[issue36560] test_functools leaks randomly 1 memory block

[STINNER Victor]

STINNER Victor <vstinner at redhat.com> added the comment:

This issue isn't a real memory leak: if I use -R 3:10 instead of -R 3:3, the test doesn't fail anymore.

But the issue is still annoying since it makes Refleaks buildbot workers fail randomly :-/

This issue remembers me the unstable multiprocessing tests:

* bpo-33735: test_multiprocessing_spawn leaked [1, 2, 1] memory blocks on AMD64 Windows8.1 Refleaks 3.7
* bpo-33984: test_multiprocessing_forkserver leaked [1, 2, 1] memory blocks on x86 Gentoo Refleaks 3.x



Patch to always display memory allocations differences:

diff --git a/Lib/test/libregrtest/refleak.py b/Lib/test/libregrtest/refleak.py
index d68ea63b5b..997be819fa 100644
--- a/Lib/test/libregrtest/refleak.py
+++ b/Lib/test/libregrtest/refleak.py
@@ -118,6 +118,8 @@ def dash_R(the_module, test, indirect_test, huntrleaks):
                 print(msg, file=refrep)
                 refrep.flush()
             failed = True
+    if not failed:
+        print(alloc_deltas[nwarmup:])
     return failed
 

Truncated output with the patch:

vstinner at apu$ ./python -m test -F -r -j1 -R 3:10 test_functools
Using random seed 4308771
Run tests in parallel using 1 child processes
0:00:04 load avg: 0.91 [  1] test_functools passed
[0, 1, 2, 0, 0, 0, 0, 0, 0, 0]
...
0:00:13 load avg: 0.92 [  3] test_functools passed
[2, 1, 0, 0, 0, 0, 0, 0, 0, 0]
...
0:00:17 load avg: 0.93 [  4] test_functools passed
[0, 3, 0, 0, 0, 0, 0, 0, 0, 0]
...
0:00:21 load avg: 0.93 [  5] test_functools passed
[0, 1, 0, 0, 2, 0, 0, 0, 0, 0]
...
0:00:26 load avg: 0.93 [  6] test_functools passed
[0, 4, 0, 0, 0, 0, 0, 0, 0, 0]
...
0:00:34 load avg: 0.87 [  8] test_functools passed
[0, 1, 0, 2, 0, 0, 0, 0, 0, 0]
...
0:01:06 load avg: 1.15 [ 15] test_functools passed
[0, 1, 0, 2, 0, -1, 1, 0, 0, 0]
...
0:01:10 load avg: 1.46 [ 16] test_functools passed
[0, 4, 0, 0, 0, 0, 0, 0, -1, 1]
...

The maximum sum() of these list is around 5 on 10 runs: not every run leaks a memory block. It looks more like a internal cache which is "unstable" if you look at the number of allocated memory blocks.

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Python tracker <report at bugs.python.org>
<https://bugs.python.org/issue36560>
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