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.
.. code:: bash
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``
.. code:: python
pip install memory_utils import memory_utils memory_utils.set_memory_limit(200 * memory_utils.MEGABYTES) # .... else where memory_utils.check_memory()
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 .. code:: python for account in memory_watcher(Account.objects): account.do_something_memory_intensive() account.save() This will call ``check_memory`` before each iteration
``set_verbose`` ^^^^^^^^^^^^^^^ 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 .. code:: python import memory_utils memory_utils.set_verbose(True) ``set_memory_limit`` ^^^^^^^^^^^^^^^^^^^^ 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 .. code:: python import memory_utils memory_utils.set_memory_limit(500 * memory_utils.MEGABYTES) ``set_out`` ^^^^^^^^^^^ By default, we will print to standard out. Feel free to override here like so .. code:: python import memory_utils from StringIO import StringIO out = StringIO() memory_utils.set_out(out) Questions / Issues ------------------ Feel free to ping me on twitter: `@tushman`_ or add issues or PRs at https://github.com/jtushman/memory_utils .. _@tushman: http://twitter.com/tushman .. |Build Status| image:: https://travis-ci.org/jtushman/proxy_tools.svg?branch=master :target: https://travis-ci.org/jtushman/memory_utils