SlideShare a Scribd company logo
FARMING HADOOP  IN THE CLOUD Steve Loughran  HP Laboratories June 2010
ABOUT ME ,[object Object]
Datacentre-scale apps & IaaS
ASF Member: stevel@apache.org
Committer: Ant, Hadoop-common, HDFS, MapReduce
Author: Ant in Action
Somewhat obsessive about testing
CLOUD ELIMINATES ,[object Object]
2+ week lead time on new hardware, storage
High Availability
Homogeneity
Static machine names, addresses and capabilities
Stable machines
A fast private network
Someone in the datacentre who cares about you
APPLICATIONS MUST BE AGILE ,[object Object]
Applications to handle moving services
Use dynamic DNS services; don’t cache IPAddrs
Don’t expect HDD content to last on a single disk
Restart VMs on any app failure Nothing is static. Nothing lasts .
CLASSIC TEAM ROLES Business Development Architecture Operations Development
Business Development Architecture Operations Development BEFORE Design Code Test Staging Live
RESPONSIBILITIES Work ,[object Object]
Developers code and test on local machines

More Related Content

What's hot

Cloudera Big Data Integration Speedpitch at TDWI Munich June 2017
Cloudera Big Data Integration Speedpitch at TDWI Munich June 2017Cloudera Big Data Integration Speedpitch at TDWI Munich June 2017
Cloudera Big Data Integration Speedpitch at TDWI Munich June 2017
Stefan Lipp
 
Intel and Cloudera: Accelerating Enterprise Big Data Success
Intel and Cloudera: Accelerating Enterprise Big Data SuccessIntel and Cloudera: Accelerating Enterprise Big Data Success
Intel and Cloudera: Accelerating Enterprise Big Data Success
Cloudera, Inc.
 
Hadoop on Virtual Machines
Hadoop on Virtual MachinesHadoop on Virtual Machines
Hadoop on Virtual Machines
Richard McDougall
 
Multi-Tenant Operations with Cloudera 5.7 & BT
Multi-Tenant Operations with Cloudera 5.7 & BTMulti-Tenant Operations with Cloudera 5.7 & BT
Multi-Tenant Operations with Cloudera 5.7 & BT
Cloudera, Inc.
 
Oracle big data appliance and solutions
Oracle big data appliance and solutionsOracle big data appliance and solutions
Oracle big data appliance and solutions
solarisyougood
 
Bare-metal performance for Big Data workloads on Docker containers
Bare-metal performance for Big Data workloads on Docker containersBare-metal performance for Big Data workloads on Docker containers
Bare-metal performance for Big Data workloads on Docker containers
BlueData, Inc.
 
Docker based Hadoop provisioning - anywhere
Docker based Hadoop provisioning - anywhere Docker based Hadoop provisioning - anywhere
Docker based Hadoop provisioning - anywhere
Janos Matyas
 
A deep dive into running data analytic workloads in the cloud
A deep dive into running data analytic workloads in the cloudA deep dive into running data analytic workloads in the cloud
A deep dive into running data analytic workloads in the cloud
Cloudera, Inc.
 
One Click Hadoop Clusters - Anywhere (Using Docker)
One Click Hadoop Clusters - Anywhere (Using Docker)One Click Hadoop Clusters - Anywhere (Using Docker)
One Click Hadoop Clusters - Anywhere (Using Docker)
DataWorks Summit
 
Hadoop Operations at LinkedIn
Hadoop Operations at LinkedInHadoop Operations at LinkedIn
Hadoop Operations at LinkedIn
DataWorks Summit
 
Intro to Apache Spark
Intro to Apache SparkIntro to Apache Spark
Intro to Apache Spark
Cloudera, Inc.
 
20150716 introduction to apache spark v3
20150716 introduction to apache spark v3 20150716 introduction to apache spark v3
20150716 introduction to apache spark v3
Andrey Vykhodtsev
 
BDTC2015 hulu-梁宇明-voidbox - docker on yarn
BDTC2015 hulu-梁宇明-voidbox - docker on yarnBDTC2015 hulu-梁宇明-voidbox - docker on yarn
BDTC2015 hulu-梁宇明-voidbox - docker on yarn
Jerry Wen
 
Hadoop Virtualization - Intel White Paper
Hadoop Virtualization - Intel White PaperHadoop Virtualization - Intel White Paper
Hadoop Virtualization - Intel White Paper
BlueData, Inc.
 
