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Kubernetes and Helm
One-click monitoring deployment
Pavel Mička (pavel.micka@zoomint.com)
Ján Tóth (jan.toth@zoomint.com)
Where are we heading
Current state of our platform
• RPMs as deployment artifacts
• Quaterly releases
• 1600+ on-premise deployments
• Monolithic architecture
Target architecture
• Containers as deployment artifacts
• Continuous delivery
• On-premise + Cloud offering
• Microservice & Streaming architecture
• Cloud native applications
• 12 factor apps
• Stateless, horizontally scalable, autoscaled,
autohealed...
Cloud native - the challenge
• We need to support both on premise and cloud
deployments
• With the same code
• We need to scale down to single machine/node
• Do disaster recovery for customers with 2 DCs
• Handle big customers with tens of servers & private clouds
• Support our own big cloud offering
Why Kubernetes?
• Automation & standardization
• Infrastructure as a Code, Immutable deployments
• Standardized service management
o Both stateless and stateful apps
• Service discovery, load balancing, auto-scaling
• Low memory footprint
• Good tweakability - (anti)affinity, memory + cpu limits
• Custom networking
• De-facto container orchestration standard
Helm
• Package manager for Kubernetes
• yum for Kubernetes
• Helm Chart (deployment descriptor)
• Provides reasonable default setup of components
• Hides internal complexity from end user
• Handles dependencies between charts
• Many production-ready charts freely available
Prometheus
• Time series database (operational)
• Pull based
• On disk: 1.3B per series sample
• Down/Up scales extremely well
• One server can handle 100s of thousands samples per second
• 1st class citizen in Kubernetes
• Kubernetes exposes metrics in Prometheus format
• Prometheus has service discovery from K8s
• Many integrations (Postgres, Rabbit, Apache, AWS...)
Deployment tools
Kops
• Kubernetes operations
• Makes easy HA K8s deployments in AWS
• Makes possible to easily modify running cluster
• Add new masters & workers
• Exchange keys
• Update cluster
• Creates resources (EC2s, security groups, ELB...)
• Generates Terraform files as a middle step
Terraform
• This step is not mandatory for all deployments
• Used for creation of infrastructure, which cannot be created by Kops
• Database servers (RDS)
• Collaboration with existing non-containerized infrastructure
o In transition period to containers
o Kops tf variables used in legacy deployments
Demo
Deployment
Namespaces
monitoring: Grafana
applications : Gogs

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Kubernetes and Helm: One click monitoring deployment

  • 1. Kubernetes and Helm One-click monitoring deployment Pavel Mička (pavel.micka@zoomint.com) Ján Tóth (jan.toth@zoomint.com)
  • 2. Where are we heading
  • 3. Current state of our platform • RPMs as deployment artifacts • Quaterly releases • 1600+ on-premise deployments • Monolithic architecture
  • 4. Target architecture • Containers as deployment artifacts • Continuous delivery • On-premise + Cloud offering • Microservice & Streaming architecture • Cloud native applications • 12 factor apps • Stateless, horizontally scalable, autoscaled, autohealed...
  • 5. Cloud native - the challenge • We need to support both on premise and cloud deployments • With the same code • We need to scale down to single machine/node • Do disaster recovery for customers with 2 DCs • Handle big customers with tens of servers & private clouds • Support our own big cloud offering
  • 6. Why Kubernetes? • Automation & standardization • Infrastructure as a Code, Immutable deployments • Standardized service management o Both stateless and stateful apps • Service discovery, load balancing, auto-scaling • Low memory footprint • Good tweakability - (anti)affinity, memory + cpu limits • Custom networking • De-facto container orchestration standard
  • 7. Helm • Package manager for Kubernetes • yum for Kubernetes • Helm Chart (deployment descriptor) • Provides reasonable default setup of components • Hides internal complexity from end user • Handles dependencies between charts • Many production-ready charts freely available
  • 8. Prometheus • Time series database (operational) • Pull based • On disk: 1.3B per series sample • Down/Up scales extremely well • One server can handle 100s of thousands samples per second • 1st class citizen in Kubernetes • Kubernetes exposes metrics in Prometheus format • Prometheus has service discovery from K8s • Many integrations (Postgres, Rabbit, Apache, AWS...)
  • 10. Kops • Kubernetes operations • Makes easy HA K8s deployments in AWS • Makes possible to easily modify running cluster • Add new masters & workers • Exchange keys • Update cluster • Creates resources (EC2s, security groups, ELB...) • Generates Terraform files as a middle step
  • 11. Terraform • This step is not mandatory for all deployments • Used for creation of infrastructure, which cannot be created by Kops • Database servers (RDS) • Collaboration with existing non-containerized infrastructure o In transition period to containers o Kops tf variables used in legacy deployments
  • 12. Demo