Role-Based Access Control (RBAC) is no longer just a nice-to-have in modern software delivery - it is the foundation of a secure CI/CD pipeline. As organizations accelerate release cycles and adopt complex multi-cloud environments, the attack surface inside the pipeline itself expands. Who can trigger a production deployment? Who can read secrets stored in your CI runner? Who is allowed to approve a merge to main? Without a clear, enforced answer to these questions, you are one compromised credential away from a supply chain breach.
This guide breaks down how to implement Role-Based Access Control across your entire CI/CD pipeline from source control to production with practical, tool-specific guidance for DevSecOps teams. Whether you are just getting started or auditing an existing setup, you will find actionable best practices, a comparison of RBAC models, and the most common mistakes teams make when locking down their pipelines.
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What is Role-Based Access Control?
Role-Based Access Control (RBAC) is an access management model in which permissions are assigned to roles rather than to individual users. A user receives access rights by being assigned to one or more roles and the role, not the user, defines what actions are permitted.
In a DevSecOps context, this means that a developer role might allow reading and writing code, running unit tests, and opening merge requests but explicitly cannot approve its own merge requests or push directly to the production branch. An operations role can trigger deployments to staging and production but cannot modify application source code. A security engineer role can view pipeline logs, access vulnerability scan results, and adjust security policies but is not a code committer.
NIST defines RBAC as a policy-neutral access control mechanism built around roles and privileges. According to the NIST Zero Trust Architecture (SP 800-207), role-based and attribute-based access controls are a foundational pillar of a zero-trust posture a standard that enterprise engineering teams are increasingly expected to meet.
In a CI/CD pipeline, RBAC means giving different access rights to each stage of the pipeline based on user roles.
The Five Core Rules of RBAC
RBAC operates on five foundational principles that, when applied together, create a robust access control framework:
Role Assignment - user can only exercise a permission if they have been assigned a role that carries that permission.
Role Authorization - user's active role must be authorized, preventing them from assuming roles they are not entitled to.
Permission Authorization - user can only exercise a permission if it is authorized for their active role.
Least Privilege - Every role carries only the minimum permissions necessary to fulfill its function.
Separation of Duties (SoD) - Conflicting permissions are distributed across different roles so no single user can complete a sensitive operation alone.
RBAC has some main parts:
Role: This is about who can do what. Each role defines what actions are allowed.
Permissions: This decides how much access someone has. It specifies what actions are not allowed or what actions are linked to the role. For instance, if you can read, write, execute, or delete.
Users: These are the people or things in the organization assigned to roles.
Role Hierarchy: This is about how roles relate to each other. Some roles might be above others, like a parent and child. This can also mean inheriting permissions from higher roles.
Policies: These are the rules that control how roles and permissions are given out and how access rules are enforced.
Why Role-Based Access Control Is Critical in CI/CD Pipelines
A CI/CD pipeline is a high-value target. It has access to source code, secrets, cloud credentials, container registries, and production infrastructure. A misconfigured pipeline is functionally equivalent to leaving the server room door unlocked and the consequences of a breach are severe.
The 2023 SolarWinds and 3CX supply chain attacks both exploited the pipeline itself, not just the application code. The Cloud Native Computing Foundation (CNCF) now considers pipeline security a tier-one concern in its cloud native security white paper, listing access control as the first line of defense for software supply chain integrity.
Key risk areas in an unprotected CI/CD pipeline: unauthorized production deploys, secret exfiltration via pipeline logs, privilege escalation through misconfigured service accounts, and insider threats from overly-permissioned developers with access to all environments simultaneously.
Benefits of RBAC in DevSecOps
Implementing RBAC in a DevSecOps environment offers several benefits, including:
Better Security: RBAC makes sure that people only have the access they need, reducing the chances of someone getting into things they shouldn't. This helps prevent security problems.
Easier Access Management: With RBAC, managing who can do what is simpler. All permissions and roles are in one place, making it easier to control access and reducing the work for administrators.
Meeting Rules: RBAC helps meet rules and regulations by organizing access control in a clear way. It also keeps track of what users are doing, which is important for audits.
Flexible: RBAC can change as the organization grows or as projects change. It's good for environments that are always evolving.
Boosts Productivity: By making sure everyone has what they need to work, RBAC helps teams work together better and make decisions faster. It keeps things running smoothly.
Implementing Role-Based Access Control in your pipeline directly addresses these risks by ensuring that pipeline actions triggering builds, approving releases, accessing secrets, modifying infrastructure are gated by role, not merely by having repository access.
Implementing RBAC in the DevSecOps pipeline
Mapping out permissions and privileges
When setting up permissions, use your CI/CD platform's built-in RBAC features or add external RBAC tools. Create roles like developer, QA tester, etc. Then, specify permissions in detail. For example, developers can read and write code but only read deployment settings.
Integration with CI/CD tools
Incorporate RBAC into CI/CD pipelines to control access during software development. Use built-in RBAC features in tools like Jenkins, GitLab CI, or CircleCI. You can also create custom solutions for managing roles and permissions. Utilize APIs to automate access control and add plugins or extensions to improve RBAC capabilities.
Automation of role assignment and permission management
Automate role and permission setups using scripts or tools for efficiency. Use Infrastructure as Code (IaC) to set RBAC rules with infrastructure settings for consistency. Integrate RBAC automation into version control and configuration tools for easy tracking. Regularly check access logs for any unusual activity and use monitoring tools like Splunk or ELK stack for alerts.
