Hi,
For Flink 1.8 (and 1.9) the only thing that you can do, is to try to limit
amount of data buffered between the nodes (check Flink network configuration
[1] for number of buffers and or buffer pool sizes). This can reduce maximal
throughput (but only if the network transfer is a significant
Hi,
I'm still facing the same issue under 1.8. Our pipeline uses end-to-end
exactly-once semantic, which means the consumer program cannot read the
messages until they are committed. So in case of an outage, the whole
runtime delay is passed over to the next stream processor application and
create
slow with high backpressure
Thanks for the explanation.
I hope that either 1.5 will solve your issue (please let us know if it
doesn’t!) or if you can’t wait, that decreasing memory buffers can mitigate the
problem.
Piotrek
> On 5 Apr 2018, at 08:13, Edward wrote:
>
> Thanks for t
Thanks for the explanation.
I hope that either 1.5 will solve your issue (please let us know if it
doesn’t!) or if you can’t wait, that decreasing memory buffers can mitigate the
problem.
Piotrek
> On 5 Apr 2018, at 08:13, Edward wrote:
>
> Thanks for the update Piotr.
>
> The reason it pre
Thanks for the update Piotr.
The reason it prevents us from using checkpoints is this:
We are relying on the checkpoints to trigger commit of Kafka offsets for our
source (kafka consumers).
When there is no backpressure this works fine. When there is backpressure,
checkpoints fail because they tak
Hi,
If I’m not mistaken this is a known issue, that we were working to resolve for
Flink 1.5 release. The problem is that with back pressure, data are being
buffered between nodes and on checkpoint, all of those data must be processed
before checkpoint can be completed. This is especially probl
I read through this thread and didn't see any resolution to the slow
checkpoint issue (just that someone resolved their backpressure issue).
We are experiencing the same problem:
- When there is no backpressure, checkpoints take less than 100ms
- When there is high backpressure, checkpoints take
Nvm i found it. Backpressure caused by aws RDS instance of mysql.
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or - Received
late message for now expired checkpoint attempt 1 from
c601dd04affa7da13a226daa222062e7 of job 303656ace348131ed7a38bb02b4fe374.
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Se
Anyidea?
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ne
>> database.
>>
>> is there recommended approach to handle backpressure?
>>
>> Version Flink 1.2.
>>
>>
>>
>>
>>
>>
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>> slow-with-high-backpressure-tp12762p13411.html
>> Sent from the Apache Flink User Mailing List archive. mailing list
>> archive at Nabble.com.
>>
>
>
e backpressure?
>
> Version Flink 1.2.
>
>
>
>
>
>
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> mailing-list-archive.2336050.n4.nabble.com/Checkpoints-
> very-slow-with-high-backpressure-tp12762p13411.html
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> at Nabble.com.
>
are also not firing so no update being made to down line
database.
is there recommended approach to handle backpressure?
Version Flink 1.2.
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Hi Ufuk,
The ProcessFunction receives elements and buffers them into a MapState, and
periodically (for example every x seconds) register processing time timers
(according to some rules which it gets from a connected rule stream). When
a timer fires, I pop next element from state, request an extern
@Yessine: no, there is no way to disable the back pressure mechanism. Do
you have more details about the two last operators? What do you mean with
the process function is slow on purpose?
@Rune: with 1.3 Flink will configure the internal buffers in a way that not
too much data is buffered in the i
Sorry I cant help you, but we're also experiencing slow checkpointing,
when having backpressure from sink.
I tried HDFS, S3, and RocksDB state backends, but to no avail -
checkpointing always times out with backpressure.
Can we somehow reduce Flink's internal buffer sizes, so checkpointing
w
Hi all,
I have a streaming pipeline as follows:
1 - read a folder continuousely from HDFS
2 - filter duplicates (using keyby(x->x) and keeping a state per key
indicating whether its is seen)
3 - schedule some future actions on the stream using ProcessFunction and
processing time timers (elements a
-- Forwarded message --
From: "Yassine MARZOUGUI"
Date: Apr 23, 2017 20:53
Subject: Checkpoints very slow with high backpressure
To:
Cc:
Hi all,
I have a Streaming pipeline as follows:
1 - read a folder continuousely from HDFS
2 - filter duplicates (using keyby
Im sorry guys if you received multiple instances of this mail, I kept
trying to send it yesterday, but looks like the mailing list was stuck and
didn't dispatch it until now. Sorry for the disturb.
On Apr 23, 2017 20:53, "Yassine MARZOUGUI"
wrote:
> Hi all,
>
> I have a Streaming pipeline as fol
Hi all,
I have a streaming pipeline as follows:
1 - read a folder continuousely from HDFS
2 - filter duplicates (using keyby(x->x) and keeping a state per key
indicating whether its is seen)
3 - schedule some future actions on the stream using ProcessFunction and
processing time timers (elements a
Re-sending as it looks like the previous mail wasn't correctly sent
---
Hi all,
I have a streaming pipeline as follows:
1 - read a folder continuousely from HDFS
2 - filter duplicates (using keyby(x->x) and keeping a state per key
indicating whether its is seen)
3
Hi all,
I have a Streaming pipeline as follows:
1 - read a folder continuousely from HDFS
2 - filter duplicates (using keyby(x->x) and keeping a state per key
indicating whether its is seen)
3 - schedule some future actions on the stream using ProcessFunction and
processing time timers (elements a
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