Harmony regression tests run a series of self contained tests to ensure no regressions occur when portions of harmony are changed.
The regression tests can be run multiple ways. Locally in Docker against SIT, UAT and Prod. This is the preferred method of verifying no regressons have occurred, when the services have been modified.
Alternatively, each test can be run locally in a browser against SIT, UAT, PROD or localhost (Harmony-In-A-Box). This is a good choice for test development and verifying service changes do not cause regression failures. Generally you run locally in the browser against a single service regression test.
- Docker - to run locally in docker
- git-lfs - to handle large files
- pre-commit - to ensure code formatting. See below.
To work with this repository, ensure git-lfs (Git Large File Storage) is installed on your system, as it's used to manage some large files stored in GitHub. To install git-lfs with Homebrew, enter the following commands:
brew install git-lfs
git lfs install
If you have already cloned this repository, delete it and re-clone.
Each test suite can be individually invoked via a GitHub workflow. Navigate to the GitHub Actions tab for this repository. Then select the "Run test suite" workflow from the lefthand menu. On the right hand side, click the "Run workflow" dropdown, and select the correct Docker image and Harmony environment. That should manually trigger the workflow.
The regression test GitHub actions can also be invoked through different event types
after a Harmony service is successfully deployed in Harmony or after a new version of
the Harmony server is deployed.
Note: Only the latest
tag of the regression docker image will be used to run the
Jupyter notebook tests.
Each test suite is run in a separate Docker container using a temporary Docker image you must build before running.
From the ./test
directory make all of the regression images with:
$ make images
make -j images
can be used to make the images in parallel (faster), although this may lead to
Docker Desktop instabilities
$ cd test
$ export HARMONY_HOST_URL=<url of Harmony in the target environment>
$ export EDL_PASSWORD=<your EDL password>
$ export EDL_USER=<your EDL username>
$ export AWS_ACCESS_KEY_ID=<key for the target environment>
$ export AWS_SECRET_ACCESS_KEY=<key secret for the target environment>
$ ./run_notebooks.sh
Outputs can be found in the tests/output/<image>
directory.
Notes:
-
All notebooks require variable
EDL_USER
andEDL_PASSWORD
to be exported for authentication against earthdata login. If you are including the NetCDF-to-Zarr (n2z) tests,AWS_ACCESS_KEY_ID
andAWS_SECRET_ACCESS_KEY
must be set to values for your current test environment to access the created Zarr store. -
It's possible to run a selection of notebooks by providing a list of images to run after the run_notebooks command. e.g.
./run_notebooks.sh hga n2z
would run theharmony GDAL adapter
andNetCDF-to-Zarr
regression tests. -
HARMONY_HOST_URL
is the harmony base url for your target environment. e.g.SIT
would behttps://harmony.sit.earthdata.nasa.gov
-
The
run_notebooks.sh
script cannot be used to test against Harmony-in-a-Box, i.e.HARMONY_HOST_URL=http://localhost:3000
, due to Docker-in-Docker issues. To test against a local Harmony instance, the notebook should be run manually on a Jupyter notebook server (e.g., in a browser).
For more information on running a local Harmony instance, see the Harmony README.
To run the tests:
-
Create an isolated python environment for the test you wish to run. You can use the environment.yml of the test to create the environment with conda or you can create the environment with another virtual env, just ensure all of the requirement from the environment.yml file are installed. They will create conda environments named
papermill-<image>
, and you should delete any existing environment before installing from the environment.yml. -
Start the jupyter server:
jupyter notebook
. -
Browse and open the jupyter notebook file for the test. (
<image>_Regression.ipynb
) -
Update the
harmony_host_url
in the notebook. -
Run the tests.
-
Create a subdirectory within
test
that contains a notebook, environment, version and supporting files, as described in the next section. For ease, it is simplest to use the same string for the subdirectory name and the suite name. -
Update the
test/Makefile
to be able to build a Docker image for the new test suite optionally including the shared utility directory:<new-suite-name>-image docker build -t ghcr.io/nasa/regression-tests-<new-suite-name>:latest -f ./Dockerfile --build-arg notebook=<new-test-notebook-name> --build-arg sub_dir=<new-suite-subdirectory> [--build-arg shared_utils=true] .
-
If you would like to use shared utilities to help ease the coding you can add the shared_util build-arg to your docker build command in the Makefile (as well as adding it as a key in the
workflow/build-all-images.yml
file). When enabed, this argument will include thetests/shared_utils
directory as a sibling directory to your tests. See thetests/shared_utils/README.md
file for more information. -
Update the
make images
rule to include building the new image.images: <pre existing rules already listed> <new-suite-name>-image
-
Update
test/run_notebooks.sh
to include the new test image inall_images
:all_images=(<pre existing test suites> <new-suite-name>)
-
Update
script/test-in-bamboo.sh
to list the new suite name inall_tests
. -
Update
config/services_tests_config_<env>.json
to associate the new suite name with a Harmony service and add it to theall
list so that it will be run when the associated Harmony service or Harmony server is deployed.
With this in place, the new test suite should be able to be built and run:
EDL_USER=...
EDL_PASSWORD=...
