SlideShare ist ein Scribd-Unternehmen logo
1 von 37
Oracle Coherence לירן זילכה מנכ"ל משותף Liran.zelkha@alunasoft.com
Aluna Israel’s leading Java/JavaEE and SOA consulting company Customers:
What is Oracle Coherence? Distributed Memory Data Management Solution (aka: Data Grid)
How Can a Data Grid Help? Provides a reliable data tier with a single, consistent view of data Enables dynamic data capacity including fault tolerance and load balancing Ensures that data capacity scales with processing capacity
DataGrid View Web Services Enterprise  Applications Real Time Clients Application Tier Data Services Coherence™ Data Grid Data Sources Databases Web Services Mainframes
Oracle Grid Computing: Enterprise Ready Enterprise Application Grid Extreme Transaction Processing XTP ,[object Object]
Data Virtualization (Data as a Service)
Middle tier scale out for Grid Based OLTP
Massive Persistent scale out with Oracle RACApplication  Tier Oracle Coherence Oracle RAC
Requirements of Enterprise Data Grid Reliable Universal Scalable Data ,[object Object]
No data loss at any volume
No interruption of service
Leverage Commodity Hardware
Cost Effective
Single view of data
Single management view
Simple programming model
Any Application
Any Data Source
Built for continuous operation
Data Fault Tolerance
Self-Diagnosis and Healing
“Once and Only Once” Processing
Data Caching
Analytics
Transaction Processing
Event Processing,[object Object]
How Does Coherence Work? ,[object Object]
In the event a node is unhealthy, other nodes diagnose state,[object Object]
Remaining nodes redistribute primary and back-up responsibilities to healthy nodesX
Customer Scenarios Caching Applications request data from the Data Grid rather than backend data sources Analytics Applications ask the Data Grid questions from simple queries to advanced scenario modeling Transactions Data Grid acts as a transactional System of Record, hosting data and business logic Events Automated processing based on event
Demo
Topology #1 - Replicated Cache
Topology #1 - Replicated Cache
Topology #2 - Partitioned Cache
Topology #2 - Guaranteed Cluster Resiliency
Topology #2 - Partitioned Failover

Weitere ähnliche Inhalte

Was ist angesagt?

Hazelcast 3.6 Roadmap Preview
Hazelcast 3.6 Roadmap PreviewHazelcast 3.6 Roadmap Preview
Hazelcast 3.6 Roadmap PreviewHazelcast
 
From cache to in-memory data grid. Introduction to Hazelcast.
From cache to in-memory data grid. Introduction to Hazelcast.From cache to in-memory data grid. Introduction to Hazelcast.
From cache to in-memory data grid. Introduction to Hazelcast.Taras Matyashovsky
 
Die 10 besten PostgreSQL-Replikationsstrategien für Ihr Unternehmen
Die 10 besten PostgreSQL-Replikationsstrategien für Ihr UnternehmenDie 10 besten PostgreSQL-Replikationsstrategien für Ihr Unternehmen
Die 10 besten PostgreSQL-Replikationsstrategien für Ihr UnternehmenEDB
 
Time to Make the Move to In-Memory Data Grids
Time to Make the Move to In-Memory Data GridsTime to Make the Move to In-Memory Data Grids
Time to Make the Move to In-Memory Data GridsHazelcast
 
Manage Microservices & Fast Data Systems on One Platform w/ DC/OS
Manage Microservices & Fast Data Systems on One Platform w/ DC/OSManage Microservices & Fast Data Systems on One Platform w/ DC/OS
Manage Microservices & Fast Data Systems on One Platform w/ DC/OSMesosphere Inc.
 
Cloud Migration Paths: Kubernetes, IaaS, or DBaaS
Cloud Migration Paths: Kubernetes, IaaS, or DBaaSCloud Migration Paths: Kubernetes, IaaS, or DBaaS
Cloud Migration Paths: Kubernetes, IaaS, or DBaaSEDB
 
Hazelcast For Beginners (Paris JUG-1)
Hazelcast For Beginners (Paris JUG-1)Hazelcast For Beginners (Paris JUG-1)
Hazelcast For Beginners (Paris JUG-1)Emrah Kocaman
 
