Can you give us more information about what your Flink job is doing and the
distribution of the Kinesis data/keys?  The distribution of work depends a
lot on that.  Example: If you are using a kafka source with a single
partition (I think they are called shards in Kenisis) in the datastream api
and do not do a keyBy or some other partitioning operation (rebalance,
broadcast, etc.) in the job, then all processing will happen in a single
task slot.  Similarly if you do a keyBy operation and everything has the
same key, then you will get a very similar distribution of work.

On Thu, May 25, 2017 at 5:31 PM, Sathi Chowdhury <
sathi.chowdh...@elliemae.com> wrote:

> Hi Till/ flink-devs,
>
> I am trying to understand why adding slots in the task manager is having
> no impact in performance for the test pipeline.
>
> Here is my flink-conf.yaml
>
> jobmanager.rpc.address: localhost
>
> jobmanager.rpc.port: 6123
>
> jobmanager.heap.mb: 1024
>
> taskmanager.memory.preallocate: false
>
> taskmanager.numberOfTaskSlots: 8
>
> parallelism.default: 8
>
> akka.ask.timeout: 10000 s
>
> akka.lookup.timeout: 100 s
>
>
>
> I have an EMR cluster of 8 task manager …for now I wanted to use one
> taskmanager with multiple slots.
>
> So I start cluster with
>
> $FLINK_HOME/bin/yarn-session.sh -d  -n 1 -s 8 -tm 57344
>
>
>
> then I run my flink job with –p 8
>
> I see only 1 cpu core is being used in this task manager
>
>
>
> In yarn web ui , I see the node that is chosen to be the taskmanager (as I
> gave –n 1) ip-10-202-4-14.us-west-2.compute.internal:8041
>
> Under memory avail : 56GB
>
> Under vcores used : 1
>
> Under vcores Avail: 31
>
>
>
> I was expecting to see vcores used to be 8
>
>
>
> Any clue or hint why I am seeing this, also I am not seeing any
> performance gain(using flink kinesis connector to read a json file and sink
> to s3 )
>
> It is getting capped with the same performance I have seen with one slot.
>
> Thanks
>
> Sathi
>
>
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