Enviar pesquisa
Carregar
Scaling web systems ts
•
Transferir como PPTX, PDF
•
2 gostaram
•
624 visualizações
Sathyanarayana Panduranga
Seguir
Tecnologia
Negócios
Denunciar
Compartilhar
Denunciar
Compartilhar
1 de 30
Baixar agora
Recomendados
Exadata z pohledu zákazníka a novinky generace X8M - 1. část
Exadata z pohledu zákazníka a novinky generace X8M - 1. část
MarketingArrowECS_CZ
Big data oracle_introduccion
Big data oracle_introduccion
Fran Navarro
Solution Use Case Demo: The Power of Relationships in Your Big Data
Solution Use Case Demo: The Power of Relationships in Your Big Data
InfiniteGraph
Sesion covergentes 2016
Sesion covergentes 2016
Fran Navarro
NTT Data - Shinichi Yamada - Hadoop World 2010
NTT Data - Shinichi Yamada - Hadoop World 2010
Cloudera, Inc.
Consolidate and prepare for cloud efficiencies
Consolidate and prepare for cloud efficiencies
DLT Solutions
Oracle Data Protection - 1. část
Oracle Data Protection - 1. část
MarketingArrowECS_CZ
Bezpečná databáze a jak využít volně dostupný nástroj DBSAT
Bezpečná databáze a jak využít volně dostupný nástroj DBSAT
MarketingArrowECS_CZ
Recomendados
Exadata z pohledu zákazníka a novinky generace X8M - 1. část
Exadata z pohledu zákazníka a novinky generace X8M - 1. část
MarketingArrowECS_CZ
Big data oracle_introduccion
Big data oracle_introduccion
Fran Navarro
Solution Use Case Demo: The Power of Relationships in Your Big Data
Solution Use Case Demo: The Power of Relationships in Your Big Data
InfiniteGraph
Sesion covergentes 2016
Sesion covergentes 2016
Fran Navarro
NTT Data - Shinichi Yamada - Hadoop World 2010
NTT Data - Shinichi Yamada - Hadoop World 2010
Cloudera, Inc.
Consolidate and prepare for cloud efficiencies
Consolidate and prepare for cloud efficiencies
DLT Solutions
Oracle Data Protection - 1. část
Oracle Data Protection - 1. část
MarketingArrowECS_CZ
Bezpečná databáze a jak využít volně dostupný nástroj DBSAT
Bezpečná databáze a jak využít volně dostupný nástroj DBSAT
MarketingArrowECS_CZ
Oracle engineered systems executive presentation
Oracle engineered systems executive presentation
OTN Systems Hub
Reduccion TCO sistemas Integrados
Reduccion TCO sistemas Integrados
Fran Navarro
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
Sandesh Rao
Oracle Database Appliance - Introduction in Cyprus
Oracle Database Appliance - Introduction in Cyprus
Andy Panayiotou
Bay Area Hadoop User Group
Bay Area Hadoop User Group
Pentaho
Virtual Compute Appliance Oracle IaaS
Virtual Compute Appliance Oracle IaaS
Fran Navarro
Oracle Cloud – Application Performance Monitoring
Oracle Cloud – Application Performance Monitoring
MarketingArrowECS_CZ
Integrated dwh 3
Integrated dwh 3
Gwen (Chen) Shapira
Oracle Enterprise Metadata Management
Oracle Enterprise Metadata Management
Andrey Akulov
MongoDB World 2019: Implementation and Operationalization of MongoDB Sharding...
MongoDB World 2019: Implementation and Operationalization of MongoDB Sharding...
MongoDB
Oracle MAA (Maximum Availability Architecture) 18c - An Overview
Oracle MAA (Maximum Availability Architecture) 18c - An Overview
Markus Michalewicz
Autonomous Data Warehouse
Autonomous Data Warehouse
MarketingArrowECS_CZ
Future of-hadoop-analytics
Future of-hadoop-analytics
MapR Technologies
5 here today still here tomorrow new technology for big_forever_archives
5 here today still here tomorrow new technology for big_forever_archives
Dr. Wilfred Lin (Ph.D.)
IBM POWER8 Processor-Based Systems RAS
IBM POWER8 Processor-Based Systems RAS
thinkASG
Trafodion overview
Trafodion overview
Rohit Jain
Database Report
Database Report
Gagan Bhalla - ITIL®, CSM®
Meetup Oracle Database MAD_BCN: 1.2 Oracle Database 18c (autonomous database)
Meetup Oracle Database MAD_BCN: 1.2 Oracle Database 18c (autonomous database)
avanttic Consultoría Tecnológica
Data Mobility for the Oracle Database by JWilliams and RGonzalez
Data Mobility for the Oracle Database by JWilliams and RGonzalez
Markus Michalewicz
Empowering you with Democratized Data Access, Data Science and Machine Learning
Empowering you with Democratized Data Access, Data Science and Machine Learning
DataWorks Summit
Web application security
Web application security
Sathyanarayana Panduranga
Software as a Service
Software as a Service
Sathyanarayana Panduranga
Mais conteúdo relacionado
Mais procurados
Oracle engineered systems executive presentation
Oracle engineered systems executive presentation
OTN Systems Hub
Reduccion TCO sistemas Integrados
Reduccion TCO sistemas Integrados
Fran Navarro
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
Sandesh Rao
Oracle Database Appliance - Introduction in Cyprus
Oracle Database Appliance - Introduction in Cyprus
Andy Panayiotou
Bay Area Hadoop User Group
Bay Area Hadoop User Group
Pentaho
Virtual Compute Appliance Oracle IaaS
Virtual Compute Appliance Oracle IaaS
Fran Navarro
Oracle Cloud – Application Performance Monitoring
Oracle Cloud – Application Performance Monitoring
MarketingArrowECS_CZ
Integrated dwh 3
Integrated dwh 3
Gwen (Chen) Shapira
Oracle Enterprise Metadata Management
Oracle Enterprise Metadata Management
Andrey Akulov
MongoDB World 2019: Implementation and Operationalization of MongoDB Sharding...
MongoDB World 2019: Implementation and Operationalization of MongoDB Sharding...
MongoDB
Oracle MAA (Maximum Availability Architecture) 18c - An Overview
Oracle MAA (Maximum Availability Architecture) 18c - An Overview
Markus Michalewicz
Autonomous Data Warehouse
Autonomous Data Warehouse
MarketingArrowECS_CZ
Future of-hadoop-analytics
Future of-hadoop-analytics
MapR Technologies
5 here today still here tomorrow new technology for big_forever_archives
5 here today still here tomorrow new technology for big_forever_archives
Dr. Wilfred Lin (Ph.D.)
IBM POWER8 Processor-Based Systems RAS
IBM POWER8 Processor-Based Systems RAS
thinkASG
Trafodion overview
Trafodion overview
Rohit Jain
Database Report
Database Report
Gagan Bhalla - ITIL®, CSM®
Meetup Oracle Database MAD_BCN: 1.2 Oracle Database 18c (autonomous database)
Meetup Oracle Database MAD_BCN: 1.2 Oracle Database 18c (autonomous database)
avanttic Consultoría Tecnológica
Data Mobility for the Oracle Database by JWilliams and RGonzalez
Data Mobility for the Oracle Database by JWilliams and RGonzalez
Markus Michalewicz
Empowering you with Democratized Data Access, Data Science and Machine Learning
Empowering you with Democratized Data Access, Data Science and Machine Learning
DataWorks Summit
Mais procurados
(20)
Oracle engineered systems executive presentation
Oracle engineered systems executive presentation
Reduccion TCO sistemas Integrados
Reduccion TCO sistemas Integrados
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
Oracle Database Appliance - Introduction in Cyprus
Oracle Database Appliance - Introduction in Cyprus
Bay Area Hadoop User Group
Bay Area Hadoop User Group
Virtual Compute Appliance Oracle IaaS
Virtual Compute Appliance Oracle IaaS
Oracle Cloud – Application Performance Monitoring
Oracle Cloud – Application Performance Monitoring
Integrated dwh 3
Integrated dwh 3
Oracle Enterprise Metadata Management
Oracle Enterprise Metadata Management
MongoDB World 2019: Implementation and Operationalization of MongoDB Sharding...