Hazelcast Deep Dive (Paris JUG-2)
Hazelcast Deep Dive (Paris JUG-2)Hazelcast Deep Dive (Paris JUG-2)
Hazelcast Deep Dive (Paris JUG-2)Emrah Kocaman
 
Oracle Cloud DBaaS
Oracle Cloud DBaaSOracle Cloud DBaaS
Oracle Cloud DBaaSArush Jain
 
Spring Meetup Paris - Getting Distributed with Hazelcast and Spring
Spring Meetup Paris - Getting Distributed with Hazelcast and SpringSpring Meetup Paris - Getting Distributed with Hazelcast and Spring
Spring Meetup Paris - Getting Distributed with Hazelcast and SpringEmrah Kocaman
 
Accelerating Business Intelligence Solutions with Microsoft Azure pass
Accelerating Business Intelligence Solutions with Microsoft Azure   passAccelerating Business Intelligence Solutions with Microsoft Azure   pass
Accelerating Business Intelligence Solutions with Microsoft Azure passJason Strate
 
IMCSummit 2015 - Day 2 General Session - Flash-Extending In-Memory Computing
IMCSummit 2015 - Day 2 General Session - Flash-Extending In-Memory ComputingIMCSummit 2015 - Day 2 General Session - Flash-Extending In-Memory Computing
IMCSummit 2015 - Day 2 General Session - Flash-Extending In-Memory ComputingIn-Memory Computing Summit
 
Coherence RoadMap 2018
Coherence RoadMap 2018Coherence RoadMap 2018
Coherence RoadMap 2018harvraja
 
Hazelcast Essentials
Hazelcast EssentialsHazelcast Essentials
Hazelcast EssentialsRahul Gupta
 

Was ist angesagt? (20)

Hazelcast 3.6 Roadmap Preview
Hazelcast 3.6 Roadmap PreviewHazelcast 3.6 Roadmap Preview
Hazelcast 3.6 Roadmap Preview
 
Novinky v Oracle Database 18c
Novinky v Oracle Database 18cNovinky v Oracle Database 18c
Novinky v Oracle Database 18c
 
From cache to in-memory data grid. Introduction to Hazelcast.
From cache to in-memory data grid. Introduction to Hazelcast.From cache to in-memory data grid. Introduction to Hazelcast.
From cache to in-memory data grid. Introduction to Hazelcast.
 
Die 10 besten PostgreSQL-Replikationsstrategien für Ihr Unternehmen
Die 10 besten PostgreSQL-Replikationsstrategien für Ihr UnternehmenDie 10 besten PostgreSQL-Replikationsstrategien für Ihr Unternehmen
Die 10 besten PostgreSQL-Replikationsstrategien für Ihr Unternehmen
 
Hazelcast 101
Hazelcast 101Hazelcast 101
Hazelcast 101
 
Time to Make the Move to In-Memory Data Grids
Time to Make the Move to In-Memory Data GridsTime to Make the Move to In-Memory Data Grids
Time to Make the Move to In-Memory Data Grids
 
Manage Microservices & Fast Data Systems on One Platform w/ DC/OS
Manage Microservices & Fast Data Systems on One Platform w/ DC/OSManage Microservices & Fast Data Systems on One Platform w/ DC/OS
Manage Microservices & Fast Data Systems on One Platform w/ DC/OS
 
Cloud Migration Paths: Kubernetes, IaaS, or DBaaS
Cloud Migration Paths: Kubernetes, IaaS, or DBaaSCloud Migration Paths: Kubernetes, IaaS, or DBaaS
Cloud Migration Paths: Kubernetes, IaaS, or DBaaS
 
GemFire In-Memory Data Grid
GemFire In-Memory Data GridGemFire In-Memory Data Grid
GemFire In-Memory Data Grid
 
Hazelcast For Beginners (Paris JUG-1)
Hazelcast For Beginners (Paris JUG-1)Hazelcast For Beginners (Paris JUG-1)
Hazelcast For Beginners (Paris JUG-1)
 
Hazelcast Deep Dive (Paris JUG-2)
Hazelcast Deep Dive (Paris JUG-2)Hazelcast Deep Dive (Paris JUG-2)
Hazelcast Deep Dive (Paris JUG-2)
 
