Reputation:
I am wondering about the output
sys: maxRecursionDepth = 10
f1, f2, f3, f4, f5, f1,
>>> maxRecursionDepth = 2
# -----------------------------
sys: maxRecursionDepth = 10
f1, f1, f1, f1, f1, f1,
>>> maxRecursionDepth = 6
of code provided below.
What I am wondering about is: What causes that chaining of function calls compared to nesting of function calls has different impact on the by the counter counted calls to the topmost function starting the recursion? In other words, how does it come that nested calls don't reduce the depth of recursion like the chained calls do? All of the nested functions wait for their parameter being evaluated, so they should take space on the stack, but it seems that they don't.
from sys import getrecursionlimit, setrecursionlimit
setrecursionlimit(10)
print(f'sys: maxRecursionDepth = {getrecursionlimit()}')
cnt = 0
def f1():
global cnt
print('f1', end=', ')
cnt += 1
f2()
def f2():
print('f2', end=', ')
f3()
def f3():
print('f3', end=', ')
f4()
def f4():
print('f4', end=', ')
f5()
def f5():
print('f5', end=', ')
f1()
# ---
try:
f1()
except RecursionError:
print(f'\n >>> maxRecursionDepth = {cnt}') # 200
# RecursionError: maximum recursion depth exceeded
print('# -----------------------------')
#"""
from sys import getrecursionlimit, setrecursionlimit
setrecursionlimit(10)
print(f'sys: maxRecursionDepth = {getrecursionlimit()}')
cnt = 0
def f1():
global cnt
print('f1', end=', ')
cnt += 1
f2(f3(f4(f5(f1()))))
def f2(f):
print('f2', end=', ')
f(f3)
def f3(f):
print('f3', end=', ')
f(f4)
def f4(f):
print('f4', end=', ')
f5()
def f5(f):
print('f5', end=', ')
f1()
# ---
try:
f1()
except RecursionError:
print(f'\n >>> maxRecursionDepth = {cnt}') # 996
# RecursionError: maximum recursion depth exceeded
Upvotes: 0
Views: 80
Reputation: 77407
In your second example, f2(f3(f4(f5(f1()))))
, python will evaluate the innermost expression first. That's the recursive call f1()
. And since f1
calls f1
again, f2
and etc... are never called. Notice that only "f1" is printed.
If you disassemble f1
, you'll see the byte codes
7 20 LOAD_GLOBAL 2 (f2)
22 LOAD_GLOBAL 3 (f3)
24 LOAD_GLOBAL 4 (f4)
26 LOAD_GLOBAL 5 (f5)
28 LOAD_GLOBAL 6 (f1)
30 CALL_FUNCTION 0
32 CALL_FUNCTION 1
34 CALL_FUNCTION 1
36 CALL_FUNCTION 1
38 CALL_FUNCTION 1
40 POP_TOP
The byte code executor has its own stack. Some byte codes put things on the stack, some take things from the stack and replace them with some resulting value. Here, all of the functions are loaded onto the stack and then a series CALL_FUNCTION
byte codes pop and call the top thing on the stack. First f1
, then f2
and etc.
Python's "recursion count" is really a stack depth count. If it gets big, like 1000 calls big, that's likely a recursion problem, hence the name. In your first example, calls to f2
, etc increase the stack count faster than f1
increases the count. But in your second example, because f1
calls f1
before any other funtion, cnt
goes up at the same rate as the stack count.
The reason why you don't quite reach 10 is that there is already some stuff on the stack by the time your code runs.
Upvotes: 0
Reputation: 782693
When you write
f2(f3(f4(f5(f1()))))
it's roughly equivalent to
temp1 = f1()
temp2 = f5(temp1)
temp3 = f4(temp2)
temp4 = f3(temp3)
f2(temp4)
Each of the argument function calls is completed before calling the next one in the chain, so they don't add to the recursion depth.
You only add to the recursion depth when the body of a function calls another function. So you get recursion when f1()
calls f2()
, f2()
calls f3()
, ... and f5()
calls f1()
. Since this is infinite recursion, the initial f1()
call never completes, so none of the chained calls happen, either.
Upvotes: 2