Season 2 Episode 1March 12, 2014

Evening Outline

Lightning Talks:- S3mper- PigPen- STAASH- Dynomite- Aegisthus- Suro- Zeno

- Lipstick on GCE- AnsWerS- IBM- Coursera

41 projects… Now what?

● Cohesive platform

● Workshops / Training / Documentation

● Participate and contribute : netflixoss@netflix.com

Lightning talks

Lipstick, Hadoop, andBig Data on the Google CloudMatt BookmanSolutions Architect

Google Confidential and Proprietary

Google Compute Engine - VMs in Google Datacenters

● Public Preview - May 2013

● General Availability - December 2013

Google Confidential and Proprietary

Demo (Summer 2013): Pig on Compute Engine

Sweet demo!

Google Confidential and Proprietary

Netflix OSS Meetup - July 17, 2013

Google Confidential and Proprietary

Lipstick - Providing insights

Google Confidential and Proprietary

Google Confidential and Proprietary

Hadoop on GCE + Cloud Storage (GCS) Connector

Accenture:Cloud vs. Bare-Metal

● Cloud-based Hadoop deployments offer better price-performance ratios than bare-metal

● Cloud’s virtualization expands performance-tuning opportunities

● Using remote storage outperforms local disk HDFS

Google Confidential and Proprietary

Data in GCS, Lipstick DB in Cloud SQLGoogle Cloud Platform

Output Data

Lipstick Database

Hadoop Master

MapReduce JobTracker

Hadoop Worker

MapReduce TaskTrackerHadoop Worker

MapReduce TaskTrackerHadoop Worker

MapReduce TaskTrackerLipstick Server

Input Data

Google Confidential and Proprietary

● Netflix Lipstick on Google Compute Enginehttps://cloud.google.com/developers/articles/netflix-lipstick-on-google-compute-engine

● GCS Connector for Hadoophttps://developers.google.com/hadoop/google-cloud-storage-connector

● Cloud-based Hadoop Deployments: Benefits and Considerationshttp://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Cloud-Based-Hadoop-Deployments-Benefits-and-Considerations.pdf

● Apache Hadoop, Hive, and Pig on Google Compute Enginehttps://cloud.google.com/developers/articles/apache-hadoop-hive-and-pig-on-google-compute-engine

Resources

Google Confidential and Proprietary

Thank you

@Answers4AWS

Cloud Prize and Beyond

Peter Sankauskas@pas256

@Answers4AWS

March 2013

@Answers4AWS

First idea

• AsgardFormation

• CloudFormation for Asgard

@Answers4AWS

@Answers4AWS

@Answers4AWS

Requirements

• AsgardFormation

• Asgard running

• AWS Credentials

• IAM user

• Policy

• Security Group

• EC2 instance

• Asgard downloaded and configured

• Tomcat downloaded and configured

• Java downloaded and installed

• Linux configured

@Answers4AWS

@Answers4AWS

Asgard playbook

• Base

• Install usual Linux packages

• Basic system hardening and security packages

• Oracle Java 7

• Tomcat 7

• Asgard

• Latest release from GitHub

• Add BASIC authentication

@Answers4AWS

Other playbooks

• Eureka

• Edda

• Simian Army

• Ice

• Aminator

• Genie

@Answers4AWS

AMIs

• Initially built using my own scripts based on Eric Hammond’s (@esh) work

• Then using Aminator

• Created Ubuntu Foundation AMIs

• Added the Ansible Provisioner for Aminator

• Put a couple of them on the AWS Marketplace for free

@Answers4AWS

CloudFormation

• One-click deploy

• Well, about 10 going through the AWS Web Console wizard

• Designed to get you up and running quickly

• Test it out, see if you like it

• NOT production quality

• No real security

• No HA

• No scalability

@Answers4AWS

@Answers4AWS

What’s next?

@Answers4AWS

Do you do this? (this is not my slide)

@Answers4AWS

@Answers4AWS

Beta users

• From a successful CI build

• To a Fully Baked AMI

• Use in Testing and Production

• Without you doing anything

• ZERO clicks

• Signups are open

bakery.answersforaws.com

@Answers4AWS

Thank you

http://bakery.answersforaws.com/

bakery@answersforaws.com

See me at the demo station

Peter Sankauskas@pas256

IBM Scalable Services Fabricfor Netflix S2E1 Meetup

Andrew Spyker

@aspyker

History and Future

2012

SPECjEnterprise

2013

AcmeAir RunOn IBM Cloud at“Web Scale”

2014

Scalable ServicesFabric internally for

IBM Services

Scalable ServicesFabric SaaS and On-Prem?

Sample applicationcloud prize work

AcmeAir Cloud/MobileSample/Benchmark born

Codename: BlueMix

Portability cloudprize work

Scalable Service Fabric WorkNetflix OSS IBM port/enablement

Netflix “Zen” of Cloud • Worked with initial services to enable cloud native arch• Worked with initial services to enable NetflixOSS usages• Created scorecard and tests for “cloud native” readiness

Highly Available IaaS and Cloud Services

• Deployment across multiple IBM SoftLayer IaaS datacenters and global and local load balancers

• Complete automation via IBM SoftLayer IaaS API’s• Ensured facilities for automatic failure recovery

Micro-service Runtimes (Karyon, Eureka Client, Ribbon, Hystrix, Archaius)

• Ported to work with IBM SoftLayer IaaS and on the WebSphere Liberty Profile application server

• Created “eureka-sidecar” for non-Java runtimes and ElasticSearch discovery

Netflix OSS Servers(Asgard, Eureka Server, Turbine)

• Ported to work with IBM SoftLayer IaaS + RightScale• Operationalized HA and secure deployments for multiple service tenants

Adopted Chaos Testing • Ported Chaos Monkey to IBM SoftLayer IaaS• Performed manual Chaos Gorilla validation on services

Worked through devops tool chain

• Worked with initial services to enable continuous delivery with devops (and imagine baking via Animator like tool)

Come meet the team!Looks like … Tweets from … Talks about …

Adolfo @adolforod API Management and Cloud Integration, user of NetflixOSS platform. Appliances in the cloud.

