Memory Leaking

Memory leaking is one of the major issues when creating a service infra-structure. A correct detection of tese type of problems is important to provide a stable production environment.

Status

The current Python 2.7 implementation leaks memory under normal usage of the netius HTTP client so using a Python 3.4+ version is recommended for a deployment/production environment to avoid memory leaking. The leaking of memory under such environments occurs on the native (Python C) codebase so its leaking is not traceable by tools like guppy.

In 3.x range doesn't create a list, so the test above won't create 10 million int objects. Even if it did, the int type in 3.x is basically a 2.x long, which doesn't implement a freelist.

Notes

Long running Python jobs that consume a lot of memory while running may not return that memory to the operating system until the process actually terminates, even if everything is garbage collected properly. That was news to me, but it's true. What this means is that processes that do need to use a lot of memory will exhibit a "high water" behavior, where they remain forever at the level of memory usage that they required at their peak.

Note: this behavior may be Linux specific; there are anecdotal reports that Python on Windows does not have this problem.

This problem arises from the fact that the Python VM does its own internal memory management. It's commonly know as memory fragmentation. Unfortunately, there doesn't seem to be any fool-proof method of avoiding it.

Utilities

Heapy

A simple yet powerful utility that provides a mechanism to detect "pending" object between two pre-defined snapshot positions (time values) and that allows a powerful memory leak detection mechanism.

Example

import guppy
heap = guppy.hpy()
heap.setrelheap()

...

state = heap.heap()
print(state)

References