magla | free SQL-powered data pipeline

You will need to first set the following environment variables required for magla to function:.
MAGLA_DB_DATA_DIR <-- this is where your sqlite db will be written to
MAGLA_DB_NAME <-- name of your DB
MAGLA_MACHINE_CONFIG_DIR <-- this directory holds information about the current machine needed by magla
All creation and deletion methods are in magla.Root, so this is primarily a demonstration of using the creation methods in the optimal order. Each creation method will return the created MaglaEntity or in the case that a record already exists, creation will abort and return the found record instead. To instead throw an EntityAlreadyExistsError, you must call the magla.Root.create method directly and pass the 'return_existing=False` entity_test_fixtureeter. example: python magla.Root().create(magla.User, {"nickname": "foo"}, return_existing=False). This functionality is demonstrated below where the name of the shot being created is set to increment - meaning that running this script repeatedly will result in new shot and directory tree structures under the same project. The above creates a new Postgres column in the 'facilities' table and returns a MaglaFacility object pre-populated with data in the ' .data' property. Project settings are sent in as a dictionary which is stored as JSON in Postgres. At runtime a MaglaEntity object gets injected and Python's native string formatting can be used to access the object's relationships and attributes for custom naming. For relational tables the creation method will usually need more than one arg for each child SQL table. The below creates Tool, ToolVersion, ToolVersionInstallation, and FileType entities which are related via foreign keys in Postgres.