tl;dr we found a memory leak in Ruby… and it mostly wasn’t our fault!

This is a tale of that 0.00001% of the time where a problem is actually an issue in the language, and not with your own code base - though, it was a bit of that as well! We went through every possible blog post on the subject (and then some), none of which really covered our issue that took us roughly 4 months of pain, misery, and suffering to solve. Here is what we learned during that time, and the process we took to uncover this memory leak.

Step 1: Reproducing the issue

The initial issue that was brought to our attention was that there was a leak in our MetricsCollector and MetricsProcessor workers*. This was a trickle leak where we would leak roughly 5-10 MB per hour and over time this would cause the workers to exceed memory thresholds and restart. This did require some extra setup to replicate as well, as we needed something to simulate adding jobs for these workers to collect/process metrics from so they had work to do.

Since these are both “queue based” workers, this meant the work that they did was dependent on external factors, and wasn’t always a linear amount of work passed to the queue, making the growth variable and not consistent. On top of that, the jobs that caused the leak seemed to be at random, and instrumenting the code to determine the leak was hard to do in a generic fashion. That is not to say we didn’t try wrap our workers with some common Ruby profiling tools (we did… many times…), but the result usually led us to wonder if were actually dealing with the “Observer effect” and introducing new problems instead of “observing” existing ones.

Although it took much more time for it to be truly observed (around a month’s time), we also found that the MIQ Server (our worker monitor process) was leaking as well. Since it had a more predictable workflow, this was the better candidate to do some deeper process of elimination testing. It took some time to get a scenario where this was leaking consistently, but once we did it was simpler to do A/B testing with it.

Step 1.5: Reproducing the issue… fasterer

One problem with using the MiqServer process as the target for observing the leak is it took days/weeks to determine if it was leaking or just the process taking it’s sweet time time to stabilize it’s memory usage. This led to very long gaps between making a change and having observable results to view from it.

One of the least invasive things that we eventually did was decrease the server :monitor_poll and :worker_monitor_frequency values from "5.seconds" and "15.seconds" respectively to "1.second" for both. This decreased the wait time between each iteration of it’s normal work and thankfully increased the leak rate.

We also increased the frequency of normal method calls by doing things like 40.times { do_some_work } in places where we thought the leak might be, and if there was rapid growth, we knew where to probe deeper.

The suggestions in this step happened for us throughout the process of discovering the cause of the leak, and didn’t necessarily happen in between this step and the next (hence the “Step 1.5”). Steps to accelerate the feedback loop, such as this, should be done as early into the memory leak debugging process as possible.

Step 2: Narrowing down the culprits

As mentioned previously, the MiqServer process has a pretty regular work loop. In case you aren’t familiar with the workflow of the MiqServer work loop, here is a gist of the important steps:

  1. heartbeat (tell other servers that I am still alive)
  2. process any messages sent to the server process over the message queue
  3. check on the health status of other server processes (and log them)
  4. check the status of all of the workers processes (stop/start them as needed)
  5. fetch queue work to assign to worker processes

Each of these steps happen in varying intervals and happen on a mostly consistent basis, barring any database latency fluctuations or worker changes.

After trying to instrument the main loop with tools like MemoryProfiler, stackprof, and even just looking at regular Ruby memory dumps using ObjectSpace.dump_all. In the end, we up taking a more low tech approach of just removing code by commenting out the steps above and seeing if the leak still happened after a server restart.

To start off, we already had a baseline of what it looked like with the code uncommented, and it leaked pretty consistently, so we did the inverse and commented out all of the steps above to confirm the leak stopped (it did, thankfully). We then worked our way back to the baseline by re-adding the methods one by one, and observing if the leak returned. After doing that a few times, we eventually determined that uncommenting the MiqServer#monitor_workers code (step #4 from the list above) was the cause. Progress!

Now, MiqServer#monitor_workers is pretty dense, so this wasn’t the end of the line, but it allowed us to start drilling in further and try some other tests outside the context of the application.