How to deploy Apache Spark in a multi-tenant, on-premises environment
How to deploy Apache Spark in a multi-tenant, on-premises environmentHow to deploy Apache Spark in a multi-tenant, on-premises environment
How to deploy Apache Spark in a multi-tenant, on-premises environment
BlueData, Inc.
 
Hadoop on Docker
Hadoop on DockerHadoop on Docker
Hadoop on Docker
Rakesh Saha
 
Hazelcast 3.6 Roadmap Preview
Hazelcast 3.6 Roadmap PreviewHazelcast 3.6 Roadmap Preview
Hazelcast 3.6 Roadmap Preview
Hazelcast
 
VMworld 2013: Virtualizing Databases: Doing IT Right
VMworld 2013: Virtualizing Databases: Doing IT Right VMworld 2013: Virtualizing Databases: Doing IT Right
VMworld 2013: Virtualizing Databases: Doing IT Right
VMworld
 
Ceph Deployment at Target: Customer Spotlight
Ceph Deployment at Target: Customer SpotlightCeph Deployment at Target: Customer Spotlight
Ceph Deployment at Target: Customer Spotlight
Colleen Corrice
 
Faster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for production
Faster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for productionFaster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for production
Faster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for production
Cloudera, Inc.
 

What's hot (20)

Cloudera Big Data Integration Speedpitch at TDWI Munich June 2017
Cloudera Big Data Integration Speedpitch at TDWI Munich June 2017Cloudera Big Data Integration Speedpitch at TDWI Munich June 2017
Cloudera Big Data Integration Speedpitch at TDWI Munich June 2017
 
Intel and Cloudera: Accelerating Enterprise Big Data Success
Intel and Cloudera: Accelerating Enterprise Big Data SuccessIntel and Cloudera: Accelerating Enterprise Big Data Success
Intel and Cloudera: Accelerating Enterprise Big Data Success
 
Hadoop on Virtual Machines
Hadoop on Virtual MachinesHadoop on Virtual Machines
Hadoop on Virtual Machines
 
Multi-Tenant Operations with Cloudera 5.7 & BT
Multi-Tenant Operations with Cloudera 5.7 & BTMulti-Tenant Operations with Cloudera 5.7 & BT
Multi-Tenant Operations with Cloudera 5.7 & BT
 
Oracle big data appliance and solutions
Oracle big data appliance and solutionsOracle big data appliance and solutions
Oracle big data appliance and solutions
 
Bare-metal performance for Big Data workloads on Docker containers
Bare-metal performance for Big Data workloads on Docker containersBare-metal performance for Big Data workloads on Docker containers
Bare-metal performance for Big Data workloads on Docker containers
 
Docker based Hadoop provisioning - anywhere
Docker based Hadoop provisioning - anywhere Docker based Hadoop provisioning - anywhere
Docker based Hadoop provisioning - anywhere
 
A deep dive into running data analytic workloads in the cloud
A deep dive into running data analytic workloads in the cloudA deep dive into running data analytic workloads in the cloud
A deep dive into running data analytic workloads in the cloud
 
One Click Hadoop Clusters - Anywhere (Using Docker)
One Click Hadoop Clusters - Anywhere (Using Docker)One Click Hadoop Clusters - Anywhere (Using Docker)
One Click Hadoop Clusters - Anywhere (Using Docker)
 
Hadoop Operations at LinkedIn
Hadoop Operations at LinkedInHadoop Operations at LinkedIn
Hadoop Operations at LinkedIn
 
Intro to Apache Spark
Intro to Apache SparkIntro to Apache Spark
Intro to Apache Spark
 
20150716 introduction to apache spark v3
20150716 introduction to apache spark v3 20150716 introduction to apache spark v3
20150716 introduction to apache spark v3
 
