Pool
- Python course
- Expert
- Lesson 28/47
- 5 min
Learn process pools with multiprocessing.Pool.
What is multiprocessing.Pool?
multiprocessing.Pool is a pool of processes. It distributes tasks across processes automatically; .map() applies a function to every element of a list.
For example Pool().map(func, [1, 2, 3]) distributes three items across the pool's processes and returns [func(1), func(2), func(3)]. The with Pool() as p: block closes the pool automatically. map preserves input order, so the output is deterministic. This is the most practical way to do parallel mapping.
Parallel computation with map
Pool().map(func, data) applies the function to every element of data. The process count is chosen automatically; usually it equals the CPU core count.
Important: map preserves input order. Input [1, 2, 3] gives output [func(1), func(2), func(3)]; no matter which process finishes first the order stays the same. This ensures deterministic results. If you do not use a with block you must close the pool with p.close() and p.join().
Practice Compute squares with Pool
Import with from multiprocessing import Pool. Define a square function that returns x * x. Inside a with Pool() as p: block assign p.map(square, [1, 2, 3]) to result. Print result.
Starter code
# write the square function
# compute in parallel with Pool
# print the resultSample solution
from multiprocessing import Pool
def square(x):
return x * x
with Pool() as p:
result = p.map(square, [1, 2, 3])
print(result)Expected output
[1, 4, 9]
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 What does Pool().map return?
with Pool() as p:
result = p.map(func, [1, 2, 3])- A list of results Correct answer
- A queue
- A process object
- A single value
Explanation: answer: a list of results. map lists each item's result in order.
2 Match Pool concepts
| Term | Meaning |
|---|---|
Pool |
Process pool |
map |
Applies in parallel |
with Pool() |
Auto-closes |
func |
Applied function |
3 Complete the Pool map call
with Pool() as p:
result = p.____(square, [1, 2, 3])with Pool() as p:
result = p.map(square, [1, 2, 3])Explanation: p.map(func, data) applies the function in parallel.