Data-Awareness and Low-Latency on the Enterprise GridGetting the Most out of Your Grid with Enterprise IMDG

Shay Hassidim

Deputy CTO

Oct 2007

Overall Presentation Goal

• Understand the Space Based Architecture model and its

4 verbs.

• Understand the Data contention challenge and the

latency challenge with Enterprise Grid based

applications.

• Understand why typical In-Memory-Data-Grid can’t solve

the above problems and why the Enterprise IMDG can.

GigaSpaces in a Nutshell• Founded in 2000

• Founder of The Israeli Association of Grid Technologies (IGT) – OGF affiliate.

• Provides infrastructure software for applications characterized by:

• High volume transaction processing

• Very Low latency requirements

• Real time analytics

• Product: eXtreme Application Platform – XAP. 6.0 released few months ago.

• Enterprise In Memory Data Grid (Caching)

• Application Service Grid

• Customer base – about 2000 deployments around the world.

• Financial Services

• Telecom

• Defense and Government

• Presence

– US: NY (HQ), San Francisco, Atlanta

– EMEA: UK, France, Germany, Israel (R&D)

– APAC: Japan, Singapore, Hong Kong

About myself – Shay Hassidim

• B.Sc. Electrical, Computer & Telecommunications engineer. Focus on Neural

networks & Artificial Intelligence , Ben-Gurion University , Graduated 1994

• Object and Multi-Dimensional DBMS Expert

• Extensive knowledge with Object Oriented & Distributed Systems

• Consultant for Telecom, Healthcare , Defense & Finance projects

• Technical Skills: MATLAB , C, C++, .Net , PowerBuilder , Visual Basic , Java ,

XML , CORBA , J2EE , ODMG , JDO , Hibernate, SQL , JMS , JMX, IDE , GUI , Jini

, ODBMS , RDBMS , JavaSpaces

• In the past:

– Sirius Technologies Israel - VMDB Applications & Tools team Leader

– Versant Corp US. - Tools Lead Architect , R&D

• Since 2003 - GigaSpaces VP Product Management (Based in Israel)

• Since 2007 – GigaSpaces Deputy CTO (Based in NY)

GigaSpaces – Technical overview

The Basics… Data Grid: Caching Topologies

Partitioned CacheReplicated Cache

Master / Local Cache

So. . .What is Space-Based Architecture?

• Utilizing a single logical/virtual resource to share:

– Data

– Logic

– Events• Services:

– Interact with each other through the space

– Can be co-located with data/events for faster results

– Are deployed and managed in an adaptive and fail-safe way

} Objects! Data Provisioning

Event Propagation

Logic Processing

8

Space Based SOA using 4 Simple Verbs

Write TakeRead Write Notify

Write + Read = IMDG (Caching)

Write + Notify = Messaging

Write + Take = Parallel Processing

Take

Write

Read

Take

Notify

IMDG Distributed In-Memory Query Support

• Enable aggregation of data

transparently

• Support SQL Query

semantics

• Continues query via

notifications

• Local view – client side

cache

Partitioned Clustered Space

Read

Space proxy

Parallel Query

Local View updated using Continues Query

Data virtualization– IMDG Accessed by all popular API and programming languages

JDBC

Clustered Space

Map/JCache

Space

Ap

plic

ati

on

s• Provides true data grid that

supports variety of

standard based data API

• API Becomes just a view

– Same data can be

accessed via multiple API

• Combine the benefits of

the relational model with

OO model

CPP/.Net

Integration with External Database – 2 basic models

•Write/Read Through and Write behind enables lazy load of data from DB to the cache and async persistency• Complete mirroring cache data into the DB• Support also for black box persistency into RDBMS and index file (light embedded ODBMS)•Sync/Async

Hibernate Cache plug-in provides 2nd level cache for hibernate based applications

Seamless Integration with External Data Sources

The Mirror service ensures Reliable synchronization with

minimal performance overhead

Mirror Service

Data is propagated seamlesslyfrom the IMDG to the external Data source and visa versa

Through the CacheStore.

load

loadload

store store store

External Data Source

Reliable Async Replication

13

Services can be Java, C++, .Net

Content-Based Routing

Shared state to enable stateful services

SBA – Real-time SOA for Stateful Services

Enterprise Data Grid unique features

FeatureBenefits

Extended and Standard Query based on SQL, and ability to connect to IMDG using standard JDBC connector.

