Azure Event Hubs Developers Guide

Receiving Events

This guide details how to effectively receive and process events from Azure Event Hubs using various client libraries and patterns.

Core Concepts for Receiving Events

• Consumer Groups: An application or service that reads from an Event Hub. Each consumer group has its own view of the event stream.
• Partition: Event Hubs divides a data stream into partitions, allowing for parallel processing.
• Offset: A unique identifier for an event within a partition.
• Sequence Number: A sequential number assigned to each event within a partition.

Choosing a Receiving Method

Azure Event Hubs offers several ways to receive events, depending on your application's needs:

• Azure Event Hubs SDKs: Recommended for most applications, providing robust features and management. Available for .NET, Java, Python, Go, and JavaScript.
• Azure Functions: A serverless compute service that can automatically trigger based on new events in an Event Hub.
• Azure Stream Analytics: A fully managed, real-time analytics service that enables you to develop and run real-time analytics on fast-streaming data from Event Hubs.
• Apache Kafka compatible clients: For applications already using Kafka, Event Hubs supports the Kafka protocol.

Using the Event Hubs SDK (Python Example)

The Event Hubs SDK provides a straightforward way to build custom event receivers.

1. Installation

pip install azure-eventhub

2. Basic Consumer Code

import asyncio
from azure.eventhub import EventHubClient, EventPosition

async def receive_events(connection_str, event_hub_name):
    client = EventHubClient.from_connection_string(connection_str, event_hub_name)
    consumer = client.get_consumer()

    async with consumer:
        print(f"Starting to receive events from {event_hub_name}...")
        async for event_data in consumer:
            try:
                print(f"Partition: {event_data.partition_id}")
                print(f"Sequence Number: {event_data.sequence_number}")
                print(f"Offset: {event_data.offset}")
                print(f"Enqueued Time: {event_data.enqueued_time}")
                print(f"Body: {event_data.body_as_str()}")
                print("-" * 20)
            except Exception as e:
                print(f"Error processing event: {e}")

async def main():
    # Replace with your Event Hubs connection string and name
    EVENT_HUB_CONNECTION_STR = "YOUR_EVENT_HUB_CONNECTION_STRING"
    EVENT_HUB_NAME = "YOUR_EVENT_HUB_NAME"

    await receive_events(EVENT_HUB_CONNECTION_STR, EVENT_HUB_NAME)

if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())

3. Important Considerations for SDK Usage

• Error Handling: Implement robust error handling and retry mechanisms for network issues or processing failures.
• Checkpointing: For stateful processing, use checkpointing to store the progress of event processing to resume from the correct position after restarts. The SDK offers mechanisms for this.
• Consumer Group: Ensure you are using a specific consumer group to avoid interference with other applications reading from the same Event Hub.
• Event Position: You can specify an EventPosition to start receiving events from a particular point in time, offset, or sequence number.

Receiving Events with Azure Functions

Azure Functions offer a simplified, event-driven approach. You can configure an Event Hubs trigger to automatically process incoming events without managing infrastructure.

Example Azure Function (Python)

In your function.json, you would define the Event Hubs trigger:

{
  "scriptFile": "__init__.py",
  "bindings": [
    {
      "name": "myEventHubsTrigger",
      "type": "eventHubTrigger",
      "direction": "in",
      "eventHubName": "YOUR_EVENT_HUB_NAME",
      "connection": "EVENTHUB_CONNECTION_STRING_SETTING",
      "consumerGroup": "$Default"
    }
  ]
}

And in your __init__.py:

import logging
import json
import azure.functions as func

async def main(myEventHubsTrigger: func.EventHubMessage):
    logging.info(f'Python Event Hub trigger function processed a message: {myEventHubsTrigger.get_body().decode()}')
    logging.info(f'Partition ID: {myEventHubsTrigger.partition_id}')
    logging.info(f'Sequence Number: {myEventHubsTrigger.sequence_number}')
    logging.info(f'Offset: {myEventHubsTrigger.offset}')
    logging.info(f'Enqueued Time: {myEventHubsTrigger.enqueued_time}')

    # Example of processing JSON data
    try:
        data = json.loads(myEventHubsTrigger.get_body().decode())
        logging.info(f"Parsed data: {data}")
        # Your processing logic here...
    except ValueError:
        logging.warning("Message is not valid JSON.")

Best Practices for Event Reception

• Idempotency: Design your event handlers to be idempotent, meaning processing the same event multiple times has the same effect as processing it once. This is crucial for resilience.
• Scalability: Leverage consumer groups and partition awareness to scale your processing horizontally.
• Monitoring: Continuously monitor your event processing pipeline for latency, throughput, and error rates.
• Batching: When possible, process events in batches to improve efficiency and reduce overhead. The SDK often supports batched receives.

Next Steps

Explore advanced scenarios like dead-lettering, advanced filtering, and integration with other Azure services.