BDTC2015 hulu-梁宇明-voidbox - docker on yarn
BDTC2015 hulu-梁宇明-voidbox - docker on yarnBDTC2015 hulu-梁宇明-voidbox - docker on yarn
BDTC2015 hulu-梁宇明-voidbox - docker on yarn
 
Hadoop Virtualization - Intel White Paper
Hadoop Virtualization - Intel White PaperHadoop Virtualization - Intel White Paper
Hadoop Virtualization - Intel White Paper
 
How to deploy Apache Spark in a multi-tenant, on-premises environment
How to deploy Apache Spark in a multi-tenant, on-premises environmentHow to deploy Apache Spark in a multi-tenant, on-premises environment
How to deploy Apache Spark in a multi-tenant, on-premises environment
 
Hadoop on Docker
Hadoop on DockerHadoop on Docker
Hadoop on Docker
 
Hazelcast 3.6 Roadmap Preview
Hazelcast 3.6 Roadmap PreviewHazelcast 3.6 Roadmap Preview
Hazelcast 3.6 Roadmap Preview
 
VMworld 2013: Virtualizing Databases: Doing IT Right
VMworld 2013: Virtualizing Databases: Doing IT Right VMworld 2013: Virtualizing Databases: Doing IT Right
VMworld 2013: Virtualizing Databases: Doing IT Right
 
Ceph Deployment at Target: Customer Spotlight
Ceph Deployment at Target: Customer SpotlightCeph Deployment at Target: Customer Spotlight
Ceph Deployment at Target: Customer Spotlight
 
Faster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for production
Faster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for productionFaster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for production
Faster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for production
 

Viewers also liked

PSUG #52 Dataflow and simplified reactive programming with Akka-streams
PSUG #52 Dataflow and simplified reactive programming with Akka-streamsPSUG #52 Dataflow and simplified reactive programming with Akka-streams
PSUG #52 Dataflow and simplified reactive programming with Akka-streams
Stephane Manciot
 
Des principes de la démarche DevOps à sa mise en oeuvre
Des principes de la démarche DevOps à sa mise en oeuvreDes principes de la démarche DevOps à sa mise en oeuvre
Des principes de la démarche DevOps à sa mise en oeuvre
Stephane Manciot
 
Machine learning
Machine learningMachine learning
Machine learning
ebiznext
 
Packaging et déploiement d'une application avec Docker et Ansible @DevoxxFR 2015
Packaging et déploiement d'une application avec Docker et Ansible @DevoxxFR 2015Packaging et déploiement d'une application avec Docker et Ansible @DevoxxFR 2015
Packaging et déploiement d'une application avec Docker et Ansible @DevoxxFR 2015
Stephane Manciot
 
Enterprise Hadoop in the Cloud. In Minutes. | How to Run Cloudera Enterprise ...
Enterprise Hadoop in the Cloud. In Minutes. | How to Run Cloudera Enterprise ...Enterprise Hadoop in the Cloud. In Minutes. | How to Run Cloudera Enterprise ...
Enterprise Hadoop in the Cloud. In Minutes. | How to Run Cloudera Enterprise ...
Cloudera, Inc.
 
Spark / Mesos Cluster Optimization
Spark / Mesos Cluster OptimizationSpark / Mesos Cluster Optimization
Spark / Mesos Cluster Optimization
ebiznext
 
DevOps avec Ansible et Docker
DevOps avec Ansible et DockerDevOps avec Ansible et Docker
DevOps avec Ansible et Docker
Stephane Manciot
 
How to Build Multi-disciplinary Analytics Applications on a Shared Data Platform
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformHow to Build Multi-disciplinary Analytics Applications on a Shared Data Platform
How to Build Multi-disciplinary Analytics Applications on a Shared Data Platform
Cloudera, Inc.
 
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...
Cloudera, Inc.
 

