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In Spark, is it a must not to have executors running in Master node when running in cluster mode?



2019 Community Moderator ElectionWhy is Spark's LinearRegressionWithSGD very slow locally?Why Logistic regression into Spark Mllib does not use Maximum likelihood estimation?Mahout Spark shell not workingWhy does my master node get heap memory full for inbuilt SVD API in Apache Spark during calculation of inverse of a square matrix?API to find out how many executors are running my Spark jobs?Best practice for developing using SparkUnable to open application master UI in spark1.6.1 in cluster modePlotting in PySpark?Navigating the jungle of choices for scalable ML deployment










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I was running my Spark application on AWS EMR, I launched a master node of m5.xlarge its CPU and RAM usages are both very low. So master node is run for Yarn and main driver to collect calculated data for further processing only?










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    I was running my Spark application on AWS EMR, I launched a master node of m5.xlarge its CPU and RAM usages are both very low. So master node is run for Yarn and main driver to collect calculated data for further processing only?










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      I was running my Spark application on AWS EMR, I launched a master node of m5.xlarge its CPU and RAM usages are both very low. So master node is run for Yarn and main driver to collect calculated data for further processing only?







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