DevOps is a merger of Development and Operations.

It can variously be viewed as a reaction to or an extension of Agile development.

1 Motivation

Tension between Incremental Development and Traditional Operations

• Incremental development promises the availability of multiple useful versions of the software.
• Agile development suggests that these increments will be available with only short time intervals (on the order of a few weeks) between them.
• This multiples the amount of work required to systems test and deploy software
  • Tasks traditionally performed by a group separate (“siloed”) from the developers.

(figure from Atlassian)

DevOps attempts to merge development

• plan
• build
• continuous integration

with operations

• continuous deployment
• monitor
• operate

2 Challenges

• Developers often unaware of QA issues and consequent operational roadblocks.
• Lack of feedback on utility & value of implemented features.
• Unclear delineation of responsibilities for problems encountered during QA or operation

2.1 Values

• Collaboration & communication
• Agile, incremental methods
• Automation
  • Version control
  • CI Servers
  • Virtualization
  • Automated system tests
• Infrastructure as Code
• Monitoring

3.1 Puppet

• Linux tool for automating config mgmt
• Master-client architecture
• Provides a declarative language, PuppetDSL, for describing desired configuration

3.2 Chef

Systems administration tasks organized into “cookbooks” and “recipes”.

3.3 Ansible

• Written in Python, configuration based on YAML scripts
  • Still heavily procedural(?)
• Single-node architecture
  • Uses ssh to build artifact on target systems

3.4 Terraform

• Focus on provisioning, setting up servers before building the configuration
• Reputedly easier to use with Docker
• More declarative approach

4 Pipelines

Automation of continuous deployment is usually organized into pipelines.

5 Monitoring

• Identify appropriate metrics
• Continuously collected
• Real-time analytics & reporting

Identifies both operational problems and provides feedback to Agile planning.

Elastic-Logstash-Kibana (ELK)

• E: search & analytics engine
• L: server-side logging
• K: visualization

Provide for capture & visualization of properties such as

• log files
• cloud data
• availability
• network traffic