Viewers also liked (9)

PSUG #52 Dataflow and simplified reactive programming with Akka-streams
PSUG #52 Dataflow and simplified reactive programming with Akka-streamsPSUG #52 Dataflow and simplified reactive programming with Akka-streams
PSUG #52 Dataflow and simplified reactive programming with Akka-streams
 
Des principes de la démarche DevOps à sa mise en oeuvre
Des principes de la démarche DevOps à sa mise en oeuvreDes principes de la démarche DevOps à sa mise en oeuvre
Des principes de la démarche DevOps à sa mise en oeuvre
 
Machine learning
Machine learningMachine learning
Machine learning
 
Packaging et déploiement d'une application avec Docker et Ansible @DevoxxFR 2015
Packaging et déploiement d'une application avec Docker et Ansible @DevoxxFR 2015Packaging et déploiement d'une application avec Docker et Ansible @DevoxxFR 2015
Packaging et déploiement d'une application avec Docker et Ansible @DevoxxFR 2015
 
Enterprise Hadoop in the Cloud. In Minutes. | How to Run Cloudera Enterprise ...
Enterprise Hadoop in the Cloud. In Minutes. | How to Run Cloudera Enterprise ...Enterprise Hadoop in the Cloud. In Minutes. | How to Run Cloudera Enterprise ...
Enterprise Hadoop in the Cloud. In Minutes. | How to Run Cloudera Enterprise ...
 
Spark / Mesos Cluster Optimization
Spark / Mesos Cluster OptimizationSpark / Mesos Cluster Optimization
Spark / Mesos Cluster Optimization
 
DevOps avec Ansible et Docker
DevOps avec Ansible et DockerDevOps avec Ansible et Docker
DevOps avec Ansible et Docker
 
How to Build Multi-disciplinary Analytics Applications on a Shared Data Platform
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformHow to Build Multi-disciplinary Analytics Applications on a Shared Data Platform
How to Build Multi-disciplinary Analytics Applications on a Shared Data Platform
 
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...
Webinar - Sehr empfehlenswert: wie man aus Daten durch maschinelles Lernen We...
 

Similar to Farming hadoop in_the_cloud

New Roles In The Cloud
New Roles In The CloudNew Roles In The Cloud
New Roles In The Cloud
Steve Loughran
 
Cloud Foundry: Hands-on Deployment Workshop
Cloud Foundry: Hands-on Deployment WorkshopCloud Foundry: Hands-on Deployment Workshop
Cloud Foundry: Hands-on Deployment Workshop
Manuel Garcia
 
Sureh hadoop 3 years t
Sureh hadoop 3 years tSureh hadoop 3 years t
Sureh hadoop 3 years t
suresh thallapelly
 
Applications on Hadoop
Applications on HadoopApplications on Hadoop
Applications on Hadoop
markgrover
 
Hadoop past, present and future
Hadoop past, present and futureHadoop past, present and future
Hadoop past, present and future
Codemotion
 
From limited Hadoop compute capacity to increased data scientist efficiency
From limited Hadoop compute capacity to increased data scientist efficiencyFrom limited Hadoop compute capacity to increased data scientist efficiency
From limited Hadoop compute capacity to increased data scientist efficiency
Alluxio, Inc.
 
Data Orchestration Platform for the Cloud
Data Orchestration Platform for the CloudData Orchestration Platform for the Cloud
Data Orchestration Platform for the Cloud
Alluxio, Inc.
 
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
What are Hadoop Components? Hadoop Ecosystem and Architecture | EdurekaWhat are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
Edureka!
 
Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS
Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFSDiscover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS
Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS
Hortonworks
 
Big Data Integration Webinar: Getting Started With Hadoop Big Data
Big Data Integration Webinar: Getting Started With Hadoop Big DataBig Data Integration Webinar: Getting Started With Hadoop Big Data
Big Data Integration Webinar: Getting Started With Hadoop Big DataPentaho
 
Harnessing the Power of Apache Hadoop
Harnessing the Power of Apache Hadoop Harnessing the Power of Apache Hadoop
Harnessing the Power of Apache Hadoop
Cloudera, Inc.
 