- Makes the IMDG accessible to standard reporting tools.- Makes accessing the IMDG just like accessing a JDBC-compatible database, reducing the learning curve.

SQL-based continuous query support.Brings relevant data close to the local memory of the relevant application instance.

Central management, monitoring and control.Allows the entire IMDG to be controlled and viewed from an administrator’s console.

Mirror Service—transparent persistence of data from the entire IMDG to a legacy database or other data source.

Allows seamless integration with existing reporting and back-office systems.

Real-time event notification—application instances can selectively subscribe to specific events.

Provides capabilities usually provided by messaging systems, including slow-consumer support, FIFO, batching, pub/sub, content-based routing.

GigaSpaces solution for Enterprise Grid

What is the Enterprise Grid?

• Improve utilization of HW resources through:

– Multiple applications can share a pool of hardware resources.

– Resources are allocated to each application as needed.

– Applications can scale up very easily.

– The Grid provides parallelization for heavy computing jobs.

• How can I bring front office application to the

grid?

– The Latency challenge

Great, But…

• What about stateful applications?

– Data Contention challenge

The Data Contention Challenge

• Only stateless applications can scale up freely on the Grid.

• Any application that needs to:

a. Share state between more than one instance (service/process)

b. Store state using a central database

Could not scale easily!Could not scale easily!• This implies

– Partial analysis results checkpoints to enable recovery.

– Managing a workflow involving more than one process.

– Common data need to be shared between processes

The Latency Challenge

• Enterprise Grid designed for batch applications

– Each client request is submitted as a job.

– Hardware resources are allocated.

– Relevant software instances (service/process) are scheduled to run on the

resources and perform the work.

Impracticable with low-latency environments!Impracticable with low-latency environments!

• Why?

– An interactive application receives thousands of client requests per second, each

of which needs to be fulfilled within milliseconds.

– It is impossible to respond fast enough in a “job” approach.

– Throughput would be severely limited due to the need to schedule and launch

large numbers of application instances.

Three Stages Approach to the Solution

1. In Memory Data Grid (IMDG)

2. Data Aware Grid using SLA driven containers

3. Adding front office application to the Grid using

Declarative Space Based Architecture (SBA)

In Memory Data Grid (IMDG)

• Data stored in the memory of numerous physical

machines instead of, or alongside, a database.

– Eliminates I/O, network and CPU load.

– Partitions the data and moves it closer to the

application.

However, IMDG in an Enterprise distributed environment, However, IMDG in an Enterprise distributed environment,

is only a partial solution!is only a partial solution!

Stage 1

Stage 1

Data Aware Grid using SLA driven containers

Common wisdom holds that it is much easier to bring the business logic to the data than to bring the data to the business logic.

But… Not all IMDG support data & business logic co-locality! But… Not all IMDG support data & business logic co-locality!

This results:

• Unnecessary overhead caused by remote calls from business logic to IMDG instances.

• Data duplication, because business logic elements that use the same data are not necessarily concentrated around the relevant IMDG instance.

• And worst of all, data contention, because several business logic elements might access the same IMDG instance - leading to exactly the problem the IMDG was meant to solve!

Requirements for a Data-Aware Grid

• The Enterprise Grid must know which data is stored on which IMDG instances.

• There must be a way to guarantee data affinity - tasks must always be executed with the relevant data coupled to them.

Stage 2

Stage 2

Enterprise IMDG Deployment requirements

• Deploying a shared IMDG rather than specific IMDG per

application requires:

– Improved resource utilization

• With the IMDG as a shared resource, memory and CPUs available

to the IMDG instances can be shared between different applications,

depending on their current data loads. It is also much easier to scale

the IMDG to respond to changing data needs

– Lower total cost of ownership

• Installation, testing, configuration, maintenance and administration of

the IMDG is performed centrally for all the applications on the Grid.

