Innovations in Grid Computing with Oracle Coherence

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Oracle Coherence Overview

Mike Lehmann

Oracle Coherence Product Management

[email protected]

The following is intended to outline general product

use and direction. It is intended for information

purposes only, and may not be incorporated into any

contract. It is not a commitment to deliver any

material, code, or functionality, and should not be

relied upon in making purchasing decisions.

The development, release, and timing of any features

or functionality described for Oracle’s products

remains at the sole discretion of Oracle.

Presentation Objectives

To Understand…

1) How Coherence improves performance, enables

linear scalability, and provides availability for

shared services

2) How Coherence leverages its distributed

architecture to enable high performance analytics

and processing on application domain objects

3) Coherence’s roadmap and strategic direction

Oracle Fusion MiddlewareComplete, Open, Integrated, Best-in-Class

Development

Tools

User Engagement

SocialWeb Mobile

Enterprise

Management

Identity

Management

Content Management

Business Intelligence

Business Process

Management

Service Integration

Data Integration

Cloud Application

Foundation

Oracle Coherence Value Path

Write Behind Caching

C++/.NET Interop

Invocable Map

TopLink Grid

Query MapCache Map

Coherence*Web

Coherence*Web

Observable Map

Lowered Costs Increased Quality of Service

Oracle Coherence Data Grid

Reliability Resource Utilization

Linear ScalabilityPerformance

Interoperability

Availability

Caching

Be

ne

fits

Fe

atu

res

Analytics Transactions Events

Enterprise Application Scalability

Challenges

Service Clients

• Browsers, supplier and partner clients, application

clients, mobile apps, and other service consumers.

Service Implementation

• Custom applications, BPM processes, service bus

endpoints, UI services, and other service providers.

Ease of Scaling

Shared Services

• RDBMS, cloud services, supplier and partner

services, mainframe applications, and so on.

• Grows and scales

naturally

• Increasing load

• Computationally

intensive work

• Highly dependent on

shared services

• Complex and

expensive to scale

• High latency SPOB

and SPOF

Traditional Clustering

• Dependent on shared service

latency

• Increase in size of cluster

leads to increase in load on

shared serviceService

Node 1

Shared Service

Service

Node 2Service

Node N

Cached Objects

Cached Objects

Cached Objects

Service Clients

• Inconsistent view of data

across cluster nodes

• No possibility for in-memory

analytics

• Limited in-memory cache

due to

• Capacity implications

• Java garbage collection

Enters the Coherence Data Grid

• Clustered Caching

• Provides a consistent view

of data across cluster

• Partitioned Caching

• Increase in size of cluster

adds capacity but does not

decrease performance

• Distributed Data

Processing

• Enables a distributed

platform for high

performance in-memory

analytics

Service

Node 1

Ease of Scaling

Service Clients

Shared Services

In-memory Data Grid

Service

Node 2

Service

Node N

How to Integrate with Coherence

Two Options

1. Custom integration through Coherence API

• For Java, C++, and .NET applications

2. Through Existing ‘Switch-On’ Integrations

• Oracle TopLink Grid – For JPA object relational

data offload

• Oracle Service Bus – For service results

caching

• Oracle Coherence*Web – For HTTP session

caching

Coherence Basic Inteface

Custom Integration

• Application uses

Coherence API to

store/access its data

• Application cache

properties are configured

externally through XML

file

• Basic cache access

through NamedCache

Map interface

NamedCache neCache =

CacheFactory

.getCache("NetworkElement");

…

NetworkElement ne = new

NetworkElement(“ID-321”, “OC-

196”, “Nortel Networks);

neCache.put(“ID-321”, ne);

…

NetworkElement ne =

neCache.get(“ID-321”);

Replicated Caching Scheme

JVMJVM

Coherence Node

Application Logic

Business Logic

Application Logic

Business Logic

Coherence Node

Domain Objects

Ap

plic

atio

n C

lust

er

Co

her

ence

Clu

ster Object

AObject

B

Object D

Object C

Object A

Object B

Object D

Object C

Replicated Cache

Service

Distributed Caching Scheme

JVMJVM

Coherence Node

Application Logic

Business Logic

Application Logic

Business Logic

Coherence Node

Domain Objects

Ap

plic

atio

n C

lust

er

Co

her

ence

Clu

ster

Primary

Object A

Object B

Primary

Object D

Object CDistributed Cache

Service

Near-Caching Topology

JVMJVM

Coherence Node Coherence Node

Domain ObjectsAp

plic

atio

n C

lust

er

Co

her

ence

Clu

ster

Primary

Object A

Object B

Primary

Object D

Object CDistributed Cache

Service

JVM

Application Logic

Business Logic

Local Cache

JVM

Application Logic

Business Logic

Local Cache

Heterogeneous Topology

JVMJVM

Coherence Node Coherence Node

Domain ObjectsAp

plic

atio

n C

lust

er

Co

her

ence

Clu

ster

Primary

Object A

Object B

Primary

Object D

Object CDistributed Cache

Service

C++ Application

Application Logic

Business Logic

Local Cache

.NET Application

Application Logic

Business Logic

Local Cache

Reliability

Partitioned Fault Tolerance

• Automatically and

transparently manages

the fault tolerance of

your data.

• Backups are

guaranteed to be on a

separate physical

machine as the primary.

• Backup responsibilities

for one node’s data is

shared amongst the

other nodes in the grid.

