I am using KafkaMessageListenerContainer with (KafkaAdapter).
How can I "nack" offsets in case of specific error, so the next poll() will take them again?
properties.setAckMode(ContainerProperties.AckMode.BATCH);
final KafkaMessageListenerContainer<String, String> kafkaContainer = new KafkaMessageListenerContainer<>(consumerFactory , properties);
kafkaContainer.setCommonErrorHandler(new CommonErrorHandler() {
#Override
public void handleBatch(Exception thrownException, ConsumerRecords<?, ?> data, Consumer<?, ?> consumer, MessageListenerContainer container, Runnable invokeListener) {
CommonErrorHandler.super.handleBatch(thrownException, data, consumer, container, invokeListener);
}
});
Inside handleBatch I am detecting the exception, for that specific exception I would like to do nack.
Tried to throw from there RuntimeException.
using springboot 2.7
Use the DefaultErrorHandler - it does exactly that (the whole batch is retried according to the back off). You can classify which exceptions are retryable or not.
If you throw a BatchListenerFailedException you can specify exactly which record in the batch had the failure and only retry it (and the following records).
EDIT
If any other type of exception is thrown, the DefaultErrorHandler falls back to using a FallbackBatchErrorHandler which calls ErrorHandlingUtils.retryBatch() which, pauses the consumer and redelivers the whole batch without seeking and re-polling (the polls within the loop return no records because the consumer is paused).
See the documentation. https://docs.spring.io/spring-kafka/docs/current/reference/html/#retrying-batch-eh
This is required, because there is no guarantee that the batch will be fetched in the same order after a seek.
This is because we need to know the state of the batch (how many times we have retried). We can't do that if the batch keeps changing; hence the algorithm I described above.
To retry indefinitely you can, for example, use a FixedBackOff with Long.MAX_VALUE in the maxAttempts property. Or use an ExponentialBackOff with no termination.
Just be sure that the largest back off (and time to process a batch) is significantly less than max.poll.interval.ms to avoid a rebalance.
My team is writing a service that leverages the retryable topics mechanism offered by Spring Kafka (version 2.8.2). Here is a subset of the configuration:
#Bean
public ConsumerFactory<String, UploadMessage> consumerFactory() {
return new DefaultKafkaConsumerFactory<>(
this.springProperties.buildConsumerProperties(),
new StringDeserializer(),
new ErrorHandlingDeserializer<>(new KafkaMessageDeserializer()));
}
#Bean
public RetryTopicConfiguration retryTopicConfiguration(KafkaTemplate<String, Object> kafkaTemplate) {
final var retry = this.applicationProperties.retry();
return RetryTopicConfigurationBuilder.newInstance()
.doNotAutoCreateRetryTopics()
.suffixTopicsWithIndexValues()
.maxAttempts(retry.attempts())
.exponentialBackoff(retry.initialDelay(), retry.multiplier(), retry.maxDelay())
.dltHandlerMethod(DeadLetterTopicProcessor.ENDPOINT_HANDLER_METHOD)
.create(kafkaTemplate);
}
KafkaMessageDeserializer is a custom deserialiser that decodes protobuf-encoded messages and may throw a SerializationException in case of a failure. This exception is correctly captured and transformed into a DeserializationException by Spring Kafka. What I find a bit confusing is that the intercepted poison pill message then hits all of the retry topics before eventually reaching the dead letter one. Obviously it fails with exactly the same error at every step.
I know that RetryTopicConfigurationBuilder::notRetryOn may be used to skip the retry attempts for particular exception types, but what if I want to use exactly the same list of exceptions as in ExceptionClassifier::configureDefaultClassifier? Is there a way to programmatically access this information without basically duplicating the code?
That is a good suggestion; it probably should be the default behavior (or at least optionally).
Please open a feature request on GitHub.
There is a, somewhat, related discussion here: https://github.com/spring-projects/spring-kafka/discussions/2101
I am using spring-kafka 2.2.8 and setting concurrency to 2 as shown below and trying to understand how do i pause an consumer thread/instance when particular condition is met.
#KafkaListener(id = "myConsumerId", topics = "myTopic", concurrency=2)
public void listen(String in) {
System.out.println(in);
}
Now, I've two questions.
