GitLab Runners and Executors¶

Overview¶

Distinguish shared, group, and project runners; pick an executor for isolation; and use job tags so the right capacity picks up production work.

A GitLab Runner is the agent that executes jobs. The executor decides how isolation works (host shell, Docker container, Kubernetes Pod, and others). Scope (instance/shared, group, project) decides who can use the runner. Tags bind jobs to capable fleets.

This is a core tutorial in Module 3 · GitLab Runners of the REBASH Academy GitLab CI/CD for Cloud & DevOps Engineers series — written for Cloud, DevOps, Platform, and SRE engineers.

Prerequisites¶

GitLab Projects, Merge Requests, and Releases

Learning Objectives¶

By the end of this tutorial, you will be able to:

Contrast shared vs group vs project runners
Choose shell, Docker, or Kubernetes executors for a workload
Use tags so jobs land on the right fleet
Outline why autoscaling exists (cost and queue depth)

Architecture¶

This topic’s control points and relationships are shown below.

Theory¶

What it is¶

GitLab schedules jobs; runners claim them over the Runner API. Registration associates a runner with an instance, group, or project (modern registration uses authentication tokens and runner types). The executor plugin runs the job:

Executor	Isolation	Typical use
Shell	Process on the runner host	Legacy / carefully locked hosts
Docker	Container per job	Most SaaS and self-managed CI
Kubernetes	Pod per job	Cluster-backed platforms
Docker Machine / autoscaler	Ephemeral VMs	Burst capacity (evolving tooling)

Shared (instance) runners serve many projects (GitLab.com shared runners). Group runners serve all projects in a group. Project runners are scoped to one project — useful for privileged or regulated workloads.

Why it matters¶

Wrong executor choices create security and reliability debt: shell executors share a host filesystem; untagged “any runner” jobs can land on laptops registered as runners; missing capacity creates hour-long queues. Platform teams treat runners as product infrastructure — sized, tagged, monitored, and patched like any other fleet.

How it works¶

Admin registers a runner with GitLab (token / runner authentication).
Runner polls for jobs that match its tags and access scope.
For Docker: pull image: (or default), mount the build directory, run script.
For Kubernetes: create a build Pod in a configured namespace, stream logs, clean up.
Status and artefacts return to GitLab; autoscalers add/remove runner capacity from queue metrics.

Job authors select capacity with tags: [docker, linux] (example). Without tags, any untagged runner in scope may take the job — usually undesirable in production.

You can study YAML without owning runners: GitLab.com free tier provides shared runners; gitlab-ci-local runs many jobs on your laptop; glab ci lint validates syntax.

Key concepts and comparisons¶

Scope	Who can use it	Ops note
Shared / instance	Broad set of projects	Minute quotas, noisy neighbour
Group	Projects under the group	Standard platform fleet
Project	One project	Privileged / air-gapped builds

Autoscaling overview: idle VMs or cluster nodes cost money; static fleets waste capacity. Autoscaling adds runners when the pending queue grows and removes them when idle — design for warm pools if cold starts hurt MR feedback.

Common pitfalls¶

Registering a personal laptop as an unprotected shared runner.
Using the shell executor for untrusted open-source MRs.
Forgetting tags so GPU or privileged jobs never run (or run everywhere).
Equating “Docker executor” with “Docker-in-Docker” — DinD is a separate, higher-risk pattern.

Hands-on Lab¶

Objective¶

Document runner capacity in runner-tags.yaml, author a .gitlab-ci.yml where jobs target specific runner tags, and validate executor choices offline with PyYAML.

Prerequisites¶

Python 3 with PyYAML (pip install pyyaml)
Optional: GitLab project with group or project runners registered

Lab environment¶

Workspace: ~/rebash-gitlab/module-03

File-first lab. Push to GitLab only when tagged runners exist to claim jobs.

