Microservices deployment

History of deployment options:

• Physical machines: 1990s. Fast deployment, best performance. Configuring/reconfiguring cumbersome.
• Virtual machines (VMs): 2000s. AWS EC2 released in 2006. AWS Elastic Beanstalk is an easy way to deploy. Can create a base image & add new instances. But virtualizing entire VM adds overhead.
• Containers: 2013-initial Docker release (competitor: Solaris Zones). Containers virtualize only OS. Quicker. Need to administer container orchestration solution (eg: Kubernetes or Docker Swarm) or go with hosted solution like Google Container Engine or AWS ECS. Sample load balancer: AWS Elastic Load Balancer (ELB).
• 'Serverless': 2014-AWS Lambda. Managing OS security patches also abstracted out. Competitors: Google Cloud with functions, Microsoft Azure with functions. Open source: Apache Openwhisk & Fission for Kubernetes. Underlying server infrastructure is hidden & abstracted away from specific programming languages. Usage based pricing. But can take time to start up & service the 1st request (long-tail latency) & not designed for long-running services.

Docker:

• Dockerfile
• Push to registry

Kubernetes:

• Cluster resource management: Cluster of machines as pool of CPU, memory, storage.
• Scheduling & service management

Kubernetes Architecture:

• API server: REST API
• Etcd: NoSQL db
• Scheduler
• Controller manager

Kubernetes Node:

• Kubelet: creates/manages pods on node
• Kube-proxy: networking, load balancing
• Pods: App services

Kubernetes Concepts:

• Pod: Single container or sidecar containers that implement supporting functions.
• Deployment: # of instances, versioning with rolling upgrades & rollbacks called 'zero-runtime'.
• Service: IP, DNS, load balancing
• ConfigMap: External config, allows storing passwords as a 'Secret'.

Source: Microservices Patterns by Chris Richardson