azure-eventhub-py

CategoryCoding
AuthorAgentic Awesome Skills 社区
LicenseMIT
Rating4.70/5
Uses8.3K

Azure Event Hubs SDK for Python

Big data streaming platform for high-throughput event ingestion.

Installation

bash
pip install azure-eventhub azure-identity

For checkpointing with blob storage

pip install azure-eventhub-checkpointstoreblob-aio

Environment Variables

bash
EVENT_HUB_FULLY_QUALIFIED_NAMESPACE=<namespace>.servicebus.windows.net
EVENT_HUB_NAME=my-eventhub
STORAGE_ACCOUNT_URL=https://<account>.blob.core.windows.net
CHECKPOINT_CONTAINER=checkpoints

Authentication

python
from azure.identity import DefaultAzureCredential
from azure.eventhub import EventHubProducerClient, EventHubConsumerClient

credential = DefaultAzureCredential()
namespace = "<namespace>.servicebus.windows.net"
eventhub_name = "my-eventhub"


Producer

producer = EventHubProducerClient(
    fully_qualified_namespace=namespace,
    eventhub_name=eventhub_name,
    credential=credential
)

Consumer

consumer = EventHubConsumerClient(
    fully_qualified_namespace=namespace,
    eventhub_name=eventhub_name,
    consumer_group="$Default",
    credential=credential
)

Client Types

| Client | Purpose |
|--------|---------|
| EventHubProducerClient | Send events to Event Hub |
| EventHubConsumerClient | Receive events from Event Hub |
| BlobCheckpointStore | Track consumer progress |

Send Events

python
from azure.eventhub import EventHubProducerClient, EventData
from azure.identity import DefaultAzureCredential

producer = EventHubProducerClient(
    fully_qualified_namespace="<namespace>.servicebus.windows.net",
    eventhub_name="my-eventhub",
    credential=DefaultAzureCredential()
)


with producer:
    # Create batch (handles size limits)
    event_data_batch = producer.create_batch()
    
    for i in range(10):
        try:
            event_data_batch.add(EventData(f"Event {i}"))
        except ValueError:
            # Batch is full, send and create new one
            producer.send_batch(event_data_batch)
            event_data_batch = producer.create_batch()
            event_data_batch.add(EventData(f"Event {i}"))
    
    # Send remaining
    producer.send_batch(event_data_batch)

Send to Specific Partition

python
# By partition ID
event_data_batch = producer.create_batch(partition_id="0")

By partition key (consistent hashing)

event_data_batch = producer.create_batch(partition_key="user-123")

Receive Events

Simple Receive

python
from azure.eventhub import EventHubConsumerClient

def on_event(partition_context, event):
    print(f"Partition: {partition_context.partition_id}")
    print(f"Data: {event.body_as_str()}")
    partition_context.update_checkpoint(event)


consumer = EventHubConsumerClient(
    fully_qualified_namespace="<namespace>.servicebus.windows.net",
    eventhub_name="my-eventhub",
    consumer_group="$Default",
    credential=DefaultAzureCredential()
)


with consumer:
    consumer.receive(
        on_event=on_event,
        starting_position="-1",  # Beginning of stream
    )

With Blob Checkpoint Store (Production)

python
from azure.eventhub import EventHubConsumerClient
from azure.eventhub.extensions.checkpointstoreblob import BlobCheckpointStore
from azure.identity import DefaultAzureCredential

checkpoint_store = BlobCheckpointStore(
    blob_account_url="https://<account>.blob.core.windows.net",
    container_name="checkpoints",
    credential=DefaultAzureCredential()
)


consumer = EventHubConsumerClient(
    fully_qualified_namespace="<namespace>.servicebus.windows.net",
    eventhub_name="my-eventhub",
    consumer_group="$Default",
    credential=DefaultAzureCredential(),
    checkpoint_store=checkpoint_store
)


def on_event(partition_context, event):
    print(f"Received: {event.body_as_str()}")
    # Checkpoint after processing
    partition_context.update_checkpoint(event)


with consumer:
    consumer.receive(on_event=on_event)

Async Client

python
from azure.eventhub.aio import EventHubProducerClient, EventHubConsumerClient
from azure.identity.aio import DefaultAzureCredential
import asyncio

async def send_events():
    credential = DefaultAzureCredential()
    
    async with EventHubProducerClient(
        fully_qualified_namespace="<namespace>.servicebus.windows.net",
        eventhub_name="my-eventhub",
        credential=credential
    ) as producer:
        batch = await producer.create_batch()
        batch.add(EventData("Async event"))
        await producer.send_batch(batch)


async def receive_events():
    async def on_event(partition_context, event):
        print(event.body_as_str())
        await partition_context.update_checkpoint(event)
    
    async with EventHubConsumerClient(
        fully_qualified_namespace="<namespace>.servicebus.windows.net",
        eventhub_name="my-eventhub",
        consumer_group="$Default",
        credential=DefaultAzureCredential()
    ) as consumer:
        await consumer.receive(on_event=on_event)


asyncio.run(send_events())

Event Properties

python
event = EventData("My event body")

Set properties

event.properties = {"custom_property": "value"}
event.content_type = "application/json"

Read properties (on receive)

print(event.body_as_str())
print(event.sequence_number)
print(event.offset)
print(event.enqueued_time)
print(event.partition_key)

Get Event Hub Info

python
with producer:
    info = producer.get_eventhub_properties()
    print(f"Name: {info['name']}")
    print(f"Partitions: {info['partition_ids']}")
    
    for partition_id in info['partition_ids']:
        partition_info = producer.get_partition_properties(partition_id)
        print(f"Partition {partition_id}: {partition_info['last_enqueued_sequence_number']}")

Best Practices

1. Use batches for sending multiple events
2. Use checkpoint store in production for reliable processing
3. Use async client for high-throughput scenarios
4. Use partition keys for ordered delivery within a partition
5. Handle batch size limits — catch ValueError when batch is full
6. Use context managers (with/async with) for proper cleanup
7. Set appropriate consumer groups for different applications

Reference Files

| File | Contents |
|------|----------|
| references/checkpointing.md | Checkpoint store patterns, blob checkpointing, checkpoint strategies |
| references/partitions.md | Partition management, load balancing, starting positions |
| scripts/setup_consumer.py | CLI for Event Hub info, consumer setup, and event sending/receiving |

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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