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Yeah Memory Issues!!

Memory Issues happen to the best of us. memory_utils will give you simple tools to quickly isolate the cuplrit, and ideally, warn you before you run into issues.

From my experience, there is no silver-bullet in dealing with memory issues. You just have to roll up your sleeve and get dirty with print statements. In our team's recent fight with a memory issue, we created memory_utils and we wanted to share.

memory_utils deals primarily with RSS memory (Resident Set Size). The most important memory concept to understand when dealing with memory constrained systems: RSS, the resident set size, is the portion of a process's memory that is held in RAM. The rest of the memory exists in the swap of the file system.


pip install memory_utils



The workhorse of this package is print_memory It simply prints out 3 columns of data: the current memory, the delta since the previous statement and an message that you pass it. If there is additional memory used -- the line will be printed RED and if there is a decrease, the line will be printed GREEN.

It is a very simple approach, but it really helped us find out where the issue was, at glance. The output could look like this:

RSS                  Delta                Message
14,393,344           14,393,344          BEFORE BLOAT
14,397,440           4,096               DURING BLOAT (1)
14,413,824           16,384              DURING BLOAT (102)
14,417,920           4,096               DURING BLOAT (211)
14,438,400           20,480              DURING BLOAT (1002)
14,442,496           4,096               DURING BLOAT (2034)
14,462,976           20,480              DURING BLOAT (2056)

memory_watcher and check_memory

We have worker processes that run in containers. I like to fail hard and early. So we have two helper functions that help us with that


Will check the current rss memory against the memory_utils set memory limit. And if it crosses that limit it will raise a MemoryTooBigException
pip install memory_utils

import memory_utils
memory_utils.set_memory_limit(200 * memory_utils.MEGABYTES)

# .... else where



Often you will want to do your check_memory at a _safe_ place. Also memory leaks often happen within a loop. We created memory_watcher with those concepts in mind

for account in memory_watcher(Account.objects):

This will call check_memory before each iteration



By default print_memory will only print statements that move the memory and memory_watcher will not print its memory usage. If you want additional verbosity set this to true

import memory_utils


By default, the memory limit at 200 MB.

Use this method to change the default.

This setting is used in print_memory and memory_watcher

Note: you can also override this limit at the function level as well

import memory_utils
memory_utils.set_memory_limit(500 * memory_utils.MEGABYTES)


By default, we will print to standard out. Feel free to override here like so

import memory_utils
from StringIO import StringIO

out = StringIO()

Questions / Issues

Feel free to ping me on twitter: @tushman or add issues or PRs at


Utilities to help fight and prevent memory leaks







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