Antipattern: Too fine-grained tasks¶
TLDR: Avoid over-parallelizing. Parallelizing tasks has higher overhead than using normal functions.
Parallelizing or distributing tasks usually comes with higher overhead than an ordinary function call. Therefore, if you parallelize a function that executes very quickly, the overhead could take longer than the actual function call!
To handle this problem, we should be careful about parallelizing too much. If you have a function or task that’s too small, you can use a technique called batching to make your tasks do more meaningful work in a single task.
@ray.remote def double(number): return number * 2 numbers = list(range(10000)) doubled_numbers =  for i in numbers: doubled_numbers.append(ray.get(double.remote(i)))
Better approach: Use batching.
@ray.remote def double_list(list_of_numbers): return [number * 2 for number in list_of_numbers] numbers = list(range(10000)) doubled_list_refs =  BATCH_SIZE = 100 for i in range(0, len(numbers), BATCH_SIZE): batch = numbers[i : i + BATCH_SIZE] doubled_list_refs.append(double_list.remote(batch)) doubled_numbers =