Terminal

mkdir -p ~/rebash-gitlab/module-03 && cd ~/rebash-gitlab/module-03

Real-world scenario¶

Your platform team operates two runner fleets: docker-linux runners for containerised builds and shell-legacy runners for a locked-down deployment host. Untagged jobs caused production deploys to land on developer laptops. You encode the runner matrix in YAML and tag every job explicitly.

Step-by-step tasks¶

Task 1 – Document the runner fleet matrix¶

Create runner-tags.yaml:

runner-tags.yaml

runners:
  - name: shared-docker-linux
    scope: group
    executor: docker
    tags:
      - docker-linux
    image_default: docker:27-cli
    notes: "Default fleet for build and test jobs"
  - name: deploy-shell-host
    scope: project
    executor: shell
    tags:
      - shell-legacy
    notes: "Single locked host — never use for untrusted MRs"
policy:
  require_tags_on_production_jobs: true
  forbid_untagged_jobs: true

Validate:

Terminal

cd ~/rebash-gitlab/module-03
python3 -c "
import yaml
m = yaml.safe_load(open('runner-tags.yaml'))
assert m['runners'][0]['executor'] == 'docker'
assert m['runners'][1]['executor'] == 'shell'
print('OK executors', [r['executor'] for r in m['runners']])
"

Expected output

Prints OK executors ['docker', 'shell'].

Task 2 – Create tagged pipeline jobs¶

Create src/check.py:

check.py

print("runner-tags ok")

Create .gitlab-ci.yml:

.gitlab-ci.yml

stages:
  - build
  - deploy

docker_build:
  stage: build
  tags:
    - docker-linux
  image: docker:27-cli
  script:
    - docker version
    - python3 -c "print('build on docker executor')"

shell_deploy_stub:
  stage: deploy
  tags:
    - shell-legacy
  script:
    - echo "Deploy stub — shell executor on locked host"
    - uname -a
  rules:
    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH

# Anti-pattern example — commented for review; do not enable in production
# untagged_job:
#   stage: build
#   script:
#     - echo "Any runner may claim this — avoid"

Validate offline:

Terminal

cd ~/rebash-gitlab/module-03
python3 -c "
import yaml
d = yaml.safe_load(open('.gitlab-ci.yml'))
assert d['docker_build']['tags'] == ['docker-linux']
assert d['shell_deploy_stub']['tags'] == ['shell-legacy']
print('OK tagged jobs')
"

Expected output

Prints OK tagged jobs.

Task 3 – Simulate docker-stage logic locally¶

Run the build script path without a runner:

Terminal

cd ~/rebash-gitlab/module-03
python3 src/check.py | tee runner-out.txt
python3 -c "print('build on docker executor')" | tee -a runner-out.txt
grep -q 'runner-tags ok' runner-out.txt

Expected output

runner-out.txt contains both runner-tags ok and build on docker executor.

Validation steps¶

runner-tags.yaml lists docker and shell executors with tags
docker_build requires tag docker-linux
shell_deploy_stub requires tag shell-legacy and runs only on default branch
No production job is left untagged
Local simulation of build output succeeds

Common errors and fixes¶

Error	Cause	Fix
Job stuck `pending`	No runner with matching tag	Register a runner with `docker-linux` or `shell-legacy` tags
Job runs on wrong host	Tag typo	Match tags exactly between runner config and `.gitlab-ci.yml`
Shell executor on public MR	Untrusted code on host filesystem	Use Docker or Kubernetes executors for MR pipelines
`docker: command not found` in job	Shell executor used for docker job	Ensure `docker_build` lands on a Docker executor runner

Challenge exercise¶

Add a lint_mr job tagged docker-linux with rules: [{ if: $CI_PIPELINE_SOURCE == "merge_request_event" }] so MR feedback never uses the shell executor.