Separate environments
Create separate spaces, either physically or virtually, for different stages of work (such as development, staging, and production). Apply specific RBAC rules to each space. Use conditional access policies to add extra layers of security, like requiring more authentication or approvals, especially for critical areas like production.
Implementing Role-Based Access Control in your pipeline directly addresses these risks by ensuring that pipeline actions triggering builds, approving releases, accessing secrets, modifying infrastructure are gated by role, not merely by having repository access.
Core Components of RBAC in a CI/CD Pipeline
Understanding these building blocks is essential before designing any RBAC implementation for your DevSecOps pipeline:
ComponentDescriptionCI/CD Pipeline ExampleUsers / PrincipalsHuman users, service accounts, or automated agents that need pipeline accessDeveloper, QA engineer, CI runner service account, deploy botRolesNamed collections of permissions representing a job functionDeveloper, Reviewer, Release Manager, SecOps, Read-Only AuditorPermissionsSpecific actions that can be allowed or deniedpush-to-main, trigger-deploy, read-secrets, approve-release, view-logsResourcesObjects or pipeline stages that permissions apply toGit branches, pipeline jobs, secret vaults, artifact registries, environmentsRole HierarchyParent-child relationships where higher roles inherit lower-role permissionsRelease Manager inherits Developer permissions + additional deployment rightsPoliciesRules governing how roles are assigned and how access is enforcedNo direct push to production without MR approval; two-person rule for releaseEnvironmentsScoped contexts where different RBAC rules applyDev, Staging, Production each with its own role permissions
RBAC vs. ABAC vs. PBAC: Choosing the Right Model
Role-Based Access Control is the most widely adopted model in CI/CD environments, but it is not the only option. Understanding the differences helps you decide when to use RBAC alone and when to layer in additional models.
Role-Based Access Control (RBAC)
Permissions are assigned to roles, and users are assigned to roles. Simple to manage at scale, well-supported by tooling (GitLab, GitHub, Jenkins, Azure DevOps all have native RBAC), and easy to audit. Best suited for most CI/CD pipelines where job functions map cleanly to roles.
Attribute-Based Access Control (ABAC)
Access decisions are made based on attributes of the user, resource, environment, and action evaluated in real time. More granular than RBAC (e.g., "allow deployment only if the environment is staging AND the time is between 09:00 - 18:00 AND the user's team attribute is 'ops'"), but significantly more complex to configure and maintain. Tools like Open Policy Agent (OPA) can implement ABAC policies in pipelines.
Policy-Based Access Control (PBAC)
A superset that combines role and attribute information into centralized, declarative policies often codified as Policy-as-Code. This is the most expressive model and aligns well with zero-trust architectures. Increasingly common in Kubernetes-native pipelines using tools like Kyverno or OPA/Gatekeeper.
Practical recommendation: Start with RBAC as your baseline across all CI/CD tooling. Layer ABAC or PBAC on top specifically for high-risk gates such as production deployments, secrets access, and infrastructure changes where time, environment context, or team attributes should influence the decision.
Implementing Role-Based Access Control Across the DevSecOps Pipeline
A complete RBAC implementation covers every layer of your pipeline: source control, the CI platform itself, secrets management, artifact registries, and target deployment environments. Here is how to approach each layer.
1. Source Control (Git)
Git is the first gate. Branch protection rules enforced by role are the most important RBAC control in your source control layer. Configure your Git platform so that:
Only the Maintainer / Release Manager role can push directly to main or release/* branches.
All code changes including from senior engineers must go through a merge request reviewed by a peer who did not write the code.
Force-push is disabled globally for protected branches.
Status checks (CI passing, security scan clean) are required before merge, regardless of the requester's role.
In GitLab, this is managed via project-level Member roles (Guest, Reporter, Developer, Maintainer, Owner) combined with Protected Branch settings. In GitHub, use Branch Protection Rules and the repository Collaborators & Teams page. In Azure DevOps, use Branch Policies and Security Groups at the repository level.
2. CI Platform Access (Jenkins, GitLab CI, GitHub Actions, Azure DevOps)
Each CI platform has its own RBAC model. The critical controls are: who can trigger pipeline runs, who can modify pipeline configuration (.gitlab-ci.yml, Jenkinsfile, workflow files), and which jobs run with elevated permissions.
For Jenkins, use the Role Strategy Plugin and the Matrix Authorization Strategy. Define fine-grained permissions at the folder and job level not just globally. Restrict who can configure jobs to a Maintainer role only.
For GitLab CI, RBAC extends naturally from your project member roles. Additionally, use protected environments and protected variables so that only Maintainers can trigger deployments to production, and only protected jobs can access production secrets.
For GitHub Actions, use Environments with required reviewers for production workflows, limit the GITHUB_TOKEN permissions to the minimum required per-workflow using the permissions key, and use repository-level or organization-level OIDC token federation instead of long-lived credentials.
For Azure DevOps, configure Environment approvals and checks so that production deployment pipelines require explicit approval from designated Release Managers before execution.
3. Secrets Management
Secrets should never be broadly available to every job in a pipeline. Apply RBAC at the secrets management layer using your vault tool of choice:
HashiCorp Vault: Use Vault policies mapped to AppRoles or JWT/OIDC tokens issued by your CI platform. Each pipeline role requests only the secrets it needs for that stage.
