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  • Introduction
  • Blog
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  • Roadmap
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  • Getting started
    • Quickstart
    • GenAI
    • Architecture
      • Overview
      • Language
      • Push vs Pull Model
      • Write Patterns
      • Feature Transformation
      • Feature Serving and Model Inference
      • Role-Based Access Control (RBAC)
    • Concepts
      • Overview
      • Project
      • Data ingestion
      • Entity
      • Feature view
      • Feature retrieval
      • Point-in-time joins
      • [Alpha] Saved dataset
      • Permission
      • Tags
    • Use Cases
    • Components
      • Overview
      • Registry
      • Offline store
      • Online store
      • Feature server
      • Batch Materialization Engine
      • Provider
      • Authorization Manager
      • OpenTelemetry Integration
    • Third party integrations
    • FAQ
  • Tutorials
    • Sample use-case tutorials
      • Driver ranking
      • Fraud detection on GCP
      • Real-time credit scoring on AWS
      • Driver stats on Snowflake
    • Validating historical features with Great Expectations
    • Building streaming features
    • Retrieval Augmented Generation (RAG) with Feast
    • MCP - AI Agent Example
  • How-to Guides
    • Running Feast with Snowflake/GCP/AWS
      • Install Feast
      • Create a feature repository
      • Deploy a feature store
      • Build a training dataset
      • Load data into the online store
      • Read features from the online store
      • Scaling Feast
      • Structuring Feature Repos
    • Running Feast in production (e.g. on Kubernetes)
    • Customizing Feast
      • Adding a custom batch materialization engine
      • Adding a new offline store
      • Adding a new online store
      • Adding a custom provider
    • Adding or reusing tests
    • Starting Feast servers in TLS(SSL) Mode
  • Reference
    • Codebase Structure
    • Type System
    • Data sources
      • Overview
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • Push
      • Kafka
      • Kinesis
      • Spark (contrib)
      • PostgreSQL (contrib)
      • Trino (contrib)
      • Azure Synapse + Azure SQL (contrib)
      • Couchbase (contrib)
    • Offline stores
      • Overview
      • Dask
      • Snowflake
      • BigQuery
      • Redshift
      • DuckDB
      • Couchbase Columnar (contrib)
      • Spark (contrib)
      • PostgreSQL (contrib)
      • Trino (contrib)
      • Azure Synapse + Azure SQL (contrib)
      • Clickhouse (contrib)
      • Remote Offline
    • Online stores
      • Overview
      • SQLite
      • Snowflake
      • Redis
      • Dragonfly
      • IKV
      • Datastore
      • DynamoDB
      • Bigtable
      • Remote
      • PostgreSQL
      • Cassandra + Astra DB
      • Couchbase
      • MySQL
      • Hazelcast
      • ScyllaDB
      • SingleStore
      • Milvus
    • Registries
      • Local
      • S3
      • GCS
      • SQL
      • Snowflake
      • Remote
    • Providers
      • Local
      • Google Cloud Platform
      • Amazon Web Services
      • Azure
    • Batch Materialization Engines
      • Snowflake
      • AWS Lambda (alpha)
      • Spark (contrib)
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feature servers
      • Python feature server
      • [Alpha] Go feature server
      • MCP Feature Server
      • Offline Feature Server
      • Registry server
    • [Beta] Web UI
    • [Beta] On demand feature view
    • [Alpha] Vector Database
    • [Alpha] Data quality monitoring
    • [Alpha] Streaming feature computation with Denormalized
    • Feast CLI reference
    • Python API reference
    • Usage
  • Project
    • Contribution process
    • Development guide
    • Backwards Compatibility Policy
      • Maintainer Docs
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
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  • Getting started
  • Decision making process
  • Pull requests
  • Resources

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  1. Project

Contribution process

Getting started

After familiarizing yourself with the documentation, the simplest way to get started is to:

  1. Setup your developer environment by following development guide.

  2. Either create a GitHub issue or make a draft PR (following development guide) to get the ball rolling!

Decision making process

See governance for more details here

We follow a process of lazy consensus. If you believe you know what the project needs then just start development. As long as there is no active opposition and the PR has been approved by maintainers or CODEOWNERS, contributions will be merged.

We use our GitHub issues, and GitHub pull requests to communicate development ideas.

Note: There may not always a corresponding CODEOWNER for the affected code, in which case the responsibility falls on other maintainers or contributors with write access to review + merge the PR

Pull requests

Please submit a PR to the master branch of the Feast repository once you are ready to submit your contribution. Code submission to Feast (including submission from project maintainers) require review and approval from maintainers or code owners.

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.

See also Making a pull request for other guidelines on making pull requests in Feast.

Resources

  • Community for other ways to get involved with the community

  • Development guide for tips on how to contribute

  • Feast GitHub issues to see what others are working on

  • Feast RFCs for a folder of previously written RFCs

PreviousUsageNextDevelopment guide

Last updated 1 year ago

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