Azure Event Hubs Guides

Receiving Events from Azure Event Hubs

This guide covers the essential concepts and methods for receiving events from Azure Event Hubs. Event Hubs is a highly scalable data streaming platform and event ingestion service that can receive and process millions of events per second.

Understanding Consumer Groups

Consumer groups allow multiple applications or services to read from the same Event Hub independently without interfering with each other. Each consumer group has its own state and offset within the event stream.

• Default Consumer Group ($Default): Every Event Hub has a built-in consumer group named '$Default'.
• Custom Consumer Groups: You can create your own consumer groups to isolate your processing logic or to support different applications reading the same data.

Receiving Events with SDKs

Azure provides SDKs for various languages to easily consume events. Here are examples for common languages:

Python Example

from azure.eventhub import EventHubConsumerClient

consumer_group = "$Default"
event_hub_name = "your_event_hub_name"
namespace_connection_str = "your_namespace_connection_string"

def process_event(event):
    print(f"Received event: {event.body_as_json()}")

def main():
    client = EventHubConsumerClient.from_connection_string(
        namespace_connection_str,
        consumer_group,
        event_hub_name=event_hub_name
    )

    with client:
        client.subscribe(process_event)
        # The client will run indefinitely, processing events as they arrive.
        # You might want to add logic to stop this gracefully in a real application.
        print("Starting to receive events. Press Ctrl+C to stop.")
        try:
            # Keep the main thread alive to allow event processing
            while True:
                import time
                time.sleep(1)
        except KeyboardInterrupt:
            print("Stopping event receiver.")

if __name__ == "__main__":
    main()
                

C# Example

using Azure.Messaging.EventHubs;
using Azure.Messaging.EventHubs.Consumer;
using System;
using System.Text;
using System.Threading.Tasks;

string fullyQualifiedNamespace = "your_namespace.servicebus.windows.net";
string eventHubName = "your_event_hub_name";
string consumerGroup = "$Default";
string connectionString = "Endpoint=sb://your_namespace.servicebus.windows.net/;SharedAccessKeyName=RootManageSharedAccessKey;SharedAccessKey=YOUR_KEY";

EventProcessorClient processorClient = new EventProcessorClient(
    new EventHubConsumerClient(consumerGroup).Identifier,
    fullyQualifiedNamespace,
    eventHubName,
    connectionString);

processorClient.ProcessEventAsync += (ProcessEventArgs args) =>
{
    string eventBody = Encoding.UTF8.GetString(args.Data.EventBody.ToArray());
    Console.WriteLine($"Received event: {eventBody}");

    // Acknowledge the event after processing
    return args.CheckpointAsync();
};

processorClient.ProcessErrorAsync += (ProcessErrorEventArgs args) =>
{
    Console.WriteLine($"Error processing event: {args.Exception.Message}");
    return Task.CompletedTask;
};

Console.WriteLine("Starting event processor. Press Ctrl+C to stop.");
await processorClient.StartProcessingAsync();

// Keep the application running until interrupted
await Task.Delay(Timeout.Infinite);

Console.WriteLine("Stopping event processor.");
await processorClient.StopProcessingAsync();
                

Checkpointing

Checkpointing is crucial for reliable event processing. It allows a consumer to record its progress in reading events. If a consumer restarts, it can resume from the last checkpoint instead of reprocessing all events from the beginning. This is typically managed by the consumer library itself but is a concept you should be aware of.

• Partition Ownership: Consumer groups coordinate to assign ownership of partitions to individual consumers.
• Offset Management: When a consumer processes an event, it updates its offset for the partition it's reading from. This offset is stored in a checkpoint store (e.g., Azure Blob Storage).

Handling Large Volumes of Data

For very high throughput scenarios, consider:

• Scaling Consumers: Run multiple instances of your consumer application, each in the same consumer group, to distribute the load across partitions.
• Efficient Deserialization: Optimize the deserialization of event data to reduce processing overhead.
• Asynchronous Processing: Use asynchronous patterns within your event handler to avoid blocking the main processing loop.