Oracle Cloud DBaaS
Oracle Cloud DBaaSOracle Cloud DBaaS
Oracle Cloud DBaaS
 
Deploying Big-Data-as-a-Service (BDaaS) in the Enterprise
Deploying Big-Data-as-a-Service (BDaaS) in the EnterpriseDeploying Big-Data-as-a-Service (BDaaS) in the Enterprise
Deploying Big-Data-as-a-Service (BDaaS) in the Enterprise
 
Spring Meetup Paris - Getting Distributed with Hazelcast and Spring
Spring Meetup Paris - Getting Distributed with Hazelcast and SpringSpring Meetup Paris - Getting Distributed with Hazelcast and Spring
Spring Meetup Paris - Getting Distributed with Hazelcast and Spring
 
Queues, Pools, Caches
Queues, Pools, CachesQueues, Pools, Caches
Queues, Pools, Caches
 
Accelerating Business Intelligence Solutions with Microsoft Azure pass
Accelerating Business Intelligence Solutions with Microsoft Azure   passAccelerating Business Intelligence Solutions with Microsoft Azure   pass
Accelerating Business Intelligence Solutions with Microsoft Azure pass
 
IMCSummit 2015 - Day 2 General Session - Flash-Extending In-Memory Computing
IMCSummit 2015 - Day 2 General Session - Flash-Extending In-Memory ComputingIMCSummit 2015 - Day 2 General Session - Flash-Extending In-Memory Computing
IMCSummit 2015 - Day 2 General Session - Flash-Extending In-Memory Computing
 
Coherence RoadMap 2018
Coherence RoadMap 2018Coherence RoadMap 2018
Coherence RoadMap 2018
 
Hazelcast Essentials
Hazelcast EssentialsHazelcast Essentials
Hazelcast Essentials
 
My Dissertation 2016
My Dissertation 2016My Dissertation 2016
My Dissertation 2016
 

Andere mochten auch

Coherence Implementation Patterns - Sig Nov 2011
Coherence Implementation Patterns - Sig Nov 2011Coherence Implementation Patterns - Sig Nov 2011
Coherence Implementation Patterns - Sig Nov 2011Ben Stopford
 
Hadoop Tutorial
Hadoop TutorialHadoop Tutorial
Hadoop Tutorialawesomesos
 
Think Distributed: The Hazelcast Way
Think Distributed: The Hazelcast WayThink Distributed: The Hazelcast Way
Think Distributed: The Hazelcast WayRahul Gupta
 
Spark SQL - 10 Things You Need to Know
Spark SQL - 10 Things You Need to KnowSpark SQL - 10 Things You Need to Know
Spark SQL - 10 Things You Need to KnowKristian Alexander
 
Design principles of scalable, distributed systems
Design principles of scalable, distributed systemsDesign principles of scalable, distributed systems
Design principles of scalable, distributed systemsTinniam V Ganesh (TV)
 
Spark SQL Deep Dive @ Melbourne Spark Meetup
Spark SQL Deep Dive @ Melbourne Spark MeetupSpark SQL Deep Dive @ Melbourne Spark Meetup
Spark SQL Deep Dive @ Melbourne Spark MeetupDatabricks
 
[OracleCode SF] In memory analytics with apache spark and hazelcast
[OracleCode SF] In memory analytics with apache spark and hazelcast[OracleCode SF] In memory analytics with apache spark and hazelcast
[OracleCode SF] In memory analytics with apache spark and hazelcastViktor Gamov
 
Real time Analytics with Apache Kafka and Apache Spark
Real time Analytics with Apache Kafka and Apache SparkReal time Analytics with Apache Kafka and Apache Spark
Real time Analytics with Apache Kafka and Apache SparkRahul Jain
 
Distributed Systems: scalability and high availability
Distributed Systems: scalability and high availabilityDistributed Systems: scalability and high availability
Distributed Systems: scalability and high availabilityRenato Lucindo
 
Hadoop & HDFS for Beginners
Hadoop & HDFS for BeginnersHadoop & HDFS for Beginners
Hadoop & HDFS for BeginnersRahul Jain
 

Andere mochten auch (10)

Coherence Implementation Patterns - Sig Nov 2011
Coherence Implementation Patterns - Sig Nov 2011Coherence Implementation Patterns - Sig Nov 2011
Coherence Implementation Patterns - Sig Nov 2011
 
