- Building A CI/CD Pipeline
- Setting Up Gitlab
- Docs.gitlab.com › Ee › CiGitLab CI/CD Script Syntax | GitLab
- Gitlab Ci Run Python Script
- How Make A If Statement In The CI File
- See Full List On Section.io
In the root of your project, create a.gitlab-ci.yml file, set the following code, and substitute the #S3NAME# with the Amazon S3 bucket name you made to save the package: image: python:3.8 stages: - deploy Dev: stage: deploy beforescript: - pip3 install awscli -upgrade - pip3 install aws-sam-cli -upgrade script: - sam build - sam package. First thing after importing repository into gitlab we need to create.gitlab-ci.yml. Gitlab itself has a lot of useful information about CI configuration, for example this and this. Previously I used to do following steps to test/compile/deploy manually: npm run build - to build all js/css; build.sh - to build docker container.
On this page
- Python Guide
- Values
- Linting
- Unit Testing
- Values
Python Guide
It is our collective responsibility to enforce this Style Guide since our chosen linter does not catch everything.
Values
Campsite rule - As these guidelines are themselves a WIP, if you work with any code which does not currently adhere to the style guide update it when you see it.
Linting
We use Black as our linter. We use the default configuration.
There is a manual CI job in the review
stage that will lint the entire repo and return a non-zero exit code if files need to be formatted. It is up to both the MR author and the reviewer to make sure that this job passes before the MR is merged. To lint the entire repo, just execute black .
from the top of the repo.
Spacing
Following PEP8 we recommend you put blank lines around logical sections of code. When starting a for
loop or if/else
block, add a new line above the section to give the code some breathing room. Newlines are cheap - brain time is expensive.
Type Hints
All function signatures should contain type hints, including for the return type, even if it is None
. This is good documentation and can also be used with mypy for type checking and error checking.
Examples:
Import Order
Imports should follow the PEP8 rules and furthermore should be ordered with any import ...
statements coming before from .... import ...
Building A CI/CD Pipeline
Example:
Docstrings
Setting Up Gitlab
Docstrings should be used in every single function. Since we are using type hints in the function signature there is no requirement to describe each parameter.Docstrings should use triple double-quotes and use complete sentences with punctuation.
Examples:
How to integrate Environment Variables
To make functions as reusable as possible, it is highly discouraged (unless there is a very good reason) from using environment variables directly in functions (there is an example of this below).Instead, the best practice is to either pass in the variable you want to use specifically or pass all of the environment variables in as a dictionary.This allows you to pass in any dictionary and have it be compatible while also not requiring the variables to being defined at the environment level.
Examples:
Package Aliases
We use a few standard aliases for common third-party packages. They are as follows:
import pandas as pd
import numpy as np
Variable Naming Conventions
Adding the type to the name is good self-documenting code.When possible, always use descriptive naming for variables, especially with regards to data type. Here are some examples:
data_df
is a dataframeparams_dict
is a dictionaryretries_int
is an intbash_command_str
is a string
If passing a constant through to a function, name each variable that is being passed so that it is clear what each thing is.
Lastly, try and avoid redundant variable naming.
Examples:
Making your script executable
If your script is not able to be run even though you've just made it, it most likely needs to be executable. Run the following:
For an explanation of chmod 755 read this askubuntu page.
Mutable default function arguments
Using mutable data structures as default arguments in functions can introduce bugs into your code. This is because a new mutable data structure is created once when the function is defined, and the data structure is used in each successive call.
Example:
Output:
Reference: https://docs.python-guide.org/writing/gotchas/
Folder structure for new extracts
- All client specific logic should be stored in /extract, any API Clients which may be reused should be stored in /orchestration
- Pipeline specific operations should be stored in /extract.
- The folder structure in extract should include a file called
extract_{source}_{dataset_name}
likeextract_qualtrics_mailingsends
orextract_qualtrics
if the script extracts multiple datasets. This script can be considered the main function of the extract, and is the file which gets run as the starting point of the extract DAG.
Docs.gitlab.com › Ee › CiGitLab CI/CD Script Syntax | GitLab
When not to use Python
Gitlab Ci Run Python Script
Since this style guide is for the entire data team, it is important to remember that there is a time and place for using Python and it is usually outside of the data modeling phase.Stick to SQL for data manipulation tasks where possible.
Unit Testing
Pytest is used to run unit tests in the Analytics project. The tests are executed from the root directory of the project with the python_pytest
CI pipeline job. The job produces a JUnit report of test results which is then processed by GitLab and displayed on merge requests.
How Make A If Statement In The CI File
Writing New Tests
New testing file names should follow the pattern test_*.py
so they are found by pytest and easily recognizable in the repository. New testing files should be placed in a directory named test
. The test directory should share the same parent directory as the file that is being tested.
A testing file consists of one or more tests. An individual test is created by defining a function that has one or many plain Python assert
statements. If the asserts are all true, the test passes. If any asserts are false, then the test will fail.
When writing imports, it is important to remember that tests are executed from the root directory. In the future, additional directories may be added to the PythonPath for ease of testing as need allows.
Exception handling
When writing a python class to extract data from an API it is the responsibility of that class to highlight any errors in the API process. Data modelling, source freshness and formatting issues should be highlighted using dbt tests.
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Avoid use of general try/except blocks ie: