Azure Data Tables client library for Python

Azure Data Tables is a NoSQL data storage service that can be accessed from anywhere in the world via authenticated calls using HTTP or HTTPS. Tables scales as needed to support the amount of data inserted, and allow for the storing of data with non-complex accessing. The Azure Data Tables client can be used to access Azure Storage or Cosmos accounts.

Source code | Package (PyPI) | API reference documentation | Samples

Getting started

The Azure Data Tables SDK can access an Azure Storage or CosmosDB account.

Prerequisites

• Python 2.7, or 3.6 or later is required to use this package.
• You must have an Azure subscription and either
  • an Azure Storage account or
  • an Azure Cosmos Account.

Create account

• To create a new storage account, you can use Azure Portal, Azure PowerShell, or Azure CLI:
• To create a new cosmos storage account, you can use the Azure CLI or Azure Portal.

Install the package

Install the Azure Data Tables client library for Python with pip:

pip install --pre azure-data-tables

Create the client

The Azure Data Tables library allows you to interact with two types of resources:

• the tables in your account
• the entities within those tables. Interaction with these resources starts with an instance of a client. To create a client object, you will need the account's table service endpoint URL and a credential that allows you to access the account. The account_url can be found on the page for your storage account in the Azure Portal under the "Access Keys" section or by running the following Azure CLI command:

# Get the table service URL for the account
az storage account show -n mystorageaccount -g MyResourceGroup --query "primaryEndpoints.table"

Once you have the account URL, it can be used to create the service client:

from azure.data.tables import TableServiceClient
service = TableServiceClient(account_url="https://<my_account_name>.table.core.windows.net/", credential=credential)

For more information about table service URL's and how to configure custom domain names for Azure Storage check out the official documentation

Types of credentials

The credential parameter may be provided in a number of different forms, depending on the type of authorization you wish to use. The Tables library supports the following authorizations:

• Shared Key
• Connection String
• Shared Access Signature Token

Creating the client from a shared key

To use an account shared key (aka account key or access key), provide the key as a string. This can be found in your storage account in the Azure Portal under the "Access Keys" section or by running the following Azure CLI command:

az storage account keys list -g MyResourceGroup -n MyStorageAccount

Use the key as the credential parameter to authenticate the client:

    from azure.data.tables import TableServiceClient
    service = TableServiceClient(account_url="https://<my_account_name>.table.core.windows.net", credential="<account_access_key>")

Creating the client from a connection string

Depending on your use case and authorization method, you may prefer to initialize a client instance with a connection string instead of providing the account URL and credential separately. To do this, pass the connection string to the client's from_connection_string class method. The connection string can be found in your storage account in the Azure Portal under the "Access Keys" section or with the following Azure CLI command:

az storage account show-connection-string -g MyResourceGroup -n MyStorageAccount

    from azure.data.tables import TableServiceClient
    connection_string = "DefaultEndpointsProtocol=https;AccountName=<my_account_name>;AccountKey=<my_account_key>;EndpointSuffix=core.windows.net"
    service = TableServiceClient.from_connection_string(conn_str=connection_string)

Creating the client from a SAS token

To use a shared access signature (SAS) token, provide the token as a string. If your account URL includes the SAS token, omit the credential parameter. You can generate a SAS token from the Azure Portal under Shared access signature or use one of the generate_*_sas() functions to create a sas token for the account or table:

    from datetime import datetime, timedelta
    from azure.data.tables import TableServiceClient, generate_account_sas, ResourceTypes, AccountSasPermissions

    sas_token = generate_account_sas(
        account_name="<account-name>",
        account_key="<account-access-key>",
        resource_types=ResourceTypes(service=True),
        permission=AccountSasPermissions(read=True),
        expiry=datetime.utcnow() + timedelta(hours=1)
    )

    table_service_client = TableServiceClient(account_url="https://<my_account_name>.table.core.windows.net", credential=sas_token)

Key concepts

Common uses of the Table service included:

• Storing TBs of structured data capable of serving web scale applications
• Storing datasets that do not require complex joins, foreign keys, or stored procedures and can be de-normalized for fast access
• Quickly querying data using a clustered index
• Accessing data using the OData protocol and LINQ filter expressions

The following components make up the Azure Data Tables Service:

• The account
• A table within the account, which contains a set of entities
• An entity within a table, as a dictionary

The Azure Data Tables client library for Python allows you to interact with each of these components through the use of a dedicated client object.

Clients

Two different clients are provided to interact with the various components of the Table Service:

1. TableServiceClient -
   • Get and set account setting
   • Query, create, and delete tables within the account.
   • Get a TableClient to access a specific table using the get_table_client method.
2. TableClient -
   • Interacts with a specific table (which need not exist yet).
   • Create, delete, query, and upsert entities within the specified table.
   • Create or delete the specified table itself.

