innovations in grid computing with oracle coherence

1 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Insert Information Protection Policy Classification from Slide 8

Innovations in Grid Computing with Oracle Coherence Craig Blitz Oracle Coherence Product Management


Thefollowingisintendedtooutlinegeneralproductuseanddirec4on.Itisintendedforinforma4onpurposesonly,andmaynotbeincorporatedintoanycontract.Itisnotacommitmenttodeliveranymaterial,code,orfunc4onality,andshouldnotberelieduponinmakingpurchasingdecisions.

Thedevelopment,release,and4mingofanyfeaturesorfunc4onalitydescribedforOracle’sproductsremainsatthesolediscre4onofOracle.


Presentation Objectives To Understand…

1)WhatisdrivingtheneedforDistributedCachingandIn‐MemoryDataGridproducts?

2) HowCoherenceimprovesperformance,enableslinearscalability,andprovidesavailabilityforsharedservices

3)Coherence’sStrategicDirec8onandLatestFeatures


Internet Scale

Problem:Howtodealwiththefloodofcustomerdemandforservicesthatoccurswhenyouexposeyourinfrastructuretoalarge,poten4allyunboundednetwork.

‐Or,statedmoreposi4vely‐

Opportunity:Howtobuildinfrastructurethatallowsyoutoscalebusinessandimprovemarginsbyexposingyourservicestoalarge,poten4allyunboundednetwork.


The Internet Scale Problem is Multi-Faceted

•  Virtually unlimited number of customers •  Services exposed to partners’ overuse • Customers’ experience relies on partners’ services • Global and Real-time


Scaling Infrastructure to Meet the Problem

•  Applications must act on data in real-time – Access, query, and aggregate the data –  Trigger business actions based on changes to data

• Must scale predictably and cost-effectively –  Failure to do so results in loss of revenue and cost overruns – No time to re-architect in Internet time

• Need a new paradigm in data management


Oracle Coherence

•  Elastic caching is “the secret sauce for internet scale” •  In-Memory Data Grid pioneer since 2001 •  Gartner’s Leader’s Quadrant for Enterprise Application Servers,

2009


More. Faster. Cheaper.

• Scaletomoreusersandmoredata

• Scalepredictablyandcost‐effec4vely

• Actondatainreal‐4me

• Reducespendingonbackendsystems GetontheGrid


Oracle Coherence

•  Coherence: in-memory data grid –  Distributed caching to applications –  Scalable extreme transaction

processing –  Real-time eventing, query, and map/

reduce aggregations –  Abstraction from back-end data

sources –  High-availability to applications

EndUsers

Applica4onServers

Mainframes,Databases,etc.

Applica4onObjects


Oracle Fusion Middleware Complete, Open, Integrated, Best-in-Class

DevelopmentTools

UserEngagement

SocialWeb Mobile

EnterpriseManagement

Iden4tyManagement

ContentManagement

BusinessIntelligence

BusinessProcessManagement

ServiceIntegra4on

DataIntegra4on

CloudApplica4onFounda4on


Enterprise Application Scalability Challenges

ServiceClientsBrowsers,supplierandpartnerclients,applica4onclients,mobileapps,andotherserviceconsumers.

ServiceImplementa8onCustomapplica4ons,BPMprocesses,servicebusendpoints,UIservices,andotherserviceproviders.

EaseofScaling

SharedServicesRDBMS,cloudservices,supplierandpartnerservices,mainframeapplica4ons,andsoon.

•  Growsandscalesnaturally

•  Increasingload•  Computa4onallyintensivework

•  Highlydependentonsharedservices

•  Complexandexpensivetoscale

•  Highlatency,SPOB,andSPOF


Traditional Clustering

•  Dependent on shared service latency

•  Increase in size of cluster leads to increase in load on shared service

ServiceNode1

SharedService

ServiceNode2

ServiceNodeN

CachedObjects

CachedObjects

CachedObjects

ServiceClients

•  Inconsistent view of data across cluster nodes

•  No possibility for in-memory analytics

•  Limited in-memory cache due to •  Capacity implications •  Java garbage collection


Enter the Coherence Data Grid

•  Clustered Caching – 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 scalable platform

for high performance in-memory analytics

ServiceNode1

EaseofScaling

ServiceClients

SharedServices

In‐memoryDataGrid

ServiceNode2

ServiceNodeN


How to Integrate with Coherence Two Options

Custom integration through Coherence API

- or –

Through Existing ‘Switch-On’ OOTB Integrations

Oracle TopLink Grid: JPA object-relational data Oracle Coherence*Web: HTTP sessions Other Oracle Products that integrate Coherence


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”);


Data Management Partitioned Caching

•  Extreme Scalability: Automatically, dynamically and transparently partitions the data set across the members of the grid.

