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Trino (contrib)

Description

The Trino offline store provides support for reading TrinoSources.
  • Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to Trino as a table in order to complete join operations.

Disclaimer

The Trino offline store does not achieve full test coverage. Please do not assume complete stability.

Getting started

In order to use this offline store, you'll need to run pip install 'feast[trino]'. You can then run feast init, then swap out feature_store.yaml with the below example to connect to Trino.

Example

feature_store.yaml
project: feature_repo
registry: data/registry.db
provider: local
offline_store:
type: feast_trino.trino.TrinoOfflineStore
host: localhost
port: 8080
catalog: memory
connector:
type: memory
user: trino
source: feast-trino-offline-store
http-scheme: https
ssl-verify: false
x-trino-extra-credential-header: foo=bar, baz=qux
# enables authentication in Trino connections, pick the one you need
# if you don't need authentication, you can safely remove the whole auth block
auth:
# Basic Auth
type: basic
config:
username: foo
password: $FOO
# Certificate
type: certificate
config:
cert-file: /path/to/cert/file
key-file: /path/to/key/file
# JWT
type: jwt
config:
token: $JWT_TOKEN
# OAuth2 (no config required)
type: oauth2
# Kerberos
type: kerberos
config:
config-file: /path/to/kerberos/config/file
service-name: foo
mutual-authentication: true
force-preemptive: true
hostname-override: custom-hostname
sanitize-mutual-error-response: true
principal: principal-name
delegate: true
ca_bundle: /path/to/ca/bundle/file
online_store:
path: data/online_store.db
The full set of configuration options is available in TrinoOfflineStoreConfig.

Functionality Matrix

The set of functionality supported by offline stores is described in detail here. Below is a matrix indicating which functionality is supported by the Trino offline store.
Text
Trino
get_historical_features (point-in-time correct join)
yes
pull_latest_from_table_or_query (retrieve latest feature values)
yes
pull_all_from_table_or_query (retrieve a saved dataset)
yes
offline_write_batch (persist dataframes to offline store)
no
write_logged_features (persist logged features to offline store)
no
Below is a matrix indicating which functionality is supported by TrinoRetrievalJob.
Text
Trino
export to dataframe
yes
export to arrow table
yes
export to arrow batches
no
export to SQL
yes
export to data lake (S3, GCS, etc.)
no
export to data warehouse
no
export as Spark dataframe
no
local execution of Python-based on-demand transforms
yes
remote execution of Python-based on-demand transforms
no
persist results in the offline store
no
preview the query plan before execution
yes
read partitioned data
yes
To compare this set of functionality against other offline stores, please see the full functionality matrix.