AWS Secrets Manager / SSM Parameter Store: IAM roles attached to pipeline runners should follow least privilege a build role should not have access to production database credentials.
GitLab / GitHub Secrets: Scope secrets to specific environments (e.g., production) and protect them so they are only exposed to jobs running on protected branches.
4. Container Registries and Artifact Stores
Only CI service accounts with an explicit "push" role should write to your container registry. Developers, QA engineers, and read-only consumers should have pull-only access. In Kubernetes environments, use image signing (Sigstore/Cosign) combined with admission controllers that enforce role-tagged image provenance before allowing a deploy.
5. Deployment Environments
Apply environment-scoped RBAC so that the permissions required to deploy to dev are completely separate from those required to deploy to production. A developer should be able to self-serve deployments to development and staging. Production deployments should always require a second role approval implementing a "four-eyes" principle enforced by your platform, not just by convention.
Best Practices for Role-Based Access Control in DevSecOps
Enforce the Principle of Least Privilege at Every Layer
Every role including your CI service accounts should carry exactly the permissions required for its task and no more. Service accounts used by pipeline runners are often over-permissioned because they were set up quickly during initial configuration and never revisited. Audit these accounts quarterly. A build runner does not need write access to your production environment, and a test runner does not need access to production secrets.
Implement Separation of Duties (SoD) as Policy
Separation of duties must be enforced by the platform not by trust. Use your CI/CD platform's native controls to ensure that the engineer who writes the code cannot be the same person who approves it for production. Define this as a branch protection rule or environment approval policy, not a team agreement.
In a mature DevSecOps pipeline, the critical SoD controls are: separate write-code and approve-code roles; separate trigger-deploy and approve-deploy roles; and a dedicated security-review role that is independent of both development and operations.
Use Just-in-Time (JIT) Access for Elevated Permissions
Permanent elevated access is the most common source of privilege abuse in pipeline environments. Instead of assigning a Release Manager role persistently, use Just-in-Time access controls that grant elevated permissions for a specific, time-bounded window for example, the duration of a release window and automatically revoke them afterward.
Tools like HashiCorp Vault's dynamic secrets, AWS IAM temporary credentials via STS, or purpose-built PAM solutions can automate JIT access within your pipeline workflow.
Automate Role Assignment via Infrastructure as Code
Never manage RBAC manually in the console. Codify your role assignments, permission bindings, and policy definitions in Terraform, Pulumi, or your platform's native IaC tooling. This ensures that role changes go through code review, are tracked in version control, and can be audited by your security team via the same processes used for infrastructure changes. This is the only way to maintain RBAC consistency at scale across multiple teams and environments.
Conduct Quarterly Access Reviews
Role assignments drift over time. Engineers change teams, project phases end, and temporary access granted for specific tasks never gets revoked. Implement quarterly access reviews either automated (using tools that compare current assignments against your IaC definitions) or manual for smaller teams. Pay particular attention to service accounts, which are often forgotten and accumulate permissions silently.
Integrate RBAC Audit Logs into Your SIEM
Every role assumption, permission escalation, and access denial in your pipeline should generate an audit log event. Feed these events into your SIEM or observability stack and create alerts for anomalous patterns: off-hours deployments, role assumptions from unexpected IP ranges, bulk secret reads, or failed authorization attempts at production gates. This connects your RBAC policy to real-time threat detection.
Common RBAC Mistakes in CI/CD Pipelines (and How to Avoid Them)
Mistake 1: Using a Single All-Powerful Service Account
It is tempting to create one powerful CI service account that has access to everything secrets, deployment targets, registries, cloud APIs so that pipelines "just work." This is also the configuration that turns a single pipeline compromise into a complete environment takeover. The fix is to create scoped service accounts per pipeline stage, each with only the permissions that stage requires. Automate this in Terraform so it scales without manual overhead.
Mistake 2: Conflating Repository Access with Pipeline Access
Having read access to a repository does not mean a user should be able to trigger a production deployment from a pipeline associated with that repository. Many teams configure CI/CD platforms so that any contributor can manually trigger any pipeline run including production deploys. Explicitly restrict manual pipeline triggers for protected environments to named roles.
Mistake 3: Not Protecting Pipeline Configuration Files
Your .gitlab-ci.yml, Jenkinsfile, or GitHub Actions workflow files are code that runs with elevated permissions. A developer who can modify the pipeline configuration file can effectively grant themselves any permission the pipeline runner holds. Treat pipeline configuration changes like infrastructure changes require peer review, protect the relevant branches, and run a policy-as-code check on pipeline config changes in your security scanning stage.
Mistake 4: Ignoring Role Hierarchy Inheritance
Most RBAC systems support role inheritance, where a higher-level role automatically inherits permissions from a lower-level role. This is convenient but dangerous if not mapped explicitly. Audit your role hierarchy to ensure that no inherited permission chain grants an unintended escalation path for example, a "Staging Deployer" role that inherits from a role that has production secret access.
Mistake 5: Neglecting RBAC in Kubernetes Clusters
If your CI/CD pipeline deploys to Kubernetes, Kubernetes RBAC is a separate, equally important control surface. Pipeline service accounts that interact with the cluster should have namespace-scoped roles, not ClusterAdmin. Use admission controllers (OPA/Gatekeeper or Kyverno) to enforce that workloads requesting elevated Kubernetes RBAC permissions are explicitly approved. The Linux Foundation's Kubernetes Security Specialist certification curriculum covers this in detail as a mandatory competency for any team running production workloads on Kubernetes.
