Access Azure Datalake Gen1 with fsspec and dask
Project description
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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file adlfs-2022.10.0.tar.gz.
File metadata
- Download URL: adlfs-2022.10.0.tar.gz
- Upload date:
- Size: 41.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 8542f9e6799aeb77adb5cb9854f76b7dce874610dc0f2ff006cce960526ffad6 |
|
| MD5 | 63af75730c900c202e4634069ded363b |
|
| BLAKE2b-256 | 67137b57e13f06393caeec97fb9930a1c59a9e2015d8d2afe7e0be50854737a1 |
Provenance
File details
Details for the file adlfs-2022.10.0-py3-none-any.whl.
File metadata
- Download URL: adlfs-2022.10.0-py3-none-any.whl
- Upload date:
- Size: 24.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | dc63b248f109bca2000d306f7a51c33d6859e4692dda7c6198b6300f8e29faaf |
|
| MD5 | 1818c29f4350fb955678957f27c12aa5 |
|
| BLAKE2b-256 | 20ae4a0eee777f120dc95c3cd356084a8c3fb910da62335377b3585d3ceb8145 |