Stage 2

Stage 2

Enterprise IMDG requirements for grid environments

• Sensitivity to Demand for Data vs. Available Resources

– Free (Memory) resources when there is no need for them

• Multi-Tenancy

• Continuous High-Availability

– Hot fail-over

– Versioning—it should be possible to upgrade or update the IMDG instances without affecting the

data or interrupting access.

– Configuration changes—it should be possible to change configuration without affecting availability

of the IMDG instances.

– Schema evolution—changing the data structure (i.e. adding or modifying classes) should not affect

the existing data and should not require downtime.

• Isolation (Groups, instances, Data)

• Content-Based Security

• Explicit Control over IMDG Instance Locations (manual relocation while the system is

running)

• Integration with Existing Systems

Stage 2

Stage 2

Strategies for adding data awareness to the grid

ScenarioMethod of Providing Data Awareness

IMDG instances deployed directly by Enterprise Grid (without SLA-Driven Containers).

Integration using affinity keys—the Enterprise Grid and users submitting tasks share special keys that identify the data relevant to each task. In this way the Enterprise Grid can execute tasks on the same machine as the relevant data.

SLA-Driven Containers are launched by Enterprise Grid (each container launches relevant IMDG instances).

Provides data awareness implicitly—data-intensive procedures can run in the SLA-Driven Container, together (co-located) with the IMDG instances. Because the container itself is data aware, data affinity can be guaranteed, without making the Enterprise Grid itself data aware.

Stage 2

Stage 2

Stage 3

Stage 3

Adding front-office to the grid using Declarative SBA

• All services are collocated on the same machine

• Transparent data affinity via content based routing (i.e. hash based load-balancing)

• Sharing can be done in local memory => the lowest possible latency.

Stage 3

Stage 3

Processing unit

27

Declarative SBA (cont.)

• So what it this “processing unit”?

– A mini-application which can perform the

entire business process.

– Accept a user request, perform all steps of

the transaction on its own, and provide a

result.

– Removes the need for sharing of state and

partial results between different

components of the application running on

different physical machines.

Stage 3

Stage 3

• Provides built-in support for deployment of Spring based

applications

• Virtualize the network and physical resources from the

application

• Handles Fail Over, Scaling and Relocation policies using SLA

based definitions.

• Provides distributed dependency injection to handle partial

failure and deployment dependency.

• Provides single point of access for monitoring and management

SLA Driven Application Service Container Stage 3

Stage 3

SLA:• Failover policy• Scaling policy• Ststem requirements• Space cluster topology

PU Services beans definition

SLA Driven Deployment Stage 3

Stage 3

Fail-OverFailure

Continuous High Availability Stage 3

Stage 3

VM 1 ,2GGSCGSC

VM 3 , 2GGSCGSC

Dynamic Partitioning = Dynamic Capacity Growth

VM 2 ,2GGSCGSC

Max Capacity=2GMax Capacity=4GMax Capacity=6G

E FPartition 1Partition 1

A BPartition 2Partition 2

C D

Partition 3Partition 3

In some point VM 1 free memory is below 20 % - it about the time to increase the capacity – lets move Partitions 1 to another GSC and

recover the data from the running backup!

Later .. Partition 2 needs to move… After the move ,

data is recovered from the backup

VM 5 , 4GGSCGSCVM 4 ,4GGSCGSCA B

Partition 2Partition 2

E FPartition 1Partition 1

C D

Partition 3Partition 3

P - PrimaryP - Primary

B - BackupB - Backup

PP

PP

PP

BB

BB BB

A closer look at OpenSpaces and Declarative

SBA Development

• Step 1:• Implement POJO domain model

• Step 2:• Implement the POJO Services

• Step 3:

• Wire the services through spring

• Step 4:

• Packaging

• Deploy to Grid (Scale-Out)

Declarative Spring-SBA – How it works.

@SpaceClasspublic class Data {

@SpaceId(autoGenerate = true) public String getId() { return id; }

@SpaceRouting public Long getType() { return type; }

public void setProcessed(boolean processed) { this.processed = processed; }

}

SpaceClass indicate that this is a SpaceEntry – SpaceClass includes classlevel attributes such as FIFO,Persistent…

SpaceId used to define the key for that entry.

