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Hopsworks Documentation

Storage Connector

logicalclocks/hopsworks

Storage Connector#

Retrieval#

get_storage_connector#

FeatureStore.get_storage_connector(name)

Get a previously created storage connector from the feature store.

Storage connectors encapsulate all information needed for the execution engine to read and write to specific storage. This storage can be S3, a JDBC compliant database or the distributed filesystem HOPSFS.

If you want to connect to the online feature store, see the get_online_storage_connector method to get the JDBC connector for the Online Feature Store.

Example

# connect to the Feature Store
fs = ...

sc = fs.get_storage_connector("demo_fs_meb10000_Training_Datasets")

Arguments

name str: Name of the storage connector to retrieve.

Returns

StorageConnector. Storage connector object.

get_online_storage_connector#

FeatureStore.get_online_storage_connector()

Get the storage connector for the Online Feature Store of the respective project's feature store.

The returned storage connector depends on the project that you are connected to.

Example

# connect to the Feature Store
fs = ...

online_storage_connector = fs.get_online_storage_connector()

Returns

StorageConnector. JDBC storage connector to the Online Feature Store.

HopsFS#

Properties#

description#

User provided description of the storage connector.

id#

Id of the storage connector uniquely identifying it in the Feature store.

name#

Name of the storage connector.

Methods#

read#

HopsFSConnector.read(query=None, data_format=None, options={}, path=None)

Reads a query or a path into a dataframe using the storage connector.

Note, paths are only supported for object stores like S3, HopsFS and ADLS, while queries are meant for JDBC or databases like Redshift and Snowflake.

Arguments

query Optional[str]: By default, the storage connector will read the table configured together with the connector, if any. It's possible to overwrite this by passing a SQL query here. Defaults to None.
data_format Optional[str]: When reading from object stores such as S3, HopsFS and ADLS, specify the file format to be read, e.g. csv, parquet.
options dict: Any additional key/value options to be passed to the connector.
path Optional[str]: Path to be read from within the bucket of the storage connector. Not relevant for JDBC or database based connectors such as Snowflake, JDBC or Redshift.

Returns

DataFrame.

refetch#

HopsFSConnector.refetch()

Refetch storage connector.

spark_options#

HopsFSConnector.spark_options()

Return prepared options to be passed to Spark, based on the additional arguments.

to_dict#

HopsFSConnector.to_dict()

update_from_response_json#

HopsFSConnector.update_from_response_json(json_dict)

JDBC#

Properties#

arguments#

Additional JDBC arguments. When running hsfs with PySpark/Spark in Hopsworks, the driver is automatically provided in the classpath but you need to set the driver argument to com.mysql.cj.jdbc.Driver when creating the Storage Connector

connection_string#

JDBC connection string.

description#

User provided description of the storage connector.

id#

Id of the storage connector uniquely identifying it in the Feature store.

name#

Name of the storage connector.

Methods#

read#

JdbcConnector.read(query, data_format=None, options={}, path=None)

Reads a query into a dataframe using the storage connector.

Arguments

query str: A SQL query to be read.
data_format Optional[str]: Not relevant for JDBC based connectors.
options dict: Any additional key/value options to be passed to the JDBC connector.
path Optional[str]: Not relevant for JDBC based connectors.

Returns

DataFrame.

refetch#

JdbcConnector.refetch()

Refetch storage connector.

spark_options#

JdbcConnector.spark_options()

Return prepared options to be passed to Spark, based on the additional arguments.

to_dict#

JdbcConnector.to_dict()

update_from_response_json#

JdbcConnector.update_from_response_json(json_dict)

S3#

Properties#

access_key#

Access key.

bucket#

Return the bucket for S3 connectors.

description#

User provided description of the storage connector.

iam_role#

IAM role.

id#

Id of the storage connector uniquely identifying it in the Feature store.

name#

Name of the storage connector.

path#

If the connector refers to a path (e.g. S3) - return the path of the connector

secret_key#

Secret key.

server_encryption_algorithm#

Encryption algorithm if server-side S3 bucket encryption is enabled.

server_encryption_key#

Encryption key if server-side S3 bucket encryption is enabled.

session_token#

Session token.

Methods#

prepare_spark#

S3Connector.prepare_spark(path=None)

Prepare Spark to use this Storage Connector.

conn.prepare_spark()

spark.read.format("json").load("s3a://[bucket]/path")

# or
spark.read.format("json").load(conn.prepare_spark("s3a://[bucket]/path"))

Arguments

path Optional[str]: Path to prepare for reading from cloud storage. Defaults to None.

read#

S3Connector.read(query=None, data_format=None, options={}, path=None)

Reads a query or a path into a dataframe using the storage connector.

