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Development guide

Table of Contents

Overview

This guide is targeted at developers looking to contribute to Feast components in the main Feast repository:
Please see this page for more details on the structure of the entire codebase.

Compatibility

The compatibility policy for Feast can be found here, and should be followed for all changes proposed, by maintainers or contributors.

Community

See Contribution process and Community for details on how to get more involved in the community.
A quick few highlights:

Making a pull request

We use the convention that the assignee of a PR is the person with the next action.
If the assignee is empty it means that no reviewer has been found yet. If a reviewer has been found, they should also be the assigned the PR. Finally, if there are comments to be addressed, the PR author should be the one assigned the PR.
PRs that are submitted by the general public need to be identified as ok-to-test. Once enabled, Prow will run a range of tests to verify the submission, after which community members will help to review the pull request.

Pull request checklist

A quick list of things to keep in mind as you're making changes:
  • As you make changes
  • When you make the PR
    • Make a pull request from the forked repo you made
    • Ensure the title of the PR matches semantic release conventions (e.g. start with feat: or fix: or ci: or chore: or docs:). Keep in mind that any PR with feat: or fix: will directly make it into the change log of a release, so make sure they are understandable!
    • Ensure you add a GitHub label (i.e. a kind tag to the PR (e.g. kind/bug or kind/housekeeping)) or else checks will fail.
    • Ensure you leave a release note for any user facing changes in the PR. There is a field automatically generated in the PR request. You can write NONE in that field if there are no user facing changes.
    • Please run tests locally before submitting a PR (e.g. for Python, the local integration tests)
    • Try to keep PRs smaller. This makes them easier to review.

Good practices to keep in mind

  • Fill in the description based on the default template configured when you first open the PR
    • What this PR does/why we need it
    • Which issue(s) this PR fixes
    • Does this PR introduce a user-facing change
  • Add WIP: to PR name if more work needs to be done prior to review

Forking the repo

Fork the Feast Github repo and clone your fork locally. Then make changes to a local branch to the fork.

Pre-commit Hooks

Setup pre-commit to automatically lint and format the codebase on commit:
  1. 1.
    Ensure that you have Python (3.7 and above) with pip, installed.
  2. 2.
    Install pre-commit with pip & install pre-push hooks
pip install pre-commit
pre-commit install --hook-type pre-commit --hook-type pre-push
  1. 3.
    On push, the pre-commit hook will run. This runs make format and make lint.

Signing off commits

​
⚠
 Warning: using the default integrations with IDEs like VSCode or IntelliJ will not sign commits. When you submit a PR, you'll have to re-sign commits to pass the DCO check.
Use git signoffs to sign your commits. See https://docs.github.com/en/github/authenticating-to-github/managing-commit-signature-verification for details
Then, you can sign off commits with the -s flag:
git commit -s -m "My first commit"
GPG-signing commits with -S is optional.

Incorporating upstream changes from master

Our preference is the use of git rebase [master] instead of git merge : git pull -r.
Note that this means if you are midway through working through a PR and rebase, you'll have to force push: git push --force-with-lease origin [branch name]

Feast Python SDK / CLI

Environment Setup

Setting up your development environment for Feast Python SDK / CLI:
  1. 1.
    Ensure that you have Docker installed in your environment. Docker is used to provision service dependencies during testing, and build images for feature servers and other components.
  2. 2.
    Ensure that you have make, Python (3.8 and above) with pip, installed.
  3. 3.
    Recommended: Create a virtual environment to isolate development dependencies to be installed
# create & activate a virtual environment
python -m venv venv/
source venv/bin/activate
  1. 4.
    Upgrade pip if outdated
pip install --upgrade pip
  1. 5.
    (M1 Mac only): Follow the dev guide​
  2. 6.
    Install pip-tools
pip install piptools
  1. 7.
    (Optional): Install Node & Yarn. Then run the following to build Feast UI artifacts for use in feast ui
make build-ui
  1. 8.
    Install mysql (needed for ci dependencies)
brew install mysql
  1. 9.
    Install development dependencies for Feast Python SDK / CLI
pip install -e ".[dev]"
This will allow the installed feast version to automatically reflect changes to your local development version of Feast without needing to reinstall everytime you make code changes.

Code Style & Linting

Feast Python SDK / CLI codebase:
  • Conforms to Black code style​
  • Has type annotations as enforced by mypy
  • Has imports sorted by isort
  • Is lintable by flake8
To ensure your Python code conforms to Feast Python code standards:
  • Autoformat your code to conform to the code style:
make format-python
  • Lint your Python code before submitting it for review:
make lint-python
Setup pre-commit hooks to automatically format and lint on commit.

