Managed Cloud to GitOps: Deploying Several Client Clusters
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DB Systel GmbH explored different approaches to deploying client clusters in a managed cloud, including using Ansible, CloudFormation with GitLab pipelines, and Terraform with a GitOps workflow. They settled on the third approach of using Terraform and GitOps to deploy multiple environments for separate customers. This met requirements for isolating customers in separate clusters and complying with ISO 20000 standards for change and release management. While advantages included simple customer creation, rolling updates, and automated monitoring, two open questions still remained.
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Managed Cloud to GitOps: Deploying Several Client Clusters
- 1. Managed Cloud to GitOps Deploying Several Client Clusters DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas
- 2. Digitalpartner of Deutsche Bahn DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 2
- 3. DB Content Hub DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 3 Icons made by Freepik, Good Ware, Sprang and Smashicons from www.flaticon.com
- 4. Requirement: Separate Cluster for each Customer DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 4 Icons made by Freepik, Good Ware, Sprang and Smashicons from www.flaticon.com
- 5. Requirement: ISO 20000 DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 5
- 6. Requirement: ISO 20000 DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 6 ‒Changemanagement ‒Releasemanagement ISO/IEC 2000-1 Service management system requirements
- 7. First Try: Ansible to Managed Cloud DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 7 Free Software Foundation - [1], FAL, https://commons.wikimedia.org/w/index.php?curid=53428398 Icons made by Freepik, Good Ware, Sprang and Smashicons from www.flaticon.com
- 8. Second Try: Cloudformation Monorepository + Gitlab Pipeline DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 8 Icons made by Freepik, Good Ware, Sprang and Smashicons from www.flaticon.com
- 9. Third Try: Terraform + Gitops DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 9 Icons made by Freepik, Good Ware, Sprang and Smashicons from www.flaticon.com
- 10. Third Try: Deploy Multiple Environments DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 10 Icons made by Freepik, Good Ware, Sprang and Smashicons from www.flaticon.com
- 11. Third Try: Terraform + Gitops DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 11
- 12. Wrap Up DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 12 Icons made by Freepik, Good Ware, Sprang and Smashicons from www.flaticon.com
- 13. Wrap Up - Advantages DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 13 Icons made by Freepik, Good Ware, Sprang and Smashicons from www.flaticon.com ‒Create new customers is simple ‒Hands free rolling updates ‒Automatic connection to monitoring ‒Compliance and Security Updates
- 14. DB Systel GmbH | Johannes Dienst @JohannesDienst | Jan Kohlhaas 14 Which two questions are still open? Johannes.Dienst@deutschebahn.com @JohannesDienst Jan.J.Kohlhaas@deutschebahn.com
Editor's Notes
- Johannes + Jan
- Johannes
- Jan Base Setup: Headless CMS, generic data model Provide Content as a Service Infrastructure customer cluster: Linux-Server(s), central database, elasticsearch, network storage (EFS) centralized API to lighten data-access, controlling requests / traffic
- Jan Requirements of the product: Deployment for several customers Data privacy protection -> Strict thread (physical) separation for each customer API implements a routing mechanism using unique keys per customer Main goal: Automatic deployment of new customers and updates of existing customer
- Johannes
- Johannes Beispiele was da drunter fällt -> Request for Change (MR) Release Notes Testing Planning Changes -> Centralized Tool
- Johannes * First customer * Convenient and fast * Compliance is a problem -> What is deployed is not always declared in repository
- Johannes Advantages: Compliance easier to achieve because configuration and infrastructure is automatically verifiable over Code Pipeline deploys more reliable Disadvantages: Monorepository -> Chance of changing customer configuration of wrong customer Hard to maintain Deployment has to manual or pipeline very complex
- Jan Final Approach (for now): Repository per asset (Docker, Plugin, AMI, Infrastructure), Gitlab-Repository per customer with configuration-ability Automatically create and initialize new customer-repository Updates of the asset (Docker, Plugin, AMI, Infrastructure): Update / accepted MR in the customer-repository Ensures always having the latest version in the customer-repository „Release-Day“: Executing whole infrastructure-update by clicking one button Executes the infrastructure-pipeline + passing customer-configuration as Gitlab-Variables + configuring terraform Final infrastructure: Cluster with several servers, database server, elasticsearch-servers and network file systems
- Jan Tried to follow the gitflow-approach using different branches representing different environments Environment: Cluster in the AWS, development, staging, production Each environment configured in the corresponding branch in the customer-repository Updates in the branch of the specific asset (e.g. docker-container for development is updated) will result in a MR in the corresponding branch in the customer repository
- Jan
- Johannes Several Clusters -> Data Security Request for Change Add stages to fulfill process requirements -> Testing, Informing customers
- Johannes + Jan im Wechsel * Script for creating a new customer * Rolling updates where possible * Sidecars automatically installed. Tagging for monitoring over customer configuration repository * Compliance checks in pipeline possible * Security updates automatically merged into customer repository