Spark behavior on native file system

Spark behavior on native file system

We are experimenting to run Spark in our project without Hadoop and no distributed storage like HDFS. Spark is installed on a single node with 10 Cores and 16GB RAM and this node is not part of any cluster. Assuming Spark driver takes 2 cores and the rest of them are consumed by executors(2 each) at the time of execution.

We are experimenting to run Spark in our project without Hadoop and no distributed storage like HDFS. Spark is installed on a single node with 10 Cores and 16GB RAM and this node is not part of any cluster. Assuming Spark driver takes 2 cores and the rest of them are consumed by executors(2 each) at the time of execution.

If we process a big CSV file (of size 1 GB) stored in local disk in Spark as RDD and repartition it to 4 different partitions, will executors process each partition in parallel? What would executors do if we don't repartition the RDD to 4 diff partitions? Do we loose the power of distributed computing and parallelism if dont use HDFS?

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