Filesystem 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 you can use the protocol abfs or az:

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('az://{CONTAINER}/folder.parquet', storage_options=storage_options)

Accepted protocol / uri formats include:
'PROTOCOL://container/path-part/file'
'PROTOCOL://container@account.dfs.core.windows.net/path-part/file'

or optionally, if AZURE_STORAGE_ACCOUNT_NAME and an AZURE_STORAGE_<CREDENTIAL> is 
set as an environmental variable, then storage_options will be read from the environmental
variables

To read from a public storage blob you are required to specify the 'account_name'. For example, you can access NYC Taxi & Limousine Commission as:

storage_options = {'account_name': 'azureopendatastorage'}
ddf = dd.read_parquet('az://nyctlc/green/puYear=2019/puMonth=*/*.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 Azure Blob Storage Python SDK.

The filesystem can be instantiated with a variety of credentials, including:
    account_name
    account_key
    sas_token
    connection_string
    Azure ServicePrincipal credentials (which requires tenant_id, client_id, client_secret)
    anon
    location_mode:  valid value are "primary" or "secondary" and apply to RA-GRS accounts

The following enviornmental variables can also be set and picked up for authentication:
    "AZURE_STORAGE_CONNECTION_STRING"
    "AZURE_STORAGE_ACCOUNT_NAME"
    "AZURE_STORAGE_ACCOUNT_KEY"
    "AZURE_STORAGE_SAS_TOKEN"
    "AZURE_STORAGE_CLIENT_SECRET"
    "AZURE_STORAGE_CLIENT_ID"
    "AZURE_STORAGE_TENANT_ID"

The default value for anon (anonymous) is True.  If no explicit credentials are set, the
AzureBlobFileSystem will assume the account_name points to a public container, and attempt to use an anonymous login.

If anon (anonymous) is False, AzureBlobFileSystem will attempt to authenticate using Azure's 
DefaultAzureCredential() library.  Specifics of the types of authentication permitted can be
found [here](https://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-pythonhttps://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-python)

The AzureBlobFileSystem accepts all of the Async BlobServiceClient arguments.

By default, write operations create BlockBlobs in Azure, which, once written can not be appended.  It is possible to create an AppendBlob using an `mode="ab"` when creating, and then when operating on blobs.  Currently AppendBlobs are not available if hierarchical namespaces are enabled.