Hadoop Tutorial
Hadoop TutorialHadoop Tutorial
Hadoop Tutorial
 
Think Distributed: The Hazelcast Way
Think Distributed: The Hazelcast WayThink Distributed: The Hazelcast Way
Think Distributed: The Hazelcast Way
 
Spark SQL - 10 Things You Need to Know
Spark SQL - 10 Things You Need to KnowSpark SQL - 10 Things You Need to Know
Spark SQL - 10 Things You Need to Know
 
Design principles of scalable, distributed systems
Design principles of scalable, distributed systemsDesign principles of scalable, distributed systems
Design principles of scalable, distributed systems
 
Spark SQL Deep Dive @ Melbourne Spark Meetup
Spark SQL Deep Dive @ Melbourne Spark MeetupSpark SQL Deep Dive @ Melbourne Spark Meetup
Spark SQL Deep Dive @ Melbourne Spark Meetup
 
[OracleCode SF] In memory analytics with apache spark and hazelcast
[OracleCode SF] In memory analytics with apache spark and hazelcast[OracleCode SF] In memory analytics with apache spark and hazelcast
[OracleCode SF] In memory analytics with apache spark and hazelcast
 
Real time Analytics with Apache Kafka and Apache Spark
Real time Analytics with Apache Kafka and Apache SparkReal time Analytics with Apache Kafka and Apache Spark
Real time Analytics with Apache Kafka and Apache Spark
 
Distributed Systems: scalability and high availability
Distributed Systems: scalability and high availabilityDistributed Systems: scalability and high availability
Distributed Systems: scalability and high availability
 
Hadoop & HDFS for Beginners
Hadoop & HDFS for BeginnersHadoop & HDFS for Beginners
Hadoop & HDFS for Beginners
 

Ähnlich wie Oracle Coherence

Server Farms and XML Web Services
Server Farms and XML Web ServicesServer Farms and XML Web Services
Server Farms and XML Web ServicesJorgen Thelin
 
Atmosphere 2014: Switching from monolithic approach to modular cloud computin...
Atmosphere 2014: Switching from monolithic approach to modular cloud computin...Atmosphere 2014: Switching from monolithic approach to modular cloud computin...
Atmosphere 2014: Switching from monolithic approach to modular cloud computin...PROIDEA
 
Microsoft Azure Cloud Basics Tutorial
Microsoft Azure Cloud Basics TutorialMicrosoft Azure Cloud Basics Tutorial
Microsoft Azure Cloud Basics TutorialIIMSE Edu
 
In search of the perfect IoT Stack - Scalable IoT Architectures with MQTT
In search of the perfect IoT Stack - Scalable IoT Architectures with MQTTIn search of the perfect IoT Stack - Scalable IoT Architectures with MQTT
In search of the perfect IoT Stack - Scalable IoT Architectures with MQTTDominik Obermaier
 
Stephane Lapointe & Alexandre Brisebois: Développer des microservices avec Se...
Stephane Lapointe & Alexandre Brisebois: Développer des microservices avec Se...Stephane Lapointe & Alexandre Brisebois: Développer des microservices avec Se...
Stephane Lapointe & Alexandre Brisebois: Développer des microservices avec Se...MSDEVMTL
 
Handling Data in Mega Scale Systems
Handling Data in Mega Scale SystemsHandling Data in Mega Scale Systems
Handling Data in Mega Scale SystemsDirecti Group
 
Designing distributed systems
Designing distributed systemsDesigning distributed systems
Designing distributed systemsMalisa Ncube
 
Web session replication with Hazelcast
Web session replication with HazelcastWeb session replication with Hazelcast
Web session replication with HazelcastEmrah Kocaman
 
Oracle 10g rac_overview
Oracle 10g rac_overviewOracle 10g rac_overview
Oracle 10g rac_overviewRobel Parvini
 
Climbing the beanstalk
Climbing the beanstalkClimbing the beanstalk
Climbing the beanstalkgordonyorke
 
Microsoft azure platforms
Microsoft azure platformsMicrosoft azure platforms
Microsoft azure platformsMotty Ben Atia
 
Big Data Streams Architectures. Why? What? How?
Big Data Streams Architectures. Why? What? How?Big Data Streams Architectures. Why? What? How?
Big Data Streams Architectures. Why? What? How?Anton Nazaruk
 
