Access Azure Datalake Gen1 with fsspec and dask
Project description
Dask interface to Azure-Datalake Gen1 and Gen2 Storage Quickstart
This package can be installed using:
pip install adlfs
or
conda install -c conda-forge adlfs
The adl:// and abfs:// protocols are included in fsspec's known_implementations registry
in fsspec > 0.6.1, otherwise users must explicitly inform fsspec about the supported adlfs protocols.
To use the Gen1 filesystem:
import dask.dataframe as dd
STORAGE_OPTIONS={'tenant_id': TENANT_ID, 'client_id': CLIENT_ID, 'client_secret': CLIENT_SECRET}
dd.read_csv('adl://{STORE_NAME}/{FOLDER}/*.csv', storage_options=STORAGE_OPTIONS)
To use the Gen2 filesystem:
import dask.dataframe as dd
STORAGE_OPTIONS={'account_name': ACCOUNT_NAME, 'account_key': ACCOUNT_KEY}
ddf = dd.read_csv('abfs://{CONTAINER}/{FOLDER}/*.csv', storage_options=STORAGE_OPTIONS)
ddf = dd.read_parquet('abfs://{CONTAINER}/folder.parquet', storage_options=STORAGE_OPTIONS)
Details
The package includes pythonic filesystem implementations for both Azure Datalake Gen1 and Azure Datalake Gen2, that facilitate interactions between both Azure Datalake implementations and Dask. This is done leveraging the intake/filesystem_spec base class and Azure Python SDKs.
Operations against both Gen1 Datalake currently only work with an Azure ServicePrincipal with suitable credentials to perform operations on the resources of choice.
Operations against the Gen2 Datalake are implemented by leveraging multi-protocol access, using the Azure Blob Storage Python SDK. The AzureBlobFileSystem accepts all of the BlockBlobService arguments.
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