•  Pros: –  Linear scalability of data capacity –  Processing power scales with data

capacity. –  Fixed cost per data access

•  Cons: –  Cost Per Access: High percentage

chance that each data access will go across the wire.

•  Primary Use: •  Large in-memory storage environments •  Parallel processing environments


Data Management Partitioned Fault Tolerance

• Automatically, dynamically 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.


Data Management Near Caching

•  Extreme Scalability and Extreme Performance: The best of both worlds between the Replicated and Partitioned topologies. Most recently/frequently used data is stored locally.

•  Pros: –  All of the same Pros as the Partitioned

topology plus… –  High percentage chance data is local to

request. •  Cons:

–  Cost Per Update: There is a cost associated with each update to a piece of data that is stored locally on other nodes.

•  Primary Use: –  Large in-memory storage environments

with likelihood of repetitive data access.


Data Source Integration Read Through / Write Through

• Read Through – All data reads occur through cache –  If there is a cache miss, the cache will load the data from the data

source automatically

• Write Through – All data writes occur through cache – Updates to the cache are written synchronously to the data

source


Data Source Integration Write Behind

•  All data writes occur through cache • Updates to the cache are written asynchronously to the

data source


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 to where the data is in the grid

–  Once and only once guarantees –  Processing is automatically fault-tolerant

•  Processing can be: • Targeted to a specific key or collection of keys • Targeted to any object that matches a specific criteria (i.e. Filter)


Data Processing Events

•  Build Complex Event-Driven Apps •  Java Bean Model •  Key-Based •  Filter-Based





3)Coherence’sStrategicDirec8onandLatestFeatures


Release Overview

• Oracle Coherence 3.7.1 – Released September 23 – Strategic investment in:

• Exalogic Innovation • Ease of Use •  Integration Points

– Significant hardening

© 2011 Oracle Corporation

28 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Insert Information Protection Policy Classification from Slide 8 © 2011 Oracle Corporation

"OracleCoherencehasbeenamarketleaderhelpingcustomersscaleapplica4onstomeetthedemandsofinternet‐scalecompu4ngforoveradecadenow.Coherence3.7.1con4nuestoreducecomplexity,easeintegra4on,andaccelerate4metomarketofscalablesolu4ons...Atthesame4me,whenrunningonanOracleExalogicengineeredsystem,Coherence3.7.1leveragesOracle'sExabustechnologytoachievethelowestlatenciesandhighestthroughput.”

CameronPurdyVPDevelopment,Oracle


Elastic Data Virtual Memory for Your Data

•  Revolutionizes the scale of data grids •  Use block storage for cached data

–  Optimized for flash –  Works well with NAS, Disk, etc.

•  Simplifies capacity planning and deployment –  Configure amount of on-heap data to store –  Overflow data written to block storage –  Data stored in buffers until flushed to journal –  Reduces chances of Out-Of-Memory errors

CacheServer

1

Heap

Flash

CacheServer

2

Heap

Flash

CacheServer

3

Heap

Flash


REST Support

•  REST API – cache creation – data access & modification – parallel aggregation/

processing/querying •  Easy Integration

–  iOS, Ruby, PHP, Python… – Via the HTTP protocol

• Compliments C++/.NET/Java Support



Coherence API Types

MapAPIReal‐TimeClientAPI

QueryFilterAPI

Aggrega4on(Map‐Reduce)

API

GridProcessingAPI

ScalableDistributedCachetoScalable,Event‐DrivenReal‐TimeApplica4ons

CoherenceCluster

CoherenceCluster

WANReplica4on

PerMemberCacheStore/LoaderAPI

DataSource

Java/C++/.NET

REST (Python, PHP,…)


Query Explain Plan

•  Evaluate query cost and index effectiveness

•  Quicker time to market of optimized Coherence solutions

•  Explain Plan •  Provides the estimated cost of

evaluating a filter as part of a query operation

•  Trace •  Performs the associated query •  Provides the actual cost of

evaluating a filter as part of a query operation.



Live Objects New Coherence Incubator Project

•  Building blocks for an event-driven finite-state machine –  …that is distributed –  …and scalable –  …and resistant to machine failure

•  Architectural possibilities: – Stated Event Driven Processing – Ripple Effect


Coherence on Exalogic Engineered System Optimized Scalability and Performance in a Box

•  Coherence optimized for Exabus •  Pre-configured network/compute nodes •  Elastic Data: Expand Capacity with Flash •  Easy deployment as demand spikes •  Grow from ¼ to multi-rack

4x Throughput, 1/6th Latency!!!


