I'm working through a production-level High Level Consumer app and have a couple of error/shutdown questions to understand how the offset storage is handled.
Test case - simulate an error writing to destination application, for example a database, offset is 'lost'
Scenario - write 500 messages for each topic/partition - use the example High Level Consumer code I wrote for the Wiki - Change the code so that every 10th read from the 'hasNext()' ConsumerIterator breaks out of the loop and returns from the thread, simulating a hard error. I write the offset to System.out to see what was provided - startup again and look to see what offset was first emitted for a partition
Issue: Kafka treats the offset for the message read that caused me to break out of the loop as processed (as expected), but I really failed. How do I tell Kafka that I didn't really consume that offset?
System.out.println("Shutting down Thread: " + m_threadNumber); }
I understand that handling 'hard' errors like JVM crashes, kill -9 etc. may leave the offsets in ZooKeeper incorrect, but I'm trying to understand what happens in a clean shutdown where Kafka and the Consumer are behaving correctly but I can't process what I read.
This also feels like I'm blurring SimpleConsumer theory into this, but except for the exception/shutdown case High Level Consumer does everything I want. Thanks,
It seems like you're not explicitly controlling the offsets. Is that correct?
If so, the moment you pull a message from the stream, the client framework considers it processed. So if your app subsequently crashes before the message is fully processed, and "auto-commit" updates the offsets in Zookeeper, you will drop that message.
The solution to this to call commitOffsets() explicitly.
On Tue, Jul 9, 2013 at 11:16 AM, Chris Curtin <[EMAIL PROTECTED]>wrote:
Correct, I don't want to explicitly control the offset committing. The ConsumerConnector handles that well enough except for when I want to shutdown and NOT have Kafka think I consumed that last message for a stream. This isn't the crash case, it is a case where the logic consuming the message detects and error and wants to cleanly exit until that issue can be resolved, but not lose the message it was trying to process when the problem is resolved.
My understanding is that the commitOffsets() call is across all threads, not just for the stream my thread is reading from. So knowing it is okay to call this requires coordination across all my threads, which makes a High Level Consumer a lot harder to write correctly.
Thinking about what I'd like to happen is: my code hands the message back to the KafkaStream (or whatever level knows about the consumed offsets) and says - set the next start offset for this topic/partition to this message in ZooKeeper - cleanly shutdown the stream from the broker(s) - don't force a rebalance on the consumer since something is wrong with processing of the data in the message, not the message. - If I try to use the stream again I should get an exception - I don't think I would want this to cause a complete shutdown of the ConsumerConnector, in case other threads are still processing. If all threads have the same issue they will all fail soon enough and do the same logic. But if only one thread fails, our Operations teams will need to resolve the issue then do a clean restart to recover.
I think this logic would only happen when the down stream system was having issues since the iterator would be drained correctly when the 'shutdown' call to ConsumerConnector is made.
On Tue, Jul 9, 2013 at 11:21 AM, Philip O'Toole <[EMAIL PROTECTED]> wrote:
It sounds like you're requesting functionality that the high-level consumer simply doesn't have. As I am sure you know, there is no API call that supports "handing back a message".
I might be missing something, but if you need this kind of control, I think you need to code your application differently. You could try creating a ConsumerConnection per partition (your clients will then need to know the number of partitions out there). That way commitOffsets() will actually only apply to that partition. Auto-commit the same way. It might give you the level of control you need.
On Tue, Jul 9, 2013 at 2:22 PM, Chris Curtin <[EMAIL PROTECTED]> wrote:
Thanks. I know I can write a SimpleConsumer to do this, but it feels like the High Level consumer is _so_ close to being robust enough to handle what I'd think people want to do in most applications. I'm going to submit an enhancement request.
I'm trying to understand the level of data loss in this situation, so I looked deeper into the KafkaStream logic: it looks like a KafkaStream includes a BlockingQueue for transferring the messages to my code from Kafka. If I call shutdown() when I detect the problem, are the messages already in the BlockingQueue considered 'read' by Kafka, or does the shutdown peek into the Queue to see what is still there before updating ZooKeeper?
My concern is if that queue is not empty I'll be losing more than the one message that led to the failure.
I'm also curious how others are handling this situation. Do you assume the message that is causing problems is lost or somehow know to go get it later? I'd think others would have this problem too.
On Tue, Jul 9, 2013 at 3:23 PM, Philip O'Toole <[EMAIL PROTECTED]> wrote:
The way I handled this in my application using the High Level Consumer was to turn off auto-commit and commit manually after finishing a batch of messages (obviously you could do it after every message, but for my purposes it was better to have batches)
Ian Friedman On Tuesday, July 9, 2013 at 4:09 PM, Chris Curtin wrote:
Is your consumer multi-threaded? If so can you share how you coordinated each of the threads so you knew it was 'okay' to commit across all the threads? I'm stuck on how to do this without really complicating the consumer.
Chris On Tue, Jul 9, 2013 at 5:51 PM, Ian Friedman <[EMAIL PROTECTED]> wrote:
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