SlideShare a Scribd company logo
1 of 17
Climbing the Beanstalk  Scaling Java Persistence to the Cloud Gordon Yorke JPA 2.1 Expert Group EclipseLink Architecture Council
EclipseLink Project ,[object Object],[object Object],[object Object],[object Object],[object Object]
Improving the system ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Optimization Steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Concurrency ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Locking ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Scaling Data / Data Affinity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Entity Caching ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Considerations with cache concurrency ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cache Co-ordination ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Caching in the Grid ,[object Object],[object Object],[object Object],[object Object]
TopLink Grid ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Grid Architecture ,[object Object],[object Object],[object Object],[object Object]
Grid Cache ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Grid Read ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Grid Entity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

More Related Content

What's hot

Azure database services for PostgreSQL and MySQL
Azure database services for PostgreSQL and MySQLAzure database services for PostgreSQL and MySQL
Azure database services for PostgreSQL and MySQLAmit Banerjee
 
EDB Postgres Platform
EDB Postgres PlatformEDB Postgres Platform
EDB Postgres PlatformEDB
 
Scalable analytics for iaas cloud availability
Scalable analytics for iaas cloud availabilityScalable analytics for iaas cloud availability
Scalable analytics for iaas cloud availabilityPapitha Velumani
 
Oracle 10g rac_overview
Oracle 10g rac_overviewOracle 10g rac_overview
Oracle 10g rac_overviewRobel Parvini
 
SQL Server 2016 novelties
SQL Server 2016 noveltiesSQL Server 2016 novelties
SQL Server 2016 noveltiesMSDEVMTL
 
EDB Postgres Replication Server
EDB Postgres Replication ServerEDB Postgres Replication Server
EDB Postgres Replication ServerEDB
 
Practical examples of using extended events
Practical examples of using extended eventsPractical examples of using extended events
Practical examples of using extended eventsDean Richards
 
Scalable analytics for iaas cloud availability
Scalable analytics for iaas cloud availabilityScalable analytics for iaas cloud availability
Scalable analytics for iaas cloud availabilityPapitha Velumani
 
Using extended events for troubleshooting sql server
Using extended events for troubleshooting sql serverUsing extended events for troubleshooting sql server
Using extended events for troubleshooting sql serverAntonios Chatzipavlis
 
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
 
high performance databases
high performance databaseshigh performance databases
high performance databasesmahdi_92
 
Be Proactive: A Good DBA Goes Looking for Signs of Trouble | IDERA
Be Proactive: A Good DBA Goes Looking for Signs of Trouble | IDERABe Proactive: A Good DBA Goes Looking for Signs of Trouble | IDERA
Be Proactive: A Good DBA Goes Looking for Signs of Trouble | IDERAIDERA Software
 
Data guard architecture
Data guard architectureData guard architecture
Data guard architectureVimlendu Kumar
 
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
 
"What database can tell about application issues? What application can tell a...
"What database can tell about application issues? What application can tell a..."What database can tell about application issues? What application can tell a...
"What database can tell about application issues? What application can tell a...Fwdays
 
Enterprise-class security with PostgreSQL - 1
Enterprise-class security with PostgreSQL - 1Enterprise-class security with PostgreSQL - 1
Enterprise-class security with PostgreSQL - 1Ashnikbiz
 
Nabil Nawaz Oracle Oracle 12c Data Guard Deep Dive Presentation
Nabil Nawaz Oracle Oracle 12c Data Guard Deep Dive PresentationNabil Nawaz Oracle Oracle 12c Data Guard Deep Dive Presentation
Nabil Nawaz Oracle Oracle 12c Data Guard Deep Dive PresentationNabil Nawaz
 
Enterprise-class security with PostgreSQL - 2
Enterprise-class security with PostgreSQL - 2Enterprise-class security with PostgreSQL - 2
Enterprise-class security with PostgreSQL - 2Ashnikbiz
 
Caching up is hard to do: Improving your Web Services' Performance
Caching up is hard to do: Improving your Web Services' PerformanceCaching up is hard to do: Improving your Web Services' Performance
Caching up is hard to do: Improving your Web Services' PerformanceRTigger
 

What's hot (20)

Azure database services for PostgreSQL and MySQL
Azure database services for PostgreSQL and MySQLAzure database services for PostgreSQL and MySQL
Azure database services for PostgreSQL and MySQL
 