GoldenGate/Coherence Integration

•  Simplifies Coherence use in shared database environment –  Propagates DB updates to Coherence –  GoldenGate captures changes to

database tables –  TopLink maps database changes to

cached objects –  Availability—2012 –  GoldenGate 11gR2 –  Available as patch to WebLogic 12.1.1

and 11.1.1.6/10.3.6 –  Oracle database only

Database

CoherenceEnabled

Applica4on

Coherence

Read/WriteThrough

CustomPackagedApplica4on

DirectD

atabaseUpdates

UpdateCoherence

UpdateCoherence

GoldenGateJavaClient

TopLinkGridCoherence

CacheUpdater

DatabaseLogStream

Capture

<entity class="Address"> <table name="ADDR”/> <attributes> <id name="id"> <generated-value /> </id> <version name="ver“/> </attributes> </entity>

JPAMetadata


CoherenceIntegra8ons

Meta‐dataServices(MDS)

Real‐8meDecision

SOAInfrastructure

OracleADFOracleGlassFish

OracleAccessManager

OracleDataIntegra8on

TopLink

WebLogicPortal

OracleServiceBus

ServiceDeliveryPlaQorm

WebLogicServer

BIPublisher

OraclePeopleSoR

WebCenter

OracleBEAM

ATG

OracleCEP

OracleEn8tlementServices

OracleSecureTokenService

OracleIGBU

OracleCGBU

ATGHostedServicesDataCacheSimpleClustering

DataGridAdvancedCapabili4es

OracleIden8tyFedera8on

OracleWebServicesManager

SOATechnologyAdapters

SOABusinessRules

SOAHumanWorkflow

SOABPEL

SOACoherenceAdapter

OracleGoldenGate


Oracle Enterprise Pack for Eclipse (OEPE)

• Coherence Project Configuration –  Facets –  Library Management – Descriptor Generation

• Runtime Configuration –  Launch Config Editor – Run/Deploy/Debug from IDE

• Configuration Support –  tangosol-coherence-override.xml editor – Validation – Context Sensitive Help Integration – Cache Configuration Editor (OEPE 1.8)



For More Information

• General Information: http://coherence.oracle.com • Coherence Training: http://education.oracle.com • Coherence Discussion Forum: http://forums.oracle.com • Coherence User Group on Linkedin •  “Oracle Coherence 3.5” by Aleks Seovic • My email: Email: [email protected]


Q & (hopefully) A


PerformanceMonitoring&Diagnos4cs

Administra4on

Configura4onManagement Provisioning

Oracle Enterprise Manager 12C Comprehensive Coherence Management


• Completelycustomizableperformanceviews–savedifferentviewsfordifferentmonitoringrequirementsoftheenterprise

• Proac4veAlertsandno4fica4onsbasedongranularthresholds• Topologyviewtoshowassocia4onsanddependencies• Logmonitoring–generateAlertsbasedonlogpajers• Advancedmonitoring–pushreplica4on,reapsessions,transac4onalcaches,etc

DeepPerformanceVisibility&AlertsBasedProac4veMonitoring

• Real‐4methreadsanalysis–findcallstack,locks,methodlocalvariables,etc• Real‐4meheapanalysis–heapdistribu4onindifferentspaces(eden,perm‐gen,etc),garbagecollec4on

• Differen4alheapanalysis–pinpointheapleaksbycomparingtwosnapshots

Real‐4meJVMDiagnos4csforCoherenceNodes

Oracle Enterprise Manager 12C Monitoring & Diagnostics


•  Querybaseddataopera4ons•  CentralUIforkeycacheopera4ons–view,update,import,export,purge,add/removeindexes

•  Dras4callysaves4meincacheopera4onsandmakesiteasyforadministrators

•  Savequeriesforfuturereferences

CentralizedCacheDataManagement

•  Maintain‘goldimage’inthesolwarelibrary•  Provisioningnewclusteroraddnodestoanexis4ngcluster

•  SupportUnicastAddrressaswellasWKAbasedclusters

AutomatedProvisioning

Oracle Enterprise Manager 12C Administration & Provisioning

innovations in grid computing with oracle coherence

Technology

data writes

stored locally

enableslinear

rights reserved

whatisdrivingtheneedfordistributedcachingandin

presentation

specific criteria

coherence