Sql saturday pig session (wes floyd) v2
Sql saturday   pig session (wes floyd) v2Sql saturday   pig session (wes floyd) v2
Sql saturday pig session (wes floyd) v2Wes Floyd
 
CCD-410 Cloudera Study Material
CCD-410 Cloudera Study MaterialCCD-410 Cloudera Study Material
CCD-410 Cloudera Study Material
Roxycodone Online
 
Sunshine consulting mopuru babu cv_java_j2ee_spring_bigdata_scala
Sunshine consulting mopuru babu cv_java_j2ee_spring_bigdata_scalaSunshine consulting mopuru babu cv_java_j2ee_spring_bigdata_scala
Sunshine consulting mopuru babu cv_java_j2ee_spring_bigdata_scala
Mopuru Babu
 
Introduction to the Hadoop EcoSystem
Introduction to the Hadoop EcoSystemIntroduction to the Hadoop EcoSystem
Introduction to the Hadoop EcoSystem
Shivaji Dutta
 
Vmware Serengeti - Based on Infochimps Ironfan
Vmware Serengeti - Based on Infochimps IronfanVmware Serengeti - Based on Infochimps Ironfan
Vmware Serengeti - Based on Infochimps Ironfan
Jim Kaskade
 

Similar to Farming hadoop in_the_cloud (20)

New Roles In The Cloud
New Roles In The CloudNew Roles In The Cloud
New Roles In The Cloud
 
Monika_Raghuvanshi
Monika_RaghuvanshiMonika_Raghuvanshi
Monika_Raghuvanshi
 
Cloud Foundry: Hands-on Deployment Workshop
Cloud Foundry: Hands-on Deployment WorkshopCloud Foundry: Hands-on Deployment Workshop
Cloud Foundry: Hands-on Deployment Workshop
 
Sureh hadoop 3 years t
Sureh hadoop 3 years tSureh hadoop 3 years t
Sureh hadoop 3 years t
 
Applications on Hadoop
Applications on HadoopApplications on Hadoop
Applications on Hadoop
 
Hortonworks.bdb
Hortonworks.bdbHortonworks.bdb
Hortonworks.bdb
 
Big Data
Big DataBig Data
Big Data
 
Hadoop past, present and future
Hadoop past, present and futureHadoop past, present and future
Hadoop past, present and future
 
From limited Hadoop compute capacity to increased data scientist efficiency
From limited Hadoop compute capacity to increased data scientist efficiencyFrom limited Hadoop compute capacity to increased data scientist efficiency
From limited Hadoop compute capacity to increased data scientist efficiency
 
Data Orchestration Platform for the Cloud
Data Orchestration Platform for the CloudData Orchestration Platform for the Cloud
Data Orchestration Platform for the Cloud
 
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
What are Hadoop Components? Hadoop Ecosystem and Architecture | EdurekaWhat are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
 
Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS
Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFSDiscover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS
Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS
 
Big Data Integration Webinar: Getting Started With Hadoop Big Data
Big Data Integration Webinar: Getting Started With Hadoop Big DataBig Data Integration Webinar: Getting Started With Hadoop Big Data
Big Data Integration Webinar: Getting Started With Hadoop Big Data
 
Harnessing the Power of Apache Hadoop
Harnessing the Power of Apache Hadoop Harnessing the Power of Apache Hadoop
Harnessing the Power of Apache Hadoop
 
Sql saturday pig session (wes floyd) v2
Sql saturday   pig session (wes floyd) v2Sql saturday   pig session (wes floyd) v2
Sql saturday pig session (wes floyd) v2
 
CCD-410 Cloudera Study Material
CCD-410 Cloudera Study MaterialCCD-410 Cloudera Study Material
CCD-410 Cloudera Study Material
 
Prashanth Kumar_Hadoop_NEW
Prashanth Kumar_Hadoop_NEWPrashanth Kumar_Hadoop_NEW
Prashanth Kumar_Hadoop_NEW
 
Sunshine consulting mopuru babu cv_java_j2ee_spring_bigdata_scala
Sunshine consulting mopuru babu cv_java_j2ee_spring_bigdata_scalaSunshine consulting mopuru babu cv_java_j2ee_spring_bigdata_scala
Sunshine consulting mopuru babu cv_java_j2ee_spring_bigdata_scala
 
Introduction to the Hadoop EcoSystem
Introduction to the Hadoop EcoSystemIntroduction to the Hadoop EcoSystem
Introduction to the Hadoop EcoSystem
 
