We did the same and started using maxReadTime and put the application to run on a recurring schedule of 5 minutes. It works fine end to end without any error.
But the problem is that it always starts reading from the beginning of the Kinesis stream when it stop-starts. When I did some investigation on that, I found that when you set maxReadTime, it will run using BoundedReadFromUnboundedSource mode. That essentially converts source in to a bounded one. This means checkpointing or watermark no longer supported. Reader just reads for x number of time and exists. Is there anyway recommended way to resume reading from the position it finished? Either using maxReadTime or in unboundedSource mode? Could some point me to a sample pipeline code that uses Kinesis as source? Regards, Mani From: Lars Almgren Schwartz <lars.almg...@tink.com> Sent: Thursday, June 25, 2020 7:53 AM To: user@beam.apache.org Subject: Re: KinesisIO checkpointing CAUTION: This email originated from outside of D&B. Please do not click links or open attachments unless you recognize the sender and know the content is safe. We had the exact same problem, but have not spent any time trying to solve it, we just skipped checkpointing for now. When we saw this problem we ran Spark 2.4.5 (local mode) and Kinesis 2.18 and 2.19. On Wed, Jun 24, 2020 at 6:22 PM Sunny, Mani Kolbe <sun...@dnb.com<mailto:sun...@dnb.com>> wrote: We are on spark 2.4 and Beam 2.22.0 From: Alexey Romanenko <aromanenko....@gmail.com<mailto:aromanenko....@gmail.com>> Sent: Wednesday, June 24, 2020 5:15 PM To: user@beam.apache.org<mailto:user@beam.apache.org> Subject: Re: KinesisIO checkpointing CAUTION: This email originated from outside of D&B. Please do not click links or open attachments unless you recognize the sender and know the content is safe. Yes, KinesisIO supports restart from checkpoints and it’s based on runner checkpoints support [1]. Could you specify which version of Spark and Beam you use? [1] https://stackoverflow.com/questions/62259364/how-apache-beam-manage-kinesis-checkpointing/62349838#62349838<https://nam03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F62259364%2Fhow-apache-beam-manage-kinesis-checkpointing%2F62349838%2362349838&data=02%7C01%7CSunnyM%40dnb.com%7Cfc04d9e9af094878c42208d818d47035%7C19e2b708bf12437597198dec42771b3e%7C0%7C1%7C637286647986523098&sdata=dwQxTL7iaK0xBrFl8xrq7Y35OSJWqejuWdgtJCBwhGM%3D&reserved=0> On 24 Jun 2020, at 14:13, Sunny, Mani Kolbe <sun...@dnb.com<mailto:sun...@dnb.com>> wrote: Hello, We are developing a beam pipeline which runs on SparkRunner on streaming mode. This pipeline read from Kinesis, do some translations, filtering and finally output to S3 using AvroIO writer. We are using Fixed windows with triggers based on element count and processing time intervals. Outputs path is partitioned by window start timestamp. allowedLateness=0sec This is working fine, but I have noticed that whenever we restarts streaming, application is starting to read from Kinesis TRIM_HORIZON. That is, it is not resuming from last checkpoint position. Then I found that the checkpoint directory is based on --jobName and --checkpointDir properties. So I tried running as below: spark-submit --master yarn --deploy-mode cluster --conf spark.dynamicAllocation.enabled=false \ --driver-memory 1g --executor-memory 1g --num-executors 1 --executor-cores 1 \ --class com.dnb.optimus.prime.processor.PrimeStreamProcessor \ --conf spark.executor.extraClassPath=/etc/hbase/conf \ /tmp/stream-processor-0.0.0.8-spark.jar \ --runner=SparkRunner \ --jobName=PrimeStreamProcessor \ --checkpointDir=hdfs:///tmp/PrimeStreamProcessor checkpoint \ --useWindow=true \ --windowDuration=60s --windowLateness=0s --windowElementCount=1 \ --maxReadTime=-1 \ --streaming=true I can see that it is able to fetch checkpoint data from checkpointDir path provided. But When the driver tries to broadcast this information to executors, it is failing with below exception. 20/06/12 15:35:28 ERROR yarn.Client: Application diagnostics message: User class threw exception: org.apache.beam.sdk.Pipeline$PipelineExecutionException: java.lang.UnsupportedOperationException: Accumulator must be registered before send to executor at org.apache.beam.runners.spark.SparkPipelineResult.beamExceptionFrom(SparkPipelineResult.java:71) at org.apache.beam.runners.spark.SparkPipelineResult.access$000(SparkPipelineResult.java:44) .... at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:678) Caused by: java.lang.UnsupportedOperationException: Accumulator must be registered before send to executor at org.apache.spark.util.AccumulatorV2.writeReplace(AccumulatorV2.scala:162) at sun.reflect.GeneratedMethodAccessor44.invoke(Unknown Source) Any idea? Is resuming from checkpoint position on application restart is actually supported on KinesisIO? Regards, Mani
