Upload

class Upload

Uploads are the interface for bringing data into Redivis. They are associated with a particular table, and can be created on any table belonging to an unreleased version of a dataset. Multiple uploads can be added to a table, in which case they are "stacked" together (equivalent to a union join, with mixed schemas supported).

Constructors

table.upload(upload_name)	Construct a reference to an upload based on its name.

Examples

See more upload examples ->

Upload file

dataset = redivis.user("user_name").dataset("dataset_name", version="next")
table = dataset.table("table_name")

upload = table.upload("data.csv").create(
        "./data.csv", 
        type="delimited",
        remove_on_fail=True,    # Remove the upload if a failure occurs
        wait_for_finish=True,   # Wait for the upload to finish processing
        raise_on_fail=True      # Raise an error on failure
    )

Upload multiple files

dataset = redivis.user("user_name").dataset("dataset_name", version="next")
table = dataset.table("table_name")

for filename in os.listdir("/path/to/data/directory"):
    upload = table.upload(filename).create(
        os.path.join(directory, filename), 
        type="delimited"
    )

Attributes

table	A reference to the Table instance that constructed this upload.
properties	A dict containing the API resource representation of the upload. This will only be populated after certain methods are called, particularly the get method.

Methods

upload.create(data[, ...])	Create an upload with the provided data and/or configuration.
upload.delete()	Delete an upload.
upload.exists()	Check whether an upload exists.
upload.get()	Fetch an upload and populate upload.properties
upload.insert_rows(rows, *[, update_schema])	Insert rows into an upload of type "stream"
upload.list_variables([max_results])	List the variables associated with a given upload.
upload.to_*([max_results, *, ...])	Various methods to read data out of an imported (but not yet released) upload. Mirrors the various Table.to_* methods (e.g., upload.to_pandas_dataframe()