Vmware Serengeti - Based on Infochimps Ironfan
Vmware Serengeti - Based on Infochimps IronfanVmware Serengeti - Based on Infochimps Ironfan
Vmware Serengeti - Based on Infochimps Ironfan
 

More from Steve Loughran

Hadoop Vectored IO
Hadoop Vectored IOHadoop Vectored IO
Hadoop Vectored IO
Steve Loughran
 
The age of rename() is over
The age of rename() is overThe age of rename() is over
The age of rename() is over
Steve Loughran
 
What does Rename Do: (detailed version)
What does Rename Do: (detailed version)What does Rename Do: (detailed version)
What does Rename Do: (detailed version)
Steve Loughran
 
Put is the new rename: San Jose Summit Edition
Put is the new rename: San Jose Summit EditionPut is the new rename: San Jose Summit Edition
Put is the new rename: San Jose Summit Edition
Steve Loughran
 
@Dissidentbot: dissent will be automated!
@Dissidentbot: dissent will be automated!@Dissidentbot: dissent will be automated!
@Dissidentbot: dissent will be automated!
Steve Loughran
 
PUT is the new rename()
PUT is the new rename()PUT is the new rename()
PUT is the new rename()
Steve Loughran
 
Extreme Programming Deployed
Extreme Programming DeployedExtreme Programming Deployed
Extreme Programming Deployed
Steve Loughran
 
Testing
TestingTesting
I hate mocking
I hate mockingI hate mocking
I hate mocking
Steve Loughran
 
What does rename() do?
What does rename() do?What does rename() do?
What does rename() do?
Steve Loughran
 
Dancing Elephants: Working with Object Storage in Apache Spark and Hive
Dancing Elephants: Working with Object Storage in Apache Spark and HiveDancing Elephants: Working with Object Storage in Apache Spark and Hive
Dancing Elephants: Working with Object Storage in Apache Spark and Hive
Steve Loughran
 
Apache Spark and Object Stores —for London Spark User Group
Apache Spark and Object Stores —for London Spark User GroupApache Spark and Object Stores —for London Spark User Group
Apache Spark and Object Stores —for London Spark User Group
Steve Loughran
 
Spark Summit East 2017: Apache spark and object stores
Spark Summit East 2017: Apache spark and object storesSpark Summit East 2017: Apache spark and object stores
Spark Summit East 2017: Apache spark and object stores
Steve Loughran
 
Hadoop, Hive, Spark and Object Stores
Hadoop, Hive, Spark and Object StoresHadoop, Hive, Spark and Object Stores
Hadoop, Hive, Spark and Object Stores
Steve Loughran
 
Apache Spark and Object Stores
Apache Spark and Object StoresApache Spark and Object Stores
Apache Spark and Object Stores
Steve Loughran
 
Household INFOSEC in a Post-Sony Era
Household INFOSEC in a Post-Sony EraHousehold INFOSEC in a Post-Sony Era
Household INFOSEC in a Post-Sony Era
Steve Loughran
 
Hadoop and Kerberos: the Madness Beyond the Gate: January 2016 edition
Hadoop and Kerberos: the Madness Beyond the Gate: January 2016 editionHadoop and Kerberos: the Madness Beyond the Gate: January 2016 edition
Hadoop and Kerberos: the Madness Beyond the Gate: January 2016 edition
Steve Loughran
 
Hadoop and Kerberos: the Madness Beyond the Gate
Hadoop and Kerberos: the Madness Beyond the GateHadoop and Kerberos: the Madness Beyond the Gate
Hadoop and Kerberos: the Madness Beyond the Gate
Steve Loughran
 
Slider: Applications on YARN
Slider: Applications on YARNSlider: Applications on YARN
Slider: Applications on YARN
Steve Loughran
 
YARN Services
YARN ServicesYARN Services
YARN Services
Steve Loughran
 

More from Steve Loughran (20)

Hadoop Vectored IO
Hadoop Vectored IOHadoop Vectored IO
Hadoop Vectored IO
 
The age of rename() is over
The age of rename() is overThe age of rename() is over
The age of rename() is over
 
What does Rename Do: (detailed version)
What does Rename Do: (detailed version)What does Rename Do: (detailed version)
What does Rename Do: (detailed version)
 
Put is the new rename: San Jose Summit Edition
Put is the new rename: San Jose Summit EditionPut is the new rename: San Jose Summit Edition
Put is the new rename: San Jose Summit Edition
 
@Dissidentbot: dissent will be automated!
@Dissidentbot: dissent will be automated!@Dissidentbot: dissent will be automated!
@Dissidentbot: dissent will be automated!
 
