Hi Robert, With the use of manual save points, I was able to obtain exactly-once output with Kafka and HDFS rolling sink.
Thanks to you and Fabian for the help. From: Robert Metzger <rmetz...@apache.org<mailto:rmetz...@apache.org>> Reply-To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Date: Tuesday, May 17, 2016 at 10:02 AM To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Subject: Re: Flink recovery Hi, Savepoints are exactly for that use case: https://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/savepoints.html http://data-artisans.com/how-apache-flink-enables-new-streaming-applications/ Regards, Robert On Tue, May 17, 2016 at 4:25 PM, Madhire, Naveen <naveen.madh...@capitalone.com<mailto:naveen.madh...@capitalone.com>> wrote: Hey Robert, What is the best way to stop the streaming job in production if I want to upgrade the application without loosing messages and causing duplicates. How can I test this scenario? We are testing few recovery mechanisms like job failure, application upgrade and node failure. Thanks, Naveen From: Robert Metzger <rmetz...@apache.org<mailto:rmetz...@apache.org>> Reply-To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Date: Tuesday, May 17, 2016 at 6:58 AM To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Subject: Re: Flink recovery Hi Naveen, I think cancelling a job is not the right approach for testing our exactly-once guarantees. By cancelling a job, you are discarding the state of your job. Restarting from scratch (without using a savepoint) will cause duplicates. What you can do to validate the behavior is randomly killing a task manager running your job. Then, the job should restart on the remaining machines (make sure that enough slots are available even after the failure) and you shouldn't have any duplicates in HDFS. Regards, Robert On Tue, May 17, 2016 at 11:27 AM, Stephan Ewen <se...@apache.org<mailto:se...@apache.org>> wrote: Hi Naveen! I assume you are using Hadoop 2.7+? Then you should not see the ".valid-length" file. The fix you mentioned is part of later Flink releases (like 1.0.3) Stephan On Mon, May 16, 2016 at 11:46 PM, Madhire, Naveen <naveen.madh...@capitalone.com<mailto:naveen.madh...@capitalone.com>> wrote: Thanks Fabian. Actually I don’t see a .valid-length suffix file in the output HDFS folder. Can you please tell me how would I debug this issue or do you suggest anything else to solve this duplicates problem. Thank you. From: Fabian Hueske <fhue...@gmail.com<mailto:fhue...@gmail.com>> Reply-To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Date: Saturday, May 14, 2016 at 4:10 AM To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Subject: Re: Flink recovery The behavior of the RollingFileSink depends on the capabilities of the file system. If the file system does not support to truncate files such as older HDFS versions, an additional file with a .valid-length suffix is written to indicate how much of the file is valid. All records / data that come after the valid-length are duplicates. Please refer to the JavaDocs of the RollingFileSink for details [1]. If the .valid-length file does not solve the problem, you might have found a bug and we should have a closer look at the problem. Best, Fabian [1] https://ci.apache.org/projects/flink/flink-docs-master/api/java/org/apache/flink/streaming/connectors/fs/RollingSink.html 2016-05-14 4:17 GMT+02:00 Madhire, Naveen <naveen.madh...@capitalone.com<mailto:naveen.madh...@capitalone.com>>: Thanks Fabian. Yes, I am seeing few records more than once in the output. I am running the job and canceling it from the dashboard, and running again. And using different HDFS file outputs both the times. I was thinking when I cancel the job, it’s not doing a clean cancel. Is there anything else which I have to use to make it exactly once in the output? I am using a simple read from kafka, transformations and rolling file sink pipeline. Thanks, Naveen From: Fabian Hueske <fhue...@gmail.com<mailto:fhue...@gmail.com>> Reply-To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Date: Friday, May 13, 2016 at 4:26 PM To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Subject: Re: Flink recovery Hi Naveen, the RollingFileSink supports exactly-once output. So you should be good. Did you see events being emitted multiple times (should not happen with the RollingFileSink) or being processed multiple times within the Flink program (might happen as explained before)? Best, Fabian 2016-05-13 23:19 GMT+02:00 Madhire, Naveen <naveen.madh...@capitalone.com<mailto:naveen.madh...@capitalone.com>>: Thank you Fabian. I am using HDFS rolling sink. This should support the exactly once output in case of failures, isn’t it? I am following the below documentation, https://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/fault_tolerance.html#fault-tolerance-guarantees-of-data-sources-and-sinks If not what other Sinks can I use to have the exactly once output since getting exactly once output is critical for our use case. Thanks, Naveen From: Fabian Hueske <fhue...@gmail.com<mailto:fhue...@gmail.com>> Reply-To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Date: Friday, May 13, 2016 at 4:13 PM To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Subject: Re: Flink recovery Hi, Flink's exactly-once semantics do not mean that events are processed exactly-once but that events will contribute exactly-once to the state of an operator such as a counter. Roughly, the mechanism works as follows: - Flink peridically injects checkpoint markers into the data stream. This happens synchronously across all sources and markers. - When an operator receives a checkpoint marker from all its sources, it checkpoints its state and forwards the marker - When the marker was received by all sinks, the distributed checkpoint is noted as successful. In case of a failure, the state of all operators is reset to the last successful checkpoint and the sources are reset to the point when the marker was injected. Hence, some events are sent a second time to the operators but the state of the operators was reset as well. So the repeated events contribute exactly once to the state of an operator. Note, you need a SinkFunction that supports Flink's checkpointing mechanism to achieve exactly-once output. Otherwise, it might happen that results are emitted multiple times. Cheers, Fabian 2016-05-13 22:58 GMT+02:00 Madhire, Naveen <naveen.madh...@capitalone.com<mailto:naveen.madh...@capitalone.com>>: I checked the JIRA and looks like FLINK-2111 should address the issue which I am facing. I am canceling the job from dashboard. I am using kafka source and HDFS rolling sink. https://issues.apache.org/jira/browse/FLINK-2111 Is this JIRA part of Flink 1.0.0? Thanks, Naveen From: "Madhire, Venkat Naveen Kumar Reddy" <naveen.madh...@capitalone.com<mailto:naveen.madh...@capitalone.com>> Reply-To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Date: Friday, May 13, 2016 at 10:58 AM To: "user@flink.apache.org<mailto:user@flink.apache.org>" <user@flink.apache.org<mailto:user@flink.apache.org>> Subject: Flink recovery Hi, We are trying to test the recovery mechanism of Flink with Kafka and HDFS sink during failures. I’ve killed the job after processing some messages and restarted the same job again. Some of the messages I am seeing are processed more than once and not following the exactly once semantics. Also, using the checkpointing mechanism and saving the state checkpoints into HDFS. Below is the checkpoint code, envStream.enableCheckpointing(11); envStream.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE); envStream.getCheckpointConfig().setCheckpointTimeout(60000); envStream.getCheckpointConfig().setMaxConcurrentCheckpoints(4); envStream.setStateBackend(new FsStateBackend("hdfs://ipaddr/mount/cp/checkpoint/")); One thing I’ve noticed is lowering the time to checkpointing is actually lowering the number of messages processed more than once and 11ms is the lowest I can use. Is there anything else I should try to have exactly once message processing functionality. 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