Microsoft Azure
Daft is able to read/write data to/from Azure Blob Store, and understands natively the URL protocols az:// and abfs:// as referring to data that resides
in Azure Blob Store.
Warning
Daft currently only supports globbing and listing files in storage accounts with hierarchical namespaces enabled.
Hierarchical namespaces enable Daft to use its embarrassingly parallel globbing algorithm to improve performance of listing large nested directories of data.
Please file an issue if you need support for non-hierarchical namespace buckets! We’d love to support your use-case.
Authorization/Authentication
In Azure Blob Service, data is stored under the hierarchy of:
Storage Account
Container (sometimes referred to as “bucket” in S3-based services)
Object Key
URLs to data in Azure Blob Store come in the form: az://{CONTAINER_NAME}/{OBJECT_KEY}.
Given that the Storage Account is not a part of the URL, you must provide this separately.
Rely on Environment
You can rely on Azure’s environment variables
to have Daft automatically discover credentials.
Please be aware that when doing so in a distributed environment such as Ray, Daft will pick these credentials up from worker machines and thus each worker machine needs to be appropriately provisioned.
If instead you wish to have Daft use credentials from the “driver”, you may wish to manually specify your credentials.
Manually specify credentials
You may also choose to pass these values into your Daft I/O function calls using an daft.io.AzureConfig config object.
daft.set_planning_config() is a convenient way to set your daft.io.IOConfig as the default config to use on any subsequent Daft method calls.
from daft.io import IOConfig, AzureConfig
# Supply actual values for the storage_account and access key here
io_config = IOConfig(azure=AzureConfig(storage_account="***", access_key="***"))
# Globally set the default IOConfig for any subsequent I/O calls
daft.set_planning_config(default_io_config=io_config)
# Perform some I/O operation
df = daft.read_parquet("az://my_container/my_path/**/*")
Alternatively, Daft supports overriding the default IOConfig per-operation by passing it into the io_config= keyword argument. This is extremely flexible as you can
pass a different daft.io.AzureConfig per function call if you wish!
# Perform some I/O operation but override the IOConfig
df2 = daft.read_csv("az://my_container/my_other_path/**/*", io_config=io_config)
Connect to Microsoft Fabric/OneLake
If you are connecting to storage in OneLake or another Microsoft Fabric service, set the use_fabric_endpoint parameter to True in the daft.io.AzureConfig object.
from daft.io import IOConfig, AzureConfig
io_config = IOConfig(
azure=AzureConfig(
storage_account="onelake",
use_fabric_endpoint=True,
# Set credentials as needed
)
)
df = daft.read_deltalake('abfss://[WORKSPACE]@onelake.dfs.fabric.microsoft.com/[LAKEHOUSE].Lakehouse/Tables/[TABLE]', io_config=io_config)
