azure-servicebus-py

Azure Service Bus SDK for Python messaging. Use for queues, topics, subscriptions, and enterprise messaging patterns. Triggers: "service bus", "ServiceBusClient", "queue", "topic", "subscription", "message broker".

npx skills add https://github.com/microsoft/skills --skill azure-servicebus-py

Azure Service Bus SDK for Python

Enterprise messaging for reliable cloud communication with queues and pub/sub topics.

Installation

pip install azure-servicebus azure-identity

Environment Variables

SERVICEBUS_FULLY_QUALIFIED_NAMESPACE=<namespace>.servicebus.windows.net  # Required for all auth methods
SERVICEBUS_QUEUE_NAME=myqueue  # Required for queue operations
SERVICEBUS_TOPIC_NAME=mytopic  # Required for topic operations
SERVICEBUS_SUBSCRIPTION_NAME=mysubscription  # Required for subscription operations
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication & Lifecycle

πŸ”‘ Two rules apply to every code sample below:

  1. Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys β€” they bypass Entra audit and rotation.
    • Local dev: DefaultAzureCredential works as-is.
    • Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.
  2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:
    • Sync: with <Client>(...) as client:
    • Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)

Snippets may abbreviate this setup, but production code should always follow both rules.

from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.servicebus import ServiceBusClient

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
namespace = "<namespace>.servicebus.windows.net"

with ServiceBusClient(
    fully_qualified_namespace=namespace,
    credential=credential
) as client:
    # Use client here (see following sections for operations)
    ...

Client Types

ClientPurposeGet From
ServiceBusClientConnection managementDirect instantiation
ServiceBusSenderSend messagesclient.get_queue_sender() / get_topic_sender()
ServiceBusReceiverReceive messagesclient.get_queue_receiver() / get_subscription_receiver()

Send Messages (Async)

import asyncio
from azure.servicebus.aio import ServiceBusClient
from azure.servicebus import ServiceBusMessage
from azure.identity.aio import DefaultAzureCredential

async def send_messages():
    credential = DefaultAzureCredential()
    
    async with ServiceBusClient(
        fully_qualified_namespace="<namespace>.servicebus.windows.net",
        credential=credential
    ) as client:
        sender = client.get_queue_sender(queue_name="myqueue")
        
        async with sender:
            # Single message
            message = ServiceBusMessage("Hello, Service Bus!")
            await sender.send_messages(message)
            
            # Batch of messages
            messages = [ServiceBusMessage(f"Message {i}") for i in range(10)]
            await sender.send_messages(messages)
            
            # Message batch (for size control)
            batch = await sender.create_message_batch()
            for i in range(100):
                try:
                    batch.add_message(ServiceBusMessage(f"Batch message {i}"))
                except ValueError:  # Batch full
                    await sender.send_messages(batch)
                    batch = await sender.create_message_batch()
                    batch.add_message(ServiceBusMessage(f"Batch message {i}"))
            await sender.send_messages(batch)

asyncio.run(send_messages())

Receive Messages (Async)

async def receive_messages():
    credential = DefaultAzureCredential()
    
    async with ServiceBusClient(
        fully_qualified_namespace="<namespace>.servicebus.windows.net",
        credential=credential
    ) as client:
        receiver = client.get_queue_receiver(queue_name="myqueue")
        
        async with receiver:
            # Receive batch
            messages = await receiver.receive_messages(
                max_message_count=10,
                max_wait_time=5  # seconds
            )
            
            for msg in messages:
                print(f"Received: {str(msg)}")
                await receiver.complete_message(msg)  # Remove from queue

asyncio.run(receive_messages())

Receive Modes

ModeBehaviorUse Case
PEEK_LOCK (default)Message locked, must complete/abandonReliable processing
RECEIVE_AND_DELETERemoved immediately on receiveAt-most-once delivery
from azure.servicebus import ServiceBusReceiveMode

receiver = client.get_queue_receiver(
    queue_name="myqueue",
    receive_mode=ServiceBusReceiveMode.RECEIVE_AND_DELETE
)