Note, paths are only supported for object stores like S3, HopsFS and ADLS, while queries are meant for JDBC or databases like Redshift and Snowflake.

Arguments

query Optional[str]: Not relevant for S3 connectors.
data_format Optional[str]: The file format of the files to be read, e.g. csv, parquet.
options dict: Any additional key/value options to be passed to the S3 connector.
path Optional[str]: Path within the bucket to be read.

Returns

DataFrame.

refetch#

S3Connector.refetch()

Refetch storage connector.

spark_options#

S3Connector.spark_options()

Return prepared options to be passed to Spark, based on the additional arguments.

to_dict#

S3Connector.to_dict()

update_from_response_json#

S3Connector.update_from_response_json(json_dict)

Redshift#

Properties#

arguments#

Additional JDBC, REDSHIFT, or Snowflake arguments.

auto_create#

Database username for redshift cluster.

cluster_identifier#

Cluster identifier for redshift cluster.

database_driver#

Database endpoint for redshift cluster.

database_endpoint#

Database endpoint for redshift cluster.

database_group#

Database username for redshift cluster.

database_name#

Database name for redshift cluster.

database_password#

Database password for redshift cluster.

database_port#

Database port for redshift cluster.

database_user_name#

Database username for redshift cluster.

description#

User provided description of the storage connector.

expiration#

Cluster temporary credential expiration time.

iam_role#

IAM role.

id#

Id of the storage connector uniquely identifying it in the Feature store.

name#

Name of the storage connector.

table_name#

Table name for redshift cluster.

Methods#

read#

RedshiftConnector.read(query=None, data_format=None, options={}, path=None)

Reads a table or query into a dataframe using the storage connector.

Arguments

query Optional[str]: By default, the storage connector will read the table configured together with the connector, if any. It's possible to overwrite this by passing a SQL query here. Defaults to None.
data_format Optional[str]: Not relevant for JDBC based connectors such as Redshift.
options dict: Any additional key/value options to be passed to the JDBC connector.
path Optional[str]: Not relevant for JDBC based connectors such as Redshift.

Returns

DataFrame.

refetch#

RedshiftConnector.refetch()

Refetch storage connector in order to retrieve updated temporary credentials.

spark_options#

RedshiftConnector.spark_options()

Return prepared options to be passed to Spark, based on the additional arguments.

to_dict#

RedshiftConnector.to_dict()

update_from_response_json#

RedshiftConnector.update_from_response_json(json_dict)

Azure Data Lake Storage#

Properties#

account_name#

Account name of the ADLS storage connector

application_id#

Application ID of the ADLS storage connector

container_name#

Container name of the ADLS storage connector

description#

User provided description of the storage connector.

directory_id#

Directory ID of the ADLS storage connector

generation#

Generation of the ADLS storage connector

id#

Id of the storage connector uniquely identifying it in the Feature store.

name#

Name of the storage connector.

path#

If the connector refers to a path (e.g. ADLS) - return the path of the connector

service_credential#

Service credential of the ADLS storage connector

Methods#

prepare_spark#

AdlsConnector.prepare_spark(path=None)

Prepare Spark to use this Storage Connector.

conn.prepare_spark()

spark.read.format("json").load("abfss://[container-name]@[account_name].dfs.core.windows.net/[path]")

# or
spark.read.format("json").load(conn.prepare_spark("abfss://[container-name]@[account_name].dfs.core.windows.net/[path]"))

Arguments

path Optional[str]: Path to prepare for reading from cloud storage. Defaults to None.

read#

AdlsConnector.read(query=None, data_format=None, options={}, path=None)

Reads a query or a path into a dataframe using the storage connector.

Note, paths are only supported for object stores like S3, HopsFS and ADLS, while queries are meant for JDBC or databases like Redshift and Snowflake.

Arguments

query Optional[str]: By default, the storage connector will read the table configured together with the connector, if any. It's possible to overwrite this by passing a SQL query here. Defaults to None.
data_format Optional[str]: When reading from object stores such as S3, HopsFS and ADLS, specify the file format to be read, e.g. csv, parquet.
options dict: Any additional key/value options to be passed to the connector.
path Optional[str]: Path to be read from within the bucket of the storage connector. Not relevant for JDBC or database based connectors such as Snowflake, JDBC or Redshift.

