[jira] [Commented] (BEAM-4803) Beam spark runner not working properly with kafka

["Alexey Romanenko (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/BEAM-4803?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16573139#comment-16573139 ] 

Alexey Romanenko commented on BEAM-4803:
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[~vivek_17] Yes, as you properly noticed, runner should cache readers and it uses {{com.google.common.cache.Cache}} internally for that purpose and this cache is created with {{expireAfterAccess(long duration, TimeUnit unit)}}, where {{duration = readerCacheInterval}}. 
Seems that it's not enough for microbatches and cache entry expires very quickly. Perhaps, we need to rethink this cache policy for streaming mode. 

> Beam spark runner not working properly with kafka
> -------------------------------------------------
>
>                 Key: BEAM-4803
>                 URL: https://issues.apache.org/jira/browse/BEAM-4803
>             Project: Beam
>          Issue Type: Bug
>          Components: io-java-kafka, runner-spark
>    Affects Versions: 2.4.0, 2.5.0
>            Reporter: Vivek Agarwal
>            Assignee: Jean-Baptiste Onofré
>            Priority: Major
>
> 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: DStream@SparkUnboumdedSource.java: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? 



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