DevOps concepts

From Traditional Delivery to DevOps

Traditional software delivery in many organizations follows a linear, siloed model:

• Developers write code, then “throw it over the wall” to operations.
• Operations is responsible for stability and is often risk‑averse to change.
• Testing and security can be separate teams, introduced late in the process.
• Releases are infrequent, large, and risky.

DevOps emerged as a response to these pain points. It is not a product or a single tool, but a set of practices, cultural principles, and automation patterns that bring development and operations (and increasingly security, QA, and business) into a continuous, collaborative flow.

In the context of OpenShift, DevOps concepts are realized through Kubernetes-native workflows: declarative configuration, pipelines, GitOps, and automated platform capabilities, but the underlying ideas remain tool‑agnostic.

Core Principles of DevOps

Collaboration and Shared Responsibility

DevOps dissolves strict boundaries between roles:

• Cross‑functional teams: Developers, ops, QA, and sometimes security and product work together on the same services.
• Shared goals and metrics: Teams own both speed (delivery frequency) and stability (reliability in production).
• Blameless culture: Failures are treated as opportunities to improve systems and processes, not to assign individual blame.

On OpenShift, this is often reflected in:

• Shared ownership of namespaces/projects.
• Developers with enough access to deploy and observe their apps.
• Platform teams exposing self‑service capabilities rather than tightly controlling every change.

Automation Everywhere

Manual steps are slow, error‑prone, and hard to audit. DevOps emphasizes:

• Automated build and test pipelines.
• Automated deployments and rollbacks.
• Automated environment provisioning (infrastructure as code).
• Automated compliance checks and security scans.

OpenShift provides a container‑native execution environment for this automation (for example, pipeline runs as pods), but the automation principle itself is DevOps‑wide.

Continuous Feedback

DevOps is built around closing feedback loops:

• From production back to development (through metrics, logs, tracing).
• From users back to teams (through behavior analytics, support tickets).
• From pipeline results back to code (test failures, quality gates, security findings).

OpenShift’s monitoring, logging, and tracing tools contribute to these feedback loops, but the core idea is: shorten the time between change and learning.

Small, Frequent, Reversible Changes

Instead of large, rare releases:

• Changes are small: limited scope, easier to understand and test.
• Changes are frequent: deployed many times per day or week.
• Changes are reversible: easy rollbacks or roll‑forwards.

This aligns perfectly with containerized deployments on OpenShift, where new container images and configurations can be rolled out and rolled back declaratively.

The DevOps Lifecycle

A typical DevOps lifecycle is often summarized as an infinite loop or cycle:

1. Plan
2. Code
3. Build
4. Test
5. Release
6. Deploy
7. Operate
8. Monitor
9. Feedback into Plan

These stages are continuous, not strictly sequential. In OpenShift‑based workflows, many of these steps are automated and codified using pipelines and Git repositories.

Plan and Code

• Work is planned with a focus on small increments and clear acceptance criteria.
• Code is written with testability, observability, and deployability in mind from the start.
• The entire system—application code, container definitions, Kubernetes/OpenShift manifests, pipeline definitions—is stored in version control (often referred to as “Git as source of truth”).

Build and Test

• Builds produce immutable artifacts (container images, Helm charts, YAML manifests).
• Automated tests run at different levels: unit, integration, acceptance, performance, etc.
• Quality and security checks are integrated into the pipeline (static code analysis, dependency checks, container scanning).

Release and Deploy

• Releases are not one‑off events; they are repeatable processes implemented as code or pipeline stages.
• Deployment strategies (such as rolling, canary, or blue‑green) aim to minimize user impact and risk.
• Configuration and environment differences are managed declaratively and automatically.

Operate and Monitor

• Operations focuses on reliability engineering—availability, performance, capacity—in collaboration with development.
• Observability data (logs, metrics, traces) is collected and analyzed to understand behavior in production.
• Incidents are handled with runbooks, post‑incident reviews, and improvements to both application and automation.

Key DevOps Practices (Conceptual)

Infrastructure as Code (IaC)

Infrastructure and platform configuration are managed as code:

• Descriptions of resources and environments (networks, nodes, storage, policies) are stored in version control.
• Changes are made via code review and automated pipelines, not by manual clicks.
• Environments can be reproduced consistently.

OpenShift’s declarative API and YAML manifests for cluster resources are a natural expression of IaC at the platform and application level.

Continuous Integration (CI)

Continuous Integration focuses on frequently integrating code changes and validating them automatically:

• Developers merge changes into a shared main branch often.
• Each change triggers an automated build and test suite.
• Problems are caught early, close to where they were introduced.

On an OpenShift‑oriented platform, CI pipelines often build new container images and run tests within containers, although the underlying CI concepts apply regardless of the tool.

Continuous Delivery and Continuous Deployment (CD)

These related concepts extend CI beyond testing:

• Continuous Delivery: The system is always in a releasable state. Deployments to production are still a decision, but the act of deploying is largely automated.
• Continuous Deployment: Every change that passes automated checks is automatically deployed to production, without manual approval.

OpenShift’s automation and declarative deployment model supports both patterns, but the key DevOps idea is making releases routine instead of special events.

Shift‑Left Testing and Shift‑Left Security

“Shift‑left” means moving checks earlier in the lifecycle:

• Testing is not just a phase at the end; it is integrated from the first commits.
• Security becomes part of the development process (sometimes called DevSecOps), with early vulnerability scanning, policy checks, and secure defaults.

This reduces surprises late in the pipeline and aligns with the continuous nature of DevOps.

Observability and Continuous Improvement

DevOps emphasizes learning from production and improving:

• Define service‑level indicators (SLIs) and service‑level objectives (SLOs) for each service.
• Use performance and reliability data to drive work priorities.
• Run experiments (such as A/B tests or controlled rollouts) to validate changes.

In a DevOps culture, operations data is visible and understandable to developers and product owners, not just to a central ops team.

Culture, Organization, and Processes

From Silos to Product Teams

DevOps often implies organizational changes:

• Teams are formed around products or services, not technologies or functions.
• Each team owns the full lifecycle: development, deployment, and operations of their service.
• Platform and shared services teams provide enabling capabilities (like OpenShift clusters, CI/CD tooling, logging, and monitoring) as reusable services.

Autonomy with Guardrails

Teams get autonomy to move fast, but within a framework:

• Standardized build and deployment patterns.
• Common security and compliance baselines.
• Reusable templates and shared tools.

This reduces friction and risk when many teams build and operate applications on the same OpenShift environment.

Measurement and Accountability

DevOps relies on measurable outcomes, not just outputs:

• Deployment frequency.
• Lead time for changes.
• Change failure rate.
• Mean time to recover (MTTR).

These metrics guide process improvements and platform investments.

How DevOps Concepts Align with OpenShift

While the detailed mechanics of CI/CD and GitOps are addressed in other chapters, it is useful to summarize how DevOps ideas map naturally onto an OpenShift‑based platform:

• Everything as code: Applications, configurations, and often infrastructure are expressed as Kubernetes/OpenShift manifests and stored in Git.
• Automated pipelines run in cluster as containers, enforcing DevOps practices like CI, testing, and promotion between environments.
• Self‑service and automation: Developers can deploy, scale, and observe their applications without manual intervention from a central ops team.
• Standardization with flexibility: Platform teams provide templates, Operators, and policies; application teams use them to implement their own DevOps workflows.

Understanding these DevOps concepts provides the conceptual foundation for the more practical chapters that cover pipelines, GitOps, and integration of external CI tools on OpenShift.