Returns

DataFrame.

refetch#

AdlsConnector.refetch()

Refetch storage connector.

spark_options#

AdlsConnector.spark_options()

Return prepared options to be passed to Spark, based on the additional arguments.

to_dict#

AdlsConnector.to_dict()

update_from_response_json#

AdlsConnector.update_from_response_json(json_dict)

Snowflake#

Properties#

account#

Account of the Snowflake storage connector

application#

Application of the Snowflake storage connector

database#

Database of the Snowflake storage connector

description#

User provided description of the storage connector.

id#

Id of the storage connector uniquely identifying it in the Feature store.

name#

Name of the storage connector.

options#

Additional options for the Snowflake storage connector

password#

Password of the Snowflake storage connector

role#

Role of the Snowflake storage connector

schema#

Schema of the Snowflake storage connector

table#

Table of the Snowflake storage connector

token#

OAuth token of the Snowflake storage connector

url#

URL of the Snowflake storage connector

user#

User of the Snowflake storage connector

warehouse#

Warehouse of the Snowflake storage connector

Methods#

read#

SnowflakeConnector.read(query=None, data_format=None, options={}, path=None)

Reads a table or query into a dataframe using the storage connector.

Arguments

query Optional[str]: By default, the storage connector will read the table configured together with the connector, if any. It's possible to overwrite this by passing a SQL query here. Defaults to None.
data_format Optional[str]: Not relevant for Snowflake connectors.
options dict: Any additional key/value options to be passed to the engine.
path Optional[str]: Not relevant for Snowflake connectors.

Returns

DataFrame.

refetch#

SnowflakeConnector.refetch()

Refetch storage connector.

snowflake_connector_options#

SnowflakeConnector.snowflake_connector_options()

In order to use the snowflake.connector Python library, this method prepares a Python dictionary with the needed arguments for you to connect to a Snowflake database.

import snowflake.connector

sc = fs.get_storage_connector("snowflake_conn")
ctx = snowflake.connector.connect(**sc.snowflake_connector_options())

spark_options#

SnowflakeConnector.spark_options()

Return prepared options to be passed to Spark, based on the additional arguments.

to_dict#

SnowflakeConnector.to_dict()

update_from_response_json#

SnowflakeConnector.update_from_response_json(json_dict)

Google Cloud Storage#

This storage connector provides integration to Google Cloud Storage (GCS). Once you create a connector in FeatureStore, you can transact data from a GCS bucket into a spark dataframe by calling the read API.

Authentication to GCP is handled by uploading the JSON keyfile for service account to the Hopsworks Project. For more information on service accounts and creating keyfile in GCP, read Google Cloud documentation.

The connector also supports the optional encryption method Customer Supplied Encryption Key by Google. The encryption details are stored as Secrets in the FeatureStore for keeping it secure. Read more about encryption on Google Documentation.

The storage connector uses the Google gcs-connector-hadoop behind the scenes. For more information, check out Google Cloud Storage Connector for Spark and Hadoop

Properties#

algorithm#

Encryption Algorithm

bucket#

GCS Bucket

description#

User provided description of the storage connector.

encryption_key#

Encryption Key

encryption_key_hash#

Encryption Key Hash

id#

Id of the storage connector uniquely identifying it in the Feature store.

key_path#

JSON keyfile for service account

name#

Name of the storage connector.

path#

the path of the connector along with gs file system prefixed

Methods#

prepare_spark#

GcsConnector.prepare_spark(path=None)

Prepare Spark to use this Storage Connector.

conn.prepare_spark()
spark.read.format("json").load("gs://bucket/path")
# or
spark.read.format("json").load(conn.prepare_spark("gs://bucket/path"))

Arguments

path Optional[str]: Path to prepare for reading from Google cloud storage. Defaults to None.

read#

GcsConnector.read(query=None, data_format=None, options={}, path=None)

Reads GCS path into a dataframe using the storage connector.

conn.read(data_format='spark_formats',path='gs://BUCKET/DATA')

Arguments

query Optional[str]: Not relevant for GCS connectors.
data_format Optional[str]: Spark data format. Defaults to None.
options dict: Spark options. Defaults to None.
path Optional[str]: GCS path. Defaults to None.

Raises

ValueError: Malformed arguments.

Returns

Dataframe: A Spark dataframe.

refetch#

GcsConnector.refetch()

Refetch storage connector.

spark_options#

GcsConnector.spark_options()

Return prepared options to be passed to Spark, based on the additional arguments.

to_dict#

GcsConnector.to_dict()

update_from_response_json#

GcsConnector.update_from_response_json(json_dict)

BigQuery#

The BigQuery storage connector provides integration to Google Cloud BigQuery. You can use it to run bigquery on your GCP cluster and load results into spark dataframe by calling the read API.

