dataforge-sdk
SDK for creating DataForge extensions.
Example projects and usage patterns: https://github.com/dataforgelabs/dataforge-sdk
Postgres Utilities
The dataforge.pg module provides helper functions to execute SQL operations against the DataForge Postgres metastore:
from dataforge.pg import select, update, pull
# Execute a SELECT query and return a Spark DataFrame
df = select("SELECT * FROM my_table")
# Execute an UPDATE/INSERT/DELETE query
update("UPDATE my_table SET col = 'value'")
# Trigger a new data pull for source_id 123
pull(123)
IngestionSession
The IngestionSession class manages a custom data ingestion process lifecycle.
from dataforge import IngestionSession
# Initialize a session (production use)
session = IngestionSession()
# Initialize a session (optional source_name/project_name for testing)
session = IngestionSession(source_name="my_source", project_name="my_project")
# Ingest data
# pass a function returning a DataFrame (recommended to integrate logging with DataForge)
session.ingest(lambda: spark.read.csv("s3://bucket/path/input.csv"))
# pass a DataFrame (can be used for testing, not recommended for production deployment)
df = spark.read.csv("s3://bucket/path/input.csv")
session.ingest(df)
# ingest empty dataframe to create 0-record input
session.ingest()
# Fail the process with error message
session.fail("Error message")
# Retrieve latest tracking fields
tracking = session.latest_tracking_fields()
# Retrieve connection parameters for the current source
connection_parameters = session.connection_parameters()
# Retrieve custom parameters for the current source
custom_parameters = session.custom_parameters()
# Retrieve system configuration for the current session
system_configuration = session.system_configuration
ParsingSession
The ParsingSession class manages a custom parse process lifecycle.
from dataforge import ParsingSession
# Initialize a session (production use)
session = ParsingSession()
# Initialize a session (optional input_id for testing)
session = ParsingSession(input_id=123)
# Retrieve custom parameters
params = session.custom_parameters()
# Retrieve system configuration for the current session
system_configuration = session.system_configuration
# Get the path of file to be parsed
path = session.file_path
# Run parsing: pass a DataFrame, a function returning a DataFrame or None (0-record file)
session.run(lambda: spark.read.json(session.file_path))
# Fail the process with error message
session.fail("Error message")
PostOutputSession
The PostOutputSession class manages a custom post-output process lifecycle.
from dataforge import PostOutputSession
# Initialize a session (production use)
session = PostOutputSession()
# Initialize a session (optional names for testing)
session = PostOutputSession(output_name="report", output_source_name="my_source", project_name="my_project")
# Get the path of file generated by preceding output process
path = session.file_path()
# Retrieve connection parameters for the current output
connection_parameters = session.connection_parameters()
# Retrieve custom parameters for the current source
custom_parameters = session.custom_parameters()
# Retrieve system configuration for the current session
system_configuration = session.system_configuration
# Run post-output logic: pass a function encapsulating custom code
session.run(lambda: print(f"Uploading file from {path}"))
# Fail the process with error message
session.fail("Error message")
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