
Http Session Clustering with HazelcastWM (based on Servlet
specification)
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USE CASES

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SUMMARY

31. Thank you for your attention!