Azure Queue storage is a service for storing large numbers of messages that can be accessed from anywhere in the world via authenticated calls using HTTP or HTTPS. A single queue message can be up to 64 KiB in size, and a queue can contain millions of messages, up to the total capacity limit of a storage account.
Common uses of Queue storage include:
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Getting started PrerequisitesInstall the Azure Storage Queues client library for Python with pip:
pip install azure-storage-queue
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
# Create the storage account
az storage account create -n my-storage-account-name -g my-resource-group
Create the client
The Azure Storage Queues client library for Python allows you to interact with three types of resources: the storage account itself, queues, and messages. Interaction with these resources starts with an instance of a client. To create a client object, you will need the storage account's queue service endpoint URL and a credential that allows you to access the storage account:
from azure.storage.queue import QueueServiceClient
service = QueueServiceClient(account_url="https://<my-storage-account-name>.queue.core.windows.net/", credential=credential)
Looking up the account URL
You can find the storage account's queue service URL using the Azure Portal, Azure PowerShell, or Azure CLI:
# Get the queue service URL for the storage account
az storage account show -n my-storage-account-name -g my-resource-group --query "primaryEndpoints.queue"
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:
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 storage account or queue:
from datetime import datetime, timedelta
from azure.storage.queue import QueueServiceClient, generate_account_sas, ResourceTypes, AccountSasPermissions
sas_token = generate_account_sas(
account_name="<storage-account-name>",
account_key="<account-access-key>",
resource_types=ResourceTypes(service=True),
permission=AccountSasPermissions(read=True),
start=datetime.utcnow(),
expiry=datetime.utcnow() + timedelta(hours=1)
)
queue_service_client = QueueServiceClient(account_url="https://<my_account_name>.queue.core.windows.net", credential=sas_token)
To use a storage account shared key (aka account key or access key), provide the key as a string. This can be found 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.storage.queue import QueueServiceClient
service = QueueServiceClient(account_url="https://<my_account_name>.queue.core.windows.net", credential="<account_access_key>")
To use an Azure Active Directory (AAD) token credential, provide an instance of the desired credential type obtained from the azure-identity library. For example, DefaultAzureCredential can be used to authenticate the client.
This requires some initial setup:
Use the returned token credential to authenticate the client:
from azure.identity import DefaultAzureCredential
from azure.storage.queue import QueueServiceClient
token_credential = DefaultAzureCredential()
queue_service_client = QueueServiceClient(
account_url="https://<my_account_name>.queue.core.windows.net",
credential=token_credential
)
Depending on your use case and authorization method, you may prefer to initialize a client instance with a storage connection string instead of providing the account URL and credential separately. To do this, pass the storage connection string to the client's from_connection_string
class method:
from azure.storage.queue import QueueServiceClient
connection_string = "DefaultEndpointsProtocol=https;AccountName=xxxx;AccountKey=xxxx;EndpointSuffix=core.windows.net"
service = QueueServiceClient.from_connection_string(conn_str=connection_string)
The connection string to your storage account can be found in the Azure Portal under the "Access Keys" section or by running the following CLI command:
az storage account show-connection-string -g MyResourceGroup -n MyStorageAccount
Key concepts
The following components make up the Azure Queue Service:
The Azure Storage Queues client library for Python allows you to interact with each of these components through the use of a dedicated client object.
Async ClientsThis 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.
Two different clients are provided to interact with the various components of the Queue Service:
get_queue_client
method.The following sections provide several code snippets covering some of the most common Storage Queue tasks, including:
Creating a queueCreate a queue in your storage account
from azure.storage.queue import QueueClient
queue = QueueClient.from_connection_string(conn_str="<connection_string>", queue_name="myqueue")
queue.create_queue()
Use the async client to create a queue
from azure.storage.queue.aio import QueueClient
queue = QueueClient.from_connection_string(conn_str="<connection_string>", queue_name="myqueue")
await queue.create_queue()
Sending messages
Send messages to your queue
from azure.storage.queue import QueueClient
queue = QueueClient.from_connection_string(conn_str="<connection_string>", queue_name="myqueue")
queue.send_message("I'm using queues!")
queue.send_message("This is my second message")
Send messages asynchronously
import asyncio
from azure.storage.queue.aio import QueueClient
queue = QueueClient.from_connection_string(conn_str="<connection_string>", queue_name="myqueue")
await asyncio.gather(
queue.send_message("I'm using queues!"),
queue.send_message("This is my second message")
)
Receiving messages
Receive and process messages from your queue
from azure.storage.queue import QueueClient
queue = QueueClient.from_connection_string(conn_str="<connection_string>", queue_name="myqueue")
response = queue.receive_messages()
for message in response:
print(message.content)
queue.delete_message(message)
# Printed messages from the front of the queue:
# >> I'm using queues!
# >> This is my second message
Receive and process messages in batches
from azure.storage.queue import QueueClient
queue = QueueClient.from_connection_string(conn_str="<connection_string>", queue_name="myqueue")
response = queue.receive_messages(messages_per_page=10)
for message_batch in response.by_page():
for message in message_batch:
print(message.content)
queue.delete_message(message)
Receive and process messages asynchronously
from azure.storage.queue.aio import QueueClient
queue = QueueClient.from_connection_string(conn_str="<connection_string>", queue_name="myqueue")
response = queue.receive_messages()
async for message in response:
print(message.content)
await queue.delete_message(message)
Optional Configuration
Optional keyword arguments that can be passed in at the client and per-operation level.
Retry Policy configurationUse the following keyword arguments when instantiating a client to configure the retry policy:
retry_total=0
if you do not want to retry on requests. Defaults to 10.False
.Other optional configuration keyword arguments that can be specified on the client or per-operation.
Client keyword arguments:
Per-operation keyword arguments:
headers={'CustomValue': value}
Storage Queue clients raise exceptions defined in Azure Core.
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
.
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.storage.queue import QueueServiceClient
# Create a logger for the 'azure.storage.queue' SDK
logger = logging.getLogger('azure.storage.queue')
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 = QueueServiceClient.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:
service_client.get_service_stats(logging_enable=True)
Next steps More sample code
Get started with our Queue samples.
Several Storage Queues 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 Storage Queues:
queue_samples_hello_world.py (async version) - Examples found in this article:
queue_samples_authentication.py (async version) - Examples for authenticating and creating the client:
queue_samples_service.py (async version) - Examples for interacting with the queue service:
queue_samples_message.py (async version) - Examples for working with queues and messages:
For more extensive documentation on Azure Queue storage, see the Azure Queue storage documentation on docs.microsoft.com.
ContributingThis 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.
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