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Upload file(s) to Azure Data Lake.

class*, local_path, remote_path, overwrite=True, nthreads=64, buffersize=4194304, blocksize=4194304, extra_upload_options=None, azure_data_lake_conn_id='azure_data_lake_default', **kwargs)[source]

Bases: airflow.models.BaseOperator

Upload file(s) to Azure Data Lake.

See also

For more information on how to use this operator, take a look at the guide: LocalFilesystemToADLSOperator

  • local_path (str) – local path. Can be single file, directory (in which case, upload recursively) or glob pattern. Recursive glob patterns using ** are not supported

  • remote_path (str) – Remote path to upload to; if multiple files, this is the directory root to write within

  • nthreads (int) – Number of threads to use. If None, uses the number of cores.

  • overwrite (bool) – Whether to forcibly overwrite existing files/directories. If False and remote path is a directory, will quit regardless if any files would be overwritten or not. If True, only matching filenames are actually overwritten

  • buffersize (int) – int [2**22] Number of bytes for internal buffer. This block cannot be bigger than a chunk and cannot be smaller than a block

  • blocksize (int) – int [2**22] Number of bytes for a block. Within each chunk, we write a smaller block for each API call. This block cannot be bigger than a chunk

  • extra_upload_options (dict[str, Any] | None) – Extra upload options to add to the hook upload method

  • azure_data_lake_conn_id (str) – Reference to the Azure Data Lake connection

template_fields: Sequence[str] = ('local_path', 'remote_path')[source]
ui_color = '#e4f0e8'[source]

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

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