Azure Event Hubs Docs

Checkpointing in Azure Event Hubs

Checkpointing is a critical mechanism for reliable event processing in Azure Event Hubs. It allows your consumer applications to keep track of their progress when reading events from partitions. By persisting the offset and sequence number of the last processed event, your application can resume processing from the correct point after a restart or failure, ensuring that no events are lost and that processing is idempotent.

What is Checkpointing?

When a consumer application reads events from an Event Hub partition, it needs a way to remember where it left off. Checkpointing involves recording the progress of a consumer group for each partition it's reading. This recorded progress is typically stored in an external system, such as Azure Blob Storage, Azure Cosmos DB, or a dedicated checkpoint store provided by an Event Hubs SDK.

Key Concept: A checkpoint marks the furthest point (identified by offset and sequence number) a consumer group has successfully processed for a specific partition.

Why is Checkpointing Important?

How Checkpointing Works

The process generally involves the following steps:

  1. A consumer instance reads a batch of events from a partition.
  2. The application processes these events.
  3. Upon successful processing of the entire batch, the consumer application records the offset and sequence number of the last processed event in the batch as a checkpoint.
  4. This checkpoint information is persisted to a designated storage.
  5. When the consumer restarts, it retrieves the latest checkpoint for each partition it's responsible for and begins reading events from the offset specified in that checkpoint.
Best Practice: It's generally recommended to checkpoint after processing a batch of events successfully, rather than after each individual event. This optimizes performance by reducing the frequency of checkpoint writes.

Checkpointing with Azure SDKs

Most Azure Event Hubs SDKs provide built-in support for checkpointing. The specific implementation details vary slightly between languages and libraries, but the core concepts remain the same. Typically, you'll need to:

Example: Python SDK with Blob Storage Checkpoint Store

The Azure SDK for Python, using the azure-eventhub-checkpointstoreblob library, provides an example of how to configure checkpointing with Azure Blob Storage.

Installation

pip install azure-eventhub azure-eventhub-checkpointstoreblob

Code Snippet


import os
import asyncio
from azure.eventhub.aio import EventHubClient
from azure.eventhub.checkpointstoreblob.aio import BlobCheckpointStore

async def process_events(event_hub_client, consumer_group, blob_storage_conn_str, blob_container_name):
    checkpoint_store = BlobCheckpointStore(
        event_hub_client.fully_qualified_namespace,
        blob_storage_conn_str,
        blob_container_name
    )

    client = EventHubClient(
        event_hub_client.fully_qualified_namespace,
        consumer_group,
        event_hub_client.event_hub_name,
        checkpoint_store
    )

    async def on_event(event):
        print(f"Received event: {event.body_as_str()}")
        # Simulate processing the event
        await asyncio.sleep(0.1)
        # Checkpoint is automatically managed by the client upon successful batch processing

    async with client:
        # Use a specific partition if needed, or process all partitions
        # For demonstration, let's assume we want to process all partitions
        # In a real scenario, you might use partition_id or partition_ids
        # and manage the load balancing yourself or rely on the client's partitioning strategy.

        # A simple way to start receiving events from all partitions:
        partitions = await client.get_partition_ids()
        receivers = [client.create_receiver(partition_id=p, offset=event.OFFSET_FROM_START) for p in partitions]
        
        async def run_receiver(receiver):
            try:
                await receiver.receive(on_event)
            except Exception as e:
                print(f"Error in receiver for partition {receiver.partition_id}: {e}")

        await asyncio.gather(*(run_receiver(r) for r in receivers))


async def main():
    connection_string = os.environ.get("EVENTHUB_CONNECTION_STRING")
    consumer_group = "$Default" # Or your custom consumer group
    blob_conn_str = os.environ.get("BLOB_STORAGE_CONNECTION_STRING")
    blob_container = "eventhub-checkpoints" # Your blob container name

    if not connection_string or not blob_conn_str:
        print("Please set EVENTHUB_CONNECTION_STRING and BLOB_STORAGE_CONNECTION_STRING environment variables.")
        return

    event_hub_client = EventHubClient.from_connection_string(connection_string, event_hub_name="your_event_hub_name") # Replace with your Event Hub name

    await process_events(event_hub_client, consumer_group, blob_conn_str, blob_container)

if __name__ == "__main__":
    asyncio.run(main())
Configuration: Ensure your application has the necessary permissions to write to the configured Azure Blob Storage container. The `BlobCheckpointStore` will automatically create the container if it doesn't exist.

Checkpointing Strategies

While SDKs often handle much of the complexity, understanding different checkpointing strategies can be beneficial:

Considerations for Checkpointing

By implementing robust checkpointing, you can build highly reliable and resilient event-driven applications using Azure Event Hubs.