Unit Tests

Unit tests (pytest) for the Feast Python SDK / CLI can run as follows:
make test-python
​
⚠
 Local configuration can interfere with Unit tests and cause them to fail:

Integration Tests

There are two sets of tests you can run:
  1. 1.
    Local integration tests (for faster development, tests file offline store & key online stores)
  2. 2.
    Full integration tests (requires cloud environment setups)

Local integration tests

For this approach of running tests, you'll need to have docker set up locally: Get Docker​
It leverages a file based offline store to test against emulated versions of Datastore, DynamoDB, and Redis, using ephemeral containers.
These tests create new temporary tables / datasets locally only, and they are cleaned up. when the containers are torn down.
make test-python-integration-local

(Advanced) Full integration tests

To test across clouds, on top of setting up Redis, you also need GCP / AWS / Snowflake setup.
Note: you can manually control what tests are run today by inspecting RepoConfiguration and commenting out tests that are added to DEFAULT_FULL_REPO_CONFIGS
GCP
  1. 1.
    You can get free credits here.
  2. 2.
    You will need to setup a service account, enable the BigQuery API, and create a staging location for a bucket.
  • Setup your service account and project using steps 1-5 here.
    • Remember to save your PROJECT_ID and your key.json. These will be your secrets that you will need to configure in Github actions. Namely, secrets.GCP_PROJECT_ID and secrets.GCP_SA_KEY. The GCP_SA_KEY value is the contents of your key.json file.
  • Follow these instructions in your project to create a bucket for running GCP tests and remember to save the bucket name.
    • Make sure to add the service account email that you created in the previous step to the users that can access your bucket. Then, make sure to give the account the correct access roles, namely objectCreator, objectViewer, objectAdmin, and admin, so that your tests can use the bucket.
  1. 3.
    Install the Cloud SDK.
  2. 4.
    Login to gcloud if you haven't already:
gcloud auth login
gcloud auth application-default login
  • When you run gcloud auth application-default login, you should see some output of the form:
    Credentials saved to file: [$HOME/.config/gcloud/application_default_credentials.json]
  • You should run export GOOGLE_APPLICATION_CREDENTIALS="$HOME/.config/gcloud/application_default_credentials.json” to add the application credentials to your .zshrc or .bashrc.
  1. 5.
    Run export GCLOUD_PROJECT=[your project id from step 2] to your .zshrc or .bashrc.
  2. 6.
    Running gcloud config list should give you something like this:
$ gcloud config list
[core]
account = [your email]
disable_usage_reporting = True
project = [your project id]
​
Your active configuration is: [default]
  1. 7.
    Export GCP specific environment variables in your workflow. Namely,
export GCS_REGION='[your gcs region e.g US]'
export GCS_STAGING_LOCATION='[your gcs staging location]'
NOTE: Your GCS_STAGING_LOCATION should be in the form gs://<bucket name> where the bucket name is from step 2.
  1. 8.
    Once authenticated, you should be able to run the integration tests for BigQuery without any failures.
AWS
  1. 1.
    Setup AWS by creating an account, database, and cluster. You will need to enable Redshift and Dynamo.
  • You can get free credits here.
  1. 2.
    To run the AWS Redshift and Dynamo integration tests you will have to export your own AWS credentials. Namely,
export AWS_REGION='[your aws region]'
export AWS_CLUSTER_ID='[your aws cluster id]'
export AWS_USER='[your aws user]'
export AWS_DB='[your aws database]'
export AWS_STAGING_LOCATION='[your s3 staging location uri]'
export AWS_IAM_ROLE='[redshift and s3 access role]'
export AWS_LAMBDA_ROLE='[your aws lambda execution role]'
export AWS_REGISTRY_PATH='[your aws registry path]'
Snowflake
  1. 1.
    See https://signup.snowflake.com/ to setup a trial.
  2. 2.
    Setup your account and if you are not an ACCOUNTADMIN (if you created your own account, you should be), give yourself the SYSADMIN role.
grant role accountadmin, sysadmin to user user2;
  • Also remember to save your account name, username, and role.
  • Your account name can be found under
  1. 3.
    Create Dashboard and add a Tile.
  2. 4.
    Create a warehouse and database named FEAST with the schemas OFFLINE and ONLINE.
create or replace warehouse feast_tests_wh with
warehouse_size='MEDIUM' --set your warehouse size to whatever your budget allows--
auto_suspend = 180
auto_resume = true
initially_suspended=true;
​
create or replace database FEAST;
use database FEAST;
create schema OFFLINE;
create schema ONLINE;
  1. 5.
    You will need to create a data unloading location(either on S3, GCP, or Azure). Detailed instructions here. You will need to save the storage export location and the storage export name. You will need to create a storage integration in your warehouse to make this work. Name this storage integration FEAST_S3.
  2. 6.
    Then to run successfully, you'll need some environment variables setup:
export SNOWFLAKE_CI_DEPLOYMENT='[your snowflake account name]'
export SNOWFLAKE_CI_USER='[your snowflake username]'
export SNOWFLAKE_CI_PASSWORD='[your snowflake pw]'
export SNOWFLAKE_CI_ROLE='[your CI role e.g. SYSADMIN]'
export SNOWFLAKE_CI_WAREHOUSE='[your warehouse]'
export BLOB_EXPORT_STORAGE_NAME='[your data unloading storage name]'
export BLOB_EXPORT_URI='[your data unloading blob uri]`
  1. 7.
    Once everything is setup, running snowflake integration tests should pass without failures.
Note that for Snowflake / GCP / AWS, running make test-python-integration will create new temporary tables / datasets in your cloud storage tables.

