Yes, you can change the GC using the env.java.opts parameter. We are not setting any GC on YARN.
On Thu, Mar 16, 2017 at 1:50 PM, Stephan Ewen <se...@apache.org> wrote: > The only immediate workaround is to use windows with "reduce" or "fold" or > "aggregate" and not "apply". And to not use an evictor. > > The good news is that I think we have a good way of fixing this soon, > making an adjustment in RocksDB. > > For the Yarn / g1gc question: Not 100% sure about that - you can check if > it used g1gc. If not, you may be able to pass this through the > "env.java.opts" parameter. (cc robert for confirmation) > > Stephan > > > > On Thu, Mar 16, 2017 at 8:31 AM, vinay patil <vinay18.pa...@gmail.com> > wrote: > >> Hi Stephan, >> >> What can be the workaround for this ? >> >> Also need one confirmation : Is G1 GC used by default when running the >> pipeline on YARN. (I see a thread of 2015 where G1 is used by default for >> JAVA8) >> >> >> >> Regards, >> Vinay Patil >> >> On Wed, Mar 15, 2017 at 10:32 PM, Stephan Ewen [via Apache Flink User >> Mailing List archive.] <[hidden email] >> <http:///user/SendEmail.jtp?type=node&node=12234&i=0>> wrote: >> >>> Hi Vinay! >>> >>> Savepoints also call the same problematic RocksDB function, >>> unfortunately. >>> >>> We will have a fix next month. We either (1) get a patched RocksDB >>> version or we (2) implement a different pattern for ListState in Flink. >>> >>> (1) would be the better solution, so we are waiting for a response from >>> the RocksDB folks. (2) is always possible if we cannot get a fix from >>> RocksDB. >>> >>> Stephan >>> >>> >>> On Wed, Mar 15, 2017 at 5:53 PM, vinay patil <[hidden email] >>> <http:///user/SendEmail.jtp?type=node&node=12225&i=0>> wrote: >>> >>>> Hi Stephan, >>>> >>>> Thank you for making me aware of this. >>>> >>>> Yes I am using a window without reduce function (Apply function). The >>>> discussion happening on JIRA is exactly what I am observing, consistent >>>> failure of checkpoints after some time and the stream halts. >>>> >>>> We want to go live in next month, not sure how this will affect in >>>> production as we are going to get above 200 million data. >>>> >>>> As a workaround can I take the savepoint while the pipeline is running >>>> ? Let's say if I take savepoint after every 30minutes, will it work ? >>>> >>>> >>>> >>>> Regards, >>>> Vinay Patil >>>> >>>> On Tue, Mar 14, 2017 at 10:02 PM, Stephan Ewen [via Apache Flink User >>>> Mailing List archive.] <[hidden email] >>>> <http:///user/SendEmail.jtp?type=node&node=12224&i=0>> wrote: >>>> >>>>> The issue in Flink is https://issues.apache.org/jira/browse/FLINK-5756 >>>>> >>>>> On Tue, Mar 14, 2017 at 3:40 PM, Stefan Richter <[hidden email] >>>>> <http:///user/SendEmail.jtp?type=node&node=12209&i=0>> wrote: >>>>> >>>>>> Hi Vinay, >>>>>> >>>>>> I think the issue is tracked here: https://github.com/faceb >>>>>> ook/rocksdb/issues/1988. >>>>>> >>>>>> Best, >>>>>> Stefan >>>>>> >>>>>> Am 14.03.2017 um 15:31 schrieb Vishnu Viswanath <[hidden email] >>>>>> <http:///user/SendEmail.jtp?type=node&node=12209&i=1>>: >>>>>> >>>>>> Hi Stephan, >>>>>> >>>>>> Is there a ticket number/link to track this, My job has all the >>>>>> conditions you mentioned. >>>>>> >>>>>> Thanks, >>>>>> Vishnu >>>>>> >>>>>> On Tue, Mar 14, 2017 at 7:13 AM, Stephan Ewen <[hidden email] >>>>>> <http:///user/SendEmail.jtp?type=node&node=12209&i=2>> wrote: >>>>>> >>>>>>> Hi Vinay! >>>>>>> >>>>>>> We just discovered a bug in RocksDB. The bug affects windows without >>>>>>> reduce() or fold(), windows with evictors, and ListState. >>>>>>> >>>>>>> A certain access pattern in RocksDB starts being so slow after a >>>>>>> certain size-per-key that it basically brings down the streaming program >>>>>>> and the snapshots. >>>>>>> >>>>>>> We are reaching out to the RocksDB folks and looking for workarounds >>>>>>> in Flink. >>>>>>> >>>>>>> Greetings, >>>>>>> Stephan >>>>>>> >>>>>>> >>>>>>> On Wed, Mar 1, 2017 at 12:10 PM, Stephan Ewen <[hidden email] >>>>>>> <http:///user/SendEmail.jtp?type=node&node=12209&i=3>> wrote: >>>>>>> >>>>>>>> @vinay Can you try to not set the buffer timeout at all? I am >>>>>>>> actually not sure what would be the effect of setting it to a negative >>>>>>>> value, that can be a cause of problems... >>>>>>>> >>>>>>>> >>>>>>>> On Mon, Feb 27, 2017 at 7:44 PM, Seth Wiesman <[hidden email] >>>>>>>> <http:///user/SendEmail.jtp?type=node&node=12209&i=4>> wrote: >>>>>>>> >>>>>>>>> Vinay, >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> The bucketing sink performs rename operations during the >>>>>>>>> checkpoint and if it tries to rename a file that is not yet >>>>>>>>> consistent that >>>>>>>>> would cause a FileNotFound exception which would fail the checkpoint. >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> Stephan, >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> Currently my aws fork contains some very specific assumptions >>>>>>>>> about the pipeline that will in general only hold for my pipeline. >>>>>>>>> This is >>>>>>>>> because there were still some open questions that I had about how to >>>>>>>>> solve >>>>>>>>> consistency issues in the general case. I will comment on the Jira >>>>>>>>> issue >>>>>>>>> with more specific. >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> Seth Wiesman >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> *From: *vinay patil <[hidden email] >>>>>>>>> <http:///user/SendEmail.jtp?type=node&node=12209&i=5>> >>>>>>>>> *Reply-To: *"[hidden email] >>>>>>>>> <http:///user/SendEmail.jtp?type=node&node=12209&i=6>" <[hidden >>>>>>>>> email] <http:///user/SendEmail.jtp?type=node&node=12209&i=7>> >>>>>>>>> *Date: *Monday, February 27, 2017 at 1:05 PM >>>>>>>>> *To: *"[hidden email] >>>>>>>>> <http:///user/SendEmail.jtp?type=node&node=12209&i=8>" <[hidden >>>>>>>>> email] <http:///user/SendEmail.jtp?type=node&node=12209&i=9>> >>>>>>>>> >>>>>>>>> >>>>>>>>> *Subject: *Re: Checkpointing with RocksDB as statebackend >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> Hi Seth, >>>>>>>>> >>>>>>>>> Thank you for your suggestion. >>>>>>>>> >>>>>>>>> But if the issue is only related to S3, then why does this happen >>>>>>>>> when I replace the S3 sink to HDFS as well (for checkpointing I am >>>>>>>>> using >>>>>>>>> HDFS only ) >>>>>>>>> >>>>>>>>> Stephan, >>>>>>>>> >>>>>>>>> Another issue I see is when I set env.setBufferTimeout(-1) , and >>>>>>>>> keep the checkpoint interval to 10minutes, I have observed that >>>>>>>>> nothing >>>>>>>>> gets written to sink (tried with S3 as well as HDFS), atleast I was >>>>>>>>> expecting pending files here. >>>>>>>>> >>>>>>>>> This issue gets worst when checkpointing is disabled as nothing >>>>>>>>> is written. >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> Regards, >>>>>>>>> >>>>>>>>> Vinay Patil >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> On Mon, Feb 27, 2017 at 10:55 PM, Stephan Ewen [via Apache Flink >>>>>>>>> User Mailing List archive.] <[hidden email]> wrote: >>>>>>>>> >>>>>>>>> Hi Seth! >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> Wow, that is an awesome approach. >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> We have actually seen these issues as well and we are looking to >>>>>>>>> eventually implement our own S3 file system (and circumvent Hadoop's >>>>>>>>> S3 >>>>>>>>> connector that Flink currently relies on): >>>>>>>>> https://issues.apache.org/jira/browse/FLINK-5706 >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> Do you think your patch would be a good starting point for that >>>>>>>>> and would you be willing to share it? >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> The Amazon AWS SDK for Java is Apache 2 licensed, so that is >>>>>>>>> possible to fork officially, if necessary... >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> Greetings, >>>>>>>>> >>>>>>>>> Stephan >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> On Mon, Feb 27, 2017 at 5:15 PM, Seth Wiesman <[hidden email] >>>>>>>>> <http://user/SendEmail.jtp?type=node&node=11943&i=0>> wrote: >>>>>>>>> >>>>>>>>> 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 <[hidden email] >>>>>>>>> <http://user/SendEmail.jtp?type=node&node=11943&i=1>> >>>>>>>>> *Reply-To: *"[hidden email] >>>>>>>>> <http://user/SendEmail.jtp?type=node&node=11943&i=2>" <[hidden >>>>>>>>> email] <http://user/SendEmail.jtp?type=node&node=11943&i=3>> >>>>>>>>> *Date: *Saturday, February 25, 2017 at 10:50 AM >>>>>>>>> *To: *"[hidden email] >>>>>>>>> <http://user/SendEmail.jtp?type=node&node=11943&i=4>" <[hidden >>>>>>>>> email] <http://user/SendEmail.jtp?type=node&node=11943&i=5>> >>>>>>>>> *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]> >>>>>>>>> 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]> 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.na >>>>>>>>> bble.com/file/n11882/Checkpointing_Failed.png> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> View this message in context: http://apache-flink-user-maili >>>>>>>>> ng-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. >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> ------------------------------ 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<http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/> >> at Nabble.com. >> > >