Message Settlement

async with receiver:
    messages = await receiver.receive_messages(max_message_count=1)
    
    for msg in messages:
        try:
            # Process message...
            await receiver.complete_message(msg)  # Success - remove from queue
        except ProcessingError:
            await receiver.abandon_message(msg)  # Retry later
        except PermanentError:
            await receiver.dead_letter_message(
                msg,
                reason="ProcessingFailed",
                error_description="Could not process"
            )
ActionEffect
complete_message()Remove from queue (success)
abandon_message()Release lock, retry immediately
dead_letter_message()Move to dead-letter queue
defer_message()Set aside, receive by sequence number

Topics and Subscriptions

# Send to topic
sender = client.get_topic_sender(topic_name="mytopic")
async with sender:
    await sender.send_messages(ServiceBusMessage("Topic message"))

# Receive from subscription
receiver = client.get_subscription_receiver(
    topic_name="mytopic",
    subscription_name="mysubscription"
)
async with receiver:
    messages = await receiver.receive_messages(max_message_count=10)

Sessions (FIFO)

# Send with session
message = ServiceBusMessage("Session message")
message.session_id = "order-123"
await sender.send_messages(message)

# Receive from specific session
receiver = client.get_queue_receiver(
    queue_name="session-queue",
    session_id="order-123"
)

# Receive from next available session
from azure.servicebus import NEXT_AVAILABLE_SESSION
receiver = client.get_queue_receiver(
    queue_name="session-queue",
    session_id=NEXT_AVAILABLE_SESSION
)

Scheduled Messages

from datetime import datetime, timedelta, timezone

message = ServiceBusMessage("Scheduled message")
scheduled_time = datetime.now(timezone.utc) + timedelta(minutes=10)

# Schedule message
sequence_number = await sender.schedule_messages(message, scheduled_time)

# Cancel scheduled message
await sender.cancel_scheduled_messages(sequence_number)

Dead-Letter Queue

from azure.servicebus import ServiceBusSubQueue

# Receive from dead-letter queue
dlq_receiver = client.get_queue_receiver(
    queue_name="myqueue",
    sub_queue=ServiceBusSubQueue.DEAD_LETTER
)

async with dlq_receiver:
    messages = await dlq_receiver.receive_messages(max_message_count=10)
    for msg in messages:
        print(f"Dead-lettered: {msg.dead_letter_reason}")
        await dlq_receiver.complete_message(msg)

Sync Client (for simple scripts)

from azure.servicebus import ServiceBusClient, ServiceBusMessage
from azure.identity import DefaultAzureCredential

with ServiceBusClient(
    fully_qualified_namespace="<namespace>.servicebus.windows.net",
    credential=DefaultAzureCredential()
) as client:
    with client.get_queue_sender("myqueue") as sender:
        sender.send_messages(ServiceBusMessage("Sync message"))
    
    with client.get_queue_receiver("myqueue") as receiver:
        for msg in receiver:
            print(str(msg))
            receiver.complete_message(msg)

Best Practices

  1. Pick sync OR async and stay consistent. Do not mix azure.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose one mode per module.
  2. Always use context managers for clients and async credentials. Wrap every client in with Client(...) as client: (sync) or async with Client(...) as client: (async) for proper cleanup. For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
  3. Use DefaultAzureCredential for portable auth across local dev and Azure (avoid connection strings / API keys when possible).
  4. Use async client for production workloads
  5. Complete messages after successful processing
  6. Use dead-letter queue for poison messages
  7. Use sessions for ordered, FIFO processing
  8. Use message batches for high-throughput scenarios
  9. Set max_wait_time to avoid infinite blocking

Reference Files

FileContents
references/patterns.mdCompeting consumers, sessions, retry patterns, request-response, transactions
references/dead-letter.mdDLQ handling, poison messages, reprocessing strategies
scripts/setup_servicebus.pyCLI for queue/topic/subscription management and DLQ monitoring

More skills from microsoft

oss-growth
microsoft
OSS growth hacker persona
agent-framework-azure-ai-py
microsoft
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS β€” from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
devops
applicationinsights-web-ts
microsoft
Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) β€” page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.
devops
azure-ai-anomalydetector-java
microsoft
Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
development
azure-ai-language-conversations-py
microsoft
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development