MongoDB World 2019: Implementation and Operationalization of MongoDB Sharding...
Oracle MAA (Maximum Availability Architecture) 18c - An Overview
Oracle MAA (Maximum Availability Architecture) 18c - An Overview
Autonomous Data Warehouse
Autonomous Data Warehouse
Future of-hadoop-analytics
Future of-hadoop-analytics
5 here today still here tomorrow new technology for big_forever_archives
5 here today still here tomorrow new technology for big_forever_archives
IBM POWER8 Processor-Based Systems RAS
IBM POWER8 Processor-Based Systems RAS
Trafodion overview
Trafodion overview
Database Report
Database Report
Meetup Oracle Database MAD_BCN: 1.2 Oracle Database 18c (autonomous database)
Meetup Oracle Database MAD_BCN: 1.2 Oracle Database 18c (autonomous database)
Data Mobility for the Oracle Database by JWilliams and RGonzalez
Data Mobility for the Oracle Database by JWilliams and RGonzalez
Empowering you with Democratized Data Access, Data Science and Machine Learning
Empowering you with Democratized Data Access, Data Science and Machine Learning
Destaque
Web application security
Web application security
Sathyanarayana Panduranga
Software as a Service
Software as a Service
Sathyanarayana Panduranga
May 2010 Issue
May 2010 Issue
Leeds Met India
Series 33 - E - History of Pirana Satpanth Part 2 of 3
Series 33 - E - History of Pirana Satpanth Part 2 of 3
Satpanth Dharm
MILF Final Working Draft on Comprehensive Compact
MILF Final Working Draft on Comprehensive Compact
GenPeace
GE 2 minutes book 06 & 07-jun-1951 -suggesting our abkkp samaj is formed by ...
GE 2 minutes book 06 & 07-jun-1951 -suggesting our abkkp samaj is formed by ...
Satpanth Dharm
Series 16 -Attachment 2 -Momin Chetamani -English
Series 16 -Attachment 2 -Momin Chetamani -English
Satpanth Dharm
Diana
Diana
guestb9c60d
R edes inte
R edes inte
Cristhian Lopez Zambrano
Sorting Objects (Math)
Sorting Objects (Math)
Analyn LaGuardia
Cradle The Future - Joel Svedlund - JCI ECM 2010
Cradle The Future - Joel Svedlund - JCI ECM 2010
Joel Svedlund
801-ADMONUMB
801-ADMONUMB
Jaqueline Sanchez
U peace peacebuilding_slideshare
U peace peacebuilding_slideshare
GenPeace
Series 7 pirana satpanth-ni_pol_ane_satya_no_prakash-d
Series 7 pirana satpanth-ni_pol_ane_satya_no_prakash-d
Satpanth Dharm
El cinema com a reflex social
El cinema com a reflex social
toas
CerdasMulia Ramadhan - Training Ramadhan Pesantren Al-Munawaroh
CerdasMulia Ramadhan - Training Ramadhan Pesantren Al-Munawaroh
Arry Rahmawan
Series 23 dasond -wrong definition on satpanth website -de
Series 23 dasond -wrong definition on satpanth website -de
Satpanth Dharm
Test doubles and EasyMock
Test doubles and EasyMock
Rafael Antonio Gutiérrez Turullols
Series 1 Satpanthi Way of Converting Hindus to Muslims -d
Series 1 Satpanthi Way of Converting Hindus to Muslims -d
Satpanth Dharm
Oe 14 Appreciation letter -to himmatbhai & team -by ahmednagar district samaj
Oe 14 Appreciation letter -to himmatbhai & team -by ahmednagar district samaj
Satpanth Dharm
Destaque
(20)
Web application security
Web application security
Software as a Service
Software as a Service
May 2010 Issue
May 2010 Issue
Series 33 - E - History of Pirana Satpanth Part 2 of 3
Series 33 - E - History of Pirana Satpanth Part 2 of 3
MILF Final Working Draft on Comprehensive Compact
MILF Final Working Draft on Comprehensive Compact
GE 2 minutes book 06 & 07-jun-1951 -suggesting our abkkp samaj is formed by ...
