On Tue, 5 Mar 2019 at 12:48, Stephen Connolly <
stephen.alan.conno...@gmail.com> wrote:
>
>
> On Fri, 1 Mar 2019 at 13:05, LINZ, Arnaud <al...@bouyguestelecom.fr>
> wrote:
>
>> Hi,
>>
>>
>>
>> I think I should go into more details to explain my use case.
>>
>> I have one non parallel source (parallelism = 1) that list binary files
>> in a HDFS directory. DataSet emitted by the source is a data set of file
>> names, not file content. These filenames are rebalanced, and sent to
>> workers (parallelism = 15) that will use a flatmapper that open the file,
>> read it, decode it, and send records (forward mode) to the sinks (with a
>> few 1-to-1 mapping in-between). So the flatmap operation is a
>> time-consuming one as the files are more than 200Mb large each; the
>> flatmapper will emit millions of record to the sink given one source record
>> (filename).
>>
>>
>>
>> The rebalancing, occurring at the file name level, does not use much I/O
>> and I cannot use one-to-one mode at that point if I want some parallelims
>> since I have only one source.
>>
>>
>>
>> I did not put file decoding directly in the sources because I have no
>> good way to distribute files to sources without a controller (input
>> directory is unique, filenames are random and cannot be “attributed” to one
>> particular source instance easily).
>>
>
> Crazy idea: If you know the task number and the number of tasks, you can
> hash the filename using a shared algorithm (e.g. md5 or sha1 or crc32) and
> then just check modulo number of tasks == task number.
>
> That would let you run the list files in parallel without sharing state.
> which would allow file decoding directly in the sources
>
if you extend RichParallelSourceFunction you will have:
int index = getRuntimeContext().getIndexOfThisSubtask();
int count = getRuntimeContext().getNumberOfParallelSubtasks();
then a hash function like:
private static int hash(String string) {
int result = 0;
for (byte b : DigestUtils.sha1(string)) {
result = result * 31 + b;
}
return result;
}
and just compare the filename like so:
for (String filename: listFiles()) {
if (Math.floorMod(hash(filename), count) != index) {
continue;
}
// this is our file
...
}
Note: if you know the file name patterns, you should tune the hash function
to distribute them evenly. The SHA1 with prime reduction of the bytes is ok
for general levelling... but may be poor over 15 buckets with your typical
data set of filenames
>
>
>> Alternatively, I could have used a dispatcher daemon separated from the
>> streaming app that distribute files to various directories, each directory
>> being associated with a flink source instance, and put the file reading &
>> decoding directly in the source, but that seemed more complex to code and
>> exploit than the filename source. Would it have been better from the
>> checkpointing perspective?
>>
>>
>>
>> About the ungraceful source sleep(), is there a way, programmatically, to
>> know the “load” of the app, or to determine if checkpointing takes too much
>> time, so that I can do it only on purpose?
>>
>>
>>
>> Thanks,
>>
>> Arnaud
>>
>>
>>
>> *De :* zhijiang <wangzhijiang...@aliyun.com>
>> *Envoyé :* vendredi 1 mars 2019 04:59
>> *À :* user <user@flink.apache.org>; LINZ, Arnaud <
>> al...@bouyguestelecom.fr>
>> *Objet :* Re: Checkpoints and catch-up burst (heavy back pressure)
>>
>>
>>
>> Hi Arnaud,
>>
>>
>>
>> Thanks for the further feedbacks!
>>
>>
>>
>> For option1: 40min still does not makes sense, which indicates it might
>> take more time to finish checkpoint in your case. I also experienced some
>> scenarios of catching up data to take several hours to finish one
>> checkpoint. If the current checkpoint expires because of timeout, the next
>> new triggered checkpoint might still be failed for timeout. So it seems
>> better to wait the current checkpoint until finishes, not expires it,
>> unless we can not bear this long time for some reasons such as wondering
>> failover to restore more data during this time.
>>
>>
>>
>> For option2: The default network setting should be make sense. The lower
>> values might cause performance regression and the higher values would
>> increase the inflighing buffers and checkpoint delay more seriously.
>>
>>
>>
>> For option3: If the resource is limited, it is still not working on your
>> side.
>>
>>
>>
>> It is an option and might work in your case for sleeping some time in
>> source as you mentioned, although it seems not a graceful way.
>>
>>
>>
>> I think there are no data skew in your case to cause backpressure,
>> because you used the rebalance mode as mentioned. Another option might use
>> the forward mode which would be better than rebalance mode if possible in
>> your case. Because the source and downstream task is one-to-one in forward
>> mode, so the total flighting buffers are 2+2+8 for one single downstream
>> task before barrier. If in rebalance mode, the total flighting buffer would
>> be (a*2+a*2+8) for one single downstream task (`a` is the parallelism of
>> source vertex), because it is all-to-all connection. The barrier alignment
>> takes more time in rebalance mode than forward mode.
>>
>>
>>
>> Best,
>>
>> Zhijiang
>>
>> ------------------------------------------------------------------
>>
>> From:LINZ, Arnaud <al...@bouyguestelecom.fr>
>>
>> Send Time:2019年3月1日(星期五) 00:46
>>
>> To:zhijiang <wangzhijiang...@aliyun.com>; user <user@flink.apache.org>
>>
>> Subject:RE: Checkpoints and catch-up burst (heavy back pressure)
>>
>>
>>
>> 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>
>> *Envoyé :* jeudi 28 février 2019 10:58
>> *À :* user <user@flink.apache.org>; LINZ, Arnaud <
>> 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>
>>
>> Send Time:2019年2月28日(星期四) 17:28
>>
>> To:user <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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>>
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