1.
CI/CD with
Azure DevOps, Pre-Commit, and Azure Databricks
30-10-2019

2.
A typical pipeline
Automate everything
• Deploy to production Efficiently & Reliably
• Allow everyone in the team to do so
• Smaller increments
• Roll-forward don’t Roll-back
2
Trigger
Version control
Test
Code
Build
Artifact
Deploy
Dev
Integration tests
Deploy
Prod
User facing
Measure
Capture performance

3.
3
Today’s pipeline

4.
Overall project structure
• src, containing the library
• input, data used while testing
• notebook, containing the application
• tests, for tests
4

5.
Testing
Our approach
• Use Pre-Commit
• Apply Black, Flake8
• Run PySpark tests in a Docker container
5
• Checkout code
• Install requirements
• Apply linters
• Run unit-tests
• Publish test/coverage

6.
Pre-Commit
Eg, solving the “Fixing lint issues” commit
• Framework for creating Git Hooks
• Eg, scripts that run on each commit
• Compare it to a local-CI

7.
Pre-Commit
.pre-commit-config.yaml
• In our case
• run black/Flake8 on each commit
• run pytest on each push
repos:
- repo: https://github.com/psf/black
rev: 19.3b0
hooks:
- id: black
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v2.3.0
hooks:
- id: flake8
- id: check-merge-conflict
- repo: https://github.com/godatadriven/pre-commit-docker-pyspark
rev: master
hooks:
- id: pyspark-docker
name: Run tests
entry: /entrypoint.sh python setup.py test
language: docker
pass_filenames: false
stages: [push]

8.
8
python setup.py test
setup.py setup.cfg conftest.py test_etl.py
docker

9.
9
Testing PySpark
conftest.py create pytest fixture called spark
def test_load_df(spark):
df = load_df(spark, "input/data.csv")
assert df.count() == 891
assert df.filter(df.Name == "Sandstrom, Miss. Marguerite Rut").count() == 1
def test_fill_na(spark):
input_df = spark.createDataFrame(
[(None, None, None)], "Age: double, Cabin: string, Fare: double"
)
output_df = fill_na(input_df)
output = df_to_list_dict(output_df)
expected_output = [{"Age": -0.5, "Cabin": "N", "Fare": -0.5}]
assert output == expected_output

10.
Test output
Integrates with Azure Devops
• Which test frequently fail
• Full stack traces of a failed test
• Code coverage
10

11.
Building
Our approach
• Python wheel of library
• Modify notebook/version.py
• Create a build artifact of notebook
11
• Checkout code
• Build wheel
• Authenticate with Azure Devops Artifacts
• Push wheel
• Publish notebook folder as Build Artifact

12.
Deployment
Our approach
• Copy version.py to the DEV workspace
• After a manual step
• Copy notebook/* to the Prod workspace
12
• Authenticate with Databricks cli
• Copy notebook/version.py to the DEV workspace
• Authenticate with Databricks cli
• Copy notebook/* to the PROD workspace

13.
13
version.py
• A successful change to master results in a new version of the library
• Deploy that version to DEV
• and maybe at a later time to PROD
Azure DevOps
Pipeline
Azure DevOps
Artifacts
Dev
Notebook
Prod
Notebook
Azure Databricks
Version: 1.0.100
Version: 1.0.200

14.
14
• On Dev only version.py is deployed by our CI/CD
• On Prod the whole notebook folder
• e.g. our application
• Using dbutils and version.py
• We can install a specific version of our library
dbutils.library

15.
15
The complete pipeline
Run black, flake8 and
pytest using pre-commit
Upload wheel to DevOps
artifacts, export Notebook
folder with modified
version.py
Copy version.py to the
DEV workspace
Copy the Notebook folder
to the PROD workspace

16.
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16
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