EDB Postgres Platform
EDB Postgres PlatformEDB Postgres Platform
EDB Postgres Platform
 
Scalable analytics for iaas cloud availability
Scalable analytics for iaas cloud availabilityScalable analytics for iaas cloud availability
Scalable analytics for iaas cloud availability
 
Oracle 10g rac_overview
Oracle 10g rac_overviewOracle 10g rac_overview
Oracle 10g rac_overview
 
SQL Server 2016 novelties
SQL Server 2016 noveltiesSQL Server 2016 novelties
SQL Server 2016 novelties
 
Oracle Dataguard
Oracle DataguardOracle Dataguard
Oracle Dataguard
 
EDB Postgres Replication Server
EDB Postgres Replication ServerEDB Postgres Replication Server
EDB Postgres Replication Server
 
Practical examples of using extended events
Practical examples of using extended eventsPractical examples of using extended events
Practical examples of using extended events
 
Scalable analytics for iaas cloud availability
Scalable analytics for iaas cloud availabilityScalable analytics for iaas cloud availability
Scalable analytics for iaas cloud availability
 
Using extended events for troubleshooting sql server
Using extended events for troubleshooting sql serverUsing extended events for troubleshooting sql server
Using extended events for troubleshooting sql server
 
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
 
high performance databases
high performance databaseshigh performance databases
high performance databases
 
Be Proactive: A Good DBA Goes Looking for Signs of Trouble | IDERA
Be Proactive: A Good DBA Goes Looking for Signs of Trouble | IDERABe Proactive: A Good DBA Goes Looking for Signs of Trouble | IDERA
Be Proactive: A Good DBA Goes Looking for Signs of Trouble | IDERA
 
Data guard architecture
Data guard architectureData guard architecture
Data guard architecture
 
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
 
"What database can tell about application issues? What application can tell a...
"What database can tell about application issues? What application can tell a..."What database can tell about application issues? What application can tell a...
"What database can tell about application issues? What application can tell a...
 
Enterprise-class security with PostgreSQL - 1
Enterprise-class security with PostgreSQL - 1Enterprise-class security with PostgreSQL - 1
Enterprise-class security with PostgreSQL - 1
 
Nabil Nawaz Oracle Oracle 12c Data Guard Deep Dive Presentation
Nabil Nawaz Oracle Oracle 12c Data Guard Deep Dive PresentationNabil Nawaz Oracle Oracle 12c Data Guard Deep Dive Presentation
Nabil Nawaz Oracle Oracle 12c Data Guard Deep Dive Presentation
 
Enterprise-class security with PostgreSQL - 2
Enterprise-class security with PostgreSQL - 2Enterprise-class security with PostgreSQL - 2
Enterprise-class security with PostgreSQL - 2
 
Caching up is hard to do: Improving your Web Services' Performance
Caching up is hard to do: Improving your Web Services' PerformanceCaching up is hard to do: Improving your Web Services' Performance
Caching up is hard to do: Improving your Web Services' Performance
 

Viewers also liked

Time management
Time management Time management
Time management Sami co
 
HETEL 2011ko ekintzen oroitidazkia
HETEL 2011ko  ekintzen oroitidazkiaHETEL 2011ko  ekintzen oroitidazkia
HETEL 2011ko ekintzen oroitidazkiaLeire Hetel
 
Revista Lanbide 2010
Revista Lanbide 2010Revista Lanbide 2010
Revista Lanbide 2010Leire Hetel
 
Revista Lanbide 2009
Revista Lanbide 2009Revista Lanbide 2009
Revista Lanbide 2009Leire Hetel
 

Viewers also liked (7)

Time management
Time management Time management
Time management
 
Future
FutureFuture
Future
 
HETEL 2011ko ekintzen oroitidazkia
HETEL 2011ko  ekintzen oroitidazkiaHETEL 2011ko  ekintzen oroitidazkia
HETEL 2011ko ekintzen oroitidazkia
 
Sports camp
Sports campSports camp
Sports camp
 
Revista Lanbide 2010
Revista Lanbide 2010Revista Lanbide 2010
Revista Lanbide 2010
 
Modals
ModalsModals
Modals
 
Revista Lanbide 2009
Revista Lanbide 2009Revista Lanbide 2009
Revista Lanbide 2009
 

