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memory-tools 1.0.5

A set of simple yet effective tools to troubleshoot memory leaks.

memory-tools

A set of simple yet effective tools to troubleshoot memory leaks.

When debugging memory issues in Python 2.6, the author had tried memory_profiler and heapy, unfortunately neither worked. And so memory-tools was born with the goal of being simple - it should always work, yet effective - it is good at helping you find memory leaks.

Quick Start Tutorial

Installation

$ pip install memory-tools

Show Memory Usage / Delta

Use the show-mem command to show system or process memory usage. When paired with watch, this becomes even more useful.

Show system memory:

$ show-mem

Commit Mem (MB):       27,852.80 total   17,278.42 used
Physical Mem (MB):     16,384.00 total   13,128.05 used

Re-run to show delta from last run:

$ show-mem

Commit Mem (MB):       27,852.80 total   17,888.59 used (delta: 310.15)
Physical Mem (MB):     16,384.00 total   13,126.40 used (delta: -1.65)

Show memory for process:

$ show-mem -p python

1 process matching "python":
  PID 26143 (MB):           4.79 rss          1.23 private

$ show-mem -p 26143

  PID 26143 (MB):           4.80 rss          1.24 private

Watch system/process memory using watch:

$ watch show-mem -s -p python

Commit Mem (MB):       27,852.80 total   17,888.59 used (delta: 310.15)
Physical Mem (MB):     16,384.00 total   13,126.40 used (delta: -1.65)

2 processes matching "python" (showing 1st & last):
  PID 26143 (MB):          40.79 rss         30.23 private
  PID 24118 (MB):           4.79 rss          1.23 private

Summarize / Save GC Objects

After running your program, view summary of gc.get_objects():

from memorytools import summarize_objects

summarize_objects()

And here is a sample output:

Objects count 3,790
Objects size 833,344

      Size Count Type
   476,864   296 <type 'dict'>
    76,320   954 <type 'wrapper_descriptor'>
    64,920   541 <type 'function'>
    ...

Count       Size Type
  954     76,320 <type 'wrapper_descriptor'>
  541     64,920 <type 'function'>
  515     37,080 <type 'builtin_function_or_method'>
  ...

Save all objects (along with the above summary) to a file:

from memorytools import save_objects

save_objects()

# Output: Wrote 3887 objects to /var/tmp/objects-45271 (882040 bytes)

Looping / Stress Testing

Use the loop command to run a command, module:method, or code in a forever loop to perform stress testing, which is useful in finding memory leaks. The command/code should, of course, act against a long running server for this to be useful.

Run a script in a loop:

$ loop show-mem 1

Physical Mem (MB):     16,384.00 total    9,415.50 used (delta: -190.67)
Physical Mem (MB):     16,384.00 total    9,415.27 used (delta: -0.23)
Physical Mem (MB):     16,384.00 total    9,415.85 used (delta: 0.58)
^C                   [ User CTRL-C here as it loops forever by default ]
Looped 3 times in 2.80 secs

Run a module:method in a loop - count of 10:

$ loop memorytools:summarize_objects 10 -c 10

# Results from summarize_objects() every 10 seconds

Looped 10 times in 100 secs

Run adhoc code in a loop - count of 2 and concurrency of 3:

$ loop 'print("Hello World!")' 0.1 -c 2 -cc 3
Hello World!
... 5 more times

Looped 2 times in 0.21 secs with concurrency of 3 (6 runs, 0.10 secs per loop, 0.03 secs per run)

Log Stack / Start Debugger on Signal

If you need to get a stacktrace of a running process, or start the debugger in specific situations to look at memory footprint, then a signal handler could help:

from memorytools import add_debug_handler

add_debug_handler(start_debugger_password='test')  # remove start_debugger_password to skip rpdb2 debugger

The above will add a handler to SIGUSR2 that will log a stacktrace on trigger and also start the rpdb2 debugger.

 
File Type Py Version Uploaded on Size
memory-tools-1.0.5.tar.gz (md5) Source 2017-06-07 15KB