Azure Event Hubs is a log-based messaging system-as-a-service in Azure cloud. It's designed to be able to handle huge amount of data, and naturally supports multiple consumers.

Event Hubs and Service Bus

While Event Hubs are formally part of Azure Service Bus family of products, in fact its model is quite different.

"Traditional" Service Bus service is organized around queues (subscriptions are just queues with the topic being the source of messages). Each consumer can peek messages from the queue, do the required processing and then complete the message to remove it from the queue, or abort the processing. Abortion will leave the message at the queue, or will move it to the Dead Letter Queue. Completion/abortion are granular per message; and the status of each message is managed by the Service Bus broker.

Service Bus Processors

Event Hubs service is different. Each Hub represnts a log of messages. Event producer appends data to the end of the log, and consumers can read this log, but they can't remove or change the status of events there.

Each event has an offset associated with it. And the only operation that is supported for consumers is "give me some messages starting at the offset X".

Event Hub Processors

While this approach might seem simplistic, it actually makes consumers more powerful:

  • The messages do not disappear from the Hub after being processed for the first time. So, if needed, the consumer can go back and re-process older events again;

  • Multiple consumers are always supported, out of the box. They just read the same log, after all;

  • Each consumer can go at its own pace, drop and resume processing whenever needed, with no effect on other consumers.

There are some disadvantages too:

  • Consumers have to manage their own state of the processing progress, i.e. they have to save the offset of the last processed event;

  • There is no way to mark any specific event as failed to be able to reprocess it later. There's no notion of Dead Letter Queue either.

Event Processor Host

To overcome the first complication, Microsoft provides the consumer API called EventProcessorHost. This API has an implementation of consumers based on checkpointing. All you need to do is to provide a callback to process a batch of events, and then call CheckpointAsync method, which saves the current offset of the last message into Azure Blob Storage. If the consumer restarts at any point in time, it will read the last checkpoint to find the current offset, and will then continue processing from that point on.

It works great for some scenarios, but the event delivery/processing guarantees are relatively low in this case:

  • Any failures are ignored: there's no retry or Dead Letter Queue

  • There are no transactions between event hub checkpoints and the data sinks that the processor works with (i.e. data stores where processed messages end up at)

In this post I want to focus on a way to process events with higher consistency requirements, in particular:

  • Event Hub processor modifies data in a SQL Database, and such processing is transactional per batch of messages

  • Each event should be (successfully) processed exactly once

  • If event processing failed, it should be marked as failed and kept available to be reprocessed at later point in time

While end-to-end exactly-once processing would require changes of the producers too, we will only focus on consumer side in this post.

Transactional Checkpoints in SQL

If checkpoint information is stored in Azure Blobs, there is no obvious way to implement distributed transactions between SQL Database and Azure Storage.

However, we can override the default checkpointing mechanism and implement our own checkpoints based on a SQL table. This way each checkpoint update can become part of a SQL transaction and be committed or rolled back with normal guarantees provided by SQL Server.

Here is a table that I created to hold my checkpoints:

CREATE TABLE EventHubCheckpoint (
  Topic varchar(100) NOT NULL,
  PartitionID varchar(100) NOT NULL,
  SequenceNumber bigint NOT NULL,
  Offset varchar(20) NOT NULL,
  CONSTRAINT PK_EventHubCheckpoint PRIMARY KEY CLUSTERED (Topic, PartitionID)

For each topic and partition of Event Hubs, we store two values: sequence number and offset, which together uniquely identify the consumer position.

Conveniently, Event Host Processor provides an extensibility point to override the default checkpoint manager with a custom one. For that we need to implement ICheckpointManager interface to work with our SQL table.

The implementation mainly consists of 3 methods: CreateCheckpointIfNotExistsAsync, GetCheckpointAsync and UpdateCheckpointAsync. The names are pretty much self-explanatory, and my Dapper-based implementation is quite trivial. You can find the code here.

For now, I'm ignoring the related topic of lease management and corresponding interface ILeaseManager. It's quite a subject on its own; for the sake of simplicity I'll assume we have just one consumer process per partition, which makes proper lease manager redundand.

Dead Letter Queue

Now, we want to be able to mark some messages as failed and to re-process them later. To make Dead Letters transactional, we need another SQL table to hold the failed events:

CREATE TABLE EventHubDeadLetter (
  Topic varchar(100) NOT NULL,
  PartitionID varchar(100) NOT NULL,
  SequenceNumber bigint NOT NULL,
  Offset varchar(20) NOT NULL,
  FailedAt datetime NOT NULL,
  Error nvarchar(max) NOT NULL,

This table looks very similar to EventHubCheckpoint that I defined above. That is because they are effectively storing pointers to events in a hub. Dead Letters have two additional columns to store error timestamp and text.

There is no need to store the message content, because failed events still sit in the event hub anyway. You could still log it for diagnostics purpose - just make an extra varbinary column.

There's no notion of dead letters in Event Hubs SDK, so I defined my own interface IDeadLetterManager with a single AddFailedEvents method:

public interface IDeadLetterManager
    Task AddFailedEvents(IEnumerable<DeadLetter<EventData>> deadLetters);

public class DeadLetter<T>
    public T Data { get; set; }
    public DateTime FailureTime { get; set; }
    public Exception Exception { get; set; }

Dapper-based implementation is trivial again, you can find the code here.

Putting It Together: Event Host

My final solution is still using EventHostProcessor. I pass SQLCheckpointManager into its constructor, and then I implement IEventProcessor's ProcessEventsAsync method in the following way:

  1. Instantiate a list of items to store failed events
  2. Start a SQL transaction
  3. Loop through all the received events in the batch
  4. Process each item inside a try-catch block
  5. If exception happens, add the current event to the list of failed events
  6. After all items are processed, save failed events to Dead Letter table
  7. Update the checkpoint pointer
  8. Commit the transaction

The code block that illustrates this workflow:

public async Task ProcessEventsAsync(
    PartitionContext context, 
    IEnumerable<EventData> eventDatas)
    // 1. Instantiate a list of items to store failed events
    var failedItems = new List<DeadLetter<EventData>>();

    // 2. Start a SQL transaction
    using (var scope = new TransactionScope())
        // 3. Loop through all the received events in the batch
        foreach (var eventData in eventDatas)
                // 4. Process each item inside a try-catch block
                var item = this.Deserialize(eventData);
                await this.DoWork(item);
            catch (Exception ex)
                // 5. Add a failed event to the list
                failedItems.Add(new DeadLetter<EventData>(eventData, DateTime.UtcNow, ex));

        if (failedItems.Any())
            // 6. Save failed items to Dead Letter table
            await this.dlq.AddFailedEvents(failedItems);

        // 7. Update the checkpoint pointer
        await context.CheckpointAsync();

        // 8. Commit the transaction


My implementation of Event Hubs consumer consists of 3 parts: checkpoint manager that saves processing progress per partition into a SQL table; dead letter manager that persists information about processing errors; and an event host which uses both to provide transactional processing of events.

The transaction scope is limited to SQL Server databases, but it might be sufficient for many real world scenarios.