(Advanced) Running specific provider tests or running your test against specific online or offline stores

  1. 1.
    If you don't need to have your test run against all of the providers(gcp, aws, and snowflake) or don't need to run against all of the online stores, you can tag your test with specific providers or stores that you need(@pytest.mark.universal_online_stores or @pytest.mark.universal_online_stores with the only parameter). The only parameter selects specific offline providers and online stores that your test will test against. Example:
# Only parametrizes this test with the sqlite online store
@pytest.mark.universal_online_stores(only=["sqlite"])
def test_feature_get_online_features_types_match():
  1. 2.
    You can also filter tests to run by using pytest's cli filtering. Instead of using the make commands to test Feast, you can filter tests by name with the -k parameter. The parametrized integration tests are all uniquely identified by their provider and online store so the -k option can select only the tests that you need to run. For example, to run only Redshift related tests, you can use the following command:
python -m pytest -n 8 --integration -k Redshift sdk/python/tests

(Experimental) Run full integration tests against containerized services

Test across clouds requires existing accounts on GCP / AWS / Snowflake, and may incur costs when using these services.
For this approach of running tests, you'll need to have docker set up locally: Get Docker​
It's possible to run some integration tests against emulated local versions of these services, using ephemeral containers. These tests create new temporary tables / datasets locally only, and they are cleaned up. when the containers are torn down.
The services with containerized replacements currently implemented are:
  • Datastore
  • DynamoDB
  • Redis
  • Trino
  • HBase
  • Postgres
  • Cassandra
You can run make test-python-integration-container to run tests against the containerized versions of dependencies.

Contrib integration tests

(Contrib) Running tests for Spark offline store

You can run make test-python-universal-spark to run all tests against the Spark offline store. (Note: you'll have to run pip install -e ".[dev]" first).
Not all tests are passing yet

(Contrib) Running tests for Trino offline store

You can run make test-python-universal-trino to run all tests against the Trino offline store. (Note: you'll have to run pip install -e ".[dev]" first)

(Contrib) Running tests for Postgres offline store

You can run test-python-universal-postgres-offline to run all tests against the Postgres offline store. (Note: you'll have to run pip install -e ".[dev]" first)

(Contrib) Running tests for Postgres online store

You can run test-python-universal-postgres-online to run all tests against the Postgres offline store. (Note: you'll have to run pip install -e ".[dev]" first)

(Contrib) Running tests for HBase online store

TODO

(Experimental) Feast UI

Feast Java Serving

See also development instructions related to the helm chart below at Developing the Feast Helm charts​

Developing the Feast Helm charts

There are 2 helm charts:
  • Feast Java feature server
  • Feast Python feature server
Generally, you can override the images in the helm charts with locally built Docker images, and install the local helm chart.
All README's for helm charts are generated using helm-docs. You can install it (e.g. with brew install norwoodj/tap/helm-docs) and then run make build-helm-docs.

Feast Java Feature Server Helm Chart

See the Java demo example (it has development instructions too using minikube) here​
It will:
  • run make build-java-docker-dev to build local Java feature server binaries
  • configure the included application-override.yaml to override the image tag to use the locally built dev images.
  • install the local chart with helm install feast-release ../../../infra/charts/feast --values application-override.yaml

Feast Python Feature Server Helm Chart

See the Python demo example (it has development instructions too using minikube) here​
It will:
  • run make build-feature-server-dev to build a local python feature server binary
  • install the local chart with helm install feast-release ../../../infra/charts/feast-feature-server --set image.tag=dev --set feature_store_yaml_base64=$(base64 feature_store.yaml)

Testing with Github Actions workflows

Please refer to the maintainers doc if you would like to locally test out the github actions workflow changes. This document will help you setup your fork to test the ci integration tests and other workflows without needing to make a pull request against feast-dev master.

Feast Data Storage Format

Feast data storage contracts are documented in the following locations:
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Last modified 21d ago