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.
On 12.10.2016 17:43, Robert Metzger wrote:
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:
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
On Wed, Oct 12, 2016 at 12:52 PM, Jürgen Thomann
we currently have an issue with Flink, as it allocates many tasks
to the same task manager and as a result it overloads it. I
reduced the amount of task slots per task manager (keeping the CPU
count) and added some more servers but that did not help to
distribute the load.
Is there some way to force Flink to distribute the load/tasks on a
standalone cluster? I saw that
<https://issues.apache.org/jira/browse/FLINK-1003> would maybe
provide what we need, but that is currently not worked on as it seems.