Can you try specifying the number of partitions when you load the data to equal the number of executors?  If your ETL changes the number of partitions, you can also repartition before calling KMeans.

On Thu, Mar 26, 2015 at 8:04 PM, Xi Shen <> wrote:

I have a large data set, and I expects to get 5000 clusters.

I load the raw data, convert them into DenseVector; then I did repartition and cache; finally I give the RDD[Vector] to KMeans.train().

Now the job is running, and data are loaded. But according to the Spark UI, all data are loaded onto one executor. I checked that executor, and its CPU workload is very low. I think it is using only 1 of the 8 cores. And all other 3 executors are at rest.

Did I miss something? Is it possible to distribute the workload to all 4 executors?