Update :
Option  1 does not work. It still fails at the end of the timeout, no matter 
its value.
Should I implement a “bandwidth” management system by using artificial 
Thread.sleep in the source depending on the back pressure ?

De : LINZ, Arnaud
Envoyé : jeudi 28 février 2019 15:47
À : 'zhijiang' <wangzhijiang...@aliyun.com>; user <user@flink.apache.org>
Objet : RE: Checkpoints and catch-up burst (heavy back pressure)

Hi Zhihiang,

Thanks for your feedback.

  *   I’ll try option 1 ; time out is 4min for now, I’ll switch it to 40min and 
will let you know. Setting it higher than 40 min does not make much sense since 
after 40 min the pending output is already quite large.
  *   Option 3 won’t work ; I already take too many ressources, and as my 
source is more or less a hdfs directory listing, it will always be far faster 
than any mapper that reads the file and emits records based on its content or 
sink that store the transformed data, unless I put “sleeps” in it (but is this 
really a good idea?)
  *   Option 2: taskmanager.network.memory.buffers-per-channel and 
taskmanager.network.memory.buffers-per-gate are currently unset in my 
configuration (so to their default of 2 and 8), but for this streaming app I 
have very few exchanges between nodes (just a rebalance after the source that 
emit file names, everything else is local to the node). Should I adjust their 
values nonetheless ? To higher or lower values ?
Best,
Arnaud
De : zhijiang <wangzhijiang...@aliyun.com<mailto:wangzhijiang...@aliyun.com>>
Envoyé : jeudi 28 février 2019 10:58
À : user <user@flink.apache.org<mailto:user@flink.apache.org>>; LINZ, Arnaud 
<al...@bouyguestelecom.fr<mailto:al...@bouyguestelecom.fr>>
Objet : Re: Checkpoints and catch-up burst (heavy back pressure)

Hi Arnaud,

I think there are two key points. First the checkpoint barrier might be emitted 
delay from source under high backpressure for synchronizing lock.
Second the barrier has to be queued in flighting data buffers, so the 
downstream task has to process all the buffers before barriers to trigger 
checkpoint and this would take some time under back pressure.

There has three ways to work around:
1. Increase the checkpoint timeout avoid expire in short time.
2. Decrease the setting of network buffers to decrease the amount of flighting 
buffers before barrier, you can check the config of  
"taskmanager.network.memory.buffers-per-channel" and 
"taskmanager.network.memory.buffers-per-gate".
3. Adjust the parallelism such as increasing it for sink vertex in order to 
process source data faster, to avoid backpressure in some extent.

You could check which way is suitable for your scenario and may have a try.

Best,
Zhijiang
------------------------------------------------------------------
From:LINZ, Arnaud <al...@bouyguestelecom.fr<mailto:al...@bouyguestelecom.fr>>
Send Time:2019年2月28日(星期四) 17:28
To:user <user@flink.apache.org<mailto:user@flink.apache.org>>
Subject:Checkpoints and catch-up burst (heavy back pressure)

Hello,

I have a simple streaming app that get data from a source and store it to HDFS 
using a sink similar to the bucketing file sink. Checkpointing mode is “exactly 
once”.
Everything is fine on a “normal” course as the sink is faster than the source; 
but when we stop the application for a while and then restart it, we have a 
catch-up burst to get all the messages emitted in the meanwhile.
During this burst, the source is faster than the sink, and all checkpoints fail 
(time out) until the source has been totally caught up. This is annoying 
because the sink does not “commit” the data before a successful checkpoint is 
made, and so the app release all the “catch up” data as a atomic block that can 
be huge if the streaming app was stopped for a while, adding an unwanted stress 
to all the following hive treatments that use the data provided in micro 
batches and to the Hadoop cluster.

How should I handle the situation? Is there something special to do to get 
checkpoints even during heavy load?

The problem does not seem to be new, but I was unable to find any practical 
solution in the documentation.

Best regards,
Arnaud





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