Azure DataLake service client library for Python

Overview

This preview package for Python includes ADLS Gen2 specific API support made available in Storage SDK. This includes:

1. New directory level operations (Create, Rename, Delete) for hierarchical namespace enabled (HNS) storage account. For HNS enabled accounts, the rename/move operations are atomic.
2. Permission related operations (Get/Set ACLs) for hierarchical namespace enabled (HNS) accounts.

Source code | Package (PyPi) | Package (Conda) | API reference documentation | Product documentation | Samples

Getting started

Prerequisites

• Python 3.9 or later is required to use this package. For more details, please read our page on Azure SDK for Python version support policy.
• You must have an Azure subscription and an Azure storage account to use this package.

Install the package

Install the Azure DataLake Storage client library for Python with pip:

pip install azure-storage-file-datalake --pre

Create a storage account

If you wish to create a new storage account, you can use the Azure Portal, Azure PowerShell, or Azure CLI:

# Create a new resource group to hold the storage account -
# if using an existing resource group, skip this step
az group create --name my-resource-group --location westus2

# Install the extension 'Storage-Preview'
az extension add --name storage-preview

# Create the storage account
az storage account create --name my-storage-account-name --resource-group my-resource-group --sku Standard_LRS --kind StorageV2 --hierarchical-namespace true

Authenticate the client

Interaction with DataLake Storage starts with an instance of the DataLakeServiceClient class. You need an existing storage account, its URL, and a credential to instantiate the client object.

Get credentials

To authenticate the client you have a few options:

1. Use a SAS token string
2. Use an account shared access key
3. Use a token credential from azure.identity

Alternatively, you can authenticate with a storage connection string using the from_connection_string method. See example: Client creation with a connection string.

You can omit the credential if your account URL already has a SAS token.

Create client

Once you have your account URL and credentials ready, you can create the DataLakeServiceClient:

from azure.storage.filedatalake import DataLakeServiceClient

service = DataLakeServiceClient(account_url="https://<my-storage-account-name>.dfs.core.windows.net/", credential=credential)

Key concepts

DataLake storage offers four types of resources:

• The storage account
• A file system in the storage account
• A directory under the file system
• A file in a the file system or under directory

Async Clients

This library includes a complete async API supported on Python 3.5+. To use it, you must first install an async transport, such as aiohttp. See azure-core documentation for more information.

Async clients and credentials should be closed when they're no longer needed. These objects are async context managers and define async close methods.

Clients

The DataLake Storage SDK provides four different clients to interact with the DataLake Service:

1. DataLakeServiceClient - this client interacts with the DataLake Service at the account level. It provides operations to retrieve and configure the account properties as well as list, create, and delete file systems within the account. For operations relating to a specific file system, directory or file, clients for those entities can also be retrieved using the get_file_client, get_directory_client or get_file_system_client functions.
2. FileSystemClient - this client represents interaction with a specific file system, even if that file system does not exist yet. It provides operations to create, delete, or configure file systems and includes operations to list paths under file system, upload, and delete file or directory in the file system. For operations relating to a specific file, the client can also be retrieved using the get_file_client function. For operations relating to a specific directory, the client can be retrieved using the get_directory_client function.
3. DatalakeDirectoryClient - this client represents interaction with a specific directory, even if that directory does not exist yet. It provides directory operations create, delete, rename, get properties and set properties operations.
4. DatalakeFileClient - this client represents interaction with a specific file, even if that file does not exist yet. It provides file operations to append data, flush data, delete, create, and read file.
5. DatalakeLeaseClient - this client represents lease interactions with a FileSystemClient, DataLakeDirectoryClient or DataLakeFileClient. It provides operations to acquire, renew, release, change, and break leases on the resources.

Examples

The following sections provide several code snippets covering some of the most common Storage DataLake tasks, including:

• Client creation with a connection string
• Uploading a file
• Downloading a file
• Enumerating paths

Client creation with a connection string

Create the DataLakeServiceClient using the connection string to your Azure Storage account.

from azure.storage.filedatalake import DataLakeServiceClient

service = DataLakeServiceClient.from_connection_string(conn_str="my_connection_string")

Uploading a file

Upload a file to your file system.

from azure.storage.filedatalake import DataLakeFileClient

data = b"abc"
file = DataLakeFileClient.from_connection_string("my_connection_string",
                                                 file_system_name="myfilesystem", file_path="myfile")
file.create_file ()
file.append_data(data, offset=0, length=len(data))
file.flush_data(len(data))

Downloading a file

Download a file from your file system.

from azure.storage.filedatalake import DataLakeFileClient

file = DataLakeFileClient.from_connection_string("my_connection_string",
                                                 file_system_name="myfilesystem", file_path="myfile")

with open("./BlockDestination.txt", "wb") as my_file:
    download = file.download_file()
    download.readinto(my_file)

Enumerating paths

List the paths in your file system.

from azure.storage.filedatalake import FileSystemClient

file_system = FileSystemClient.from_connection_string("my_connection_string", file_system_name="myfilesystem")

paths = file_system.get_paths()
for path in paths:
    print(path.name + '\n')

Optional Configuration

Optional keyword arguments that can be passed in at the client and per-operation level.

Retry Policy configuration

Use the following keyword arguments when instantiating a client to configure the retry policy:

• retry_total (int): Total number of retries to allow. Takes precedence over other counts. Pass in retry_total=0 if you do not want to retry on requests. Defaults to 10.
• retry_connect (int): How many connection-related errors to retry on. Defaults to 3.
• retry_read (int): How many times to retry on read errors. Defaults to 3.
• retry_status (int): How many times to retry on bad status codes. Defaults to 3.
• retry_to_secondary (bool): Whether the request should be retried to secondary, if able. This should only be enabled of RA-GRS accounts are used and potentially stale data can be handled. Defaults to False.

Other client / per-operation configuration

Other optional configuration keyword arguments that can be specified on the client or per-operation.

**Client