PROFESSIONAL WORK · 2025
Kubernetes Deployment and CI/CD Reliability
Investigated Kubernetes deployment failures and improved CI/CD workflows by diagnosing configuration mismatches, pod failures, and pipeline behaviour.
- Kubernetes
- GitLab CI
- YAML
- Debugging
This case study is a sanitized explanation of my contribution. Internal names, architecture details, and business information have been omitted or generalized.
Context
Enterprise services deployed on Kubernetes with GitLab CI pipelines. This work focused on deployment reliability and CI/CD diagnosis, not on owning a broader Kubernetes platform.
Problem
Deployments failed intermittently across environments with a mix of pod-level, configuration, and pipeline-stage root causes, slowing delivery.
Constraints
- Failures often reproduced only in specific environments
- Pipeline-stage failures and application failures looked the same to a casual reader of the log
- Fixes had to be backwards-compatible with existing deployment manifests
My contribution
Investigated and contributed to
Investigated deployment failures (pod startup, CrashLoopBackOff, configuration mismatches), pipeline-stage failures, and environment-specific issues; contributed deployment workflow improvements.
Technical approach
- Analysed Kubernetes events and pod and container logs
- Diagnosed pod startup failures and CrashLoopBackOff loops
- Identified configuration mismatches in YAML and deployment manifests
- Traced pipeline-stage failures to environment-specific causes
- Drove configuration consistency across environments
- Contributed deployment workflow improvements based on recurring failure patterns
One important engineering decision
Decision
Start every deployment investigation from Kubernetes events and pod descriptions rather than from CI pipeline logs.
Why
CI logs were showing symptoms; in the failures I investigated, the actual cause (image pull, readiness probe, config map mismatch) was typically visible in events much earlier.
Trade-off
Investigations took an extra cluster-context step before opening the CI log, which felt slower for the first few minutes but converged on the real cause faster overall.
Alternatives considered
- Reading the CI pipeline log top-to-bottom on every failure (familiar, but consistently led people to the wrong layer first)
- Re-running the pipeline immediately to see if it was 'just flake' (rejected because it hid real, reproducible failures)
Failure cases and edge cases
- Readiness probes that passed once and then failed under load during rollout
- Config map updates that did not propagate until a pod was manually restarted
- Pipeline stages that timed out waiting for a pod that had been evicted
Technologies used
- Kubernetes
- kubectl
- YAML
- GitLab CI
- Shell scripting
Challenges
- Reproducing environment-specific failures outside the original environment
- Distinguishing transient pipeline failures from real deployment regressions
Verified outcome
Recurring deployment and pipeline failure modes were diagnosed and addressed, and reviewers had a more consistent way to triage a failing deployment.
What I learned
Several deployment failures initially classified as flaky had identifiable causes in Kubernetes events, deployment configuration, or environment state. Consistent environments cost less than one bad incident.
What I would improve
I would automate a small post-failure diagnostic step in the pipeline that collects pod descriptions, recent events, and config map versions into a single artifact so engineers reviewing pipeline failures do not have to recreate that context by hand.
Ownership breakdown
Wider system context
- The broader Kubernetes platform and pipeline infrastructure was owned by the wider team
My contribution
- Deployment workflow improvements based on recurring patterns
Components I investigated
- Pod startup failures and CrashLoopBackOff loops
- Configuration mismatches in deployment manifests
- Environment-specific pipeline-stage failures
Components I validated
- Configuration consistency across environments