EventHub Consumer Callback Not Triggered for Existing Messages Without New Arrivals

Hello! Amine HAJ KHLIFA

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This scenario, where a consumer's on_event callback is only triggered for new messages and not for existing, unprocessed messages until new messages arrive, is a common issue with certain message queue or event hub clients, particularly when dealing with checkpointing or offset management. This behaviour often suggests a configuration or implementation detail related to how the consumer fetches and processes messages.

Here's why this might be happening and potential solutions, focusing on general principles applicable to various Python consumer libraries (e.g., azure-eventhub, confluent-kafka-python):

Possible Causes:

• Checkpointing/Offset Management: The consumer might be correctly recording its last processed offset but only fetching messages after that offset. If no new messages arrive, there's nothing "new" to fetch beyond the last checkpoint.
• Fetch/Poll Timeout: The consumer's poll or fetch operation might have a long timeout or be configured to wait indefinitely for new messages, rather than immediately processing any available existing messages.
• Client Library Behaviour: Some client libraries might prioritize or optimize for real-time stream processing, leading to this behaviour when no new data is actively being produced.
• Rebalancing Issues: In distributed consumer setups (like Kafka consumer groups), rebalances can sometimes lead to partitions not being properly assigned or fetched from immediately after a rebalance, requiring new messages to "kickstart" the process.

 Is this the expected behaviour?

Yes, the behaviour you're observing in Scenario B is generally the expected behaviour for the on_event callback in the azure-eventhub Python SDK when using EventHubConsumerClient.receive().

Here's why:

• Stream-Oriented Processing: Event Hubs is fundamentally a streaming platform. The receive() method, particularly when operating continuously, is designed to react to new events as they arrive. It operates on the principle of "push" or event-driven processing.
• Idle Timeout/Polling: While the client does maintain a connection and an offset, it's not constantly polling for old events if no new events are being pushed to the partition. The on_event callback is primarily triggered when the client receives a batch of events from the service. If there are no new events to form a batch (or within a configured max_wait_time), the callback won't fire, even if there are unread messages from a previous session.
• Checkpoint Store's Role: The checkpoint store correctly records your last processed offset. When the consumer starts, it will indeed begin reading from the last checkpointed offset. However, if it reaches the "end" of the stream (i.e., the current high-water mark of the partition) and no new events are published, the on_event callback won't be invoked until events arrive. It doesn't continuously re-process static historical data unless prompted by new data pushing the offset forward.

Configuration Issue?

You're not missing a configuration parameter to force the on_event callback for existing messages without new arrivals in the receive() loop. The parameters like max_batch_size and max_wait_time affect how many messages are grouped or how long to wait for a batch, but they don't force the callback to trigger if no new data is flowing into the partition beyond the current read point.

Alternative Approaches for Processing a "Snapshot" of Existing Messages

Your goal of processing a "snapshot" of existing messages without requiring new arrivals is common, especially for initial loads, re-processing historical data, or backfilling scenarios. The EventHubConsumerClient's receive() method, as you've observed, isn't optimized for this "batch-like" or "finite-read" pattern directly.

Here are the recommended ways to achieve this without your "ping" workaround:

1. Read from a Specific Offset/Time (for finite reads): If you want to process all messages up to a certain point (e.g., all messages between the last checkpoint and the current end of the stream at startup), you need to explicitly define the stop condition.

• Manual Iteration with receive(): You can use receive() with a very small max_wait_time (e.g., 1 second) and break out of the loop when you consistently get empty batches, indicating you've caught up. This is a "pull" model you implement yourself.
• Get current "end of stream" offset: You can get the current "end of stream" (latest sequence number or enqueue time) for a partition using EventHubConsumerClient.get_partition_properties(). This gives you a target offset to read up to.

Solutions and Workarounds:

• Explicit Polling/Fetching: Ensure your consumer loop explicitly calls poll() or fetch() with an appropriate timeout to regularly check for messages, even if no new messages are expected. A small timeout (e.g., poll(timeout_ms=1000)) will allow the consumer to process existing messages if available.
• Offset Reset/Configuration: o    If you want to reprocess existing messages, ensure your auto.offset.reset configuration is set appropriately (e.g., earliest in Kafka) when the consumer starts. This dictates where the consumer begins reading if no prior offset is found for its consumer group. o    Be mindful of how your checkpointing or offset storage mechanism works. If it's persistent, the consumer will resume from the last committed offset, regardless of whether new messages have arrived.

 Also, could you please clarify "Scenario B (Doesn't Work)" further?

• When you start the consumer in Scenario B, and there are unprocessed messages between the last checkpoint and the current offset, does on_event callback:
  • Never trigger at all, even for this initial backlog of existing messages?
  • Trigger for the initial backlog of existing messages (from checkpoint to current end of stream), but then stop triggering and not process anything further until new messages arrive?

If the answer is (1), then there might be a problem with your setup or how receive() is handling the initial read. If the answer is (2), then the behaviour is expected for a continuous streaming client that processes up to the "live" end of the stream and then waits.

Addressing "Snapshot" Processing (Assuming Case 2 above is true, i.e., it processes backlog then waits):

If you truly want a "snapshot" like behaviour (process X number of existing messages, then stop), the receive() method isn't the most direct way to stop after a snapshot. You'd typically:

1. Get the last_enqueued_sequence_number (or last_enqueued_offset) for each partition using client.get_partition_properties(). This gives you the "end" of your snapshot.
2. Start client.receive() in a loop (potentially in separate threads for each partition or using asyncio).
3. Inside your on_event callback, track the sequence number (or offset) of the last processed event for each partition.
4. Implement logic to stop receiving for a partition once its event.sequence_number meets or exceeds its last_enqueued_sequence_number from step 1.

This would involve more manual state management and potentially stopping the client or specific partition receivers once the snapshot is complete across all partitions.

Alternatively, for a truly one-off "snapshot" or "backfill" without ongoing streaming, sometimes people use other tools (like Spark/Databricks, or custom scripts using lower-level Event Hubs APIs) that are designed for batch reads over a specific offset range.

Let's start try with these first then we can also explore more on clarifying Scenario B.

Please "Accept as Answer" and Upvote if the answer provided is useful, so that you can help others in the community looking for remediation for similar issues.

Thanks

Pratyush