fra enkel j2se til grid computing med gigaspaces xap

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NEO

DB DB DB

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NEO

DB DB

In

memory

data grid

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Node 1 Node 2 Node 3

In memory data grid aka «Space’et»

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DB

XAP PREMIUM –END-TO-END SCALING

End-to-end-scalable execution environment with elastic deployment to meet extreme throughput requirements

Any environment, anytime, anywhere – traditional data center, public/private cloud, or hybrid

Any platform Any language Any framework Any API

Optimize IT resource utilization

Production-grade control and visibility

16 ® Copyright 2011 Gigaspaces Ltd. All Rights Reserved

IMDG BASIC OPERATIONS

Application

Space

Take

Application

Space

Read

Application

Space

WriteMultipleApplication

Space

Write

Application

Space

ReadMultiple

Application

Space

TakeMultiple

Application

Space

ExecuteApplication

Space

Notify

http://www.gigaspaces.com/docs/JavaDoc8.0/org/openspaces/core/GigaSpace.html

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Back to key scenarios

Feeder

DATA PARTITIONING PRINCIPLES

Virtual Machine Virtual MachineVirtual Machine

Virtual MachineVirtual Machine Virtual MachineVirtual Machine

Replication

Primary 1Backup 1

Replication

Backup 2Primary 2

Partitioned Data with Backup Per Partition

Feeder

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Partitioned Data

Back to key scenarios

DATA SCALING ARCHITECTURES

System of Records (SoR) with Write Behind

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System of Records (SoR) with Local Caching

Back to key scenarios

DATA CACHING ARCHITECTURES

Side Cache with Local Caching

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Side Cache

Back to key scenarios

EASY INTEGRATION

DATA

Native:

Highly optimized, POJO driven API

which exposes all the unique

capabilities of the platform

JPA:

Support data grid access using the

standard JPA API for seamlessly

scaling your JEE data access layer

Key-Value:

Simple and intuitive Map-based

interface for simple caching

scenarios

Document:

Completely schema-free data API

that supports upgrading the

application’s data model on the fly

.Net:

Native C# interface that enables any

.NET application to access the data

grid

C++:

Native C++ interface that allows any

.NET application to access the data

grid

Cross Language Access:

Java, C# and C++ API support for

heterogeneous environments, with

seamless interoperability among them

all

Back to scenarios

PROCESSING

Processing

Master-Worker Support:

Intuitive and highly scalable master-

worker implementation for

distributing computation-intensive

tasks

Distributed Code Execution:

Dynamic code shipment and

map/reduce- like execution across the

grid for optimized processing and data

access

Back to scenarios

PROCESSING

Processing

Master-Worker Support:

Intuitive and highly scalable master-

worker implementation for

distributing computation-intensive

tasks

Distributed Code Execution:

Dynamic code shipment and

map/reduce- like execution across the

grid for optimized processing and data

access

public interface SimpleService {

String say(String message);

Future<String> asyncSay(String message);

}

Master-Worker Support:

Intuitive and highly scalable master-

worker implementation for

distributing computation-intensive

tasks

Distributed Code Execution:

Dynamic code shipment and

map/reduce- like execution across the

grid for optimized processing and data

access

PROCESSING

Processing

public class MyTask implements Task<Integer> { private int value; public MyTask(int value) { this.value = value; } public Integer execute() throws Exception { return value; } }

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