GE 2 minutes book 06 & 07-jun-1951 -suggesting our abkkp samaj is formed by ...
Series 16 -Attachment 2 -Momin Chetamani -English
Series 16 -Attachment 2 -Momin Chetamani -English
Diana
Diana
R edes inte
R edes inte
Sorting Objects (Math)
Sorting Objects (Math)
Cradle The Future - Joel Svedlund - JCI ECM 2010
Cradle The Future - Joel Svedlund - JCI ECM 2010
801-ADMONUMB
801-ADMONUMB
U peace peacebuilding_slideshare
U peace peacebuilding_slideshare
Series 7 pirana satpanth-ni_pol_ane_satya_no_prakash-d
Series 7 pirana satpanth-ni_pol_ane_satya_no_prakash-d
El cinema com a reflex social
El cinema com a reflex social
CerdasMulia Ramadhan - Training Ramadhan Pesantren Al-Munawaroh
CerdasMulia Ramadhan - Training Ramadhan Pesantren Al-Munawaroh
Series 23 dasond -wrong definition on satpanth website -de
Series 23 dasond -wrong definition on satpanth website -de
Test doubles and EasyMock
Test doubles and EasyMock
Series 1 Satpanthi Way of Converting Hindus to Muslims -d
Series 1 Satpanthi Way of Converting Hindus to Muslims -d
Oe 14 Appreciation letter -to himmatbhai & team -by ahmednagar district samaj
Oe 14 Appreciation letter -to himmatbhai & team -by ahmednagar district samaj
Semelhante a Scaling web systems ts
Migrate and Modernize Your Database
Migrate and Modernize Your Database
Amazon Web Services
Preparing Your Data for Cloud Analytics & AI/ML
Preparing Your Data for Cloud Analytics & AI/ML
Amazon Web Services
Chug building a data lake in azure with spark and databricks
Chug building a data lake in azure with spark and databricks
Brandon Berlinrut
Deep Dive - Amazon Relational Database Services_AWSPSSummit_Singapore
Deep Dive - Amazon Relational Database Services_AWSPSSummit_Singapore
Amazon Web Services
Using AWS Purpose-Built Databases to Modernize your Applications
Using AWS Purpose-Built Databases to Modernize your Applications
Amazon Web Services
What_to_expect_from_oracle_database_12c
What_to_expect_from_oracle_database_12c
Maria Colgan
Simplify IT: Oracle SuperCluster
Simplify IT: Oracle SuperCluster
Fran Navarro
Oracle databáze – Konsolidovaná Data Management Platforma
Oracle databáze – Konsolidovaná Data Management Platforma
MarketingArrowECS_CZ
Amazon Aurora and AWS Database Migration Service
Amazon Aurora and AWS Database Migration Service
Amazon Web Services
Oracle Cloud
Oracle Cloud
MarketingArrowECS_CZ
Big data journey to the cloud rohit pujari 5.30.18
Big data journey to the cloud rohit pujari 5.30.18
Cloudera, Inc.
AWS Database Services @ Scale
AWS Database Services @ Scale
Amazon Web Services
Why to Use an Oracle Database?
Why to Use an Oracle Database?
