This could be a duplicate of Error in Kafka Streams using kafka-node - negative timestamp, but certainly not. My Kafka Streams app does some transformation logic on each message and forwards it to a new topic. There is no time-based aggregation/processing in the app, so there is no need of using any custom timestamp extractor. This app was running fine for several days, but all of sudden the app thrown a negative timestamp exception.
Exception in thread "StreamThread-4" org.apache.kafka.streams.errors.StreamsException: Extracted timestamp value is negative, which is not allowed.
After throwing this exception from all StreamThreads (10 in total), the app was kind of frozen as there was no further progress on the stream for several hours. There was no exception thrown after that. When I restarted the app, it started to process only the newly coming messages.
Now the question is, what happened to those messages that came in between (after throwing the exception and before restarting the app). In case, those missing messages had no embedded timestamp (Highly impossible as no changes happened in the broker and producer), isn't that the app should have thrown an exception for each such message? Or is't like the app stop the stream progress when it detects the negative timestamp in the message at first time? Is there a way to handle this situation so that the app can progress the stream, even after detecting any negative timestamp?My app uses Kafka Streams library version 0.10.0.1-cp1.
Note: I can easily put up a custom timestamp extractor which can check the negative timestamp in each message, but that is a lot of unnecessary overhead for my app. All I want to understand is why was the stream not progressed after detecting a message with negative timestamp.
Even if you do not have any time based operator, a Kafka Streams application checks if timestamps returned from timestamp extractor are valid, because timestamps are used to determine processing order of records from different partitions, to ensure records are processes in-order and all partitions are consumed in an time-based aligned manner.
If a negative timestamp is detected, the application (or actually the corresponding thread) dies. Unfortunately, it is currently not possible to recover from such an exception and you would need to restart your application. See also Confluent FAQs: http://docs.confluent.io/3.1.1/streams/faq.html#invalid-timestamp-exception
If your application dies and you restart it, it will resume processing where it left off. Unfortunately, in Kafka
0.10.0.1
there is a bug (fixed in upcoming release0.10.2
) and in case of failure an incorrect offset can get committed and the application "steps over" some records. I assume this happened in your case, and if you have only some records with an invalid timestamp, those record might have been skipped allowing your application to resume after restart. This behavior is actually a bug -- without the bug, Kafka Stream would try to process those records with invalid timestamp again and again and fail every time until you provide a custom timestamp extractor that fixes the problem by returning a valid timestamp.How to fix it:
The correct fix would be to provide a custom timestamp extractor that does never return an invalid (ie, negative) timestamp.
I have no explanation why you got invalid timestamps though... This is quite strange and you might want to investigate your producer setup and try to figure out if there is the possibility that your producer puts and invalid timestamp (even if this is unlikely -- I have no other idea what the root cause of the problem could be).
Further remarks:
In the next release (
0.10.2
), handling invalid timestamps gets simplified and Kafka Streams provides more built-in timestamp extractors that handle records with invalid timestamps differently. For example, this allows you to auto-skip records with invalid timestamps instead of raising an error (current behavior). For more details see KIP-93: https://cwiki.apache.org/confluence/display/KAFKA/KIP-93%3A+Improve+invalid+timestamp+handling+in+Kafka+Streams