Hi Yun, Thank you for the response.
1. You could tune your job to avoid backpressure. Maybe you can upgrade your flink engine to at least flink-1.13 to know how to monitor the back pressure status [1]. [VIDYA] - In the view of my organization, it's a very big activity to upgrade to Flink version from our current one(1.11). I need to continue for my dev activity with 1.11 only. 1. You can refer to [2] to know how to custom your serializer. [VIDYA] - Thanks for providing me with the link references for custom serializer. I am wondering, how is the serialization part in the incremental checkpointing is different from Full checkpointing. My pipeline logic is same for both Full checkpoint and Incremental checkpoint, except the checkpoint.type variable change and some other env variables. But, the code pipeline logic should be same for both types of checkpoints. - Full checkpoint of pipeline is not taking considerably long time when compared to incremental checkpointing at the end of the window. I see the backpressure is High and CPU utilization is high with incremental checkpointing. Thread dump shows the stack related to serialization. How is the serialization part different between full checkpointing vs Incremental checkpointing? I know, RocksDB library has some serializers for Incremental. - While I am not writing custom serializer for my pipeline in case of Full checkpointing, is it the general pattern to implement custom serializer in case of Incremental? - With respect with serializers for Full vs Incremental checkpointing, What's the general usage pattern across the Flink community? If I write custom serializer for Incremental, how does it go with Full checkpointing. Please clarify. Thanks, Vidya. [1] https://nightlies.apache.org/flink/flink-docs-release-1.13/docs/ops/monitoring/back_pressure/ [2] https://nightlies.apache.org/flink/flink-docs-release-1.13/docs/dev/datastream/fault-tolerance/custom_serialization/ On Sun, Mar 6, 2022 at 12:11 AM Yun Tang <myas...@live.com> wrote: > Hi Vidya, > > > 1. You could tune your job to avoid backpressure. Maybe you can > upgrade your flink engine to at least flink-1.13 to know how to monitor the > back pressure status [1] > 2. You can refer to [2] to know how to custom your serializer. > > > > [1] > https://nightlies.apache.org/flink/flink-docs-release-1.13/docs/ops/monitoring/back_pressure/ > [2] > https://nightlies.apache.org/flink/flink-docs-release-1.13/docs/dev/datastream/fault-tolerance/custom_serialization/ > > Best, > Yun Tang > ------------------------------ > *From:* Vidya Sagar Mula <mulasa...@gmail.com> > *Sent:* Sunday, March 6, 2022 4:16 > *To:* Yun Tang <myas...@live.com> > *Cc:* user <user@flink.apache.org> > *Subject:* Re: Incremental checkpointing & RocksDB Serialization > > Hi Yun Tang, > Thank you for the reply. I have follow up questions and need some more > details. Can you please clarify my inline questions? > > > Why is the incremental checkpointing taking more time for the snapshot > at the end of the window duration? > > I guess that this is because the job is under back pressure on end of > window. You can expand the checkpoint details to see whether that the async > duration of each task is much slower than the e2e duration? If so, this > caused the checkpoint barrier stay in the channel longer. > > *<VIDYA> - Yes, I expanded the checkpoint details and noticed e2e duration > is much higher than async duration. Attaching the screenshot > here(Checkpoint #59) Can you give elaborate more on "checkpoint barrier > stay in the channel longer." What are the suggested ways to mitigate this > issue? I am wondering how can this be avoided as it is happening only at > the end of the window.* > > > > Do you suggest any change in the serializer type in the RocksDB? (Kryo > vs Avro) > > From our experience, kryo is not a good choice in most cases. > > > *<VIDYA> - What are your recommendations on other serializers? I tried to > change it to Avro by enabling the flag "forceAvro" to TRUE in the Execution > Config. But, it RocksDB is still going picking KryoSerializer. This is > because the Transformation is KeyType is assigned as GenericType. I am not > sure what changes need to made to my class/pojo to take the Avro > Serialzer. Can you please suggest the way to change to other better > serializers?* > > > > On Fri, Mar 4, 2022 at 2:06 AM Yun Tang <myas...@live.com> wrote: > > Hi Vidya, > > > Why is the incremental checkpointing taking more time for the snapshot > at the end of the window duration? > > I guess that this is because the job is under back pressure on end of > window. You can expand the checkpoint details to see whether that the async > duration of each task is much slower than the e2e duration? If so, this > caused the checkpoint barrier stay in the channel longer. > > > Why is RocksDB serialization causing the CPU peak? > > This is caused by the implementation of your serializer. > > > Do you suggest any change in the serializer type in the RocksDB? (Kryo > vs Avro) > > From our experience, kryo is not a good choice in most cases. > > Best > Yun Tang > ------------------------------ > *From:* Vidya Sagar Mula <mulasa...@gmail.com> > *Sent:* Friday, March 4, 2022 17:00 > *To:* user <user@flink.apache.org> > *Subject:* Incremental checkpointing & RocksDB Serialization > > Hi, > > I have a cluster that contains the Flink 1.11 version with AWS - S3 > backend. I am trying the incremental checkpointing on this set up. I have a > pipeline with a 10 mins window and incremental checkpointing happens every > 2 mins. > > Observation: > ------------- > I am observing the long duration while taking the snapshot at the end of > each window, which means every last checkpoint of the window (almost all > the times). > I am attaching the Flink UI, checkpoint history. > > My set up details: > ------------------- > Cluster: Cloud cluster with instance storage. > Memory : 20 GB, > Heap : 10 GB > Flink Managed Memory: 4.5 GB > Flink Version : 1.11 > CPUs : 2 > > ROCKSDB_WRITE_BUFFER_SIZE: "2097152000" ## 2GB > > ROCKSDB_BLOCK_CACHE_SIZE: "104857600" ## 100 Mb > > ROCKSDB_BLOCK_SIZE: "5242880" ## 5 Mb > > ROCKSDB_CHECKPOINT_TRANSFER_THREAD_NUM: 4 > > ROCKSDB_MAX_BACKGROUND_THREADS: 4 > > > In the analysis, I noticed that the CPU utilization is peaking to almost > 100% at the time of issue. With further analysis with thread dumps at the > time CPU peak, it is showing RocksDB serialization related call trace. All > the thread samples are pointing to this stack. > > Based on pipeline transformation class type, RocksDB is choosing Kryo > Serializer. I did try to change the serializer type, but that is not the > focal point I want to stress here. > > I would like to understand the reason for high CPU utilization. I have > tried to increase the CPU cycles to 2 and 4. But, it did not give me any > better results. I have parallelism 2. > > Please take a look at the below stack trace. Please suggest me why it is > taking a lot of CPU at the time of serialize/deserialize in the RocksDB? > > ######## > > Stack-1, Stack-2, Stack-3 are attached to this email. > > Questions: > ----------- > - Why is the incremental checkpointing taking more time for the snapshot > at the end of the window duration? > - Why is RocksDB serialization causing the CPU peak? > - Do you suggest any change in the serializer type in the RocksDB? (Kryo > vs Avro) > > Thank you, > > > > > > > > > >