Markus Michalewicz
The Evolution of Database Technologies Christian Bandulet
The Evolution of Database Technologies Christian Bandulet
Christian Bandulet
Data Catalog & ETL - Glue & Athena
Data Catalog & ETL - Glue & Athena
Amazon Web Services
Data Catalog & ETL - Glue & Athena
Data Catalog & ETL - Glue & Athena
Amazon Web Services
Tendencias Storage
Tendencias Storage
Fran Navarro
Oracle MAA Best Practices - Applications Considerations
Oracle MAA Best Practices - Applications Considerations
Markus Michalewicz
From raw data to business insights. A modern data lake
From raw data to business insights. A modern data lake
javier ramirez
Databases - Choosing the right Database on AWS
Databases - Choosing the right Database on AWS
Amazon Web Services
Semelhante a Scaling web systems ts
(20)
Migrate and Modernize Your Database
Migrate and Modernize Your Database
Preparing Your Data for Cloud Analytics & AI/ML
Preparing Your Data for Cloud Analytics & AI/ML
Chug building a data lake in azure with spark and databricks
Chug building a data lake in azure with spark and databricks
Deep Dive - Amazon Relational Database Services_AWSPSSummit_Singapore
Deep Dive - Amazon Relational Database Services_AWSPSSummit_Singapore
Using AWS Purpose-Built Databases to Modernize your Applications
Using AWS Purpose-Built Databases to Modernize your Applications
What_to_expect_from_oracle_database_12c
What_to_expect_from_oracle_database_12c
Simplify IT: Oracle SuperCluster
Simplify IT: Oracle SuperCluster
Oracle databáze – Konsolidovaná Data Management Platforma
Oracle databáze – Konsolidovaná Data Management Platforma
Amazon Aurora and AWS Database Migration Service
Amazon Aurora and AWS Database Migration Service
Oracle Cloud
Oracle Cloud
Big data journey to the cloud rohit pujari 5.30.18
Big data journey to the cloud rohit pujari 5.30.18
AWS Database Services @ Scale
AWS Database Services @ Scale
Why to Use an Oracle Database?
Why to Use an Oracle Database?
The Evolution of Database Technologies Christian Bandulet
The Evolution of Database Technologies Christian Bandulet
Data Catalog & ETL - Glue & Athena
Data Catalog & ETL - Glue & Athena
Data Catalog & ETL - Glue & Athena
Data Catalog & ETL - Glue & Athena
Tendencias Storage
Tendencias Storage
Oracle MAA Best Practices - Applications Considerations
Oracle MAA Best Practices - Applications Considerations
From raw data to business insights. A modern data lake
From raw data to business insights. A modern data lake
Databases - Choosing the right Database on AWS
Databases - Choosing the right Database on AWS
Último
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
The Digital Insurer
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
Andrey Devyatkin
AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024
The Digital Insurer
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
Overkill Security
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
sammart93
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
Rustici Software
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
UiPathCommunity
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
The Digital Insurer
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
apidays
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
apidays
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
Zilliz
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
debabhi2
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Jeffrey Haguewood
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
apidays
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Edi Saputra
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
Martijn de Jong
Último
(20)
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
Scaling web systems ts
1.
© 2010 Ariba,
Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
2.
What is scalability? ©
2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
3.
Some standard definitions The
ability of a system, network, or process to handle a growing amount of work in a capable manner or its ability to be enlarged to accommodate that growth. (Wikipedia) The ability to handle increased workload by repeatedly applying a cost effective strategy for extending a system’s capacity (SEI) © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
4.
Consider a simple
web application © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
5.
Load v/s Performance
of a nonscalable system © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
6.
Scalability Bottlenecks Memory Out
of memory Disk thrashing Fragmentation CPU Overload Context switches I/O waits Others Disk Network © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
7.
Load v/s performance
of a scalable system (Ideal) © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
8.
© 2010 Ariba,
Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
9.
Improve application performance… ©
2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
10.