The Future of Role-Based Access Control: AI-Driven and Context-Aware
The next evolution of Role-Based Access Control in DevSecOps is moving beyond static role assignments toward dynamic, context-aware access decisions informed by machine learning. Several directions are already emerging in enterprise tooling:
Behavioral anomaly detection layers ML models on top of existing RBAC logs to flag when a user or service account is behaving outside their normal pattern for example, a developer role suddenly reading production secrets at 2 AM from an unusual geographic location. This does not replace RBAC but adds a detection layer on top of it.
AI-assisted access reviews use usage telemetry to recommend permission reductions: "This role has not used its write access to the production artifact registry in 90 days consider downgrading it to read-only." Tools like Brainwave GRC and some CIEM (Cloud Infrastructure Entitlement Management) platforms already offer this capability.
Dynamic role provisioning aligned with zero-trust principles moves toward a model where a role is not assigned persistently at all. Instead, the pipeline requests a role token for a specific task, the identity provider evaluates a policy combining role, time, environment, and behavioral context, and issues a short-lived token. This is the direction that Platform Engineering teams are building toward as internal developer platforms mature.
For most teams, the practical step today is to ensure your RBAC system generates the rich audit logs that will feed these ML models when you are ready to adopt them rather than retroactively trying to add observability to a pipeline that was built without it.
Conclusion
In the future, RBAC for DevSecOps will likely integrate with emerging technologies like AI and blockchain. AI algorithms can dynamically adjust access privileges based on user behavior patterns, while blockchain offers decentralized and immutable access control solutions.
Expect the evolution of RBAC standards to address emerging security challenges and technological advancements, with a focus on interoperability and compatibility.
RBAC adoption will shape DevSecOps culture by promoting a security-first mindset and fostering collaboration between teams. Training and education on RBAC principles may become a priority, and organizations may establish dedicated access control teams or integrate RBAC into their toolchains and processes.
Fedir Kompaniiets
Co-founder & CEO, Gart Solutions · Cloud Architect & DevOps Consultant
Fedir is a technology enthusiast with over a decade of diverse industry experience. He co-founded Gart Solutions to address complex tech challenges related to Digital Transformation, helping businesses focus on what matters most — scaling. Fedir is committed to driving sustainable IT transformation, helping SMBs innovate, plan future growth, and navigate the "tech madness" through expert DevOps and Cloud managed services. Connect on LinkedIn.
In this guide, we will explore various scenarios for working with Gitlab CI/CD Pipelines. We will provide examples of using the most commonly used options when working with CI/CD. The template library will be expanded as needed.
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You can easily find what CI/CD is and why it's needed through a quick internet search. Full documentation on configuring pipelines in GitLab is also readily available. Here, I'll briefly and as seamlessly as possible describe the system's operation from a bird's-eye view:
A developer submits a commit to the repository, creates a merge request through the website, or initiates a pipeline explicitly or implicitly in some other way.
The pipeline configuration selects all tasks whose conditions allow them to run in the current context.
Tasks are organized according to their respective stages.
Stages are executed sequentially, meaning that all tasks within a stage run in parallel.
If a stage fails (i.e., if at least one task in the stage fails), the pipeline typically stops (almost always).
If all stages are completed successfully, the pipeline is considered to have passed successfully.
In summary, we have:
A pipeline: a set of tasks organized into stages, where you can build, test, package code, deploy a ready build to a cloud service, and more.
A stage: a unit of organizing a pipeline, containing one or more tasks.
A task (job): a unit of work in the pipeline, consisting of a script (mandatory), launch conditions, settings for publishing/caching artifacts, and much more.
Consequently, the task when setting up CI/CD is to create a set of tasks that implement all necessary actions for building, testing, and publishing code and artifacts.
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Templates
In this section, we will provide several ready-made templates that you can use as a foundation for writing your own pipeline.
Minimal Scenario
For small tasks consisting of a couple of jobs:
stages:
- build
TASK_NAME:
stage: build
script:
- ./build_script.sh
stages: Describes the stages of our pipeline. In this example, there is only one stage.
TASK_NAME: The name of our job.
stage: The stage to which our job belongs.
script: A set of scripts to execute.
Standard Build Cycle
Typically, the CI/CD process includes the following steps:
Building the package.
Testing.
Delivery.
Deployment.
You can use the following template as a basis for such a scenario:
stages:
- build
- test
- delivery
- deploy
build-job:
stage: build
script:
- echo "Start build job"
- build-script.sh
test-job:
stage: test
script:
- echo "Start test job"
- test-script.sh
delivery-job:
stage: delivery
script:
- echo "Start delivery job"
- delivery-script.sh
deploy-job:
stage: deploy
script:
- echo "Start deploy job"
- deploy-script.sh
Jobs
In this section, we will explore options that can be applied when defining a job. The general syntax is as follows
<TASK_NAME>:
<OPTION1>: ...
<OPTION2>: ...
We will list commonly used options, and you can find the complete list in the official documentation.
Stage
Documentation
This option specifies to which stage the job belongs. For example:
stages:
- build
- test
TASK_NAME:
...
stage: build
TASK_NAME:
...
stage: test
Stages are defined in the stages directive.
There are two special stages that do not need to be defined in stages:
.pre: Runs before executing the main pipeline jobs.
