Metadata-Version: 2.1
Name: azure-storage-file-datalake
Version: 12.6.0b1
Summary: Microsoft Azure File DataLake Storage Client Library for Python
Home-page: https://github.com/Azure/azure-sdk-for-python
Author: Microsoft Corporation
Author-email: ascl@microsoft.com
License: MIT License
Description: # 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](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/storage/azure-storage-file-datalake/azure/storage/filedatalake) | [Package (PyPi)](https://pypi.org/project/azure-storage-file-datalake/) | [API reference documentation](https://aka.ms/azsdk-python-storage-filedatalake-ref) | [Product documentation](https://docs.microsoft.com/azure/storage/) | [Samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/storage/azure-storage-file-datalake/samples)
        
        
        ## Getting started
        
        ### Prerequisites
        * Python 2.7, or 3.5 or later is required to use this package.
        * You must have an [Azure subscription](https://azure.microsoft.com/free/) and an
        [Azure storage account](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-quickstart-create-account) to use this package.
        
        ### Install the package
        Install the Azure DataLake Storage client library for Python with [pip](https://pypi.org/project/pip/):
        
        ```bash
        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](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-quickstart-create-account#create-an-account-using-the-azure-portal),
        [Azure PowerShell](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-quickstart-create-account#create-an-account-using-powershell),
        or [Azure CLI](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-quickstart-create-account#create-an-account-using-azure-cli):
        
        ```bash
        # 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](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/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](#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:
        
        ```python
        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
        
        #### 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.
        3. **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.
        4. **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](#client-creation-with-a-connection-string)
        * [Uploading a file](#uploading-a-file)
        * [Downloading a file](#downloading-a-file)
        * [Enumerating paths](#enumerating-paths)
        
        
        ### Client creation with a connection string
        Create the DataLakeServiceClient using the connection string to your Azure Storage account.
        
        ```python
        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.
        
        ```python
        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.
        
        ```python
        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.
        
        ```python
        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 keyword arguments:**
        
        * __connection_timeout__ (int): Optionally sets the connect and read timeout value, in seconds.
        * __transport__ (Any): User-provided transport to send the HTTP request.
        
        **Per-operation keyword arguments:**
        
        * __raw_response_hook__ (callable): The given callback uses the response returned from the service.
        * __raw_request_hook__ (callable): The given callback uses the request before being sent to service.
        * __client_request_id__ (str): Optional user specified identification of the request.
        * __user_agent__ (str): Appends the custom value to the user-agent header to be sent with the request.
        * __logging_enable__ (bool): Enables logging at the DEBUG level. Defaults to False. Can also be passed in at
        the client level to enable it for all requests.
        * __logging_body__ (bool): Enables logging the request and response body. Defaults to False. Can also be passed in at
        the client level to enable it for all requests.
        * __headers__ (dict): Pass in custom headers as key, value pairs. E.g. `headers={'CustomValue': value}`
        
        ## Troubleshooting
        ### General
        DataLake Storage clients raise exceptions defined in [Azure Core](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/README.md).
        
        This list can be used for reference to catch thrown exceptions. To get the specific error code of the exception, use the `error_code` attribute, i.e, `exception.error_code`.
        
        ### Logging
        This library uses the standard
        [logging](https://docs.python.org/3/library/logging.html) library for logging.
        Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO
        level.
        
        Detailed DEBUG level logging, including request/response bodies and unredacted
        headers, can be enabled on a client with the `logging_enable` argument:
        ```python
        import sys
        import logging
        from azure.storage.filedatalake import DataLakeServiceClient
        
        # Create a logger for the 'azure.storage.filedatalake' SDK
        logger = logging.getLogger('azure.storage')
        logger.setLevel(logging.DEBUG)
        
        # Configure a console output
        handler = logging.StreamHandler(stream=sys.stdout)
        logger.addHandler(handler)
        
        # This client will log detailed information about its HTTP sessions, at DEBUG level
        service_client = DataLakeServiceClient.from_connection_string("your_connection_string", logging_enable=True)
        ```
        
        Similarly, `logging_enable` can enable detailed logging for a single operation,
        even when it isn't enabled for the client:
        ```py
        service_client.list_file_systems(logging_enable=True)
        ```
        
        ## Next steps
        
        ### More sample code
        
        Get started with our [Azure DataLake samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/storage/azure-storage-file-datalake/samples).
        
        Several DataLake Storage Python SDK samples are available to you in the SDK's GitHub repository. These samples provide example code for additional scenarios commonly encountered while working with DataLake Storage:
        
        * [`datalake_samples_access_control.py`](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/storage/azure-storage-file-datalake/samples/datalake_samples_access_control.py) - Examples for common DataLake Storage tasks:
            * Set up a file system
            * Create a directory
            * Set/Get access control for the directory
            * Create files under the directory
            * Set/Get access control for each file
            * Delete file system
        
        * [`datalake_samples_upload_download.py`](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/storage/azure-storage-file-datalake/samples/datalake_samples_upload_download.py) - Examples for common DataLake Storage tasks:
            * Set up a file system
            * Create file
            * Append data to the file
            * Flush data to the file
            * Download the uploaded data
            * Delete file system
        
        
        ### Additional documentation
        
        Table for [ADLS Gen1 to ADLS Gen2 API Mapping](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/storage/azure-storage-file-datalake/GEN1_GEN2_MAPPING.md)
        For more extensive REST documentation on Data Lake Storage Gen2, see the [Data Lake Storage Gen2 documentation](https://docs.microsoft.com/rest/api/storageservices/datalakestoragegen2/filesystem) on docs.microsoft.com.
        
        
        ## Contributing
        This project welcomes contributions and suggestions.  Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
        
        When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
        
        This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
