LogoLogo
v0.49-branch
v0.49-branch
  • Introduction
  • Blog
  • Community & getting help
  • Roadmap
  • Changelog
  • Getting started
    • Quickstart
    • 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
  • 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
    • 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
      • 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+
Powered by GitBook
On this page
  • Description
  • Disclaimer
  • Examples
  • Supported Types

Was this helpful?

Edit on GitHub
Export as PDF
  1. Reference
  2. Data sources

Trino (contrib)

PreviousPostgreSQL (contrib)NextAzure Synapse + Azure SQL (contrib)

Last updated 26 days ago

Was this helpful?

Description

Trino data sources are Trino tables or views. These can be specified either by a table reference or a SQL query.

Disclaimer

The Trino data source does not achieve full test coverage. Please do not assume complete stability.

Examples

Defining a Trino source:

from feast.infra.offline_stores.contrib.trino_offline_store.trino_source import (
    TrinoSource,
)

driver_hourly_stats = TrinoSource(
    event_timestamp_column="event_timestamp",
    table_ref="feast.driver_stats",
    created_timestamp_column="created",
)

The full set of configuration options is available .

Supported Types

Trino data sources support all eight primitive types, but currently do not support array types. For a comparison against other batch data sources, please see .

here
here