Containers at Netflx - An Evolving Story QConSF2015

In a fragmented mobile landscape, developing mobile applications can be challenging, especially when creating enterprise mobile applications. Targeting the wrong audience, lack of security and a good integration can introduce surprises and pitfalls along your enterprise mobile journey. During this session, we will cover those enterprise mobility challenges by explaining and exploring MADP with its benefits, such as delivering fully native apps with 60-90% re-use of code across device platforms, decreasing test time by 90% and app project costs by 40% and the possibility of building fully reusable components in JavaScript.

Container adoption and use at Netflix

Software

Containers at Netﬂix - An
Evolving Story
Sangeeta Narayanan
Engineering Manager @Netﬂix
@sangeetan

Going Global
Recent Launches: Italy, Spain, Portugal
Early 2016: Korea, Hong Kong, Taiwan, Singapore

Jan
Apr
Jul
O
ct
2015
A/B Tests
New features

Jan
Apr
Jul
O
ct
2015
A/B Tests
New features
Device Launches

New Considerations
Diversity - Users, Tech stacks, Workloads
Dev Ex - Dev iterations, Debugging, Packaging
Rapid Delivery
Resource Sharing

Dev-centric packaging &
reproducibility
Infrastructure abstraction
Faster dev iterations
Resource sharing
Why Docker?

Docker @Netﬂix - Use Cases
Algorithms Engineering
Data Pipeline
Edge Engineering

Algorithms Engineering
R&D of algorithmic components for product

Precompute
Online
Services
Model Trainer
New data available

Infrastructure abstraction
Polyglot support & Local Dev Ex
Dynamic resource allocation &
distributed job scheduling
Requirements

Model Trainer Online
Services
Precompute
New data available
Distributed scheduler
+

Distributed Scheduling & Task Management
Model Trainer Spinnaker Other Apps
Fenzo

Infrastructure abstraction
Polyglot + Local Dev
Distributed Scheduling
Num of Containers

Data Pipeline
Publish, Collect, Process, Move Data

Local Dev Ex
Infrastructure abstraction
Fine-grained resource management
Requirements

Infrastructure abstraction
Local Dev Ex
Fine grained resource Management
Num of Containers

(~10K steady state)

Edge Engineering
Entry point to Netﬂix for all device trafﬁc

Infrastructure abstraction
Local dev, Polyglot - Java, Groovy,
Node.js
Fine-grained resource
management
Requirements

Ops Tooling
Networking, IPC
Security
Insights
Failure Testing
Performance
And more…
Requirements++
Long running Services
vs.
Batch Jobs

Local Dev
Test/
Debug
Open PR
CI
Build/Test
AMI
Bakery
Deploy
Spinnaker
Dist Job
Sched

Gradle docker plugin to generate Dockerﬁle
Spinnaker CloudProvider provides abstraction
Bakery support for image generation
Docker registry for internal images
libnetwork plugin for addressable IP
Building out the pieces

Dev Ex Wins
Faster dev iterations
Run prod image locally; attach debugger
Multiple branch/conﬁg variations
Hot reloading of ﬁles
‘Roman Riding’ with AMIs

‘PaaS-like' Dev Experience
Production scale/Operational readiness
Optimized resource utilization

Infrastructure Abstraction
Dev Ex
Production ready Online Services
Polyglot env, Packaging

The Netﬂix Algorithms Engineering & Data Pipeline
teams
Acknowledgements
Photo Credits
https://goo.gl/sP5T4X

https://goo.gl/JaNlyf

https://goo.gl/rZXws1

http://goo.gl/V4UFmR

https://goo.gl/R08GPQ

http://tastyk.deviantart.com/art/Evolution-149719618

https://goo.gl/W2UKv0

https://goo.gl/fRcpPN

https://goo.gl/tF199H

https://goo.gl/K1OH9T wildﬂowers

https://goo.gl/KSaL5s roadmap

https://goo.gl/8JCDQy odometer

https://goo.gl/c4RuSR takeaway box

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This presentation covers the following topics: What is logging? The purpose of logging: Debugging The purpose of logging: Security The purpose of logging: Stats & analytics Traditional logging Traditional logging: Advantages Traditional logging: Disadvantages The solution: Large-scale logging Large-scale logging: Core principles Large-scale logging: Solution types Large-scale logging: Cloud vs on-prem Large-scale logging: Operational complexity Large-scale logging: Security Large-scale logging: Costs Large-scale logging: On-prem comparison - Elasticsearch - Grafana Loki - VictoriaLogs On-prem comparison: Setup and operation On-prem comparison: Costs On-prem comparison: Full-text search support On-prem comparison: How to efficiently query 100TB of logs? On-prem comparison: Integration with CLI tools VictoriaLogs for large-scale logging VictoriaLogs demo instance - Ingestion rate: 3600 messages / minute - The number of log messages: 1.1 billion - Uncompressed log messages’ size: 1.5TB - Compressed log messages’ size: 23GB - Compression ratio: 47x - Memory usage: 150MB VictoriaLogs CLI integration demo - Which errors have occurred in all the apps during the last hour? - How many errors have occurred during the last hour? - Which apps generated the most of errors during the last hour? - The number of per-minute errors for the last 10 minutes - Status codes for the last hour - Non-200 status codes for the last week - Top client IPs for the last 4 weeks with 404 and 500 response status codes - Per-month stats for the given IP across all the logs Large-scale logging solution MUST provide excellent CLI integration VictoriaLogs: (temporary) drawbacks VictoriaLogs: Recap - Easy to setup and operate - The lowest RAM usage and disk space usage (up to 30x less than Elasticsearch and Grafana Loki) - Fast full-text search - Excellent integration with traditional command-line tools for log analysis - Accepts logs from popular log shippers (Filebeat, Fluentbit, Logstash, Vector, Promtail, Grafana Agent) - Open source and free to use!

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