Our Flink streaming workflow publishes messages to Kafka. KafkaProducer's
'retry' mechanism doesn't kick in until a message is added to it's internal
buffer.
If there's an exception before that, KafkaProducer will throw that
exception, and seems like Flink isn't handling that. In this case there will
be a data loss.
Related Flink code (FlinkKafkaProducerBase):
if (logFailuresOnly) {
callback = new Callback() {
@Override
public void onCompletion(RecordMetadata metadata, Exception
e) {
if (e != null) {
LOG.error("Error while sending record to Kafka: " +
e.getMessage(), e);
}
acknowledgeMessage();
}
};
}
else {
callback = new Callback() {
@Override
public void onCompletion(RecordMetadata metadata, Exception
exception) {
if (exception != null && asyncException == null) {
asyncException = exception;
}
acknowledgeMessage();
}
};
}
Here are the scenario's we've identified that will cause data loss:
All kafka brokers are down.
In this case, before appending a message to it's buffer, KafkaProducer tries
to fetch metadata. If the KafkaProducer isn't able to fetch the metadata in
configured timeout, it throws an exception.
-Memory records not writable (Existing bug in kafka 0.9.0.1 library)
https://issues.apache.org/jira/browse/KAFKA-3594
In both the above cases, KafkaProducer won't retry, and Flink will ignore
the messages. the messages aren't even logged. The exception is, but not the
messages which failed.
Possible workarounds (Kafka settings):
A very high value for metadata timeout (metadata.fetch.timeout.ms)
A very high value for buffer expiry (request.timeout.ms)
We're still investigating the possible side effects of changing the above
kafka settings.
So, is our understanding correct? Or is there a way we can avoid this data
loss by modifying some Flink settings?
Thanks.
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