kennknowles opened a new issue, #19017: URL: https://github.com/apache/beam/issues/19017
We are running a beam stream processing job on a spark runner, which reads from a kafka topic using kerberos authentication. We are using java-io-kafka v2.4.0 to read from kafka topic in the pipeline. The issue is that the kafkaIO client is continuously creating a new kafka consumer with specified config, doing kerberos login every time. Also, there are spark streaming jobs which get spawned for the unbounded source, every second or so even when there is no data in the kafka topic. Log has these jobs- INFO SparkContext: Starting job: [email protected]:172 We can see in the logs INFO MicrobatchSource: No cached reader found for split: [org.apache.beam.sdk.io.kafka.KafkaUnboundedSource@2919a728]. Creating new reader at checkpoint mark... And then it creates new consumer doing fresh kerberos login, which is creating issues. We are unsure of what should be correct behavior here and why so many spark streaming jobs are getting created. We tried the beam code with flink runner and did not find this issue there. Can someone point to the correct settings for using unbounded kafka source with spark runner using beam? Imported from Jira [BEAM-4803](https://issues.apache.org/jira/browse/BEAM-4803). Original Jira may contain additional context. Reported by: vivek_17. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
