A DevOps Success Story

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Experience report of a successful DevOps transformation in a small company in Zürich/Switzerland. DevOps Enthusiast Meetup Zürich, 2017-04-04.

Software

A DevOps
Success Story
@matiasfrndz
DevOps Enthusiast Meetup Zürich – 2017-04-04

Matías E. Fernández
@matiasfrndz
https://matiasfrndz.ch

Dev Ops
Flow
Feedback
Continual  
Learning & Experimentation

The Company
• Hosting provider based in Switzerland
• Specialised in highly available and scalable platforms
• Approx. 1’500 servers in 3 data centres
• Approx. 16’000 checks/metrics collected every 5 min.
• Powering revenue of CHF 2 billion per year
• 24/7 operations with on-call rotation

2011 – The Beginning
• Approx. 10 people in 1 team
• Wide range of customer segments (B2B, B2C)
• No documented processes
• No defined service portfolio

Choosing the Right Team
• Find innovators and early adopters
• Build critical mass & silent majority
• Identify holdouts

– Ron van Kemenade, CIO of ING
“Leading change requires courage,
especially in corporate environments
where people are scared and ﬁght you.
But if you start small, you really have
nothing to fear. Any leader needs to be
brave enough to allocate teams to some
calculated risk-taking.”

The Strangler Pattern
https://continuousdelivery.com/implementing/architecture/

Day-to-Day Business 
versus 
Project Work

–The DevOps Handbook
“Improvement of daily work is more
important than daily work itself.”

Increasing Focus 
by introducing a 
Service Portfolio

Our First 
Telemetry Pipeline
dashing.io
Nagios 
+ 
check_mk
Wow!

Git Branching Model
• Trunk based Development
• Feature branches
• Pull requests

Improved Feedback Loop
Dev Ops
Nagios + check_mk

Hypothesis Driven Development
• We Believe that increasing the size of hotel images
on the booking page
• Will Result in improved customer engagement and
conversion
• We Will Have Confidence To Proceed When we
see a 5% increase in customers who review hotel
images who then proceed to book in forty-eight
hours.

Lean Startup 
and the Scientific Method
Build
MeasureLearn
Idea
Requirement
Hypothesis
Data
Product
Experiment
allows collecting 
the required data
required to prove the hypothesis a.k.a. empirical evidence

The Hypothesis  
Validation Engine
Dev Ops
✓
✘
✓
new:
validated:

Establishing a  
Culture of Learning
Dev Ops

High Trust 
Enables Experimentation and Risk Taking

Core Development Principles
• Consistency
• Explicitness over Implicitness
• The Boy Scout Rule
• Don’t Repeat Yourself (DRY)
• Zero Waste
• You Ain’t Gonna Need It (YAGNI)

Convert Local Discoveries 
into Global Improvements

• Drinking coffee together
• Brown-bag sessions
• Mandatory trainings
• Video recordings
• DevDays
• Public conferences and meetups

Main Successes
• New hires were able to work on tickets and deploy
to production on their second working day
• New features were implemented and deployed to
production within days
• New servers (physical and virtual) where
deployed within 10 minutes
• Successful ISO 9001 certification

–Apple, Inc. television commercial from 1997
“Because the people who are crazy
enough to think they can change
the world, are the ones who do.”

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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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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

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A DevOps Success Story

1. A DevOps Success Story @matiasfrndz DevOps Enthusiast Meetup Zürich – 2017-04-04

2. Matías E. Fernández @matiasfrndz https://matiasfrndz.ch

3. The Purpose  of a Business

4. value pay business customer

5. The Three Ways  of DevOps

6. Dev Ops business customer

7. Dev Ops Flow Feedback Continual   Learning & Experimentation

8. Experience Report

9. The Company • Hosting provider based in Switzerland • Specialised in highly available and scalable platforms • Approx. 1’500 servers in 3 data centres • Approx. 16’000 checks/metrics collected every 5 min. • Powering revenue of CHF 2 billion per year • 24/7 operations with on-call rotation

10. 2011 – The Beginning • Approx. 10 people in 1 team • Wide range of customer segments (B2B, B2C) • No documented processes • No defined service portfolio

11.

12. Getting Into the Flow Dev Ops

13. Scrum vs. Kanban

14. Choosing the Right Team • Find innovators and early adopters • Build critical mass & silent majority • Identify holdouts

15. – Ron van Kemenade, CIO of ING “Leading change requires courage, especially in corporate environments where people are scared and ﬁght you. But if you start small, you really have nothing to fear. Any leader needs to be brave enough to allocate teams to some calculated risk-taking.”

16. Our First Deployment Pipeline

17. Configuration as Code

18. Service Oriented Architecture (SOA)

19. The Strangler Pattern https://continuousdelivery.com/implementing/architecture/

20. Team Retrospectives

21. Day-to-Day Business  versus  Project Work

22. –The DevOps Handbook “Improvement of daily work is more important than daily work itself.”

23. Increasing Focus  by introducing a  Service Portfolio

24. Enabling Feedback Dev Ops

25. Our First  Telemetry Pipeline dashing.io Nagios  +  check_mk Wow!

26. Git Branching Model • Trunk based Development • Feature branches • Pull requests

27. Improved Feedback Loop Dev Ops Nagios + check_mk

28. DevDays  with Customers

29. Hypothesis Driven Development • We Believe that increasing the size of hotel images on the booking page • Will Result in improved customer engagement and conversion • We Will Have Confidence To Proceed When we see a 5% increase in customers who review hotel images who then proceed to book in forty-eight hours.

30. Lean Startup  and the Scientific Method Build MeasureLearn Idea Requirement Hypothesis Data Product Experiment allows collecting  the required data required to prove the hypothesis a.k.a. empirical evidence

31. The Hypothesis   Validation Engine Dev Ops ✓ ✘ ✓ new: validated:

32. Establishing a   Culture of Learning Dev Ops

33. Blameless Postmortems

34. High Trust  Enables Experimentation and Risk Taking

35. Core Development Principles • Consistency • Explicitness over Implicitness • The Boy Scout Rule • Don’t Repeat Yourself (DRY) • Zero Waste • You Ain’t Gonna Need It (YAGNI)

36. Convert Local Discoveries  into Global Improvements

37. • Drinking coffee together • Brown-bag sessions • Mandatory trainings • Video recordings • DevDays • Public conferences and meetups

38. ChatOps

39. Main Successes • New hires were able to work on tickets and deploy to production on their second working day • New features were implemented and deployed to production within days • New servers (physical and virtual) where deployed within 10 minutes • Successful ISO 9001 certification

40. Start Your DevOps Journey Today!

41. –Apple, Inc. television commercial from 1997 “Because the people who are crazy enough to think they can change the world, are the ones who do.”

42. https://devopsdays.ch/

A DevOps Success Story

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