featurebyte.SparkDetails

class SparkDetails(

*,

host: StrictStr="127.0.0.1",

port: int=10000,

http_path: StrictStr="cliservice",

use_http_transport: bool=False,

use_ssl: bool=False,

storage_type: StorageType,

storage_url: str,

storage_spark_url: StrictStr,

featurebyte_catalog: StrictStr,

featurebyte_schema: StrictStr

)

Description

Model for details used to connect to a Spark data source.

Parameters

• host: StrictStr
  default: "127.0.0.1"
  The server where your spark cluster is hosted.
  
  
• port: int
  default: 10000
  The port your spark cluster is hosted on.
  
  
• http_path: StrictStr
  default: "cliservice"
  Spark compute resource URL.
  
  
• use_http_transport: bool
  default: False
  Configuration on whether to use HTTP as our transport layer. Defaults to Thrift
  
  
• use_ssl: bool
  default: False
  Configuration on whether to use SSL. Only applicable if use_http_transport is set to True.
  
  
• storage_type: StorageType
  Storage type of where we will be persisting the feature store to.
  
  
• storage_url: str
  URL of where we will be uploading our custom UDFs to.
  
  
• storage_spark_url: StrictStr
  URL of where we will be reading our data from. Note that this technically points to the same location as the storage_url. However, the format that the warehouse accepts differs between the read and write path, and as such, we require two fields.
  
  
• featurebyte_catalog: StrictStr
  Name of the database that holds metadata about the actual data. This is commonly filled as hive_metastore.
  
  
• featurebyte_schema: StrictStr
  The name of the schema containing the tables and columns.
  
  

Examples

>>> details = fb.SparkDetails(
...   host="<host>",
...   port=10003,
...   featurebyte_catalog="spark_catalog",
...   featurebyte_schema="<schema_name>",
...   storage_type=fb.StorageType.S3,
...   storage_url="<storage_url>",
...   storage_spark_url="gs://dataproc-cluster-staging/{<schema_name>}"
... )