Similar to Climbing the beanstalk

Optimizing your java applications for multi core hardware
Optimizing your java applications for multi core hardwareOptimizing your java applications for multi core hardware
Optimizing your java applications for multi core hardwareIndicThreads
 
J2EE Batch Processing
J2EE Batch ProcessingJ2EE Batch Processing
J2EE Batch ProcessingChris Adkin
 
226 team project-report-manjula kollipara
226 team project-report-manjula kollipara226 team project-report-manjula kollipara
226 team project-report-manjula kolliparaManjula Kollipara
 
Building High Performance MySQL Query Systems and Analytic Applications
Building High Performance MySQL Query Systems and Analytic ApplicationsBuilding High Performance MySQL Query Systems and Analytic Applications
Building High Performance MySQL Query Systems and Analytic ApplicationsCalpont
 
Building High Performance MySql Query Systems And Analytic Applications
Building High Performance MySql Query Systems And Analytic ApplicationsBuilding High Performance MySql Query Systems And Analytic Applications
Building High Performance MySql Query Systems And Analytic Applicationsguest40cda0b
 
Big Data Glossary of terms
Big Data Glossary of termsBig Data Glossary of terms
Big Data Glossary of termsKognitio
 
EOUG95 - Client Server Very Large Databases - Paper
EOUG95 - Client Server Very Large Databases - PaperEOUG95 - Client Server Very Large Databases - Paper
EOUG95 - Client Server Very Large Databases - PaperDavid Walker
 
Handling Data in Mega Scale Systems
Handling Data in Mega Scale SystemsHandling Data in Mega Scale Systems
Handling Data in Mega Scale SystemsDirecti Group
 
Google Megastore
Google MegastoreGoogle Megastore
Google Megastorebergwolf
 
Data management in cloud study of existing systems and future opportunities
Data management in cloud study of existing systems and future opportunitiesData management in cloud study of existing systems and future opportunities
Data management in cloud study of existing systems and future opportunitiesEditor Jacotech
 
Mow2012 data services
Mow2012 data servicesMow2012 data services
Mow2012 data servicesSyed Shaaf
 
Orca: A Modular Query Optimizer Architecture for Big Data
Orca: A Modular Query Optimizer Architecture for Big DataOrca: A Modular Query Optimizer Architecture for Big Data
Orca: A Modular Query Optimizer Architecture for Big DataEMC
 
Gavin M
Gavin MGavin M
Gavin MOntico
 
Apache ignite as in-memory computing platform
Apache ignite as in-memory computing platformApache ignite as in-memory computing platform
Apache ignite as in-memory computing platformSurinder Mehra
 
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
 
Hadoop and Voldemort @ LinkedIn
Hadoop and Voldemort @ LinkedInHadoop and Voldemort @ LinkedIn
Hadoop and Voldemort @ LinkedInHadoop User Group
 

Similar to Climbing the beanstalk (20)

Optimizing your java applications for multi core hardware
Optimizing your java applications for multi core hardwareOptimizing your java applications for multi core hardware
Optimizing your java applications for multi core hardware
 
J2EE Batch Processing
J2EE Batch ProcessingJ2EE Batch Processing
J2EE Batch Processing
 
226 team project-report-manjula kollipara
226 team project-report-manjula kollipara226 team project-report-manjula kollipara
226 team project-report-manjula kollipara
 
Building High Performance MySQL Query Systems and Analytic Applications
Building High Performance MySQL Query Systems and Analytic ApplicationsBuilding High Performance MySQL Query Systems and Analytic Applications
Building High Performance MySQL Query Systems and Analytic Applications
 
Building High Performance MySql Query Systems And Analytic Applications
Building High Performance MySql Query Systems And Analytic ApplicationsBuilding High Performance MySql Query Systems And Analytic Applications
Building High Performance MySql Query Systems And Analytic Applications
 
11g R2
11g R211g R2
11g R2
 
Big Data Glossary of terms
Big Data Glossary of termsBig Data Glossary of terms
Big Data Glossary of terms
 
EOUG95 - Client Server Very Large Databases - Paper
EOUG95 - Client Server Very Large Databases - PaperEOUG95 - Client Server Very Large Databases - Paper
EOUG95 - Client Server Very Large Databases - Paper
 
Clustering van IT-componenten
Clustering van IT-componentenClustering van IT-componenten
Clustering van IT-componenten
 
Handling Data in Mega Scale Systems
Handling Data in Mega Scale SystemsHandling Data in Mega Scale Systems
Handling Data in Mega Scale Systems
 