.post: Executes at the end, after the main pipeline jobs have completed.
For example:
stages:
- build
- test
getVersion:
stage: .pre
script:
- VERSION=$(cat VERSION_FILE)
- echo "VERSION=${VERSION}" > variables.env
artifacts:
reports:
dotenv: variables.env
In this example, before starting all build jobs, we define a variable VERSION by reading it from a file and pass it as an artifact as a system variable.
Image
Documentation
Specifies the name of the Docker image to use if the job runs in a Docker container:
TASK_NAME:
...
image: debian:11
Before_script
Documentation
This option defines a list of commands to run before the script option and after obtaining artifacts:
TASK_NAME:
...
before_script:
- echo "Run before_script"
Script
Documentation
The main section where job tasks are executed is described in the script option. Let's explore it further.
Describing an array of commands: Simply list the commands that need to be executed sequentially in your job:
TASK_NAME:
...
script:
- command1
- command2
Long commands split into multiple lines: You may need to execute commands as part of a script (with comparison operators, for example). In this case, a multiline format is more convenient. You can use different indicators:
Using |:
TASK_NAME:
...
script:
- |
command_line1
command_line2
Using >:
TASK_NAME:
...
script:
- >
command_line1
command_line1_continue
command_line2
command_line2_continue
After_script
Documentation
A set of commands that are run after the script, even if the script fails:
TASK_NAME:
...
script:
...
after_script:
- command1
- command2
Artifacts
Documentation
Artifacts are intermediate builds or files that can be passed from one stage to another.
You can specify which files or directories will be artifacts:
TASK_NAME:
...
artifacts:
paths:
- ${PKG_NAME}.deb
- ${PKG_NAME}.rpm
- *.txt
- configs/
In this example, artifacts will include all files with names ending in .txt, ${PKG_NAME}.deb, ${PKG_NAME}.rpm, and the configs directory. ${PKG_NAME} is a variable (more on variables below).
In other jobs that run afterward, you can use these artifacts by referencing them by name, for example:
TASK_NAME_2:
...
script:
- cat *.txt
- yum -y localinstall ${PKG_NAME}.rpm
- apt -y install ./${PKG_NAME}.deb
You can also pass system variables that you defined in a file:
TASK_NAME:
...
script:
- echo -e "VERSION=1.1.1" > variables.env
...
artifacts:
reports:
dotenv: variables.env
In this example, we pass the system variable VERSION with the value 1.1.1 through the variables.env file.
If necessary, you can exclude specific files by name or pattern:
TASK_NAME:
...
artifacts:
paths:
...
exclude:
- ./.git/**/
In this example, we exclude the .git directory, which typically contains repository metadata. Note that unlike paths, exclude does not recursively include files and directories, so you need to explicitly specify objects.
Extends
Documentation
Allows you to separate a part of the script into a separate block and combine it with a job. To better understand this, let's look at a specific example:
.my_extend:
stage: build
variables:
USERNAME: my_user
script:
- extend script
TASK_NAME:
extends: .my_extend
variables:
VERSION: 123
PASSWORD: my_pwd
script:
- task script
In this case, in our TASK_NAME job, we use extends. As a result, the job will look like this:
TASK_NAME:
stage: build
variables:
VERSION: 123
PASSWORD: my_pwd
USERNAME: my_user
script:
- task script
What happened:
stage: build came from .my_extend.
Variables were merged, so the job includes VERSION, PASSWORD, and USERNAME.
The script is taken from the job (key values are not merged; the job's value takes precedence).
Environment
Documentation
Specifies a system variable that will be created for the job:
TASK_NAME:
...
environment:
RSYNC_PASSWORD: rpass
EDITOR: vi
Release
Documentation
Publishes a release on the Gitlab portal for your project:
TASK_NAME:
...
release:
name: 'Release $CI_COMMIT_TAG'
tag_name: '$CI_COMMIT_TAG'
description: 'Created using the release-cli'
assets:
links:
- name: "myprogram-${VERSION}"
url: "https://gitlab.com/master.dmosk/project/-/jobs/${CI_JOB_ID}/artifacts/raw/myprogram-${VERSION}.tar.gz"
rules:
- if: $CI_COMMIT_TAG
Please note that we use the if rule (explained below).
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Rules and Constraints Directives
To control the behavior of job execution, you can use directives with rules and conditions. You can execute or skip jobs depending on certain conditions. Several useful directives facilitate this, which we will explore in this section.
Rules
Documentation
Rules define conditions under which a job can be executed. Rules regulate different conditions using:
if
changes
exists
allow_failure
variables
The if Operator: Allows you to check a condition, such as whether a variable is equal to a specific value:
TASK_NAME:
...
rules:
- if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
In this example, the commit must be made to the default branch.
Changes: Checks whether changes have affected specific files. This check is performed using the changes option:In this example, it checks if the script.sql file has changed:
TASK_NAME:
...
rules:
- changes:
- script.sql
Multiple Conditions: You can have multiple conditions for starting a job. Let's explore some examples.
a) If the commit is made to the default branch AND changes affect the script.sql file:
TASK_NAME:
...
rules:
- if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
changes:
- script.sql
b) If the commit is made to the default branch OR changes affect the script.sql file:
TASK_NAME:
...
rules:
- if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
- changes:
- script.sql
Checking File Existence: Determined using exists:
TASK_NAME:
...
rules:
- exists:
- script.sql
The job will only execute if the script.sql file exists.
