How spark loads the data into memory

Question

I have total confusion in the spark execution process. I have referred may articles and tutorials, nobody is discussing in detailed. I might be wrongly understanding spark. Please correct me.

I have my file of 40GB distributed across 4 nodes (10GB each node) of the 10 node cluster. When I say spark.read.textFile("test.txt") in my code, will it load data(40GB) from all the 4 nodes into driver program (master node)? Or this RDD will be loaded in all the 4 nodes separately. In that case, each node RDD should hold 10GB of physical data, is it? And the whole RDD holds 10GB data and perform tasks for each partition i.e 128MB in spark 2.0. And finally shuffles the output to the driver program (master node)

And I read somewhere "numbers of cores in Cluster = no. of partitions" does it mean, the spark will move the partitions of one node to all 10 nodes for processing?

Answer 1

Spark doesn't have to read the whole file into memory at once. That 40GB file is split into many 128MB (or whatever your partition size is) partitions. Each of those partitions is a processing task. Each core will only work on one task at a time, with a preference to work on tasks where the data partition is stored on the same node. Only the 128MB partition that is being worked on needs to be read, the rest of the file is not read. Once the task completes (and produces some output) then the 128MB for the next task cab be read in and the data read in for the first task can be freed from memory. Because of this only the small amount of data being processed at a time needs to be loaded in to memory and not the entire file at once.

Also strictly speaking spark.read.textFile("test.txt") does nothing. It reads no data and does no processing. It creates an RDD but an RDD doesn't contain any data. And RDD is just an execution plan. spark.read.textFile("test.txt") declared that the file test.txt will be read an used as a source of data if and when the RDD is evaluated but doesn't do anything on its own.