PUT is the new rename()
PUT is the new rename()PUT is the new rename()
PUT is the new rename()
 
Extreme Programming Deployed
Extreme Programming DeployedExtreme Programming Deployed
Extreme Programming Deployed
 
Testing
TestingTesting
Testing
 
I hate mocking
I hate mockingI hate mocking
I hate mocking
 
What does rename() do?
What does rename() do?What does rename() do?
What does rename() do?
 
Dancing Elephants: Working with Object Storage in Apache Spark and Hive
Dancing Elephants: Working with Object Storage in Apache Spark and HiveDancing Elephants: Working with Object Storage in Apache Spark and Hive
Dancing Elephants: Working with Object Storage in Apache Spark and Hive
 
Apache Spark and Object Stores —for London Spark User Group
Apache Spark and Object Stores —for London Spark User GroupApache Spark and Object Stores —for London Spark User Group
Apache Spark and Object Stores —for London Spark User Group
 
Spark Summit East 2017: Apache spark and object stores
Spark Summit East 2017: Apache spark and object storesSpark Summit East 2017: Apache spark and object stores
Spark Summit East 2017: Apache spark and object stores
 
Hadoop, Hive, Spark and Object Stores
Hadoop, Hive, Spark and Object StoresHadoop, Hive, Spark and Object Stores
Hadoop, Hive, Spark and Object Stores
 
Apache Spark and Object Stores
Apache Spark and Object StoresApache Spark and Object Stores
Apache Spark and Object Stores
 
Household INFOSEC in a Post-Sony Era
Household INFOSEC in a Post-Sony EraHousehold INFOSEC in a Post-Sony Era
Household INFOSEC in a Post-Sony Era
 
Hadoop and Kerberos: the Madness Beyond the Gate: January 2016 edition
Hadoop and Kerberos: the Madness Beyond the Gate: January 2016 editionHadoop and Kerberos: the Madness Beyond the Gate: January 2016 edition
Hadoop and Kerberos: the Madness Beyond the Gate: January 2016 edition
 
Hadoop and Kerberos: the Madness Beyond the Gate
Hadoop and Kerberos: the Madness Beyond the GateHadoop and Kerberos: the Madness Beyond the Gate
Hadoop and Kerberos: the Madness Beyond the Gate
 
Slider: Applications on YARN
Slider: Applications on YARNSlider: Applications on YARN
Slider: Applications on YARN
 
YARN Services
YARN ServicesYARN Services
YARN Services
 

Recently uploaded

Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Kari Kakkonen
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
Alpen-Adria-Universität
 
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdfFIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance
 
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
名前 です男
 
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
DianaGray10
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Paige Cruz
 
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptxSecstrike : Reverse Engineering & Pwnable tools for CTF.pptx
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
nkrafacyberclub
 
GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...
ThomasParaiso2
 
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
DianaGray10
 
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
James Anderson
 
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionGenerative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Aggregage
 
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofszkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
Alex Pruden
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Neo4j
 
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 daysPushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Adtran
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Neo4j
 
Introduction to CHERI technology - Cybersecurity
Introduction to CHERI technology - CybersecurityIntroduction to CHERI technology - Cybersecurity
Introduction to CHERI technology - Cybersecurity
mikeeftimakis1
 
Epistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI supportEpistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI support
Alan Dix
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
Uni Systems S.M.S.A.
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Quotidiano Piemontese
 
GraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge GraphGraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge Graph
Guy Korland
 

Recently uploaded (20)

Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
 
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdfFIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
 
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
 
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
 
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptxSecstrike : Reverse Engineering & Pwnable tools for CTF.pptx
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
 
GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...
 