Write Behind

• All data writes occur through cache

• Updates to the cache are written asynchronously to

the data source

Coherence Elastic Data | In-Memory Cache

Seamless management of data

across memory and disk-based

devices, including RAM, Solid

State Disk (SSD), and Storage

Area Networks (SAN)

Tuned journaling algorithms

enabling near memory speed

access to data regardless of

storage medium

4x increase in data capacity

Very high performance, low latency

Massively concurrent reads and

writes (to external storage)

DIS

K

FLAS

H

RAM

Coherence

Preliminary Data - Elastic Data on ExaLogic

Existing AssetsKey

Low-Latency Infrastructure

Data

IntegrationData Grid

Activity

Monitoring

High

Availability

Event

Processing

In Memory

Trading

Pricing

Risk

Order

Management

Modelling

Existing Systems

TradingProfit & Loss

Risk Management

Management &

Compliance

Modelling &

Development

Compliance

Profit & Loss

Risk

Management

Business Insight

Multi-Channel Management

Shared Dynamic Infrastructure

Trading & Risk PlatformExample of conceptual architecture

New Components

En

terp

rise

Ma

na

ge

me

nt

Contin

uous M

onito

ring

SLA

Managem

ent

Perfo

rmance T

unin

g

Market Data

Positions Trades Instruments

OrdersModels

High Performance Persistent Data

Market Reference Data Instruments PositionsValuation Models Historic Market Data

Market Feeds

News Feeds

New Interface

Trading Desk Portals

Algorithms

Real-time Alerts

Content

Oracle Confidential and Proprietary

Presentation Objectives

To Understand…

1) How Coherence improves performance, enables

linear scalability, and provides availability for

shared services

2) How Coherence leverages its distributed

architecture to enable high performance analytics

and processing on application domain objects

3) Coherence’s roadmap and strategic direction

Data Processing

Parallel Query

• Programmatic query mechanism

• Queries performed in parallel across the grid

• Standard indexes provided out-of-the-box and supports implementing

your own custom indexes

Data Processing

Continuous Query Cache

• Automatically, transparently and dynamically maintains a view locally

based on a specific criteria (i.e. Filter)

• Same API as all other Coherence caches

Data Processing

Invocable Map

• The inverse of caching

• Sends the processing (e.g. EntryProcessors) to where the data is in the

grid

• Standard EntryProcessors provided Out-of-the-box

• Once and only once guarantees

• Processing is automatically fault-tolerant

• Processing can be:

• Targeted to a specific key

• Targeted to a collection of keys

• Targeted to any object that matches a specific criteria (i.e. Filter)

Data Processing

Invocable Map

Data Processing

Events

• Java Bean Model

• Key-Based

• Filter-Based

Presentation Objectives

To Understand…

1) How Coherence improves performance, enables

linear scalability, and provides availability for

shared services

2) How Coherence leverages its distributed

architecture to enable high performance analytics

and processing on application domain objects

3) Coherence’s roadmap and strategic direction

Coherence Roadmap

Theme Features Benefit

Ease Of Use Cache Configuration GUI and

tooling

Eclipse user-friendly configuration

Shared

Infrastructure

Coherence Container Increase manageability and HA

Customizable partitioning Finer grained control of

partitioning

REST API Extended client language

integration

Multi-Datacenter Replication Push replication feature full

support

RASP Query “Explain Plan” Better query visibility

Delta backups Increase write performance

Innovation Exabus: Native IB support Dramatically Lower Latency

Continuous Aggregation Real-Time Analytics

Oracle Confidential – Do Not Distribute

How to Learn More

• Attend the Oracle University (OU) course

• You can register in this course by using this link

• Read Coherence 3.5 book

• Provides a great introduction to Coherence

• Attend the Coherence SIG Meetings

• Available in San Francisco, NYC, London, and Toronto

• Use OTN Coherence Forum

• Follow-ups with Coherence Architects/PMs with deep

experience in FS

29

Q&A

Coherence Fusion Middleware Integrations

at a Glance

Enterprise Applications

Oracle

Service

Bus

WebLogic

Server

Oracle RTD

Oracle

Golden

Gate

Database

Ora

cle

EM

Cohere

nce

Managem

ent

Pack

GlassFish

Coherence*Web

for HTTP

Session Offload

Service Result Caching for

Improved Service Performance

Golden Gate Coherence Adapter for

Change Data Capture

TopLink Grid for

Database Access

Performance

Improvements

Coherence to

Improve Quality

of RTD ResultsEnd-To-End

Management

31Oracle Confidential – Do Not Distribute

Oracle Integrations By Value

Meta-data Services (MDS)

Real-time Decision

SOA Infrastructure

Oracle ADFOracle GlassFish

Oracle Access Manager

Oracle Data Integration

TopLink

WebLogic Portal

Oracle Service Bus

Service Delivery Platform

WebLogic Server

BI Publisher

Oracle PeopleSoft

WebCenter

Oracle BEAM

ATG

Oracle CEP

Oracle Entitlement Services

Oracle Secure Token Service

Oracle IGBU

Oracle CGBU

ATG Hosted Services

Data CacheSimple Clustering

Data GridAdvanced Capabilities

Valu

eOracle Identity Federation

Oracle Web Services Manager

SOA Technology Adapters

SOA Business Rules

SOA Human Workflow

SOA BPEL

SOA Coherence Adapter

Oracle Golden Gate