Hi,
I have test it on my production environment, and I find a strange thing.
After I set the kafka partition to 100, some tasks are executed very fast, but some are slow. The slow ones cost double time than fast ones(from event timeline). However, I have checked the consumer offsets, the data amount for each task should be similar, so it should be no unbalance problem.
Any one have some good idea? 

Regard,
Junfeng Chen


On Thu, Nov 8, 2018 at 12:34 AM Shahbaz <shahzadhifz@gmail.com> wrote:
Hi ,
  • Do you have adequate CPU cores allocated to handle increased partitions ,generally if you have Kafka partitions >=(greater than or equal to) CPU Cores Total (Number of Executor Instances * Per Executor Core) ,gives increased task parallelism for reader phase.
  • However if you have too many partitions but not enough cores ,it would eventually slow down the reader (Ex: 100 Partitions and only 20 Total Cores).
  • Additionally ,the next set of transformation will have there own partitions ,if its involving  shuffle ,sq.shuffle.partitions then defines next level of parallelism ,if you are not having any data skew,then you should get good performance.

Regards,
Shahbaz

On Wed, Nov 7, 2018 at 12:58 PM JF Chen <darouwan@gmail.com> wrote:
I have a Spark Streaming application which reads data from kafka and save the the transformation result to hdfs. 
My original partition number of kafka topic is 8, and repartition the data to 100 to increase the parallelism of spark job. 
Now I am wondering if I increase the kafka partition number to 100 instead of setting repartition to 100, will the performance be enhanced? (I know repartition action cost a lot cpu resource)
If I set the kafka partition number to 100, does it have any negative efficiency?  
I just have one production environment so it's not convenient for me to do the test....
 
Thanks!

Regard,
Junfeng Chen