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From Stephan Ewen <se...@apache.org>
Subject Re: Checkpointing with RocksDB as statebackend
Date Wed, 01 Mar 2017 11:10:46 GMT
@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 <swiesman@mediamath.com>
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 <vinay18.patil@gmail.com>
> *Reply-To: *"user@flink.apache.org" <user@flink.apache.org>
> *Date: *Monday, February 27, 2017 at 1:05 PM
> *To: *"user@flink.apache.org" <user@flink.apache.org>
>
> *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.nabble.com/file/n11882/Checkpointing_Failed.png>
>
>
>
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