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Work with feature tables in Unity Catalog

This page describes how to create and work with feature tables in Unity Catalog.

This page applies only to workspaces that are enabled for Unity Catalog. If your workspace is not enabled for Unity Catalog, see Work with feature tables in Workspace Feature Store (legacy).

For details about the commands and parameters used in the examples on this page, see the Feature Engineering Python API reference.

Feature Engineering in Unity Catalog requires Databricks Runtime 13.2 or above. In addition, the Unity Catalog metastore must have Privilege Model Version 1.0.

Feature Engineering in Unity Catalog has a Python client FeatureEngineeringClient. The class is available on PyPI with the databricks-feature-engineering package and is pre-installed in Databricks Runtime 13.3 LTS ML and above. If you use a non-ML Databricks Runtime, you must install the client manually. Use the compatibility matrix to find the correct version for your Databricks Runtime version.

Python

%pip install databricks-feature-engineering

dbutils.library.restartPython()

You must create a new catalog or use an existing catalog for feature tables.

To create a new catalog, you must have the CREATE CATALOG privilege on the metastore.

SQL

CREATE CATALOG IF NOT EXISTS <catalog-name>

To use an existing catalog, you must have the USE CATALOG privilege on the catalog.

SQL

USE CATALOG <catalog-name>

Feature tables in Unity Catalog must be stored in a schema. To create a new schema in the catalog, you must have the CREATE SCHEMA privilege on the catalog.

SQL

CREATE SCHEMA IF NOT EXISTS <schema-name>

Feature tables in Unity Catalog are Delta tables. Feature tables must have a primary key. Feature tables, like other data assets in Unity Catalog, are accessed using a three-level namespace: <catalog-name>.<schema-name>.<table-name>.

You can use Databricks SQL, the Python FeatureEngineeringClient, or Lakeflow Declarative Pipelines to create feature tables in Unity Catalog.

• Databricks SQL
• Python

You can use any Delta table with a primary key constraint as a feature table. The following code shows how to create a table with a primary key:

SQL

CREATE TABLE ml.recommender_system.customer_features (
  customer_id int NOT NULL,
  feat1 long,
  feat2 varchar(100),
  CONSTRAINT customer_features_pk PRIMARY KEY (customer_id)
);

To create a time series feature table, add a time column as a primary key column and specify the TIMESERIES keyword. The TIMESERIES keyword requires Databricks Runtime 13.3 LTS or above.

SQL

CREATE TABLE ml.recommender_system.customer_features (
  customer_id int NOT NULL,
  ts timestamp NOT NULL,
  feat1 long,
  feat2 varchar(100),
  CONSTRAINT customer_features_pk PRIMARY KEY (customer_id, ts TIMESERIES)
);

After the table is created, you can write data to it like other Delta tables, and it can be used as a feature table.

For details about the commands and parameters used in the following examples, see the Feature Engineering Python API reference.

1. Write the Python functions to compute the features. The output of each function should be an Apache Spark DataFrame with a unique primary key. The primary key can consist of one or more columns.
2. Create a feature table by instantiating a FeatureEngineeringClient and using create_table.
3. Populate the feature table using write_table.

Python

from databricks.feature_engineering import FeatureEngineeringClient

fe = FeatureEngineeringClient()


def compute_customer_features(data):
  ''' Feature computation code returns a DataFrame with 'customer_id' as primary key'''
  pass

customer_features_df = compute_customer_features(df)



customer_feature_table = fe.create_table(
  name='ml.recommender_system.customer_features',
  primary_keys='customer_id',
  schema=customer_features_df.schema,
  description='Customer features'
)

note

Lakeflow Declarative Pipelines support for table constraints is in Public Preview. The following code examples must be run using the Lakeflow Declarative Pipelines preview channel.

Any table published from Lakeflow Declarative Pipelines that includes a primary key constraint can be used as a feature table. To create a table in a pipeline with a primary key, you can use either Databricks SQL or the Lakeflow Declarative Pipelines Python programming interface.

