Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing.
Simple, cost-effective message queuing for asynchronous communication.
pip install azure-storage-queue azure-identity
AZURE_STORAGE_ACCOUNT_URL=https://<account>.queue.core.windows.net
from azure.identity import DefaultAzureCredential
from azure.storage.queue import QueueServiceClient, QueueClient
credential = DefaultAzureCredential()
account_url = "https://<account>.queue.core.windows.net"
# Service client
service_client = QueueServiceClient(account_url=account_url, credential=credential)
# Queue client
queue_client = QueueClient(account_url=account_url, queue_name="myqueue", credential=credential)
# Create queue
service_client.create_queue("myqueue")
# Get queue client
queue_client = service_client.get_queue_client("myqueue")
# Delete queue
service_client.delete_queue("myqueue")
# List queues
for queue in service_client.list_queues():
print(queue.name)
# Send message (string)
queue_client.send_message("Hello, Queue!")
# Send with options
queue_client.send_message(
content="Delayed message",
visibility_timeout=60, # Hidden for 60 seconds
time_to_live=3600 # Expires in 1 hour
)
# Send JSON
import json
data = {"task": "process", "id": 123}
queue_client.send_message(json.dumps(data))
# Receive messages (makes them invisible temporarily)
messages = queue_client.receive_messages(
messages_per_page=10,
visibility_timeout=30 # 30 seconds to process
)
for message in messages:
print(f"ID: {message.id}")
print(f"Content: {message.content}")
print(f"Dequeue count: {message.dequeue_count}")
# Process message...
# Delete after processing
queue_client.delete_message(message)
# Peek without hiding (doesn't affect visibility)
messages = queue_client.peek_messages(max_messages=5)
for message in messages:
print(message.content)
# Extend visibility or update content
messages = queue_client.receive_messages()
for message in messages:
# Extend timeout (need more time)
queue_client.update_message(
message,
visibility_timeout=60
)
# Update content and timeout
queue_client.update_message(
message,
content="Updated content",
visibility_timeout=60
)
# Delete after successful processing
messages = queue_client.receive_messages()
for message in messages:
try:
# Process...
queue_client.delete_message(message)
except Exception:
# Message becomes visible again after timeout
pass
# Delete all messages
queue_client.clear_messages()
# Get queue properties
properties = queue_client.get_queue_properties()
print(f"Approximate message count: {properties.approximate_message_count}")
# Set/get metadata
queue_client.set_queue_metadata(metadata={"environment": "production"})
properties = queue_client.get_queue_properties()
print(properties.metadata)
from azure.storage.queue.aio import QueueServiceClient, QueueClient
from azure.identity.aio import DefaultAzureCredential
async def queue_operations():
credential = DefaultAzureCredential()
async with QueueClient(
account_url="https://<account>.queue.core.windows.net",
queue_name="myqueue",
credential=credential
) as client:
# Send
await client.send_message("Async message")
# Receive
async for message in client.receive_messages():
print(message.content)
await client.delete_message(message)
import asyncio
asyncio.run(queue_operations())
from azure.storage.queue import QueueClient, BinaryBase64EncodePolicy, BinaryBase64DecodePolicy
# For binary data
queue_client = QueueClient(
account_url=account_url,
queue_name="myqueue",
credential=credential,
message_encode_policy=BinaryBase64EncodePolicy(),
message_decode_policy=BinaryBase64DecodePolicy()
)
# Send bytes
queue_client.send_message(b"Binary content")
dequeue_count for poison message detectionpeek_messages for monitoring without affecting queuetime_to_live to prevent stale messagesThis skill is applicable to execute the workflow or actions described in the overview.
Copy a source-pinned command for your client. You run it yourself.
Destination: .claude/skills/azure-storage-queue-py · pinned to the source commit
# Run from your project root
git clone https://github.com/sickn33/agentic-awesome-skills.git .skillboard-tmp
git -C .skillboard-tmp checkout 5cf4dfeb13ea966daa1e117897689cd7991e3f44
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/plugins/agentic-awesome-skills-claude/skills/azure-storage-queue-py" ".claude/skills/"
rm -rf .skillboard-tmpReview the source before running. This copies files into your project; it is not a one-click install and does not verify runtime safety.
sudo apt update && sudo apt install -y gitnpm install -g @anthropic-ai/claude-code# Run from your project root
git clone https://github.com/sickn33/agentic-awesome-skills.git .skillboard-tmp
git -C .skillboard-tmp checkout 5cf4dfeb13ea966daa1e117897689cd7991e3f44
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/plugins/agentic-awesome-skills-claude/skills/azure-storage-queue-py" ".claude/skills/"
rm -rf .skillboard-tmpDestination: .claude/skills/azure-storage-queue-py
Scanner static-checks@0.1.0 · commit 5cf4dfeb13ea. Static checks cannot prove runtime safety – review the source and the exact diff before installing. How checks work.
Instructs shell/process/package operations that run commands on the host.
Evidence: pip install· fingerprint 7944ec554efca445