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From Robert Metzger <rmetz...@apache.org>
Subject Re: Distributing Tasks over Task manager
Date Wed, 26 Oct 2016 09:16:52 GMT
I'm sorry for the delay. I've added Till who knows the scheduler details to
the conversation.

On Tue, Oct 18, 2016 at 3:09 PM, Jürgen Thomann <
juergen.thomann@innogames.com> wrote:

> Hi Robert,
>
> Do you already had a chance to look on it? If you need more information
> just let me know.
>
> Regards,
> Jürgen
>
>
> On 12.10.2016 21:12, Jürgen Thomann wrote:
>
>>
>> Hi Robert,
>>
>> Thanks for your suggestions. We are using the DataStream API and I tried
>> it with disabling it completely, but that didn't help.
>>
>> I attached the plan and to add some context, it starts with a Kafka
>> source followed by a map operation ( parallelism 4). The next map is the
>> expensive part with a parallelism of 18 which produces a Tuple2 which is
>> used for splitting. Starting here the parallelism is always 2 except the
>> sink with 1. Both resulting streams have two maps, a filter, one more map
>> and are ending with an assignTimestampsAndWatermarks. If there is now a
>> small box in the picture it is a filter operation and otherwise it goes
>> directly to a keyBy, timewindow and apply operation followed by a sink.
>>
>> If one task manager contains more sub tasks of the expensive map than any
>> other task manager, everything later in the stream is running on the same
>> task manager. If two task manager have the same amount of sub tasks, the
>> following tasks with a parallelism of 2 are distributed over the two task
>> manager.
>>
>> Interesting is also that the task manager have 6 task slots configured
>> and the expensive part has 6 sub tasks on one task manager but still
>> everything later in the flow is running on this task manager. This also
>> happens if operator chaining is disabled.
>>
>> Best,
>> Jürgen
>>
>>
>> On 12.10.2016 17:43, Robert Metzger wrote:
>>
>>> Hi Jürgen,
>>>
>>> Are you using the DataStream or the DataSet API?
>>> Maybe the operator chaining is causing too many operations to be
>>> "packed" into one task. Check out this documentation page:
>>> https://ci.apache.org/projects/flink/flink-docs-master/dev/
>>> datastream_api.html#task-chaining-and-resource-groups
>>> You could try to disable chaining completely to see if that resolves the
>>> issue (you'll probably pay for this by having more serialization overhead
>>> and network traffic).
>>>
>>> If my suggestions don't help, can you post a screenshot of your job plan
>>> (from the web interface) here, so that we see what operations you are
>>> performing?
>>>
>>> Regards,
>>> Robert
>>>
>>>

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