1) The document discusses managing short-lived Kubernetes deployments and outlines the steps taken to implement a DevOps process using Kubernetes and Azure Container Services. 2) Key priorities included enabling CI/CD, automatic provisioning, and minimizing the need for operations work. 3) The solution implemented Kubernetes with Azure Container Services using Azure as the IaaS provider to enable on-demand development and test environments identical to production.

1. Managing short lived
Kubernetes deployments
Martin Danielsson (@donmartin76)
Solution Architect @ Haufe Group

2. $ whoami
C:> WINDOWS.EXE
C/C++/C# Background
15+ years
$ docker ps
Containers & Kubernetes
Since ~2 years
wicked.haufe.io maintainer
OSS API Management
“Solution
Architect”
Developer
since 2006

3. Setting The Scene

4. Strategic move
to containers Modular
Architecture
Without Container
Experience
Current Occupation – A Cloud Journey
Hosted with Hoster
Long Release
cycles
(LOTS of) Manual
Work for Releases
Little Operations
Insight
Error tracking
very difficult
Non-Parity
Dev/Test/Prod
(Cost!)
Legacy Web App
(Java based)

5. Solution – Let’s go DevOps in the cloud!

6. A Process Pattern

7. Enabling
CI/CD
Automatic
Provisioni
ng
Full Insight
Minimize
Ops
Top Priorities

8. Chosen Solution Outline
Kubernetes
Azure Container Services
Azure as IaaS provider

9. Alternative Solution Outline
kops
Kubernetes
kops (kubernetes operations)
AWS as IaaS provider

10. Steps to DevOps Happiness (for us)
Provisio
n
Deploy CI/CD
Weekly for Production, Daily for Dev/Test
Ship when ready!

11. But… Why?
Target
“No-Ops”
No long-running
systems
Enable validation of
3rd Party component
upgrades
Incremental
changes
Practice Disaster
Recovery Daily
100% Reproducible
Deployments
On-demand Production
Identical Environments

12. Dem geneigten Zuhörer mag aufgefallen
sein…
Stateless Components
Stateful Components

13. Adding State (Persistence)

14. Full Provisioning
Create backup
Provision new
infrastructure
• From
backups
• Same as
disaster
recovery!
Deploy
components
• Using
deployment
pipelines
• Partly
parallelized
Top level DNS
switch
• Using DNS
traffic
manager
Destroy old
infrastructure
• If tests
succeed

15. Persistence Options
Roll your own persistence Persistence “as a service”
Self managed VMs (incl. NFS) Managed Disks
(AWS EBS, Azure Managed Disks)
DBaaS (many options)
Files as a service
(AWS EFS, Azure
Files)
Gluster/Ceph FS (cluster)

16. Persistence Requirements
A) Backup on demand (or auto)
B) Restore to other instance
AB) Clone on demand

17. On-demand Environments
Prod
Dev/Test
Load Testing…

18. Example – SQL Schema Update
Create backup
Provision new
infrastructure
Deploy
components
Top level DNS
switch
Destroy old
infrastructure
Test/Validate

19. Advantages
On Demand Dev/
Test Envs
Enables Test
Of Risky Updates
Built-In Disaster
Recovery

20. -as-a-Service
Less Complex
No Operations Overhead
Supports A+B, or AB?
If not: Can I live without Prod
Data in Dev/Test Envs?
Do I trust Service Provider
to live up to SLA?
In case of
What can I do?

21. Limitations

22. Possible Constraints
Implementation
Effort
SLA Requirements
(Downtime)
Data Size
Backup/Restore TimeTeam Size
Ops Skills Needed

23. Our Solution Vector

24. Resource Group
Kubernetes Cluster
Solution Architecture (Infrastructure)
ks8
Master
ks8
Agent
ks8
Agent n
…
NFS
VM(s)
Postgres
VM(s)
Database as
a Service
Disks
Disks

25. NFS Storage/Postgres Storage
• Backup – Cloning disks from running system
• Restore – Cloning from backups
• Very much a transient technology!
• But it works…
• Moving to DBaaS (e.g. Cosmos DB) over
time

26. Endless Variants…

27. Conclusion and Takeaways
k8s Ops possible
as a Team
Requires full (test)
automation
Team dedication Rethinking ops is
challenging
No Silver Bullet
Assess your requirements

28. Q & A

29. Thanks!
Twitter donmartin76
GitHub donmartin76
linkedin.com/in/martindanielsson/
www.haufegroup.com
work.haufegroup.io
wicked.haufe.io

30. Backup Slides

31. Persistence problems and possible
solutions
Data Type Solution Technology Backup/Restore Complexity
Plain Files NFS AB Low
CephFS/GlusterFS A+B High
SQL Database Azure SQL Server A+B Medium
Azure Postgres-aaS AB Low
AWS RDS for Postgres AB Low
NoSQL Azure Cosmos DB A+B Medium
AWS DynamoDB A+B (via tools) Medium

32. Integration
& e2e Test
Build &
Unit Test
Docker
Image
Deploy
Building blocks of CI/CD pipelines
• E.g., Blue/Green
• Rolling Updates
• Also used for initial
deployment

33. Incremental Frontend Deployment
Merge feature to
master
•After code
review
•Including test
suite changes
Build master
branch
•Includes unit
testing
•First integration
tests
Deploy to
integration system
•Run integration
tests
•Rollback if failing
Deploy to
Production
•Run e2e
integration tests
•Rollback if failing

34. Incremental Backend Deployment
Merge feature to
master
•After code
review
•Including test
suite changes
Build master
branch
•Includes unit
testing
•First integration
tests
Deploy to
integration system
•Blue/Green with
integration tests
Deploy to
Production
•Blue/Green with
integration tests