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https://issues.apache.org/jira/browse/KAFKA-12226?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Randall Hauch updated KAFKA-12226:
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Fix Version/s: 3.0.1
3.2.0
> High-throughput source tasks fail to commit offsets
> ---------------------------------------------------
>
> Key: KAFKA-12226
> URL: https://issues.apache.org/jira/browse/KAFKA-12226
> Project: Kafka
> Issue Type: Bug
> Components: KafkaConnect
> Reporter: Chris Egerton
> Assignee: Chris Egerton
> Priority: Major
> Fix For: 3.1.0, 3.0.1, 3.2.0
>
>
> The current source task thread has the following workflow:
> # Poll messages from the source task
> # Queue these messages to the producer and send them to Kafka asynchronously.
> # Add the message to outstandingMessages, or if a flush is currently active,
> outstandingMessagesBacklog
> # When the producer completes the send of a record, remove it from
> outstandingMessages
> The commit offsets thread has the following workflow:
> # Wait a flat timeout for outstandingMessages to flush completely
> # If this times out, add all of the outstandingMessagesBacklog to the
> outstandingMessages and reset
> # If it succeeds, commit the source task offsets to the backing store.
> # Retry the above on a fixed schedule
> If the source task is producing records quickly (faster than the producer can
> send), then the producer will throttle the task thread by blocking in its
> {{send}} method, waiting at most {{max.block.ms}} for space in the
> {{buffer.memory}} to be available. This means that the number of records in
> {{outstandingMessages}} + {{outstandingMessagesBacklog}} is proportional to
> the size of the producer memory buffer.
> This amount of data might take more than {{offset.flush.timeout.ms}} to
> flush, and thus the flush will never succeed while the source task is
> rate-limited by the producer memory. This means that we may write multiple
> hours of data to Kafka and not ever commit source offsets for the connector.
> When the task is lost due to a worker failure, hours of data will be
> re-processed that otherwise were successfully written to Kafka.
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