Large number of ray::IDLE using rd.read_json

when running the following simple code, a large number of “ray::IDLE” shows up in the process. The number is equal to the number of CPUs in the node.

      import ray.data as rd
      rd.read_json("example.json")

I test it in a node with 8 processor and found the following processes:

15851 user      35  15 6922232 179840  76064 S   1.0   1.1   0:02.06 ray::IDLE                   
15852 user      35  15 6996100 178016  76260 S   1.0   1.1   0:02.05 ray::IDLE                    
15853 user      35  15 6996112 181764  75852 S   1.0   1.1   0:02.03 ray::IDLE                    
15854 user      35  15 6995976 179676  75928 S   1.0   1.1   0:02.10 ray::IDLE                    
15855 user      35  15 6996096 180160  76284 S   1.0   1.1   0:02.01 ray::IDLE                   
15856 user      35  15 6969584 185676  79136 S   1.0   1.1   0:01.70 ray::IDLE                   
15857 user      35  15 6996100 180148  76288 S   1.0   1.1   0:02.00 ray::IDLE                   
15858 user      35  15 6922244 183924  76064 S   1.0   1.1   0:01.95 ray::IDLE                   

Is this a bug or a feature?