CLUSTERING JAVA APPLICATIONS

WITH TERRACOTTA AND HAZELCAST

Boris Trofimov @ Sigma Ukraine

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

Application clusterization Horizontal scaling, vertical scaling, MapReduce, MPI,...

Data Claterization Distributed Data Exchanging Distributed Coordination Distributed Heap

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KINDS OF CLASTERIZATION

SOMETIMES IT'S HARD TO CHOOSE...

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Maintained by private company

Supports Quartz and Ehcache

Terracota modes

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ABOUT TERRACOTTA

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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

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

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

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

Start Terracotta Server (local or cluster)

Launch two Java application instances through terracotta script launcher.

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EXAMPLE — LAUNCH

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

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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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

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

Partitioning with replicas

Scalable Peer-toPeer architecture

Availability over Consistency

Failover cluster

Native Client support

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CLUSTERIZATION

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

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

Thank you for your attention!