Authentication to GCP is handled by uploading the JSON keyfile for service account to the Hopsworks Project. For more information on service accounts and creating keyfile in GCP, read Google Cloud documentation.

The storage connector uses the Google spark-bigquery-connector behind the scenes. To read more about the spark connector, like the spark options or usage, check Apache Spark SQL connector for Google BigQuery.

Properties#

arguments#

Additional spark options

dataset#

BigQuery dataset (The dataset containing the table)

description#

User provided description of the storage connector.

id#

Id of the storage connector uniquely identifying it in the Feature store.

key_path#

JSON keyfile for service account

materialization_dataset#

BigQuery materialization dataset (The dataset where the materialized view is going to be created, used in case of query)

name#

Name of the storage connector.

parent_project#

BigQuery parent project (Google Cloud Project ID of the table to bill for the export)

query_project#

BigQuery project (The Google Cloud Project ID of the table)

query_table#

BigQuery table name

Methods#

read#

BigQueryConnector.read(query=None, data_format=None, options={}, path=None)

Reads results from BigQuery into a spark dataframe using the storage connector.

Reading from bigquery is done via either specifying the BigQuery table or BigQuery query. For example, to read from a BigQuery table, set the BigQuery project, dataset and table on storage connector and read directly from the corresponding path.

conn.read()

OR, to read results from a BigQuery query, set Materialization Dataset on storage connector, and pass your SQL to query argument.

conn.read(query='SQL')

Optionally, passing query argument will take priority at runtime if the table options were also set on the storage connector. This allows user to run from both a query or table with same connector, assuming all fields were set. Also, user can set the path argument to a bigquery table path to read at runtime, if table options were not set initially while creating the connector.

conn.read(path='project.dataset.table')

Arguments

query Optional[str]: BigQuery query. Defaults to None.
data_format Optional[str]: Spark data format. Defaults to None.
options dict: Spark options. Defaults to None.
path Optional[str]: BigQuery table path. Defaults to None.

Raises

ValueError: Malformed arguments.

Returns

Dataframe: A Spark dataframe.

refetch#

BigQueryConnector.refetch()

Refetch storage connector.

spark_options#

BigQueryConnector.spark_options()

Return spark options to be set for BigQuery spark connector

to_dict#

BigQueryConnector.to_dict()

update_from_response_json#

BigQueryConnector.update_from_response_json(json_dict)

Kafka#

Properties#

boostrap_servers#

Bootstrap servers string.

description#

User provided description of the storage connector.

id#

Id of the storage connector uniquely identifying it in the Feature store.

name#

Name of the storage connector.

options#

Bootstrap servers string.

security_protocol#

Bootstrap servers string.

ssl_endpoint_identification_algorithm#

Bootstrap servers string.

ssl_keystore_location#

Bootstrap servers string.

ssl_truststore_location#

Bootstrap servers string.

Methods#

read#

KafkaConnector.read(query=None, data_format=None, options={}, path=None)

NOT SUPPORTED.

read_stream#

KafkaConnector.read_stream(
    topic,
    topic_pattern=False,
    message_format="avro",
    schema=None,
    options={},
    include_metadata=False,
)

Reads a Kafka stream from a topic or multiple topics into a Dataframe.

Engine Support

Spark only

Reading from data streams using Pandas/Python as engine is currently not supported. Python/Pandas has no notion of streaming.

Arguments

topic str: Name or pattern of the topic(s) to subscribe to.
topic_pattern bool: Flag to indicate if topic string is a pattern. Defaults to False.
message_format str: The format of the messages to use for decoding. Can be "avro" or "json". Defaults to "avro".
schema Optional[str]: Optional schema, to use for decoding, can be an Avro schema string for "avro" message format, or for JSON encoding a Spark StructType schema, or a DDL formatted string. Defaults to None.
options dict: Additional options as key/value string pairs to be passed to Spark. Defaults to {}.
include_metadata bool: Indicate whether to return additional metadata fields from messages in the stream. Otherwise only the decoded value fields are returned. Defaults to False.

Raises

ValueError: Malformed arguments.

Returns

StreamingDataframe: A Spark streaming dataframe.

refetch#

KafkaConnector.refetch()

Refetch storage connector.

spark_options#

KafkaConnector.spark_options()

Return prepared options to be passed to Spark, based on the additional arguments.

to_dict#

KafkaConnector.to_dict()

update_from_response_json#

KafkaConnector.update_from_response_json(json_dict)