Allowing Job Failure: Defined with allow_failure:
TASK_NAME:
...
rules:
- allow_failure: true
In this example, the pipeline continues even if the TASK_NAME job fails.
Conditional Variable Assignment: You can conditionally assign variables using a combination of if and variables:
TASK_NAME:
...
rules:
- if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
variables:
DEPLOY_VARIABLE: "production"
- if: '$CI_COMMIT_BRANCH =~ demo'
variables:
DEPLOY_VARIABLE: "demo"
When
Documentation
Determines when a job should be run, such as on manual trigger or at specific intervals. The when directive determines when a job should run. Possible values include:
on_success (default): Runs if all previous jobs have succeeded or have allow_failure: true.
manual: Requires manual triggering (a "Run Pipeline" button appears in the GitLab CI/CD panel).
always: Runs always, regardless of previous results.
on_failure: Runs only if at least one previous job has failed.
delayed: Delays job execution. You can control the delay using the start_in directive.
never: Never runs.
Let's explore some examples:
Manual:
TASK_NAME:
...
when: manual
The job won't start until you manually trigger it in the GitLab CI/CD panel.
2. Always:
TASK_NAME:
...
when: always
The job will always run. Useful, for instance, when generating a report regardless of build results.
On_failure:
TASK_NAME:
...
on_failure: always
The job will run if there is a failure in previous stages. You can use this for sending notifications.
Delayed:
TASK_NAME:
...
when: delayed
start_in: 30 minutes
The job will be delayed by 30 minutes.
Never:
TASK_NAME:
...
when: never
The job will never run.
Using with if:
TASK_NAME:
...
rules:
- if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
when: manual
In this example, the job will only execute if the commit is made to the default branch and an administrator confirms the run.
Needs
Documentation
Allows you to specify conditions for job execution based on the presence of specific artifacts or completed jobs. With rules of this type, you can control the order in which jobs are executed.
Let's look at some examples.
Artifacts: This directive accepts true (default) and false. To start a job, artifacts must be uploaded in previous stages. Using this configuration:
TASK_NAME:
...
needs:
- artifacts: false
...allows the job to start without artifacts.
Job: You can start a job only after another job has completed:
TASK_NAME:
...
needs:
- job: createBuild
In this example, the task will only start after the job named createBuild has finished.
? Read more: Building a Robust CI/CD Pipeline for Cybersecurity Company
Variables
In this section, we will discuss user-defined variables that you can use in your pipeline scripts as well as some built-in variables that can modify the pipeline's behavior.
User-Defined Variables User-defined variables are set using the variables directive. You can define them globally for all jobs:
variables:
PKG_VER: "1.1.1"
Or for a specific job:
TASK_NAME:
...
variables:
PKG_VER: "1.1.1"
You can then use your custom variable in your script by prefixing it with a dollar sign and enclosing it in curly braces, for example:
script:
- echo ${PKG_VER}
GitLab Variables These variables help you control the build process. Let's list these variables along with descriptions of their properties:
Variable Description Example LOG_LEVEL Sets the log level. Options: debug, info, warn, error, fatal, and panic. Lower priority compared to command-line arguments --debug and --log-level. LOG_LEVEL: warning CI_DEBUG_TRACE Enables or disables debug tracing. Takes values true or false. CI_DEBUG_TRACE: true CONCURRENT Limits the number of jobs that can run simultaneously. CONCURRENT: 5 GIT_STRATEGY Controls how files are fetched from the repository. Options: clone, fetch, and none (don't fetch). GIT_STRATEGY: none
Additional Options In this section, we will cover various options that were not covered in other sections.
Workflow: Allows you to define common rules for the entire pipeline. Let's look at an example from the official GitLab documentation:
workflow:
rules:
- if: $CI_COMMIT_TITLE =~ /-draft$/
when: never
- if: $CI_PIPELINE_SOURCE == "merge_request_event"
- if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
In this example, the pipeline:
Won't be triggered if the commit title contains "draft."
Will be triggered if the pipeline source is a merge request event.
Will be triggered if changes are made to the default branch of the repository.
Default Values: Defined in the default directive. Options with these values will be used in all jobs but can be overridden at the job level.
default:
image: centos:7
tags:
- ci-test
In this example, we've defined an image (e.g., a Docker image) and tags (which may be required for some runners).
Import Configuration from Another YAML File: This can be useful for creating a shared part of a script that you want to apply to all pipelines or for breaking down a complex script into manageable parts. It is done using the include option and has different ways to load files. Let's explore them in more detail.
a) Local File Inclusion (local):
include:
- local: .gitlab/ci/build.yml
b) Template Collections (template):
include:
- template: Custom/.gitlab-ci.yml
In this example, we include the contents of the Custom/.gitlab-ci.yml file in our script.
c) External File Available via HTTP (remote):
include:
- remote: 'https://gitlab.dmosk.ru/main/build.yml'
d) Another Project:
include:
- project: 'public-project'
ref: main
file: 'build.yml'
!reference tags: Allows you to describe a script and reuse it for different stages and tasks in your CI/CD. For example:
.apt-cache:
script:
- apt update
after_script:
- apt clean all
install:
stage: install
script:
- !reference [.apt-cache, script]
- apt install nginx
update:
stage: update
script:
- !reference [.apt-cache, script]
- apt upgrade
- !reference [.apt-cache, after_script]
Let's break down what's happening in our example:
We created a task called apt-cache. The dot at the beginning of the name tells the system not to start this task automatically when the pipeline is run. The created task consists of two sections: script and after_script. There can be more sections.