SpaceRouting used to set the data affinity i.e. define the partition where this entry will be routed to.

The POJO Based Data Domain Model

public class DataProcessor implements IDataProcessor {

@SpaceDataEvent public Data processData(Data data) { data.setProcessed(true); data.setData("PROCESSED : " + data.getRawData()); // reset the id as we use auto generate true data.setId(null); System.out.println(" ------ PROCESSED : " + data); return data; }}

SpaceDataEvent annotation marks the processData method as the one that need to be called when an event is triggered

Order Processor Service Bean

<bean id="dataProcessor“ class="com.gigaspaces.pu.example1.processor.DataProcessor" />

<os-events:polling-container id="dataProcessorPollingEventContainer" giga-

space="gigaSpace">

<os-events:tx-support tx-manager="transactionManager"/>

<os-core:template>

<bean class="org.openspaces.example.data.common.Data">

<property name="processed" value="false"/>

</bean>

</os-core:template>

<os-events:listener> <os-events:annotation-adapter>

<os-events:delegate ref="dataProcessor"/>

</os-events:annotation-adapter> </os-events:listener>

</os-events:polling-container>

The PollingEventContainer will implicitly call take with template defined in the template property and invoke the method marked with @SpaceDataEevent on dataProcessor bean.

Wiring Order Processor Service Bean through Spring

Write

Space BUS

OrderProcessor

Service Bean

Polling EventContainer

Notify EventContainer

ProcessedOrders

RoutingService Bean

Take Write Notify

Data Loader

Space Proxy

Direct Data Loader Client

Order Proxy

Order ProcessorClient

SpaceServiceProxyFactoryBean

Invoke

Write

SpaceInvokeData OrderProcessorDelegator

Space BUS

OrderProcessor

Service Bean

SpaceServiceExporter

TakeSpaceInvokeData

Writeresult

ProcesData

Space Based Remoting

Order Proxy

Order ProcessorClient

SpaceServiceProxyFactoryBean

Invoke

WriteSpaceInvokeData

OrderProcessorDelegator

Space BUS

OrderProcessor

Service Bean

SpaceServiceExporter

TakeSpaceInvokeData

Writeresult

ProcessData

Space Based Remoting – Inherent Scalability/Reliability

Looking into the Future… Many Enhancements!

• Enhance Performance

– Built in infiniband support – Voltaire , Cisco

• Enhance Database integration

– Enhance the Space Mirror support (async persistency)

• Enhance partnership and integration with grid vendors

– DataSynapse , Platform Computing , Sun Grid Engine, Microsoft

Compute Cluster Server

• Enhance CPP and .Net support

– Performance optimization – first goal – same as java

– Support for complex object mapping

Conclusions and Summary

• Typical IMDG won’t help you

– You need Data Aware Enterprise IMDG to solve the data

contention and latency challenges.

– Data affinity need its twin: data & business locality

• The Enterprise IMDG co-locates the data with the

business logic

– Using self-sufficient autonomic processing unit deployed into

SLA based container that scales via the Enterprise Grid

• The Enterprise IMDG bring the Front-office into the grid

– Makes the grid a utility model for wide spectrum of applications

across the organization

Case Studies

A Dynamically Scalable Architecture for Data IntensiveTrading Analysis Applications

• Most financial organizations today use

Excel™ or Reporting Databases as the

main trading analysis tools. These are very

difficult to scale.

• The solution is to create a shared In-

Memory Data Grid (IMDG) which stores the

trading data in a shared pool of machines.

Common data calculation and analysis run

on that pool as well, leveraging the available

memory and CPU resources.

• JavaSpaces is a powerful model for

distributed persistence. GigaSpaces is a

JavaSpaces vendor providing Enterprise

features.

• Spring hides the details of the JavaSpaces

model, allows effort to be focused on

requirements rather than frameworks.

Using shared data grid for all users

Running analytics close to the data to improve performance and leverage the available resources

Reconciliation Calculation

Questions?

Thank You!shay@gigaspaces.com