How do I find what is using memory in a Python process in a production system?

Question

My production system occasionally exhibits a memory leak I have not been able to reproduce in a development environment. I've used a Python memory profiler (specifically, Heapy) with some success in the development environment, but it can't help me with things I can't reproduce, and I'm reluctant to instrument our production system with Heapy because it takes a while to do its thing and its threaded remote interface does not work well in our server.

What I think I want is a way to dump a snapshot of the production Python process (or at least gc.get_objects), and then analyze it offline to see where it is using memory. How do I get a core dump of a python process like this? Once I have one, how do I do something useful with it?

Answer 1

Using Python's gc garbage collector interface and sys.getsizeof() it's possible to dump all the python objects and their sizes. Here's the code I'm using in production to troubleshoot a memory leak:

rss = psutil.Process(os.getpid()).get_memory_info().rss # Dump variables if using more than 100MB of memory if rss > 100 * 1024 * 1024:     memory_dump()     os.abort()  def memory_dump():     dump = open("memory.pickle", 'wb')     xs = []     for obj in gc.get_objects():         i = id(obj)         size = sys.getsizeof(obj, 0)         #    referrers = [id(o) for o in gc.get_referrers(obj) if hasattr(o, '__class__')]         referents = [id(o) for o in gc.get_referents(obj) if hasattr(o, '__class__')]         if hasattr(obj, '__class__'):             cls = str(obj.__class__)             xs.append({'id': i, 'class': cls, 'size': size, 'referents': referents})     cPickle.dump(xs, dump) 

Note that I'm only saving data from objects that have a __class__ attribute because those are the only objects I care about. It should be possible to save the complete list of objects, but you will need to take care choosing other attributes. Also, I found that getting the referrers for each object was extremely slow so I opted to save only the referents. Anyway, after the crash, the resulting pickled data can be read back like this:

with open("memory.pickle", 'rb') as dump:     objs = cPickle.load(dump) 

Added 2017-11-15

The Python 3.6 version is here:

import gc import sys import _pickle as cPickle  def memory_dump():     with open("memory.pickle", 'wb') as dump:         xs = []         for obj in gc.get_objects():             i = id(obj)             size = sys.getsizeof(obj, 0)             #    referrers = [id(o) for o in gc.get_referrers(obj) if hasattr(o, '__class__')]             referents = [id(o) for o in gc.get_referents(obj) if hasattr(o, '__class__')]             if hasattr(obj, '__class__'):                 cls = str(obj.__class__)                 xs.append({'id': i, 'class': cls, 'size': size, 'referents': referents})         cPickle.dump(xs, dump) 

Answer 2

I will expand on Brett's answer from my recent experience. Dozer package is well maintained, and despite advancements, like addition of tracemalloc to stdlib in Python 3.4, its gc.get_objects counting chart is my go-to tool to tackle memory leaks. Below I use dozer > 0.7 which has not been released at the time of writing (well, because I contributed a couple of fixes there recently).

Example

Let's look at a non-trivial memory leak. I'll use Celery 4.4 here and will eventually uncover a feature which causes the leak (and because it's a bug/feature kind of thing, it can be called mere misconfiguration, cause by ignorance). So there's a Python 3.6 venv where I pip install celery < 4.5. And have the following module.

demo.py

import time  import celery    redis_dsn = 'redis://localhost' app = celery.Celery('demo', broker=redis_dsn, backend=redis_dsn)  @app.task def subtask():     pass  @app.task def task():     for i in range(10_000):         subtask.delay()         time.sleep(0.01)   if __name__ == '__main__':     task.delay().get() 

Basically a task which schedules a bunch of subtasks. What can go wrong?

I'll use procpath to analyse Celery node memory consumption. pip install procpath. I have 4 terminals:

1. procpath record -d celery.sqlite -i1 "$..children[?('celery' in @.cmdline)]" to record the Celery node's process tree stats
2. docker run --rm -it -p 6379:6379 redis to run Redis which will serve as Celery broker and result backend
3. celery -A demo worker --concurrency 2 to run the node with 2 workers
4. python demo.py to finally run the example

(4) will finish under 2 minutes.

Then I use sqliteviz (pre-built version) to visualise what procpath has recorder. I drop the celery.sqlite there and use this query:

SELECT datetime(ts, 'unixepoch', 'localtime') ts, stat_pid, stat_rss / 256.0 rss FROM record  

And in sqliteviz I create a line chart trace with X=ts, Y=rss, and add split transform By=stat_pid. The result chart is:

This shape is likely pretty familiar to anyone who fought with memory leaks.

Finding leaking objects

Now it's time for dozer. I'll show non-instrumented case (and you can instrument your code in similar way if you can). To inject Dozer server into target process I'll use Pyrasite. There are two things to know about it:

• To run it, ptrace has to be configured as "classic ptrace permissions": echo 0 | sudo tee /proc/sys/kernel/yama/ptrace_scope, which is may be a security risk
• There are non-zero chances that your target Python process will crash

With that caveat I:

• pip install https://github.com/mgedmin/dozer/archive/3ca74bd8.zip (that's to-be 0.8 I mentioned above)
• pip install pillow (which dozer uses for charting)
• pip install pyrasite

After that I can get Python shell in the target process:

pyrasite-shell 26572 

And inject the following, which will run Dozer's WSGI application using stdlib's wsgiref's server.

import threading import wsgiref.simple_server  import dozer   def run_dozer():     app = dozer.Dozer(app=None, path='/')     with wsgiref.simple_server.make_server('', 8000, app) as httpd:         print('Serving Dozer on port 8000...')         httpd.serve_forever()  threading.Thread(target=run_dozer, daemon=True).start() 

Opening http://localhost:8000 in a browser there should see something like:

After that I run python demo.py from (4) again and wait for it to finish. Then in Dozer I set "Floor" to 5000, and here's what I see:

Two types related to Celery grow as the subtask are scheduled:

• celery.result.AsyncResult
• vine.promises.promise

weakref.WeakMethod has the same shape and numbers and must be caused by the same thing.

Finding root cause

At this point from the leaking types and the trends it may be already clear what's going on in your case. If it's not, Dozer has "TRACE" link per type, which allows tracing (e.g. seeing object's attributes) chosen object's referrers (gc.get_referrers) and referents (gc.get_referents), and continue the process again traversing the graph.

But a picture says a thousand words, right? So I'll show how to use objgraph to render chosen object's dependency graph.

• pip install objgraph
• apt-get install graphviz

Then:

• I run python demo.py from (4) again
• in Dozer I set floor=0, filter=AsyncResult
• and click "TRACE" which should yield

Then in Pyrasite shell run:

objgraph.show_backrefs([objgraph.at(140254427663376)], filename='backref.png') 

The PNG file should contain:

Basically there's some Context object containing a list called _children that in turn is containing many instances of celery.result.AsyncResult, which leak. Changing Filter=celery.*context in Dozer here's what I see:

So the culprit is celery.app.task.Context. Searching that type would certainly lead you to Celery task page. Quickly searching for "children" there, here's what it says:

trail = True

If enabled the request will keep track of subtasks started by this task, and this information will be sent with the result (result.children).

Disabling the trail by setting trail=False like:

@app.task(trail=False) def task():     for i in range(10_000):         subtask.delay()         time.sleep(0.01) 

Then restarting the Celery node from (3) and python demo.py from (4) yet again, shows this memory consumption.

Problem solved!