Would my consumer span two different poll threads to poll the records?
If i'm setting an id to the consumer as shown above. How can i pause a specific consumer thread (with concurrency set to more than 1).
Please suggest.
Use the KafkaListenerEndpointRegistry.getListenerContainer(id) method to get a reference to the container.
Cast it to a ConcurrentMessageListenerContainer and call getContainers() to get a list of the child KafkaMessageListenerContainers; you can then pause/resume them individually.
You can determine which topics/partitions each one has using getAssignedPartitions().
I’m writing functionality for receiving messages from Azure Service Bus Topic and delete the specified message from Topic. Before deleting that message, I need to send that message to other Topic.
static async Task ProcessMessagesAsync(Message message, CancellationToken token)
{
// Process the message.
Console.WriteLine($"Received message: WorkOrderNumber:{message.MessageId} SequenceNumber:{message.SystemProperties.SequenceNumber} Body:{Encoding.UTF8.GetString(message.Body)}");
Console.WriteLine("Enter the WorkOrder Number you want to delete:");
string WorkOrderNubmer = Console.ReadLine();
if (message.MessageId == WorkOrderNubmer)
{
//TODO:Post message into other topic(Priority) then delete from this current topic.
var status=await SendMessageToBus(message);
if (status == true)
{
await normalSubscriptionClient.CompleteAsync(message.SystemProperties.LockToken);
Console.WriteLine($"Successfully deleted your message from Topic:{NormalTopicName}-WorkOrderNumber:" + message.MessageId);
}
else
{
Console.WriteLine($"Failed to send message to PriorityTopic:{PriorityTopicName}-WorkOrderNumber:" + message.MessageId);
}
}
else
{
Console.WriteLine($"Failed to delete your message from Topic:{NormalTopicName}-WorkOrderNumber:" + WorkOrderNubmer);
// Complete the message so that it is not received again.
// This can be done only if the subscriptionClient is created in ReceiveMode.PeekLock mode (which is the default).
await normalSubscriptionClient.CompleteAsync(message.SystemProperties.LockToken);
// Note: Use the cancellationToken passed as necessary to determine if the subscriptionClient has already been closed.
// If subscriptionClient has already been closed, you can choose to not call CompleteAsync() or AbandonAsync() etc.
// to avoid unnecessary exceptions.
}
}
My issue with this approach is:
It’s not scalable; what if the message is the 50th in the collection? We’d have to iterate through 49 times and mark i.e deleted.
It’s a long-running process.
To avoid these problems, I want to get the specified message from the queue based on Index or sequence number then I can delete that from the topic.
So, can anyone suggest me how to resolve this problem?
So if I understand your questions and comments correctly you are trying to do something like this:
Incoming messages come into either a standard topic or priority
topic.
Some process checks messages in the standard topic and
"moves" them to the priority topic based on some criteria by
deleting them from the standard topic and adding them to the
priority topic.
Messages are processed as normal.
As Sean noted, step 2 simply won't work. Service Bus is a first=in-first-out-ish system where a consumer simply picks up the next available message. You can sort through a queue by pulling out all the messages and abandoning/completing them based on specific criteria, but scaling is a problem. In addition, you can think of each topic subscription as its own separate queue- removing a message form one subscription does not remove it from any of the other subscriptions.
What I would suggest instead of trying to pull out everything from the topics and then putting back the ones you want to keep, add a sorting queue in front of the two topics. If you don't need to sort the high priority messages you could put this sorting process in front of the standard priority topic only.
This is how the process would work:
Incoming messages are added to a sorting queue Note that this is a single queue, not a topic. At this point in the process we want to ensure there is only one copy of each message.
A sorting process moves messages from the sorting queue into either the standard or priority queue as is appropriate. Using something like Azure Functions you can scale this process fairly easily.
Messages are processed from the topics as normal.
I read spring-kafka/kafka documentation back and forth, and still cannot find a way, how to do proper transactional behavior with error recovering. I believe this is not trivial question, so please read until end. I believe whole this question revolves around finding way how to reposition over failing record or how to ack in error handler. But mabye there are better ways, I don't know.
