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.

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.

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.

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