To create a table in a pipeline with a primary key, use the following syntax:

• Databricks SQL
• Python

SQL

CREATE MATERIALIZED VIEW customer_features (
  customer_id int NOT NULL,
  feat1 long,
  feat2 varchar(100),
  CONSTRAINT customer_features_pk PRIMARY KEY (customer_id)
) AS SELECT * FROM ...;

Python

import dlt

@dlt.table(
  schema="""
          customer_id int NOT NULL,
          feat1 long,
          feat2 varchar(100),
          CONSTRAINT customer_features_pk PRIMARY KEY (customer_id)
    """)
def customer_features():
  return ...

To create a time series feature table, add a time column as a primary key column and specify the TIMESERIES keyword.

• Databricks SQL
• Python

SQL

CREATE MATERIALIZED VIEW customer_features (
  customer_id int NOT NULL,
  ts timestamp NOT NULL,
  feat1 long,
  feat2 varchar(100),
  CONSTRAINT customer_features_pk PRIMARY KEY (customer_id, ts TIMESERIES)
) AS SELECT * FROM ...;

Python

import dlt

@dlt.table(
  schema="""
    customer_id int NOT NULL,
    ts timestamp NOT NULL,
    feat1 long,
    feat2 varchar(100),
    CONSTRAINT customer_features_pk PRIMARY KEY (customer_id, ts TIMESERIES)
    """)
def customer_features():
  return ...

After the table is created, you can write data to it like other Lakeflow Declarative Pipelines datasets, and it can be used as a feature table.

Any Delta table in Unity Catalog with a primary key can be a feature table in Unity Catalog, and you can use the Features UI and API with the table.

note

• Only the table owner can declare primary key constraints. The owner's name is displayed on the table detail page of Catalog Explorer.
• Verify the data type in the Delta table is supported by Feature Engineering in Unity Catalog. See Supported data types.
• The TIMESERIES keyword requires Databricks Runtime 13.3 LTS or above.

If an existing Delta table does not have a primary key constraint, you can create one as follows:

1. Set primary key columns to NOT NULL. For each primary key column, run:

   SQL

   ALTER TABLE <full_table_name> ALTER COLUMN <pk_col_name> SET NOT NULL
   

2. Alter the table to add the primary key constraint:

   SQL

   ALTER TABLE <full_table_name> ADD CONSTRAINT <pk_name> PRIMARY KEY(pk_col1, pk_col2, ...)
   

   pk_name is the name of the primary key constraint. By convention, you can use the table name (without schema and catalog) with a _pk suffix. For example, a table with the name "ml.recommender_system.customer_features" would have customer_features_pk as the name of its primary key constraint.

   To make the table a time series feature table, specify the TIMESERIES keyword on one of the primary key columns, as follows:

   SQL

   ALTER TABLE <full_table_name> ADD CONSTRAINT <pk_name> PRIMARY KEY(pk_col1 TIMESERIES, pk_col2, ...)
   

   After you add the primary key constraint on the table, the table appears in the Features UI and you can use it as a feature table.

Any streaming table or materialized view in Unity Catalog with a primary key can be a feature table in Unity Catalog, and you can use the Features UI and API with the table.

note

• Lakeflow Declarative Pipelines support for table constraints is in Public Preview. The following code examples must be run using the Lakeflow Declarative Pipelines preview channel.
• Only the table owner can declare primary key constraints. The owner's name is displayed on the table detail page of Catalog Explorer.
• Verify that Feature Engineering in Unity Catalog supports the data type in the Delta table. See Supported data types.

To set primary keys for an existing streaming table or materialized view, update the schema of the streaming table or materialized view in the notebook that manages the object. Then, refresh the table to update the Unity Catalog object.

The following is the syntax to add a primary key to a materialized view:

• Databricks SQL
• Python

SQL

CREATE OR REFRESH MATERIALIZED VIEW existing_live_table(
  id int NOT NULL PRIMARY KEY,
  ...
) AS SELECT ...

Python

import dlt

@dlt.table(
  schema="""
    id int NOT NULL PRIMARY KEY,
    ...
    """
)
def existing_live_table():
  return ...