Application server
Application serverApplication server
Application servernava rathna
 
Amazon EKS 그리고 Service Mesh (김세호 솔루션즈 아키텍트, AWS) :: Gaming on AWS 2018
Amazon EKS 그리고 Service Mesh (김세호 솔루션즈 아키텍트, AWS) :: Gaming on AWS 2018Amazon EKS 그리고 Service Mesh (김세호 솔루션즈 아키텍트, AWS) :: Gaming on AWS 2018
Amazon EKS 그리고 Service Mesh (김세호 솔루션즈 아키텍트, AWS) :: Gaming on AWS 2018Amazon Web Services Korea
 
Move fast and make things with microservices
Move fast and make things with microservicesMove fast and make things with microservices
Move fast and make things with microservicesMithun Arunan
 
ScalabilityAvailability
ScalabilityAvailabilityScalabilityAvailability
ScalabilityAvailabilitywebuploader
 
CloudBrew 2016 - Building IoT solution with Service Fabric
CloudBrew 2016 - Building IoT solution with Service FabricCloudBrew 2016 - Building IoT solution with Service Fabric
CloudBrew 2016 - Building IoT solution with Service FabricTeemu Tapanila
 
Serhiy Kalinets "Embracing architectural challenges in the modern .NET world"
Serhiy Kalinets "Embracing architectural challenges in the modern .NET world"Serhiy Kalinets "Embracing architectural challenges in the modern .NET world"
Serhiy Kalinets "Embracing architectural challenges in the modern .NET world"Fwdays
 

Ähnlich wie Oracle Coherence (20)

Server Farms and XML Web Services
Server Farms and XML Web ServicesServer Farms and XML Web Services
Server Farms and XML Web Services
 
Introduction To Cloud Computing
Introduction To Cloud ComputingIntroduction To Cloud Computing
Introduction To Cloud Computing
 
Atmosphere 2014: Switching from monolithic approach to modular cloud computin...
Atmosphere 2014: Switching from monolithic approach to modular cloud computin...Atmosphere 2014: Switching from monolithic approach to modular cloud computin...
Atmosphere 2014: Switching from monolithic approach to modular cloud computin...
 
Microsoft Azure Cloud Basics Tutorial
Microsoft Azure Cloud Basics TutorialMicrosoft Azure Cloud Basics Tutorial
Microsoft Azure Cloud Basics Tutorial
 
In search of the perfect IoT Stack - Scalable IoT Architectures with MQTT
In search of the perfect IoT Stack - Scalable IoT Architectures with MQTTIn search of the perfect IoT Stack - Scalable IoT Architectures with MQTT
In search of the perfect IoT Stack - Scalable IoT Architectures with MQTT
 
Clustering van IT-componenten
Clustering van IT-componentenClustering van IT-componenten
Clustering van IT-componenten
 
Stephane Lapointe & Alexandre Brisebois: Développer des microservices avec Se...
Stephane Lapointe & Alexandre Brisebois: Développer des microservices avec Se...Stephane Lapointe & Alexandre Brisebois: Développer des microservices avec Se...
Stephane Lapointe & Alexandre Brisebois: Développer des microservices avec Se...
 
Handling Data in Mega Scale Systems
Handling Data in Mega Scale SystemsHandling Data in Mega Scale Systems
Handling Data in Mega Scale Systems
 
Designing distributed systems
Designing distributed systemsDesigning distributed systems
Designing distributed systems
 
Web session replication with Hazelcast
Web session replication with HazelcastWeb session replication with Hazelcast
Web session replication with Hazelcast
 
Oracle 10g rac_overview
Oracle 10g rac_overviewOracle 10g rac_overview
Oracle 10g rac_overview
 
Climbing the beanstalk
Climbing the beanstalkClimbing the beanstalk
Climbing the beanstalk
 
Microsoft azure platforms
Microsoft azure platformsMicrosoft azure platforms
Microsoft azure platforms
 
Big Data Streams Architectures. Why? What? How?
Big Data Streams Architectures. Why? What? How?Big Data Streams Architectures. Why? What? How?
Big Data Streams Architectures. Why? What? How?
 