Improve application performance Identify
and fix performance bottlenecks Algorithms DB queries Thread Deadlocks I/O Why is it important When you use less resources per task (processor time, memory, N/W round trips etc)… …You can handle a lot more load © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
11.
Loose coupling paradigms
- SOA Loosely coupled interactions One-to-one communications Consumer-based trigger Synchronous © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
12.
Loose coupling paradigms
- EDA Decoupled interactions Many-to-many communications Event-based trigger Asynchronous © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
13.
Distribute work, data Motivations: Can
scale independently Failures are isolated Segment Functionality application pools Segment Data Based on functional areas Horizontal split © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
14.
Asynchronous communication Motivations Can scale
components independently Can decouple availability Can spread peak load over time Integrate different services asynchronously Point to point / publish subscribe Staged event driven architecture (SEDA) © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
15.
Point to point messaging Publish-Subscribe messaging ©
2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
16.
Aggressive Caching Motivations: Save processing
cycles Save on network round-trip delays Content caching on CDNs Caching on clients (browsers/mobile devices) Caching at application layer Distributed caching © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
17.
Cache Everywhere Distributed In memory
Cache Proxy App Server Web Browser LRU Cache Browser Cache Page Cache Query Cache CDN Resource Cache © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc. Database Result Cache
18.
Avoid or distribute
state Motivations: Save memory and processing cycles Reduce machine affinity Strive for statelessness Maintain session data in browsers if possible Store session state in distributed cache © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
19.
Database Simplify entity relationships
to aid split Use the right kind of Database lock Avoid distributed transactions Don’t select everything, read only as much data as you can use Consider NoSQL storage © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
20.
© 2010 Ariba,
Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
21.
How do we
scale… Individual community © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
22.
Scaling storage: Multi
SID Goal: To be able to scale out DB storage as required Application Realm-Schema map Persistence Layer DB Instance #1 Schema 1 Schema 2 Schema 20 DB Instance #2 Schema 1 Schema 2 Schema 20 © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
23.
Clustering v/s Sharding Clustering
Oracle RAC, Hbase Automatically scale datastore Rebalances to distribute capacity Nodes communicate with each other Very complicated Cluster manager failure! Sharding Data distributed manually Split database to add capacity Data does not move Nodes are unaware of each other Custom algorithm based on functional / key distribution © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
24.
Scaling search: Arches Goal:
To be able to scale up search/publish activities linearly © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
25.
Arches goals… Elastic
architecture with ability to add capacity on the fly Sub-second search performance Improving indexing performance with customer isolation Eventually be used to build the global search service all across Ariba Arches interfaces… Pub API: Publish endpoint exposed over one way messaging Search API: REST based search endpoint Pull API: REST based data pull endpoint to be implemented by applications Manage API: REST based management endpoint © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
26.
Lightweight Metadata: Overcoming memory
bottleneck Problem: All realms, even realms with no customization and no activity consume lots of system resources Realms with no customization have the same foot print as realms with lots of customization Important for mid-market offering Goals © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
27.
Light weight metadata
solution Shape of a Class is now shared across Variants Sub Types computed dynamically © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
28.
Dynamic capacity realms
project Goal: Remove downtimes for scaling our products. Dynamic scalability Tolerate change to cluster topology Central connection manager Goal: Improve database connection usage. Central connection manager to distribute database connections based on usage Local pools & Global pool © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
29.
ADE (Ariba data
enrichment) scalability Instance 1 Weblogic Managed Server ADE Client Weblogic Administrative Server Load balancing (EJBs & RMI) Weight based sticky session JMS Product Product Engines Engines Instance 2 Weblogic Managed Server JDBC Clustering JMS Product Product Engines Engines Instance 3 Weblogic Managed Server JMS Product Product Engines Engines ADE DB © 2010 Ariba, Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc. SDB (Ops)
30.
© 2010 Ariba,
Inc. All rights reserved. The contents of this document are confidential and proprietary information of Ariba, Inc.
Baixar agora