Learning outcomes¶

Documented runner capacity and executor types in version-controlled YAML
Routed jobs to tagged runners instead of accepting any available runner
Understood when shell versus Docker executors are appropriate
Validated pipeline and matrix YAML offline

Cleanup¶

Terminal

rm -f ~/rebash-gitlab/module-03/runner-out.txt
# Keep runner-tags.yaml and .gitlab-ci.yml for module 04

Validation¶

Lab commands run under ~/rebash-gitlab/module-03/
You can explain each Theory section in your own words
You used modern tooling where it applies to this topic
You can describe one production failure mode for this topic

Code Walkthrough¶

Production practice for GitLab Runners and Executors always combines:

Inspect before you change (status, plan, logs, dry-run)
Prefer reversible, documented changes (Git, IaC, drop-ins, version pins)
Capture evidence (command output, pipeline logs) for handovers
Prefer current tools and APIs over legacy shortcuts
Least privilege — escalate credentials only when required

Keep runbooks short enough to follow under pressure. Automate checks; keep humans for judgement.

Security Considerations¶

Treat credentials and tokens for gitlab as privileged — never commit them
Prefer short-lived auth (OIDC, roles, SSO) over long-lived keys
Validate blast radius before apply/deploy/delete operations
Restrict who can approve production changes
Collect audit logs; limit who can read sensitive traces

Common Mistakes¶

Registering a personal laptop as an unprotected shared runner.

Validate assumptions against the Theory section and official docs before changing production.

Using the shell executor for untrusted open-source MRs.

Lab shortcuts (open security groups, admin roles, skip approvals) must not ship unchanged.

Changing production without a rollback path

Always know how to revert (previous artefact, prior release, state rollback, DNS failback).

Best Practices¶

Encode GitLab Runners and Executors changes as code and review them in pull requests
Pin versions (images, modules, actions, provider plugins)
Separate environments with clear promotion gates
Alert on symptoms with runbooks attached
Destroy lab resources; tag everything with owner and expiry where possible

Troubleshooting¶

Symptom	Likely cause	Fix
Auth / permission denied	Wrong identity, policy, or scope	Check caller identity, roles, and least-privilege policies
Timeout / no route	Network, DNS, security group, or endpoint	Trace path, DNS, and allow-lists before retrying
Drift / unexpected plan	Manual change or wrong state/workspace	Reconcile desired vs actual; avoid click-ops on managed resources
Pipeline/job red	Flaky step, cache, or missing secret	Read failing step logs; bisect recent workflow/config changes
Cost spike	Idle load balancer, NAT, oversized compute	Inventory billable resources; stop/delete labs promptly

Summary¶

GitLab Runners and Executors is essential for Cloud and DevOps engineers working with gitlab. Practise the lab until the inspection and change path is muscle memory, then continue the track.

Interview Questions¶

Compare shell, Docker, and Kubernetes executors for isolation and cost.
Jobs stuck in pending — what runner factors do you verify first?
Why tag runners instead of relying on shared untagged runners?
What security risk does a privileged Docker runner introduce?
How do protected runners interact with protected branches/variables?

Sample answer — question 2

Check runner online status, matching tags, concurrent job limits, and whether the project may use that runner. Pending almost always means no eligible runner.

Sample answer — question 4

Privileged mode and Docker socket mounts can let jobs escape to the host. Prefer unprivileged executors and dedicated tags for production.

GitLab Runners and Executors¶

Overview¶

Prerequisites¶

Learning Objectives¶

Architecture¶

Theory¶

What it is¶

Why it matters¶

How it works¶

Key concepts and comparisons¶

Common pitfalls¶

Hands-on Lab¶

Objective¶

Prerequisites¶

Lab environment¶

Real-world scenario¶

Step-by-step tasks¶

Task 1 – Document the runner fleet matrix¶

Task 2 – Create tagged pipeline jobs¶

Task 3 – Simulate docker-stage logic locally¶

Validation steps¶

Common errors and fixes¶

Challenge exercise¶

Learning outcomes¶

Cleanup¶

Validation¶

Code Walkthrough¶

Security Considerations¶

Common Mistakes¶

Best Practices¶

Troubleshooting¶

Summary¶

Interview Questions¶

Related Tutorials¶

References¶