Application server
Application serverApplication server
Application server
 
Amazon EKS 그리고 Service Mesh (김세호 솔루션즈 아키텍트, AWS) :: Gaming on AWS 2018
Amazon EKS 그리고 Service Mesh (김세호 솔루션즈 아키텍트, AWS) :: Gaming on AWS 2018Amazon EKS 그리고 Service Mesh (김세호 솔루션즈 아키텍트, AWS) :: Gaming on AWS 2018
Amazon EKS 그리고 Service Mesh (김세호 솔루션즈 아키텍트, AWS) :: Gaming on AWS 2018
 
Move fast and make things with microservices
Move fast and make things with microservicesMove fast and make things with microservices
Move fast and make things with microservices
 
ScalabilityAvailability
ScalabilityAvailabilityScalabilityAvailability
ScalabilityAvailability
 
CloudBrew 2016 - Building IoT solution with Service Fabric
CloudBrew 2016 - Building IoT solution with Service FabricCloudBrew 2016 - Building IoT solution with Service Fabric
CloudBrew 2016 - Building IoT solution with Service Fabric
 
Serhiy Kalinets "Embracing architectural challenges in the modern .NET world"
Serhiy Kalinets "Embracing architectural challenges in the modern .NET world"Serhiy Kalinets "Embracing architectural challenges in the modern .NET world"
Serhiy Kalinets "Embracing architectural challenges in the modern .NET world"
 

Mehr von Liran Zelkha

Scaling data on public clouds
Scaling data on public cloudsScaling data on public clouds
Scaling data on public cloudsLiran Zelkha
 
OC4J to WebLogic Server Migration5
OC4J to WebLogic Server Migration5OC4J to WebLogic Server Migration5
OC4J to WebLogic Server Migration5Liran Zelkha
 
Data SLA in the public cloud
Data SLA in the public cloudData SLA in the public cloud
Data SLA in the public cloudLiran Zelkha
 
שטפונות בנגב
שטפונות בנגבשטפונות בנגב
שטפונות בנגבLiran Zelkha
 
Social Networks Optimization
Social Networks OptimizationSocial Networks Optimization
Social Networks OptimizationLiran Zelkha
 
Technology Overview
Technology OverviewTechnology Overview
Technology OverviewLiran Zelkha
 
Building Eclipse Plugins
Building Eclipse PluginsBuilding Eclipse Plugins
Building Eclipse PluginsLiran Zelkha
 
Aluna Introduction
Aluna IntroductionAluna Introduction
Aluna IntroductionLiran Zelkha
 

Mehr von Liran Zelkha (9)

Scaling data on public clouds
Scaling data on public cloudsScaling data on public clouds
Scaling data on public clouds
 
OC4J to WebLogic Server Migration5
OC4J to WebLogic Server Migration5OC4J to WebLogic Server Migration5
OC4J to WebLogic Server Migration5
 
Data SLA in the public cloud
Data SLA in the public cloudData SLA in the public cloud
Data SLA in the public cloud
 
שטפונות בנגב
שטפונות בנגבשטפונות בנגב
שטפונות בנגב
 
מתפ
מתפמתפ
מתפ
 
Social Networks Optimization
Social Networks OptimizationSocial Networks Optimization
Social Networks Optimization
 
Technology Overview
Technology OverviewTechnology Overview
Technology Overview
 
Building Eclipse Plugins
Building Eclipse PluginsBuilding Eclipse Plugins
Building Eclipse Plugins
 
Aluna Introduction
Aluna IntroductionAluna Introduction
Aluna Introduction
 

Kürzlich hochgeladen

Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clashcharlottematthew16
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 

Kürzlich hochgeladen (20)

Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clash
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 

