feast-dev

Development

Development guide for contributing to the Feast codebase. Covers environment setup, testing, linting, project structure, and PR workflow for feast-dev/feast.

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Feast Development Guide

Environment Setup

# Install development dependencies (uses uv pip sync with pinned requirements)
make install-python-dependencies-dev

# Install minimal dependencies
make install-python-dependencies-minimal

# Install pre-commit hooks (runs formatters and linters on commit)
make install-precommit

Running Tests

Unit Tests

# Run all unit tests
make test-python-unit

# Run a specific test file
python -m pytest sdk/python/tests/unit/test_unit_feature_store.py -v

# Run a specific test by name
python -m pytest sdk/python/tests/unit/test_unit_feature_store.py -k "test_apply" -v

# Run fast unit tests only (no external dependencies)
make test-python-unit-fast

Integration Tests (local)

# Run integration tests in local dev mode
make test-python-integration-local

Linting and Formatting

# Format Python code
make format-python

# Lint Python code
make lint-python

# Run all precommit checks (format + lint)
make precommit-check

# Type checking (entire codebase)
uv run bash -c "cd sdk/python && mypy feast"

# Type-check a single file
uv run bash -c "cd sdk/python && mypy feast/path/to/file.py"

Code Style

Use type hints on all function signatures
Use from __future__ import annotations at the top of new files
Follow existing patterns in the module you are modifying
PR titles must follow semantic conventions: feat:, fix:, ci:, chore:, docs:
Add a GitHub label to PRs (e.g. kind/bug, kind/feature, kind/housekeeping)
Sign off commits with git commit -s (DCO requirement)

Docker Images

# Build all Docker images
make build-docker

# Build feature server Docker image
make build-feature-server-docker

Documentation

# Build Sphinx documentation
make build-sphinx

# Build templates
make build-templates

# Build Helm docs
make build-helm-docs

Documentation and Blog Posts

Blog posts must be placed in /infra/website/docs/blog/ — do NOT place them under docs/blog/ or elsewhere.
Blog post files must include YAML frontmatter with title, description, date, and authors fields matching the format of existing posts in that directory.
All other docs go under docs/ and must be added to docs/SUMMARY.md (GitBook navigation) or they won't appear on the website.

Change type	Doc location
New online store	`docs/reference/online-stores/<name>.md` + update `README.md` and `SUMMARY.md`
New offline store	`docs/reference/offline-stores/<name>.md` + update `README.md`, `overview.md`, `SUMMARY.md`
New registry backend	`docs/reference/registries/<name>.md` + update `SUMMARY.md`
Config option	`docs/reference/feature-store-yaml.md`
CLI flag/command	`docs/reference/feast-cli-commands.md`
How-to / integration	`docs/how-to-guides/customizing-feast/` or `docs/how-to-guides/` + update `SUMMARY.md`
Architecture / concept	`docs/getting-started/architecture/` or `docs/getting-started/components/`

Project Structure

sdk/python/feast/          # Main Python SDK
  cli/cli.py               # CLI entry point (feast apply, feast materialize, etc.)
  feature_store.py         # FeatureStore class - core orchestration
  repo_config.py           # feature_store.yaml configuration parsing
  repo_operations.py       # feast apply / feast teardown logic
  infra/                   # Online/offline store implementations
    online_stores/         # Redis, DynamoDB, SQLite, etc.
    offline_stores/        # BigQuery, Snowflake, File, etc.
  transformation/          # On-demand and streaming transformations
protos/feast/              # Protobuf definitions
sdk/python/tests/          # Test suite
  unit/                    # Fast, no external deps
  integration/             # Requires infrastructure

Key Abstractions

FeatureStore (feature_store.py): Entry point for all operations
FeatureView: Defines a set of features from a data source
OnDemandFeatureView: Computed features using request-time transformations
Entity: Join key definition (e.g. driver_id, customer_id)
DataSource: Where raw data lives (BigQuery, files, Snowflake, etc.)
OnlineStore: Low-latency feature serving (Redis, DynamoDB, SQLite)
OfflineStore: Historical feature retrieval (BigQuery, Snowflake, file)