Troubleshooting and limitations
Troubleshooting
Error message: Database recommender_system does not exist in the Hive metastore.
A feature table is stored as a Delta table. The database is specified by the table name prefix, so a feature table recommender_system.customer_features will be stored in the recommender_system database.
To create the database, run:
%sql CREATE DATABASE IF NOT EXISTS recommender_system;
Error message: ModuleNotFoundError: No module named 'databricks.feature_engineering'
or ModuleNotFoundError: No module named 'databricks.feature_store'
This error occurs when databricks-feature-engineering is not installed on the Databricks Runtime you are using.
databricks-feature-engineering is available on PyPI, and can be installed with:
%pip install databricks-feature-engineering
Error message: ModuleNotFoundError: No module named 'databricks.feature_store'
This error occurs when databricks-feature-store is not installed on the Databricks Runtime you are using.
Note
For Databricks Runtime 14.3 and above, install databricks-feature-engineering instead via %pip install databricks-feature-engineering
databricks-feature-store is available on PyPI, and can be installed with:
%pip install databricks-feature-store
Error message: Invalid input. Data is not compatible with model signature. Cannot convert non-finite values...'
This error can occur when using a Feature Store-packaged model in Mosaic AI Model Serving. When providing custom feature values in an input to the endpoint, you must provide a value for the feature for each row in the input, or for no rows. You cannot provide custom values for a feature for only some rows.
Limitations
A model can use at most 50 tables and 100 functions for training.
Databricks Runtime ML clusters are not supported when using Delta Live Tables as feature tables. Instead, use a shared cluster and manually install the client using
pip install databricks-feature-engineering
. You must also install any other required ML libraries.%pip install databricks-feature-engineering
Materialized views and streaming tables are managed by Delta Live Tables pipelines.
fe.write_table()
does not update them. Instead, use the Delta Live Table pipeline to update the tables.
Workspace Feature Store does not support deleting individual features from a feature table.
A maximum of 100 on-demand features can be used in a model.
Publishing Delta Live Tables to third-party online stores is not supported. Use Databricks Online Tables as an online store.