Oracle Coherence

  • 1. Oracle Coherence לירן זילכה מנכ"ל משותף Liran.zelkha@alunasoft.com
  • 2. Aluna Israel’s leading Java/JavaEE and SOA consulting company Customers:
  • 3. What is Oracle Coherence? Distributed Memory Data Management Solution (aka: Data Grid)
  • 4. How Can a Data Grid Help? Provides a reliable data tier with a single, consistent view of data Enables dynamic data capacity including fault tolerance and load balancing Ensures that data capacity scales with processing capacity
  • 5. DataGrid View Web Services Enterprise Applications Real Time Clients Application Tier Data Services Coherence™ Data Grid Data Sources Databases Web Services Mainframes
  • 6.
  • 8. Middle tier scale out for Grid Based OLTP
  • 9. Massive Persistent scale out with Oracle RACApplication Tier Oracle Coherence Oracle RAC
  • 10.
  • 11. No data loss at any volume
  • 23. “Once and Only Once” Processing
  • 27.
  • 28.
  • 29.
  • 30. Remaining nodes redistribute primary and back-up responsibilities to healthy nodesX
  • 31. Customer Scenarios Caching Applications request data from the Data Grid rather than backend data sources Analytics Applications ask the Data Grid questions from simple queries to advanced scenario modeling Transactions Data Grid acts as a transactional System of Record, hosting data and business logic Events Automated processing based on event
  • 32. Demo
  • 33. Topology #1 - Replicated Cache
  • 34. Topology #1 - Replicated Cache
  • 35. Topology #2 - Partitioned Cache
  • 36. Topology #2 - Guaranteed Cluster Resiliency
  • 37. Topology #2 - Partitioned Failover
  • 38. Topology #2a – Cache Client/Cache Server
  • 39. Topology #3 - Near Cache
  • 40. Use Case: Coherence*Web Coherence*Web is an HTTP session-management module (built-in feature of Coherence) Supports a wide range of application servers. Does not require any changes to the application. Coherence*Web uses the NearCache technology to provide fully fault-tolerant caching, with almost unlimited scalability (to several hundred cluster nodes without issue). Heterogeneous applications running on mixed hardware/OS/application servers can share common user session data. This dramatically simplifies supporting Single-Sign-On across applications.
  • 41. Coherence*Web: Session State Management Web Tier Web Application ApplicationState Router CoherenceWeb Java EE or ServletContainer Web Application ApplicationState CoherenceWeb Java EE or ServletContainer Web Application ApplicationState CoherenceWeb Load Balanced Java EE or ServletContainer Clustered Oracle, WebLogic, WebSphere, JBoss, Tomcat In Memory Coherence Data Grid for Session State
  • 45. Transaction Implicit: Queuing of operations Virtual queue & thread per entry Explicit: Pessimistic locking Grid-Wide Mutex Transactions: Unit of work management Both optimistic and pessimistic transactions Isolation levels from read-committed through serializable Integrated with JTA
  • 47. Events Universal: All data sets provide events, regardless of the topology. Distributed: The events are always delivered efficiently to the interested listeners. Regardless of originating node Flexible: Listen to entire data sets, specific identities, and even to queries! Provides “before” and “after” state Both sync and async event models
  • 49. Query Parallel Query: A query is performed in parallel across the Data Grid, using indexing and a iterative Cost Based Optimizer. Customizable predicates Custom indexes Custom aggregators Continuous Query: Combines a query with events to provide a local materialized view. Result is up-to-date in real-time Like the Near Topology, but it always contains the desired data
  • 50. Query
  • 51. InvocableMap – Server Side Processing
  • 52. Coherence*Extend Supports “fat client” real-time applications such as trading desks, as well as other server tiers WAN support Connection to the cluster is over TCP Continuous query can be used to maintain real-time query results on the desktop!
  • 53. Tangosol Cluster Management Protocol (TCMP) Coherence’s own protocol between cluster members TCMP utilizes UDP Massively scalable Asynchronous Point-to-point UDP Multicast is used for: New JVMs to join the cluster automatically Maintaining cluster membership Multicast is not required; it may be disabled with Well Known Addresses (WKA) UDP Unicast is used for most communication Very fast and scalable TCMP guarantees packet order and delivery TCP/IP connections do not need to be maintained
  • 54. Clustering is about Consensus! Oracle Coherence Clustering is very different! Goal: Maintain Cluster Membership Consensus all times Do it as fast as physically possible Do it without a single point of failure or registry of members Ensure all members have the same responsibility and work together to maintain consensus Ensure that no voting occurs to determine membership
  • 55. Clustering is about Consensus! Why: If all members are always known… We can partition / load balance Data & Services We don’t need to hold TCP/IP connections open (resource intensive) Any member can “talk” directly with any other member (peer-to-peer) The cluster can dynamically (while running) scale to any size
  • 56.
  • 57. Scale out testing on Dual 2.3GHz PowerPC G5 Xserve
  • 58. Use of on index for direct access
  • 59.