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
 
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
 
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionGenerative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
 
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofszkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
 
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 daysPushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
 
Introduction to CHERI technology - Cybersecurity
Introduction to CHERI technology - CybersecurityIntroduction to CHERI technology - Cybersecurity
Introduction to CHERI technology - Cybersecurity
 
Epistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI supportEpistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI support
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
 
GraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge GraphGraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge Graph
 

Farming hadoop in_the_cloud

Editor's Notes

  1. 6 June 2010 HP Confidential
  2. What does all this mean? You don’t need to predict your customer load in advance, though you had better hope your supplier can offer a service to match You don’ t have to wait a few weeks for some order of hardware to get delivered. You can’t buy HA kit: RAID, L7 routers, other nice things, to address availability. You need to design these in You can’t be sure your machines will stay around, that when they come back their names and IP Addresses may change You don’t have someone with a pager in the room who will track down network problems for you 6 June 2010 HP Confidential
  3. We really need to rethink how to design apps in this world, the old ways don’t. When a VM goes, so does any transient HDD. When a machine gets terminated and re-instantiated, it can have different hostname and address. Nor can that server deal with machines moving around. Which is a pity as the simplest way to deal with app trouble is to reset the VM. No need to worry about what its previous state June 6, 2010 HP Confidential
  4. Here are some of the classic roles of back-end projects. There’s also graphic designers, marketing, content generation, etc. But this is the code side. Everyone’s job is hard. Biz dev: make sure the idea is good, predict demand , get the ops team to work with Arch and Finance to get machines to meet the demand Architecture: design something that works in the machines that ops will bring up Developers: code and test the app, produce something that works 6 June 2010 HP Confidential
  5. This is how things were built -at best- if you had a static set of machines as your target. Even if you design/code/test in a cycle, going live creates problems. Different systems, different networks, etc. Staging is meant to simplify this with a setup that mimics production, but it still has different users . June 6, 2010 HP Confidential
  6. This is how things are today. Set up for conflict. The big one is developers "ship code that is functional" and ops "run secure services". 6 June 2010 HP Confidential
  7. Once you stop needing a physical cluster of machines to test on, you can give every developer a virtual cluster which mimics that in production. You can bring up a staging site on the public server farm, let third parties play with it, switch it over when you are happy (ignoring data issues) 6 June 2010 HP Confidential
  8. Developers shouldn’t be creating the machine configurations; that’s a job for the architect and ops Ops have to move beyond the pager when a machine fails to getting an overall statistical view of what works, doesn't work, and look at the total, perceived picture. No more panicing when a machine goes down, but do worry when all the machines start to fail too often. Solution: monitoring and statistics. Datamining. Hadoop. Biz dev/management need to keep an eye on costs and revenue. Costs: machines. Revenue, things like why people are switching from free to premium, where customers are coming from. Statistics.Datamining. Hadoop. June 6, 2010 HP Confidential
  9. At this scale, datamining and statistics becomes an essential background activity Test result collection and analysis Application and VM log file capture, analysis: chukwa Application load analysis - feed into VM create/destroy User/paying customer mining -when do people pay, when do they leave? Infrastructure: how do people and their VMs behave? 6 June 2010 HP Confidential
  10. These are where Hadoop contains assumptions that are valid in the physical datacentre, but which don't work in a virtual world. 6 June 2010 HP Confidential
  11. This for everyone to create machines. You can only create machines in roles you have the right to. This is more than a constrained image, much more of the config is locked down: VM, networking, dynamic options. June 6, 2010 HP Confidential
  12. I’ve cheated and added some Hadoop-specificness in the web front end; you can create Hadoop workers and it knows to create the Master first, and passes the master hostname down so that the workers bond properly. This use case needs to be made generic June 6, 2010 HP Confidential
  13. This is a fairly weak Web UI but it’s designed to feed into portals. It also happens to test easily. 6 June 2010 HP Confidential
  14. 6 June 2010 HP Confidential