Initial job has not accepted any resources

Hi, i am running spark jobs with standalone resource manager and i am gathering several performance metrics from my cluster nodes. I am also gathering disk io metrics from my nodes and because many of my jobs are using the same dataset i am trying to prevent the operating system from caching the

PriviledgedActionException- Executor error

Hi, i am running several jobs in standalone mode and i notice this error in the log files in some of my nodes at the start of my jobs: INFO executor.CoarseGrainedExecutorBackend: Registered signal handlers for [TERM, HUP, INT] INFO spark.SecurityManager: Changing view acls to: root INFO

KMeans takeSample jobs and RDD cached

Hi, i am running k-means algorithm with initialization mode set to random and various dataset sizes and values for clusters and i have a question regarding the takeSample job of the algorithm. More specific i notice that in every application there are two sampling jobs. The first one is consuming

Executor memory in web UI

Hi, i am a bit confused with the executor-memory option. I am running applications with Standalone cluster manager with 8 workers with 4gb memory and 2 cores each and when i submit my application with spark-submit i use --executor-memory 1g. In the web ui in the completed applications table i see

Mllib kmeans #iteration

Hello, i am running the Kmeans algorithm in cluster mode from Mllib and i was wondering if i could run the algorithm with fixed number of iterations in some way. Thanks -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Mllib-kmeans-iteration-tp22353.html