Entities

Entities are similar to rows. An entity has a PartitionKey, a RowKey, and a set of properties. A property is a name value pair, similar to a column. Every entity in a table does not need to have the same properties. Entities can be represented as dictionaries like this as an example:

entity = {
    'PartitionKey': 'color',
    'RowKey': 'brand',
    'text': 'Marker',
    'color': 'Purple',
    'price': '5'
}

• create_entity - Add an entity to the table.
• delete_entity - Delete an entity from the table.
• update_entity - Update an entity's information by either merging or replacing the existing entity.
  • UpdateMode.MERGE will add new properties to an existing entity it will not delete an existing properties
  • UpdateMode.REPLACE will replace the existing entity with the given one, deleting any existing properties not included in the submitted entity
• query_entities - Query existing entities in a table using OData filters.
• get_entity - Get a specific entity from a table by partition and row key.
• upsert_entity - Merge or replace an entity in a table, or if the entity does not exist, inserts the entity.
  • UpdateMode.MERGE will add new properties to an existing entity it will not delete an existing properties
  • UpdateMode.REPLACE will replace the existing entity with the given one, deleting any existing properties not included in the submitted entity

Examples

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

• Creating a table
• Creating entities
• Querying entities

Creating a table

Create a table in your account and get a TableClient to perform operations on the newly created table:

from azure.data.tables import TableServiceClient
table_service_client = TableServiceClient.from_connection_string(conn_str="<connection_string>")
table_name = "myTable
table_client = table_service_client.create_table(table_name=table_name)

Creating entities

Create entities in the table:

from azure.data.tables import TableServiceClient
from datetime import datetime

PRODUCT_ID = '001234'
PRODUCT_NAME = 'RedMarker'

my_entity = {
    'PartitionKey': PRODUCT_NAME,
    'RowKey': PRODUCT_ID,
    'Stock': 15,
    'Price': 9.99,
    'Comments': "great product",
    'OnSale': True,
    'ReducedPrice': 7.99,
    'PurchaseDate': datetime(1973, 10, 4),
    'BinaryRepresentation': b'product_name
}

table_service_client = TableServiceClient.from_connection_string(conn_str="<connection_string>")
table_client = table_service_client.get_table_client(table_name="myTable")

entity = table_client.create_entity(entity=my_entity)

Querying entities

Querying entities in the table:

from azure.data.tables import TableClient
my_filter = "RowKey eq 'RedMarker'"
table_client = TableClient.from_connection_string(conn_str="<connection_string>", table_name="mytable")
entities = table_client.query_entities(filter=my_filter)
for entity in entities:
    for key in entity.keys():
        print("Key: {}, Value: {}".format(key, entity[key]))

Optional Configuration

Optional keyword arguments can be passed in at the client and per-operation level. The azure-core reference documentation describes available configurations for retries, logging, transport protocols, and more.

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.
• headers (dict): Pass in custom headers as key, value pairs. E.g. headers={'CustomValue': value}

Troubleshooting

General

Azure Data Tables clients raise exceptions defined in Azure Core. When you interact with the Azure table library using the Python SDK, errors returned by the service respond ot the same HTTP status codes for REST API requests. The Table service operations will throw a HttpResponseError on failure with helpful error codes.

For examples, if you try to create a table that already exists, a 409 error is returned indicating "Conflict".

from azure.data.tables import TableServiceClient
from azure.core.exceptions import HttpResponseError
table_name = 'YourTableName'

service_client = TableServiceClient.from_connection_string(connection_string)

# Create the table if it does not already exist
tc = service_client.create_table_if_not_exists(table_name)

try:
    service_client.create_table(table_name)
except HttpResponseError:
    print("Table with name {} already exists".format(table_name))

Logging

This library uses the standard logging 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:

import sys
import logging
from azure.data.tables import TableServiceClient
# Create a logger for the 'azure' SDK
logger = logging.getLogger('azure')
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 = TableServiceClient.from_connection_string("your_connection_string", logging_enable=True)

Similarly, logging_enable can enable detailed logging for a single operation, even when it is not enabled for the client:

service_client.create_entity(entity=my_entity, logging_enable=True)

Next steps

Get started with our Table samples.

Several Azure Data Tables 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 Tables.

Common Scenarios

These code samples show common scenario operations with the Azure Data tables client library. The async versions of the samples (the python sample files appended with _async) show asynchronous operations with Tables and require Python 3.5 or later.

• Create and delete tables: sample_create_delete_table.py (async version)
• List and query tables: sample_query_tables.py (async version)
• Insert and delete entities: sample_insert_delete_entities.py (async version)
• Query and list entities: sample_query_tables.py (async version)
• Update, upsert, and merge entities: sample_update_upsert_merge_entities.py (async version)
• Committing many requests in a single batch: sample_batching.py (async version)

Additional documentation

For more extensive documentation on Azure Data Tables, see the Azure Data Tables documentation on docs.microsoft.com.

Known Issues

A list of currently known issues relating to Cosmos DB table endpoints can be found here.

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. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Release History

12.0.0b5 (2020-03-09)

• This version and all future versions will require Python 2.7 or Python 3.6+, Python 3.5 is no longer supported.
• Adds SAS credential as an authentication option
• Bumps minimum requirement of azure-core to 1.10.0
• Bumped minimum requirement of msrest from 0.6.10 to 0.6.19.
• Added support for datetime entities with milliseconds

12.0.0b4 (2020-01-12)

• Fixes an issue where query_entities kwarg parameters would not work with multiple parameters or with non-string parameters. This now works with multiple parameters and numeric, string, boolean, UUID, and datetime objects.
• Fixes an issue where delete_entity will return an ClientAuthenticationError when the '@' symbol is included in the entity.

12.0.0b3 (2020-11-12)

• Add support for transactional batching of entity operations.
• Fixed deserialization bug in list_tables and query_tables where TableItem.table_name was an object instead of a string.
• Fixed issue where unrecognized entity data fields were silently ignored. They will now raise a TypeError.
• Fixed issue where query filter parameters were being ignored (#15094)

12.0.0b2 (2020-10-07)

• Adds support for Enumerable types by converting the Enum to a string before sending to the service

12.0.0b1 (2020-09-08)

This is the first beta of the azure-data-tables client library. The Azure Tables client library can seamlessly target either Azure Table storage or Azure Cosmos DB table service endpoints with no code changes.