Just wanted to throw in my 2cts. I’ve been running pipelines with similar state size using rocksdb which externalize to S3 and bucket to S3. I was getting stalls like this and ended up tracing the problem to S3 and the bucketing sink. The solution was two fold:
1) I forked hadoop-aws and have it treat flink as a source of truth. Emr uses a dynamodb table to determine if S3 is inconsistent. Instead I say that if flink believes that a file exists on S3 and we don’t see it then I am going to trust that flink is in a consistent state and S3 is not. In this case, various operations will perform a back off and retry up to a certain number of times. 2) The bucketing sink performs multiple renames over the lifetime of a file, occurring when a checkpoint starts and then again on notification after it completes. Due to S3’s consistency guarantees the second rename of file can never be assured to work and will eventually fail either during or after a checkpoint. Because there is no upper bound on the time it will take for a file on S3 to become consistent, retries cannot solve this specific problem as it could take upwards of many minutes to rename which would stall the entire pipeline. The only viable solution I could find was to write a custom sink which understands S3. Each writer will write file locally and then copy it to S3 on checkpoint. By only interacting with S3 once per file it can circumvent consistency issues all together. Hope this helps, Seth Wiesman From: vinay patil <vinay18.pa...@gmail.com> Reply-To: "user@flink.apache.org" <user@flink.apache.org> Date: Saturday, February 25, 2017 at 10:50 AM To: "user@flink.apache.org" <user@flink.apache.org> Subject: Re: Checkpointing with RocksDB as statebackend HI Stephan, Just to avoid the confusion here, I am using S3 sink for writing the data, and using HDFS for storing checkpoints. There are 2 core nodes (HDFS) and two task nodes on EMR I replaced s3 sink with HDFS for writing data in my last test. Let's say the checkpoint interval is 5 minutes, now within 5minutes of run the state size grows to 30GB , after checkpointing the 30GB state that is maintained in rocksDB has to be copied to HDFS, right ? is this causing the pipeline to stall ? Regards, Vinay Patil On Sat, Feb 25, 2017 at 12:22 AM, Vinay Patil <[hidden email]<file:////user/SendEmail.jtp%3ftype=node&node=11913&i=0>> wrote: Hi Stephan, To verify if S3 is making teh pipeline stall, I have replaced the S3 sink with HDFS and kept minimum pause between checkpoints to 5minutes, still I see the same issue with checkpoints getting failed. If I keep the pause time to 20 seconds, all checkpoints are completed , however there is a hit in overall throughput. Regards, Vinay Patil On Fri, Feb 24, 2017 at 10:09 PM, Stephan Ewen [via Apache Flink User Mailing List archive.] <[hidden email]<file:////user/SendEmail.jtp%3ftype=node&node=11913&i=1>> wrote: Flink's state backends currently do a good number of "make sure this exists" operations on the file systems. Through Hadoop's S3 filesystem, that translates to S3 bucket list operations, where there is a limit in how many operation may happen per time interval. After that, S3 blocks. It seems that operations that are totally cheap on HDFS are hellishly expensive (and limited) on S3. It may be that you are affected by that. We are gradually trying to improve the behavior there and be more S3 aware. Both 1.3-SNAPSHOT and 1.2-SNAPSHOT already contain improvements there. Best, Stephan On Fri, Feb 24, 2017 at 4:42 PM, vinay patil <[hidden email]<http://user/SendEmail.jtp?type=node&node=11891&i=0>> wrote: Hi Stephan, So do you mean that S3 is causing the stall , as I have mentioned in my previous mail, I could not see any progress for 16minutes as checkpoints were getting failed continuously. On Feb 24, 2017 8:30 PM, "Stephan Ewen [via Apache Flink User Mailing List archive.]" <[hidden email]<http://user/SendEmail.jtp?type=node&node=11887&i=0>> wrote: Hi Vinay! True, the operator state (like Kafka) is currently not asynchronously checkpointed. While it is rather small state, we have seen before that on S3 it can cause trouble, because S3 frequently stalls uploads of even data amounts as low as kilobytes due to its throttling policies. That would be a super important fix to add! Best, Stephan On Fri, Feb 24, 2017 at 2:58 PM, vinay patil <[hidden email]<http://user/SendEmail.jtp?type=node&node=11885&i=0>> wrote: Hi, I have attached a snapshot for reference: As you can see all the 3 checkpointins failed , for checkpoint ID 2 and 3 it is stuck at the Kafka source after 50% (The data sent till now by Kafka source 1 is 65GB and sent by source 2 is 15GB ) Within 10minutes 15M records were processed, and for the next 16minutes the pipeline is stuck , I don't see any progress beyond 15M because of checkpoints getting failed consistently. <http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/file/n11882/Checkpointing_Failed.png> -- View this message in context: http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Re-Checkpointing-with-RocksDB-as-statebackend-tp11752p11882.html Sent from the Apache Flink User Mailing List archive. mailing list archive at Nabble.com. ________________________________ If you reply to this email, your message will be added to the discussion below: http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Re-Checkpointing-with-RocksDB-as-statebackend-tp11752p11885.html To start a new topic under Apache Flink User Mailing List archive., email [hidden email]<http://user/SendEmail.jtp?type=node&node=11887&i=1> To unsubscribe from Apache Flink User Mailing List archive., click here. 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