Vertical Pod Autoscaler
Horizontal Pod Autoscaling
Autoscaling Workloads in Kubernetes
Scaling Your Application in Kubernetes
Init Containers
Multi-container Pods
How to Use Secrets in a Pod
How to Create Secrets in Kubernetes
Secrets in Kubernetes
How to Set Environment Variable in a Pod Using ConfigMaps
How to Create a ConfigMap in Kubernetes
ConfigMaps in Kubernetes
Type of Upgrades for Deployments in Kubernetes
Different Types of Admission Controllers
Admission Controllers in Kubernetes
Preemption in Kubernetes
How to Use Priority and Preemption
Priority Class
Pod Priority
How to Check If a Pod Is Static
How to Create Static Pods
Static Pods
DaemonSet in Kubernetes
How to Export Current Config to Edit in Yaml
How to Create a Deployment Using Kubectl
How to Control Where a Pod Runs in Kubernetes
How to Label Nodes in Kubernetes
How to Taint Nodes in Kubernetes
CNCF October Edition
Kubernetes Deployment
ReplicaSets in Kubernetes
Pods
Kubernetes Components
I finished reading Lucas Käldström’s bachelor’s thesis about the patterns and reasons behind the cloud native paradigm.
Lucas uses concepts from thermodynamics and control theory to talk about the key design principles involved in kubernetes design.
An excerpt:
The Kubernetes operator programming model has allowed subject matter experts
(that have authored) to encapsulate knowledge in a way that lets tens
of thousands of Kubernetes users “stand on the shoulders of giants” (knowledge
scalability). Furthermore, the declarative abstraction layer allows for optimizations
in the implementation (e.g., scheduling), leading to resource scalability. Finally,
through continuous reconciliation “repairing” the system as often as possible, fewer
error situations need human intervention, leading to monitoring scalability.
Link to the paper - https://github.com/luxas/research
From Oodi to a Mentor
I found a mentor at a meetup