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Netflix oss season 2 episode 1 - meetup Lightning talks

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Information about Netflix oss season 2 episode 1 - meetup Lightning talks
Technology

Published on March 13, 2014

Author: RuslanMeshenberg

Source: slideshare.net

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Season 2 Episode 1 March 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, and Big Data on the Google Cloud Matt Bookman Solutions 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 SQL Google Cloud Platform Output Data Lipstick Database Hadoop Master MapReduce JobTracker Hadoop Worker MapReduce TaskTrackerHadoop Worker MapReduce TaskTracker Hadoop Worker MapReduce TaskTrackerLipstick Server Input Data

Google Confidential and Proprietary ● Netflix Lipstick on Google Compute Engine https://cloud.google.com/developers/articles/netflix-lipstick-on-google-compute-engine ● GCS Connector for Hadoop https://developers.google.com/hadoop/google-cloud-storage-connector ● Cloud-based Hadoop Deployments: Benefits and Considerations http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Cloud-Based-Hadoop-Deployments-Benefits-and-Considerations. pdf ● Apache Hadoop, Hive, and Pig on Google Compute Engine https://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

@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

@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

@Answers4AWS Thank you http://bakery.answersforaws.com/ bakery@answersforaws.com See me at the demo station Peter Sankauskas @pas256

IBM Scalable Services Fabric for Netflix S2E1 Meetup Andrew Spyker @aspyker

History and Future 2012 SPECjEnterprise 2013 AcmeAir Run On IBM Cloud at “Web Scale” 2014 Scalable Services Fabric internally for IBM Services Scalable Services Fabric SaaS and On-Prem? Sample application cloud prize work AcmeAir Cloud/Mobile Sample/Benchmark born Codename: BlueMix Portability cloud prize work

Scalable Service Fabric Work Netflix 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 aspects Cloud messaging (in relation to Suro) Matt @matrober API Management, user of NetflixOSS platform Converted 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 platform Creator of Imaginator Will @auwilli98 API Management operations, user of NetflixOSS platform

Priam + Aegisthus @Coursera NetflixOSS 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

Zeno Why 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

Distribution FastBlob: 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 Quality Diff Reports - inspect data changes between releases

Data Quality Diff 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 Benefits Development 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

STAASH STorage 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 S3 SSTables

S3 SSTables Move to HDFS* Convert to JSON Compact Rows S3 JSON

● 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

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