GitHub Actions is a CI/CD (Continuous Integration & Continuous Delivery) platform. It enables users to automate their deployment, test, and build pipelines. Users can create workflows, which build, as well as test each pull request to a repository. Alternatively, they can also deploy the merged requests for pull to production.
To put it simply, GitHub actions have a wider scope than DevOps as it allows users to run workflows while other events are occurring in the repository. For instance, one can run a workflow for automatically adding relevant labels if someone else creates a fresh issue in a repository.
It allows macOS, Windows, and Linux virtual machines to run workflows.
You may host self-hosted runners in cloud infrastructure or data centers. Each GitHub Action automation can be handled through workflows. A workflow is a YAML file put in the workflows/GitHub directory. The directory is placed within a repository, which contains a definition of automated processes.
Core concepts
Each workflow comprises distinct core concepts. These are as follows:
- Jobs- These are a collection of steps, which run in a separate Virtual Machine and run simultaneously with other jobs.
- Events- Events are specific triggers that trigger a workflow.
- Actions: These are commands executed on runners. Action is the core component of GitHub Actions.
- Steps: These are individual tasks, which execute a job’s commands. Just one runner executes all steps present in the job.
- Runners: Runners executes the workflows if they are triggered and are a GitHub server. The runner can execute only one job at a time.
Defining Linters
These are programs, which inspect the code’s quality by showing warnings and errors. Some merits of leveraging Python linters are as follows:
- Test for syntax errors
- Cleaning up and simplifying the code
- Detecting nonessential code fragments
- Make Python code easily understandable to programmers
- Prevention of project bugs
Some instances of linters are PyFlakes, Flake8, and Pylint.
Defining Pylint
It is a tool to analyze static Python code, which detects invalid code constructs, coding standards violations, and programming errors. Additionally, we can adjust Pylint to our requirements by disabling a few reported bugs. For instance, output messages may simply include details about certain types of errors. A Pylint message comprises five errors-F, E, W, C, and R.
Merits of PYLINT
- Triggers more incorrect positives that exist in the code
- It provides the rating to indicate the code quality
- It May be customized for certain error types
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