Trino (contrib)
- 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.
The Trino offline store does not achieve full test coverage. Please do not assume complete stability.
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.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
online_store:
path: data/online_store.db
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.
Last modified 1mo ago