Hi Fabian,
Thanks a lot.
I got a better understanding.
> Operators are never GC'd (unless a job was cancelled)
That's great information.
Maybe, this is related to so called Managed Memory.
The document will be better if detail documents about Memory Management
exists.
Thank you,
Yuta
On 2017/09/18 18:03, Fabian Hueske wrote:
Hi Yuta,
you got most things right :-)
3) sources (such as Kafka connectors) are also considered operators and
start immediately because they are sources.
4) All other operators start when they need to process their first
record. Operators are never GC'd (unless a job was cancelled), so the
setup cost is a one time thing that only happens when the job is started.
Best, Fabian
2017-09-15 12:43 GMT+02:00 Yuta Morisawa <yu-moris...@kddi-research.jp
<mailto:yu-moris...@kddi-research.jp>>:
Hi Fabian,
Thank you for your description.
This is my understanding.
1, At the exact time execute() method called, Flink creates
JobGraph, submit it to JobManager, deploy tasks to TaskManagers and
DOES NOT execute each operators.
2, Operators are executed when they needed.
3, Sources(kafka-connectors) starts before operators.
4, The first time operators are called or after GC removes
operators' instance, a kind of initialization occurs, such as
classloading, instantiation, memory allocation and so on. It may
costs much time.
If there is any misunderstanding, please comment it.
If not, my question is solved.
Regards.
Yuta
On 2017/09/15 17:05, Fabian Hueske wrote:
Hi Yuta,
when the execute() method is called, the a so-called JobGraph is
constructed from all operators that have been added before by
calling map(), keyBy() and so on.
The JobGraph is then submitted to the JobManager which is the
master process in Flink. Based on the JobGraph, the master
deploys tasks to the worker processes (TaskManagers).
These are the tasks that do the actual processing and they are
subsequently started as I explained before, i.e., the source
task starts consuming from Kafka before subsequent tasks have
been started.
So, there is quite a lot happening when you call execute()
including network communication and task deployment.
Hope this helps,
Fabian
2017-09-15 4:25 GMT+02:00 Yuta Morisawa
<yu-moris...@kddi-research.jp
<mailto:yu-moris...@kddi-research.jp>
<mailto:yu-moris...@kddi-research.jp
<mailto:yu-moris...@kddi-research.jp>>>:
Hi, Fabian
> If I understand you correctly, the problem is only for
the first events
> that are processed.
Yes. More Precisely, first 300 kafka-messages.
> AFAIK, Flink lazily instantiates its operators which
means that a source
> task starts to consume records from Kafka before the
subsequent tasks
> have been started.
That's a great indication. It describe well the affair.
But, according to the document, it says "The operations are
actually
executed when the execution is explicitly triggered by an
execute()
call on the execution environment.".
What does it mean?
AFAIK, common Flink programs invoke execute() in main().
Every operators start at this time? I think maybe no.
- Flink Document
https://ci.apache.org/projects/flink/flink-docs-release-1.3/dev/api_concepts.html#lazy-evaluation
<https://ci.apache.org/projects/flink/flink-docs-release-1.3/dev/api_concepts.html#lazy-evaluation>
<https://ci.apache.org/projects/flink/flink-docs-release-1.3/dev/api_concepts.html#lazy-evaluation
<https://ci.apache.org/projects/flink/flink-docs-release-1.3/dev/api_concepts.html#lazy-evaluation>>
> Not sure if or what can be done about this behavior.
> I'll loop in Till who knows more about the lifecycle of
tasks.
Thank you very much for your kindness.
Regards, Yuta
On 2017/09/14 19:32, Fabian Hueske wrote:
Hi,
If I understand you correctly, the problem is only for
the first
events that are processed.
AFAIK, Flink lazily instantiates its operators which
means that
a source task starts to consume records from Kafka
before the
subsequent tasks have been started.
That's why the latency of the first records is higher.
Not sure if or what can be done about this behavior.
I'll loop in Till who knows more about the lifecycle of
tasks.
Best, Fabian
2017-09-12 11:02 GMT+02:00 Yuta Morisawa
<yu-moris...@kddi-research.jp
<mailto:yu-moris...@kddi-research.jp>
<mailto:yu-moris...@kddi-research.jp
<mailto:yu-moris...@kddi-research.jp>>
<mailto:yu-moris...@kddi-research.jp
<mailto:yu-moris...@kddi-research.jp>
<mailto:yu-moris...@kddi-research.jp
<mailto:yu-moris...@kddi-research.jp>>>>:
Hi,
I am worrying about the delay of the Streaming API.
My application is that it gets data from
kafka-connectors and
process them, then push data to kafka-producers.
The problem is that the app suffers a long delay
when the
first data
come in the cluster.
It takes about 1000ms to process data (I measure
the time with
kafka-timestamp). On the other hand, it works well
after
2-3 seconds
first data come in (the delay is about 200ms).
The application is so delay sensitive that I want
to solve
this problem.
Now, I think this is a matter of JVM but I have no
idea to
investigate it.
Is there any way to avoid this delay?
Thank you for your attention
Yuta