Brian @bkmartin IBM BlueMix (PaaS), enabling composable apps in PaaS

Darrell IBM Research focusing on NetflixOSS devops and on-premise deployments

David @dcurrie WebSphere Liberty Profile application server NetflixOSS development and PaaS integration

Jonathan @ma4jpb NetflixOSS portability across many aspectsCloud messaging (in relation to Suro)

Matt @matrober API Management, user of NetflixOSS platformConverted service to be cloud native

Rachel @rreinitz IBM Services, interested in helping you get to this cloud native in SaaS and on-premise

Ricky @rickymoorhouse API Management, user of NetflixOSS platformCreator of Imaginator

Will @auwilli98 API Management operations, user of NetflixOSS platform

Priam + Aegisthus@CourseraNetflixOSS Meetup

Introduction@DanielChiaJH

Software Engineer, Infrastructure Team

Coursera

Overview• Philosophy

• Priam

• Aegisthus

• Conclusion

Philosophy• Architecture Patterns

• Use what we can

• Incorporate the spirit of others

Priam – Wins• Token Management

• S3 Backup + Restore

• Config

Priam – Next Steps?• SimpleDB -> DynamoDB

• Backups blow out OS disk buffer cache

• Compatibility with newer C* versions

Aegisthus - Wins• Novel workflow

• Data reduced to one authoritative copy

• Possibility for incremental jobs

Aegisthus – What Next?• C* 1.2 / 2.0

• CQL3

• Priam <–> Aegisthus

• Better compressed SSTable support

Conclusion• Come chat with me!

• Especially if you have similar goals to me

Zeno

● In-memory data distribution platform

● Contains tools for:○ data quality management○ data serialization

● We use it to distribute and keep up to date gigabytes of video metadata on tens of thousands of servers across the globe

ZenoWhy in-memory data?

- Netflix serves billions of requests per day

- Each request requires metadata about many movies to answer

Zeno

Netflix Use Case:● Gigabytes of in-memory data

● Hundreds of thousands of in-memory cache requests per second, per application instance

● Tens of thousands of application instances

DistributionFastBlob:

Binary serialization of a complete state of data, and/or the changes in data over time.

Serialization format designed to propagate, and keep up to date, a large amount of in-memory data across many servers.

Optimized for: memory GC effects, memory footprint, data transfer size, deserialization CPU usage

Data QualityDiff Reports - inspect data changes between releases

Data QualityDiff History - inspect changes in data over time

Zeno Framework

Data Schema (Serializers)

Operation (SerializationFramework)

Input Data (POJOs)

Output

Zeno Framework

Data Schema (Serializers)

Operation (SerializationFramework)

Input Data (POJOs)

Output

JsonSerializationFramework

HashSerializationFramework

DiffSerializationFramework

FastBlobStateEngine

Zeno BenefitsDevelopment Agility:● Easy to evolve data model, no need to change serialization formats or

operation logic● Easy to create new functionality, no need to think about data model

structure or semantics● Included “Diff” tools support high data quality across releases without too

much effort

Resource Efficiency:● Included “FastBlob” optimized for Netflix scale● Ask about in-development functionality!

Suro

To Be Processed in Different Ways

A Simple Solution That Supports All These

STAASHSTorage As A Service over Http

STAASH

STAASH● Storage-Agnostic● Language-Agnostic● REST Interface to data● Pattern Automation / Aware End Points● Wrapper Around Astyanax Recipes● Possibilities: Auditing, Cascading CL, Replication

across multiple storages, MapReduce …...many more..

Dynomite!!

Dynomite

● Cross AZ & Region replication to existing Key Value stores○ memcached○ Reddis

● Thin Dynamo implementation provides the replication

● Keep existing native KV protocol○ No code refactoring

Dynomite

Dynomite

memcached

Dynomite

memcached

App 1

AZ 1 AZ 2

What do all those events mean?

{“deviceid”: 12345, “action”: “played”, “titleid”: 99999}

{“deviceid”: 12345, “action”: “played”, “titleid”: 99999}

Device C*

12345: “PS3”

{“deviceid”: 12345, “action”: “played”, “titleid”: 99999}

Device C*

12345: “PS3”

Content C*

99999: “HOC”

Don’t hurt production/our customers

Device/Content C*

“My Devices”: {“PS3:HOC”:”12345:99999”}

?!?!?

Sometimes you just want all the data

C* Priam S3SSTables

S3SSTables

Move to HDFS*

Convert to JSON

Compact Rows

S3JSON

● A splittable input format for SSTables○ Need less files from the cluster.○ Faster - just deserializing/serializing the files.

● An input format for the JSON○ Allow incremental processing of backups

● A reducer that can compact SSTables.

Big Data Platform

Eventual Consistency

Focus on Performance

● Get your job running faster

● Understand why it was slow

● Transition to Hadoop 2