HARMONY_HOST_URL=https://harmony.sit.earthdata.nasa.gov # Or UAT or production
cd test
make <new-suite-name>-image
./run_notebooks.sh <new-suite-name>
After this, the test suite will need to be integrated with the GitHub workflow
to create a new version of the test image any time the related version.txt
file is updated. To do so, simply add a new target to the
build-all-images.yml workflow in the .github/workflows
directory:
-
image: <new-suite-name>
notebook: <new-notebook-name>
The above is the basic structure for adding a new image to the CI/CD. Two additional options shared-utils
and lfs
default to off, but can be over-ridden as they are for the nsidc-icesat2 image. shared-utils
controls the addition of the tests/shared_utils
directory into your image. lfs
enables git LFS for your image and should be enabled only if you have added reference files with git LFS.
-
image: "nsidc-icesat2"
notebook: "NSIDC-ICESAT2_Regression.ipynb"
shared-utils: "true"
lfs: "true"
This section of the README describes the files that are expected in every test suite subdirectory.
For example, in the swath-projector
directory we have
├── reference_files
├── SwathProjector_Regression.ipynb
├── environment.yaml
├── utilities.py
└── version.txt
reference_files
contains golden template files for expected outputs oftests
. When you add new binary files to your test, you should configure them to to use Git LFS as well as keep them as small as possible.SwathProjector_Regression.ipynb
is the regression test Jupyter notebook itself, running tests in cells. A test suite fails when a Jupyter notebook cell returns an error from the execution. Each regression test is designed to trigger this failure state for failed tests by asserting whether the output matches expectations.environment.yaml
defines the conda environment and packages present in it. The Docker image for each test suite will use the appropriate environment file to define the conda environment the Jupyter notebook is executed within during regression testing.utilities.py
is a file containing lower level helper functions. Usually, these helper functions have been removed from the notebook itself in order to simplify the appearance of the notebook and make it easier to understand upon test failures.version.txt
contains a semantic version number for the latest version of the regression tests. This will be iterated either as new tests are added, or as the test outputs are updated. Changing this file in a PR, and then merging that PR to themain
branch will trigger the publication of a new version of that regression test Docker image.
Notebook dependencies should be listed in file named environment.yaml
at the top level of the
subdirectory. The name
field in the file should be papermill
. For example:
name: papermill-<IMAGE>
channels:
- conda-forge
- nodefaults
dependencies:
- python=3.11
- jupyter
- requests
- netcdf4
- matplotlib
- papermill
- pytest
- ipytest
- pip:
- harmony-py
The regression test notebooks follow semantic versioning:
major.minor.patch
Every time a regression test suite is updated, the version number in the
version.txt
file for that suite should be iterated by the appropriate type of
version increment. This will likely occur for one of three reasons:
- Adding, updating or removing tests within the notebook (or associated utility functionality).
- Adding or updating Python dependencies in the
environment.yaml
file for the test suite. - Updating the overall Docker image for all test suites, in which case all
suites should have their
version.txt
incremented.
The CI/CD pipeline for this repository will release a new Docker image for a
test suite to
ghcr.io
whenever a change in the relevant version.txt
file is merged to the main
branch.
To use these changes in the overall Harmony CI/CD pipeline in Bamboo, the environment variables for the appropriate regression test deployment environment (SIT, UAT or production) should also be updated.
Note - the manual update step for Bamboo environment variables is brittle, and improvements are being considered to make the choice of regression test image version more automated.
To increase runtime efficiency, the build relies on
micromamba.
Micromamba and mamba are meant to be drop in replacements for miniconda and
conda. The fast solving allows us to skip creating a conda-lock file, and the
dependency management is entirely defined by the environment.yaml
file.
Test notebooks should not rely on other forms of dependency management or expect user input.
They should utilize the harmony_host_url
global variable to communicate with Harmony
or to determine the Harmony environment. This variable is set by papermill
- see the
Harmony.ipynb
for how to make use of this variable. More information can be found
in the papermill
documentation on setting parameters.
New test suites must be added to the Makefile
. A new name-image
target (where name is the name of
the test suite) should be added (see the harmony-image
example), and the new image target
should be added as a dependency of the images
target. The docker image should have a name like
ghcr.io/nasa/regression-tests-<base_name>
, where base_name
is the name of the test suite.
To build the test images on github, add a new matrix target that includes the
image base name and notbook name to the list of targets in the
.github/workflows/build-all-images.yml
file.
Finally, add the image base name to the all_images
array in the
run_notebooks.sh
file and the all_tests
array in scripts/test-in-bamboo.sh
script. For instance,
if the new image is named ghcr.io/nasa/regression-tests-foo
, then we would add
foo
to both arrays.
The run_notebooks.sh
file can be used as described above to run the test suite. Notebooks are
expected to exit with a non-zero exit code on failure when run from papermill
.
This repository uses pre-commit to enable pre-commit checking the repository for some coding standard best practices. These include:
- Removing trailing whitespaces.
- Removing blank lines at the end of a file.
- Ensure JSON files have valid formats.
- ruff Python linting checks.
- black Python code formatting checks.
To enable these checks:
# Install pre-commit Python package:
pip install pre-commit
# Install the git hook scripts:
pre-commit install
If you have installed the hooks locally, when you commit your changes the hook will validate your changes before actually committing to your repository. If there are failures you will have to opportunity to fix them and add them to your commit.
pre-commit.ci is configured such that these same hooks will be automatically run for every pull request. Because of this, it is highly recommended that you also do this locally, since failures will prevent your PR from being merged.