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03-22-2017
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11-08-2017
09:22 AM
Thanks for your suggestions and will try to incorporate your suggestions and come back to you with more questions!
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08-08-2017
03:20 PM
Hi Ankit, I'm already using Gzip for compressing my reduce tasks output. But If i use gzip compression for map output i will not be able to split map output among reducers. correct me if i am wrong!!!? so i didnt use compression for map output. how to update sort algorithm? you have any tutorial for doing this? Also, can you explain me how to set io.sort.mb and io.sort.factor parameters?
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04-04-2017
06:53 PM
7 Kudos
@Saravanan Selvam, In yarn mode you can control the total number of executors needed for an application with --num-executor option. However, if you do not explicitly specify --num-executor for spark application in yarn mode, it would typically start one executor on each Nodemanager. Spark also has a feature called Dynamic resource allocation. It gives spark application a feature to dynamically scale the set of cluster resources allocated to your application up and down based on the workload. This way you can make sure that application is not over utilizing the resources. http://spark.apache.org/docs/1.2.0/job-scheduling.html#dynamic-resource-allocation
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07-26-2019
04:15 AM
Hello, Below are the default configuration values which will be considered by the spark job if these are not overriden at the time of submitting job to the required values. # - SPARK_EXECUTOR_INSTANCES, Number of workers to start (Default: 2) # - SPARK_EXECUTOR_CORES, Number of cores for the workers (Default: 1). # - SPARK_EXECUTOR_MEMORY, Memory per Worker (e.g. 1000M, 2G) (Default: 1G) SPARK_EXECUTOR_INSTANCES -> indicates the number of workers to be started, it means for a job maximum this many number of executors it can take from the cluster resource manager. SPARK_EXECUTOR_CORES -> indicates the number of cores in each executor, it means the spark TaskScheduler will ask this many cores to be allocated/blocked in each of the executor machine. SPARK_EXECUTOR_MEMORY -> indicates the maximum amount of RAM/MEMORY it requires in each executor. All these details are asked by the TastScheduler to the cluster manager (it may be a spark standalone, yarn, mesos and can be kubernetes starting from spark 2.0) to provide before actually the job execution starts. Also, please note that, initial number of executor instances is dependent on "--num-executors" but when the data is more to be processed and "spark.dynamicAllocation.enabled" set true, then it will be dynamically add more executors based on "spark.dynamicAllocation.initialExecutors". Note: Always "spark.dynamicAllocation.initialExecutors" should be configured greater than "--num-executors". spark.dynamicAllocation.initialExecutors spark.dynamicAllocation.minExecutors Initial number of executors to run if dynamic allocation is enabled. If `--num-executors` (or `spark.executor.instances`) is set and larger than this value, it will be used as the initial number of executors. spark.executor.memory 1g Amount of memory to use per executor process, in the same format as JVM memory strings with a size unit suffix ("k", "m", "g" or "t") (e.g. 512m , 2g ). spark.executor.cores 1 in YARN mode, all the available cores on the worker in standalone and Mesos coarse-grained modes. The number of cores to use on each executor. In standalone and Mesos coarse-grained modes, for more detail, see this description.
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