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From Stefan Richter <s.rich...@data-artisans.com>
Subject Re: Checkpointing with RocksDB as statebackend
Date Tue, 28 Mar 2017 07:58:10 GMT
Hi,

I was able to come up with a custom build of RocksDB yesterday that seems to fix the problems.
I still have to build the native code for different platforms and then test it. I cannot make
promises about the 1.2.1 release, but I would be optimistic that this will make it in.

Best,
Stefan

> Am 27.03.2017 um 19:12 schrieb vinay patil <vinay18.patil@gmail.com>:
> 
> Hi Stephan,
> 
> Just an update, last week I did a run with state size close to 18GB, I did not observe
the pipeline getting stopped in between with G1GC enabled.
> 
> I had observed checkpoint failures when the state size was close to 38GB (but in this
case G1GC was not enabled)
> 
> Is it possible to get the RocksDB fix in 1.2.1 so that I can test it out.
> 
> 
> Regards,
> Vinay Patil
> 
> On Sat, Mar 18, 2017 at 12:25 AM, Stephan Ewen [via Apache Flink User Mailing List archive.]
<[hidden email] <x-msg://1/user/SendEmail.jtp?type=node&node=12425&i=0>>
wrote:
> @vinay Let's see how fast we get this fix in - I hope yes. It may depend also a bit on
the RocksDB community.
> 
> In any case, if it does not make it in, we can do a 1.2.2 release immediately after (I
think the problem is big enough to warrant that), or at least release a custom version of
the RocksDB state backend that includes the fix.
> 
> Stephan
> 
> 
> On Fri, Mar 17, 2017 at 5:51 PM, vinay patil <[hidden email] <http://user/SendEmail.jtp?type=node&node=12276&i=0>>
wrote:
> Hi Stephan,
> 
> Is the performance related change  of RocksDB going to be part of Flink 1.2.1 ?
> 
> Regards,
> Vinay Patil
> 
> On Thu, Mar 16, 2017 at 6:13 PM, Stephan Ewen [via Apache Flink User Mailing List archive.]
<[hidden email] <http://user/SendEmail.jtp?type=node&node=12274&i=0>>
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 <[hidden email] <http://user/SendEmail.jtp?type=node&node=12243&i=0>>
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 <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/facebook/rocksdb/issues/1988 <https://github.com/facebook/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 <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
<http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/file/n11882/Checkpointing_Failed.png>>
>> 
>> 
>> 
>> --
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