We execute the install stage. In one of the execution lines, we call apt-cache (only the commands from the script section).
In the update stage, we call apt-cache twice—the first executes commands from the script section, and the second executes those from the after_script section.
These are the fundamental concepts and options of GitLab CI/CD pipelines. You can use these directives and templates as a starting point for configuring your CI/CD pipelines efficiently. For more advanced use cases and additional options, refer to the official GitLab CI/CD documentation.
In this blog post, we delve into the world of CI/CD tools, uncovering the game-changing potential of these tools in accelerating your software delivery process.
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Discover the top CI/CD tools and learn from real-life case studies where Gart, a trusted industry leader, has successfully implemented CI/CD pipelines and infrastructure for e-health and entertainment software platforms. Get inspired by their achievements and gain practical insights into optimizing your development process.
CI/CD Tools Table
CI/CD ToolDescriptionLanguage SupportIntegrationDeploymentJenkinsOpen-source automation serverExtensive support for multiple languagesWide range of plugins availableFlexible deployment optionsGitLab CI/CDIntegrated CI/CD solution within GitLabWide language supportSeamless integration with GitLab repositoriesFlexible deployment optionsCircleCICloud-based CI/CD platformSupport for various languages and frameworksIntegrates with popular version control systemsSupports deployment to multiple environmentsTravis CICloud-based CI/CD service for GitHub projectsWide language supportTight integration with GitHubEasy deployment to platforms like Heroku and AWSAzure DevOps (Azure Pipelines)Comprehensive development tools by MicrosoftExtensive language supportIntegrates with Azure servicesDeployment to Azure cloud and on-premisesTeamCityCI/CD server developed by JetBrainsSupports various build and test runnersIntegrates with JetBrains IDEs and external toolsSupports flexible deployment strategiesA comparison table for some popular CI/CD tools
Case Studies: Achieving Success with Gart
CI/CD Pipelines and Infrastructure for E-Health Platform
Gart collaborated with an e-health platform to revolutionize their software delivery process. By implementing robust CI/CD pipelines and optimizing the underlying infrastructure, Gart helped the platform achieve faster releases, improved quality, and enhanced scalability.
AWS Cost Optimization and CI/CD Automation for Entertainment Software Platform
Another notable case study involves Gart's partnership with an entertainment software platform, where they tackled the dual challenges of AWS cost optimization and CI/CD automation. Gart's expertise resulted in significant cost savings by optimizing AWS resources, while simultaneously streamlining the software delivery process through efficient CI/CD pipelines. Learn more about this successful collaboration here.
These case studies highlight Gart's prowess in tailoring CI/CD solutions to diverse industries and our ability to drive tangible benefits for their clients. By leveraging Gart's expertise, you can witness firsthand how CI/CD implementation can bring about remarkable transformations in software delivery processes.
Looking for CI/CD solutions? Contact Gart for comprehensive expertise in streamlining your software delivery process.
The Ultimate CI/CD Tools List
CI/CD (Continuous Integration/Continuous Deployment) tools are software solutions that help automate the process of building, testing, and deploying software applications. These tools enable development teams to streamline their workflows and deliver software updates more efficiently. Here are some popular CI/CD tools:
Jenkins
Jenkins is an open-source automation server that is widely used for CI/CD. It offers a vast array of plugins and integrations, allowing teams to build, test, and deploy applications across various platforms.
GitLab CI/CD
GitLab provides an integrated CI/CD solution within its platform. It enables teams to define pipelines using a YAML configuration file and offers features such as automatic testing, code quality checks, and deployment to various environments.
CircleCI
CircleCI is a cloud-based CI/CD platform that supports continuous integration and delivery. It provides a simple and intuitive interface for configuring pipelines and offers extensive support for a wide range of programming languages and frameworks.
Travis CI
Travis CI is a cloud-based CI/CD service primarily designed for projects hosted on GitHub. It offers a straightforward setup process and provides a range of features for building, testing, and deploying applications.
Azure DevOps
Azure DevOps is a comprehensive set of development tools provided by Microsoft. It includes Azure Pipelines, which allows teams to define and manage CI/CD pipelines for their applications. Azure Pipelines supports both cloud and on-premises deployments.
? Read more: CI/CD Pipelines and Infrastructure for E-Health Platform
Bamboo
Bamboo is a CI/CD server developed by Atlassian. It integrates well with other Atlassian products like Jira and Bitbucket. Bamboo offers features such as parallel builds, customizable workflows, and easy integration with external tools.
TeamCity
TeamCity is a CI/CD server developed by JetBrains. It supports a variety of build and test runners and offers a user-friendly interface for managing pipelines. TeamCity also provides advanced features like code coverage analysis and build chain visualization.
GoCD
GoCD is an open-source CI/CD tool that provides advanced workflow modeling capabilities. It enables teams to define complex pipelines and manage dependencies between different stages of the software delivery process.
Buddy
Buddy is a CI/CD platform that offers a free plan for small projects. It provides a user-friendly interface and supports a wide range of programming languages, making it suitable for developers of all levels.