So records are flowing in, and some of them are invalid. What I would like to have as a minimal solution is(in which I will then fix sevaral problems you probably see as well):
1) we cannot afford the luxury of stopping the production in case of some trivial mishap, like one or few invalid records. Thus if there is invalid record in kafka topic, I would like to log it, or resend it to different queue, but then proceed with processing following records.
2) there are permanent and temporary failures. Permanent failure is record unable to deserialize, record failing data validation. In this case, I'd like to skip the invalid record, as discussed in 1). Temporary failure might be some specific exception or state, like for example database connection errors, network issues etc. In this case, we do not want to skip failing record, we want to retry, after some delay.
Subject of this question is ONLY implementing skip/don't skip behavior.
Lets say, that this is our starting point:
private Map<String, Object> createKafkaConsumerFactoryProperties(String bootstrapServers, String groupId, Class<?> valueDeserializerClass) {
Map<String, Object> props = new HashMap<>();
props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, valueDeserializerClass);
props.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
return props;
}
#Bean(name="SomeFactory")
public ConcurrentKafkaListenerContainerFactory<String, String> kafkaListenerContainerFactory(
#Value("${…}") String bootstrapServers,
#Value("${…}") String groupId) {
ConcurrentKafkaListenerContainerFactory<String, String> factory =
new ConcurrentKafkaListenerContainerFactory<>();
ConsumerFactory<String, String> consumerFactory = new DefaultKafkaConsumerFactory<>(
createKafkaConsumerFactoryProperties(bootstrapServers, groupId, AvroDeserializer.class),
new StringDeserializer(),
new AvroDeserializer(SomeClass.class));
factory.setConsumerFactory(consumerFactory);
// factory.setConcurrency(2);
// factory.setBatchListener(true);
return factory;
}
and we have listener like:
#KafkaListener(topics = "${…}", containerFactory = "SomeFactory")
public void receive(#Valid List<SomeClass> messageList) {/*logic*/}
Now how this behave if I understand correctly:
when listener gets message, ~when we reach inside of receive method, the kafka message will be already acked, and if receive method throw an exception, the next poll will return following record. Because ack happened, and we do not have error handler defined, thus logging error handler will kick in. This is not necessarily what we want. We can use SeekToCurrentErrorHandler to reprocess the message. Or one can specify TransactionManager, and if exception 'leaks' from listener, repositioning will also happen. If someone know performance comparison of these two approaches, please tell me.
when message cannot be deserialized, deserializer will fail, message will not be acked and same record will be polled again. This is some sort of "poison packet" since kafka will spin on this message indefinitelly. We do have retry.backoff.ms to at least slow it down, but I can't see any max number retries or something. So the best thing we can do is to stop/pause container in this situation. Which is way to harsh. Btw. I'm new to kafka/spring-kafka, I did not see anywhere mention, how to manually reposition offset from outside of an application, meaning OK, listener is down, but now what? Another solution would be not to fail deserializer, and return something. But what?? KafkaNull, great, but then our listener will fail because SomeClass ClassCastException. We can send some artificial value of SomeClass, which is again horrible, because this is not a data what we actually get. Also this is architectonically incorrect.
or we can use repositioning error handler, which would be great, well if we know how to do that. I need to seek to next record. But while documentation says, that ErrorHandler should communicate which record caused the failure, it seems that it fails to do so. So even in non-batch listener I have list of records(1 failed + bunch of unprocessed), and have no idea where set offset to.
So what is the solution to this madness?
Well the best I can come up with right now is pretty ugly: do not fail in deserializer (bad), do not accept specific type in listener (bad), filter out KafkaNulls manually (bad) and finally trigger bean validation manually (bad). Is there a better way? Thanks for examplantion, I'd be grateful for every hint or direction given how to achieve this.
See the documentation for the upcoming 2.2 release (due tomorrow).
The DefaultAfterRollbackProcessor (when using transactions) and SeekToCurrentErrorHandler (when not using transactions) can now recover (skip) records that keep failing, and will do so after 10 failures, by default. They can be configured to publish failed records to a dead-letter topic.
Also see the Error Handling Deserializer which catches deserialization problems and passes them to the container so they can be sent to the error handler.