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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 DLT as feature tables. Instead, use a standard access mode compute resource and manually install the client using pip install databricks-feature-engineering. You must also install any other required ML libraries.

    Python
    %pip install databricks-feature-engineering

  • Materialized views and streaming tables are managed by DLT pipelines. fe.write_table() does not update them. Instead, use the DLT pipeline to update the tables.

  • Feature Store APIs support batch scoring of models packaged with Feature Store. Online inference is not supported.
  • Databricks legacy Workspace Feature Store does not support deleting individual features from a feature table.
  • No online stores are supported on Databricks on Google Cloud as of this release.
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