Drone
Drone is an open-source CI/CD platform that is highly flexible and scalable. It allows you to define your pipelines using a simple YAML configuration file and integrates with popular version control systems.
Strider
Strider is an open-source, customizable CI/CD platform that supports self-hosting. It offers features like parallel testing, deployment, and notification plugins to enhance your software delivery process.
Semaphore
Semaphore is a cloud-based CI/CD platform that provides a free tier for small projects. It supports popular programming languages and offers a simple and intuitive interface for configuring and managing your pipelines.
Concourse CI
Concourse CI is an open-source CI/CD system that focuses on simplicity and scalability. It provides a declarative pipeline configuration and supports powerful automation capabilities.
Codeship
Codeship is a cloud-based CI/CD platform that offers a free tier for small projects. It provides a simple and intuitive interface, supports various programming languages, and integrates with popular version control systems.
Ready to supercharge your software delivery? Contact Gart today and leverage our expertise in CI/CD to optimize your development process. Boost efficiency, streamline deployments, and stay ahead of the competition.
Bitbucket Pipelines
Bitbucket Pipelines is a CI/CD solution tightly integrated with Atlassian's Bitbucket. It enables you to define and execute pipelines directly from your Bitbucket repositories, offering seamless integration and easy configuration.
Wercker
Wercker is a cloud-based CI/CD platform that offers container-centric workflows. It provides seamless integration with popular container platforms like Docker and Kubernetes, enabling you to build, test, and deploy containerized applications efficiently.
Nevercode
Nevercode is a mobile-focused CI/CD platform that specializes in automating the build, testing, and deployment of mobile applications. It supports both iOS and Android development and provides a range of mobile-specific features and integrations.
Spinnaker
Spinnaker is an open-source multi-cloud CD platform that focuses on deployment orchestration. It enables you to deploy applications to multiple cloud providers with built-in support for canary deployments, rolling updates, and more.
Buildbot
Buildbot is an open-source CI/CD framework that allows you to automate build, test, and release processes. It provides a highly customizable and extensible architecture, making it suitable for complex CI/CD workflows.
Harness
Harness is a CI/CD platform that emphasizes continuous delivery and feature flagging. It offers advanced deployment strategies, observability, and monitoring capabilities to ensure smooth and reliable software releases.
IBM UrbanCode
IBM UrbanCode is an enterprise-grade CI/CD platform that provides end-to-end automation and release management. It offers features like environment management, deployment automation, and release coordination for complex enterprise applications.
Perforce Helix
Perforce Helix is a CI/CD and version control platform that supports large-scale development and collaboration. It provides a range of tools for source control, build automation, and release management.
Bitrise
Bitrise is a CI/CD platform designed specifically for mobile app development. It offers an extensive library of integrations, enabling you to automate workflows for building, testing, and deploying iOS and Android apps.
Codefresh
Codefresh is a cloud-native CI/CD platform built for Docker and Kubernetes workflows. It offers a visual pipeline editor, seamless integration with container registries, and advanced deployment features for modern application development.
CruiseControl
CruiseControl is an open-source CI tool that focuses on continuous integration. It provides a framework for automating builds, tests, and releases, and supports various build tools and version control systems.
These are just a few examples of popular CI/CD tools available in the market. The choice of tool depends on various factors such as project requirements, team preferences, and integration capabilities with other tools in your software development stack.
Exploring the Power of AWS CI/CD Tools
AWS (Amazon Web Services) offers a range of CI/CD tools and services to streamline software delivery. Here are some popular AWS CI/CD tools:
AWS CodePipeline: CodePipeline is a fully managed CI/CD service that enables you to automate your software release process. It integrates with other AWS services, such as CodeCommit, CodeBuild, and CodeDeploy, to build, test, and deploy your applications.
AWS CodeBuild: CodeBuild is a fully managed build service that compiles your source code, runs tests, and produces software packages. It supports various programming languages and build environments and integrates with CodePipeline for automated builds.
AWS CodeDeploy: CodeDeploy automates the deployment of applications to instances, containers, or serverless environments. It provides capabilities for blue/green deployments, automatic rollback, and integration with CodePipeline for streamlined deployments.
AWS CodeCommit: CodeCommit is a fully managed source control service that hosts Git repositories. It provides secure and scalable version control for your code and integrates seamlessly with other AWS CI/CD tools.
AWS CodeStar: CodeStar is a fully integrated development environment (IDE) for developing, building, and deploying applications on AWS. It combines various AWS services, including CodePipeline, CodeBuild, and CodeDeploy, to provide an end-to-end CI/CD experience.
These AWS CI/CD tools offer powerful capabilities to automate and streamline your software delivery process on the AWS platform. Each tool can be used independently or combined to create a comprehensive CI/CD pipeline tailored to your application requirements.
Conclusion
CI/CD tools have become indispensable in modern software development, enabling teams to streamline their delivery process, improve efficiency, and achieve faster time to market. Throughout this article, we have explored a wide range of CI/CD tools, both free and enterprise-grade, each offering unique features and capabilities. From popular options like Jenkins, GitLab CI/CD, and CircleCI to specialized tools for mobile app development and container-centric workflows, there is a tool to fit every project's requirements.
Now is the time to embark on your CI/CD journey and leverage the power of these tools. Evaluate your project requirements, explore the tools discussed in this article, and consider partnering with experts like Gart to guide you through the implementation process. Embrace the CI/CD revolution and unlock the full potential of your software development process.
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