To use a view as a feature table, you must use databricks-feature-engineering version 0.7.0 or above, which is built into Databricks Runtime 16.0 ML.

A simple SELECT view in Unity Catalog can be a feature table in Unity Catalog, and you can use the Features API with the table.

note

• A simple SELECT view is defined as a view created from a single Delta table in Unity Catalog that can be used as a feature table, and whose primary keys are selected without JOIN, GROUP BY, or DISTINCT clauses. Acceptable keywords in the SQL statement are SELECT, FROM, WHERE, ORDER BY, LIMIT, and OFFSET.
• See Supported data types for supported data types.
• Feature tables backed by views do not appear in the Features UI.
• If columns are renamed in the source Delta table, the columns in the SELECT statement for the view definition must be renamed to match.

Here is an example of a simple SELECT view that can be used as a feature table:

SQL

CREATE OR REPLACE VIEW ml.recommender_system.content_recommendation_subset AS
SELECT
    user_id,
    content_id,
    user_age,
    user_gender,
    content_genre,
    content_release_year,
    user_content_watch_duration,
    user_content_like_dislike_ratio
FROM
    ml.recommender_system.content_recommendations_features
WHERE
    user_age BETWEEN 18 AND 35
    AND content_genre IN ('Drama', 'Comedy', 'Action')
    AND content_release_year >= 2010
    AND user_content_watch_duration > 60;

Feature tables based on views can be used for offline model training and evaluation. They cannot be published to online stores. Features from these tables and models based on these features cannot be served.

You can update a feature table in Unity Catalog by adding new features or by modifying specific rows based on the primary key.

The following feature table metadata should not be updated:

• Primary key.
• Partition key.
• Name or data type of an existing feature.

Altering them will cause downstream pipelines that use features for training and serving models to break.

You can add new features to an existing feature table in one of two ways:

• Update the existing feature computation function and run write_table with the returned DataFrame. This updates the feature table schema and merges new feature values based on the primary key.
• Create a new feature computation function to calculate the new feature values. The DataFrame returned by this new computation function must contain the feature tables' primary and partition keys (if defined). Run write_table with the DataFrame to write the new features to the existing feature table using the same primary key.

Use mode = "merge" in write_table. Rows whose primary key does not exist in the DataFrame sent in the write_table call remain unchanged.

Python

from databricks.feature_engineering import FeatureEngineeringClient
fe = FeatureEngineeringClient()
fe.write_table(
  name='ml.recommender_system.customer_features',
  df = customer_features_df,
  mode = 'merge'
)

To ensure that features in feature tables always have the most recent values, Databricks recommends that you create a job that runs a notebook to update your feature table on a regular basis, such as every day. If you already have a non-scheduled job created, you can convert it to a scheduled job to ensure the feature values are always up-to-date. See Lakeflow Jobs.

Code to update a feature table uses mode='merge', as shown in the following example.

Python

from databricks.feature_engineering import FeatureEngineeringClient
fe = FeatureEngineeringClient()

customer_features_df = compute_customer_features(data)

fe.write_table(
  df=customer_features_df,
  name='ml.recommender_system.customer_features',
  mode='merge'
)

Define a feature table with a composite primary key. Include the date in the primary key. For example, for a feature table customer_features, you might use a composite primary key (date, customer_id) and partition key date for efficient reads.

Databricks recommends that you enable liquid clustering on the table for efficient reads. If you do not use liquid clustering, set the date column as a partition key for better read performance.

• Databricks SQL
• Python

SQL

CREATE TABLE ml.recommender_system.customer_features (
  customer_id int NOT NULL,
  `date` date NOT NULL,
  feat1 long,
  feat2 varchar(100),
  CONSTRAINT customer_features_pk PRIMARY KEY (`date`, customer_id)
)


COMMENT "Customer features";

Python

from databricks.feature_engineering import FeatureEngineeringClient
fe = FeatureEngineeringClient()
fe.create_table(
  name='ml.recommender_system.customer_features',
  primary_keys=['date', 'customer_id'],
  
  
  schema=customer_features_df.schema,
  description='Customer features'
)

You can then create code to read from the feature table filtering date to the time period of interest.

