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Connection#

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Connection#

hsfs.connection.Connection(
    host=None,
    port=443,
    project=None,
    engine=None,
    region_name="default",
    secrets_store="parameterstore",
    hostname_verification=True,
    trust_store_path=None,
    cert_folder="/tmp",
    api_key_file=None,
    api_key_value=None,
)

A feature store connection object.

The connection is project specific, so you can access the project's own feature store but also any feature store which has been shared with the project you connect to.

This class provides convenience classmethods accessible from the hsfs-module:

Connection factory

For convenience, hsfs provides a factory method, accessible from the top level module, so you don't have to import the Connection class manually:

import hsfs
conn = hsfs.connection()

Save API Key as File

To get started quickly, without saving the Hopsworks API in a secret storage, you can simply create a file with the previously created Hopsworks API Key and place it on the environment from which you wish to connect to the Hopsworks Feature Store.

You can then connect by simply passing the path to the key file when instantiating a connection:

    import hsfs
    conn = hsfs.connection(
        'my_instance',                      # Hostname of your Feature Store instance
        443,                                # Port to reach your Hopsworks instance, defaults to 443
        'my_project',                       # Name of your Hopsworks Feature Store project
        api_key_file='featurestore.key',    # The file containing the API key generated above
        hostname_verification=True)         # Disable for self-signed certificates
    )
    fs = conn.get_feature_store()           # Get the project's default feature store

    # or

    import hopsworks
    project = hopsworks.login()
    fs = project.get_feature_store()

Clients in external clusters need to connect to the Hopsworks Feature Store using an API key. The API key is generated inside the Hopsworks platform, and requires at least the "project" and "featurestore" scopes to be able to access a feature store. For more information, see the integration guides.

Arguments

  • host Optional[str]: The hostname of the Hopsworks instance in the form of [UUID].cloud.hopsworks.ai, defaults to None. Do not use the url including https:// when connecting programatically.
  • port int: The port on which the Hopsworks instance can be reached, defaults to 443.
  • project Optional[str]: The name of the project to connect to. When running on Hopsworks, this defaults to the project from where the client is run from. Defaults to None.
  • engine Optional[str]: Which engine to use, "spark", "python" or "training". Defaults to None, which initializes the engine to Spark if the environment provides Spark, for example on Hopsworks and Databricks, or falls back on Hive in Python if Spark is not available, e.g. on local Python environments or AWS SageMaker. This option allows you to override this behaviour. "training" engine is useful when only feature store metadata is needed, for example training dataset location and label information when Hopsworks training experiment is conducted.
  • region_name str: The name of the AWS region in which the required secrets are stored, defaults to "default".
  • secrets_store str: The secrets storage to be used, either "secretsmanager", "parameterstore" or "local", defaults to "parameterstore".
  • hostname_verification bool: Whether or not to verify Hopsworks’ certificate, defaults to True.
  • trust_store_path Optional[str]: Path on the file system containing the Hopsworks certificates, defaults to None.
  • cert_folder str: The directory to store retrieved HopsFS certificates, defaults to "/tmp". Only required when running without a Spark environment.
  • api_key_file Optional[str]: Path to a file containing the API Key, if provided, secrets_store will be ignored, defaults to None.
  • api_key_value Optional[str]: API Key as string, if provided, secrets_store will be ignored, however, this should be used with care, especially if the used notebook or job script is accessible by multiple parties. Defaults toNone`.

Returns

Connection. Feature Store connection handle to perform operations on a Hopsworks project.

Properties#

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api_key_file#

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api_key_value#

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cert_folder#

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host#

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hostname_verification#

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port#

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project#

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region_name#

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secrets_store#

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trust_store_path#

Methods#

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close#

Connection.close()

Close a connection gracefully.

This will clean up any materialized certificates on the local file system of external environments such as AWS SageMaker.

Usage is recommended but optional.

Example

import hsfs
conn = hsfs.connection()
conn.close()

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connect#

Connection.connect()

Instantiate the connection.

Creating a Connection object implicitly calls this method for you to instantiate the connection. However, it is possible to close the connection gracefully with the close() method, in order to clean up materialized certificates. This might be desired when working on external environments such as AWS SageMaker. Subsequently you can call connect() again to reopen the connection.

Example

import hsfs
conn = hsfs.connection()
conn.close()
conn.connect()

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connection#

Connection.connection(
    host=None,
    port=443,
    project=None,
    engine=None,
    region_name="default",
    secrets_store="parameterstore",
    hostname_verification=True,
    trust_store_path=None,
    cert_folder="/tmp",
    api_key_file=None,
    api_key_value=None,
)

Connection factory method, accessible through hsfs.connection().

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get_feature_store#

Connection.get_feature_store(name=None)

Get a reference to a feature store to perform operations on.

Defaulting to the project name of default feature store. To get a Shared feature stores, the project name of the feature store is required.

How to get feature store instance

import hsfs
conn = hsfs.connection()
fs = conn.get_feature_store()

# or

import hopsworks
project = hopsworks.login()
fs = project.get_feature_store()

Arguments

  • name str: The name of the feature store, defaults to None.

Returns

FeatureStore. A feature store handle object to perform operations on.