Google Megastore
Google MegastoreGoogle Megastore
Google Megastore
 
Data management in cloud study of existing systems and future opportunities
Data management in cloud study of existing systems and future opportunitiesData management in cloud study of existing systems and future opportunities
Data management in cloud study of existing systems and future opportunities
 
Kafka & Hadoop in Rakuten
Kafka & Hadoop in RakutenKafka & Hadoop in Rakuten
Kafka & Hadoop in Rakuten
 
Mow2012 data services
Mow2012 data servicesMow2012 data services
Mow2012 data services
 
Orca: A Modular Query Optimizer Architecture for Big Data
Orca: A Modular Query Optimizer Architecture for Big DataOrca: A Modular Query Optimizer Architecture for Big Data
Orca: A Modular Query Optimizer Architecture for Big Data
 
Gavin M
Gavin MGavin M
Gavin M
 
Apache ignite as in-memory computing platform
Apache ignite as in-memory computing platformApache ignite as in-memory computing platform
Apache ignite as in-memory computing platform
 
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
 
Hadoop and Voldemort @ LinkedIn
Hadoop and Voldemort @ LinkedInHadoop and Voldemort @ LinkedIn
Hadoop and Voldemort @ LinkedIn
 
Apache ignite v1.3
Apache ignite v1.3Apache ignite v1.3
Apache ignite v1.3
 

Recently uploaded

How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?XfilesPro
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsHyundai Motor Group
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersEnhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersThousandEyes
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machinePadma Pradeep
 
Azure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAzure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAndikSusilo4
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Allon Mureinik
 
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your BudgetHyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your BudgetEnjoy Anytime
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 

Recently uploaded (20)

How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersEnhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machine
 
Azure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & ApplicationAzure Monitor & Application Insight to monitor Infrastructure & Application
Azure Monitor & Application Insight to monitor Infrastructure & Application
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
 
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your BudgetHyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 

Climbing the beanstalk

  • 1. Climbing the Beanstalk Scaling Java Persistence to the Cloud Gordon Yorke JPA 2.1 Expert Group EclipseLink Architecture Council
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.

Editor's Notes

  1. Australia
  2. The Grid Read configuration a little different than the Grid Cache configuration in that rather than just caching objects in Coherence we start to execute queries for those objects against Coherence. This is for both primary key and non-primary key queries. If you noticed in the last configuration, the Grid Cache configuration, we were executing primary key queries against Coherence. In the Grid Read configuration we redirect all, both primary and non-primary key queries, to Coherence. This configuration is useful for entities is that have to be highly available. Being in Coherence they can be found a very rapidly without having to do a database round-trip. This is also useful for entities that have to have their changes written synchronously to the database. In this configuration EclipseLink is doing all the writing. Therefore you get the advantage of batch writing, JTA transaction integration, and other write optimizations and features. And you're also guaranteed that your database is correct. Each transaction runs, the database is updated synchronously, and once the transaction has committed Coherence is updated. It is possible for database failures to occur in this configuration, for example optimistic lock exceptions. If a failure does occur the transaction roles back and the changes are not applied to Coherence. So this configuration is suitable when database transaction failures can occur. The characteristics of this configuration include that the database is always correct – the database is committed before the cache is updated. All the write performance features of EclipseLink are available. But because all reads are being redirected into the grid you get the benefit of high-performance parallel query processing. And you can optionally configure a CacheLoader so that primary key queries against Coherence can load an object from the database if it's not in the grid.
  3. Grid Entity is a further incremental change on top of the previous Grid Read configuration. In this case all the reads and all the writes are executed against Coherence. In this configuration, Coherence is effectively the system of record. All the queries are redirected to it instead of the database. You may have a database behind Coherence but EclipseLink will treat Coherence as the data source. This configuration makes sense for Entities that need to be highly available and can be written asynchronously to a backing database through a CacheStore. With Coherence write behind changes can be flushed the database asynchronously at intervals. The database will not be up-to-date until Coherence flushes any pending writes. So if you're using right behind, in the period between the EclipseLink transaction commit and the flush of those changes the database will be out of sync with the cache so third-party applications that access the database may read stale data. This is nothing new for Coherence developers working with a database and using write behind. This configuration cannot benefit from all of the EclipseLink write features optimizations other available using the other two configurations.