You can also create a time series feature table which enables point-in-time lookups when you use create_training_set or score_batch. See Create a feature table in Unity Catalog.

To keep the feature table up to date, set up a regularly scheduled job to write features or stream new feature values into the feature table.

To create a streaming feature computation pipeline, pass a streaming DataFrame as an argument to write_table. This method returns a StreamingQuery object.

Python

def compute_additional_customer_features(data):
  ''' Returns Streaming DataFrame
  '''
  pass

from databricks.feature_engineering import FeatureEngineeringClient
fe = FeatureEngineeringClient()

customer_transactions = spark.readStream.table("prod.events.customer_transactions")
stream_df = compute_additional_customer_features(customer_transactions)

fe.write_table(
  df=stream_df,
  name='ml.recommender_system.customer_features',
  mode='merge'
)

Use read_table to read feature values.

Python

from databricks.feature_engineering import FeatureEngineeringClient
fe = FeatureEngineeringClient()
customer_features_df = fe.read_table(
  name='ml.recommender_system.customer_features',
)

Use the Features UI to search for or browse feature tables in Unity Catalog.

1. Click Features in the sidebar to display the Features UI.

2. Select catalog with the catalog selector to view all of the available feature tables in that catalog. In the search box, enter all or part of the name of a feature table, a feature, or a comment. You can also enter all or part of the key or value of a tag. Search text is case-insensitive.

Use get_table to get feature table metadata.

Python

from databricks.feature_engineering import FeatureEngineeringClient
fe = FeatureEngineeringClient()
ft = fe.get_table(name="ml.recommender_system.user_feature_table")
print(ft.features)

You can use tags, which are simple key-value pairs, to categorize and manage your feature tables and features.

For feature tables, you can create, edit, and delete tags using Catalog Explorer, SQL statements in a notebook or SQL query editor, or the Feature Engineering Python API.

For features, you can create, edit, and delete tags using Catalog Explorer or SQL statements in a notebook or SQL query editor.

See Apply tags to Unity Catalog securable objects and Python API.

The following example shows how to use the Feature Engineering Python API to create, update, and delete feature table tags.

Python

from databricks.feature_engineering import FeatureEngineeringClient
fe = FeatureEngineeringClient()


customer_feature_table = fe.create_table(
  
  tags={"tag_key_1": "tag_value_1", "tag_key_2": "tag_value_2", ...},
  
)


fe.set_feature_table_tag(name="customer_feature_table", key="tag_key_1", value="new_key_value")


fe.delete_feature_table_tag(name="customer_feature_table", key="tag_key_2")

You can delete a feature table in Unity Catalog by directly deleting the Delta table in Unity Catalog using Catalog Explorer or using the Feature Engineering Python API.

note

• Deleting a feature table can lead to unexpected failures in upstream producers and downstream consumers (models, endpoints, and scheduled jobs). You must delete published online stores with your cloud provider.
• When you delete a feature table in Unity Catalog, the underlying Delta table is also dropped.
• drop_table is not supported in Databricks Runtime 13.1 ML or below. Use SQL command to delete the table.

You can use Databricks SQL or FeatureEngineeringClient.drop_table to delete a feature table in Unity Catalog:

• Databricks SQL
• Python

SQL

DROP TABLE ml.recommender_system.customer_features;

Python

from databricks.feature_engineering import FeatureEngineeringClient
fe = FeatureEngineeringClient()
fe.drop_table(
  name='ml.recommender_system.customer_features'
)

A feature table in Unity Catalog is accessible to all workspaces assigned to the table's Unity Catalog metastore.

To share a feature table with workspaces that are not assigned to the same Unity Catalog metastore, use Delta Sharing.

The basic notebook shows how to create a feature table, use it to train a model, and run batch scoring using automatic feature lookup. It also shows the Feature Engineering UI, which you can use to search for features and understand how features are created and used.

The taxi example notebook illustrates the process of creating features, updating them, and using them for model training and batch inference.

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