Step 3: First Observations

The tricks from step #1.5 were applied at this point, and we started making some more nuanced observations about what affected the leak:

  • The server seemed to leak faster with more workers forked and monitored by the server process. This meant to us that it was probably calling a method that was dependent on the number workers.
  • While adding some instrumentation to do some regular GC.starts and logging of GC.stat, we noticed the number of Ruby objects didn’t really increase (some custom caching that we do caused us to question this for a bit).
  • The [heap] in /proc/<PID>/smaps was the only thing growing. Combined with the info from the last bullet, this meant that the leak surely had to be in a c-extension, or Ruby itself. (an important observation made by Joe Rafaniello)
  • None of the suspected code looked to be a possible cause of a leak so it was probably something specific to our environment.

Those were all important observations, but the last one was a double-edged sword. Throughout this process, we continued to hold the mindset that “this has to be something with our code”, and it led us to going down a lot of wrong rabbit holes searching for answers that re-enforced that bias.

Step 4: Lots of failure, torment, and agony

At this point we started looking outside of the ManageIQ code base and started writing scripts to try to reproduce the leak in a more targeted fashion. These scripts can be found here and mostly failed to reliably recreate the leak.

Of note: one the places we got hung up on was thinking DRb, which we use for light message passing between workers, was the culprit. This mostly came from the thought that most people aren’t using it in production for anything terribly complicated, but for us, it was how key features like heartbeating were implemented. Additionally, this would have explained why the leak was worse with more worker processes forked and monitored as was previously observed.

We also found that sync_workers seemed to cause the server to leak quicker when wrapped with a 40.times {} block. This was eventually proven to not be the case as well, but did lead us down a path of trying some more obscure ways of testing our theories.

Step 5: We need to go deeper…

At this point, we were about 4 months into digging into this. The rest of this article happened in the last week prior to discovering the Ruby leak, but they are the good parts!

I ended up running across this blog post by Nelson Elhage (@nelhage), which ended up giving us an idea how to use gdb to hook into an existing process and run some arbitrary Ruby code in the existing environment.

The technique described in the blog post to find that memory leak was actually to take a look at the core dumps as hex and determine where patterns of leaked memory were. We weren’t that smart to be able to understand that ourselves or make use of it properly in our case, but we did end up using the rb_eval_string("") portions to our advantage though, and that ended up being super useful.

At the time, we were under the impression that MiqServer#sync_workers was the culprit, so we ended up attaching to an existing ‘warmed up’ server process that had been running for a bit so most of the code it would load would already be autoloaded. With that, we tried running a high number of invocations of that method in a row, and watched the /proc/<PID>/smaps to see if the [heap] section grew. It looked something like this**:

(gdb) p rb_eval_string("GC.start")
(gdb) p rb_eval_string("500.times { MiqServer.my_server.sync_workers; GC.start }")

Nothing. Not even a budge. Well shoot… there goes that theory.

At this point, we looked back to the servers we had running that were modified with the known “non-leaking” portions commented out, and checked if there was anything else that we might have been missing. We noticed that in addition to MiqServer#sync_workers still being uncommented, a call to MiqWorker.status_update_all that was directly preceding it was also still being executed.

Now, on it’s own, this method looks seemed pretty tame. Effectively it:

  1. Uses basic ActiveRecord to fetch all the workers under the current server
  2. Makes a call to our own code to fetch usage data for the given worker pids
  3. Saves the data to the DB records for internal reporting/monitoring

The first and last bits were ruled out, and we were pretty sure we already tested the middle bit via some scripts outside of the context of ManageIQ, but for verbosity sake, using the same gdb process, we decided to update the 500.times {} block to include that call:

MiqServer.my_server.sync_workers; MiqWorker.status_update_all; GC.start 

And… it leaked… !?

Step 6: Wait… wut!? (aka: Replicating it without the app)

At this point, we started to look at the call to MiqProcess.processInfo(worker_pid) in MiqWorker.status_update_all since the other areas are mostly ActiveRecord. Besides: it’s easier to blame our own libraries. ;)

Now you might be thinking “that was your code… why didn’t try that in the first place”, let me explain. We had tried something similar in a test script with no leak, but ended up trying it again. The first draft of the script looked basically like this:

# Code needed to get MiqProcess.processInfo loaded and working
require 'manageiq-gems-pending'
require 'util/miq-process'
require 'active_support/all'

pid = ARGV[0]

loop do
  sleep 0.5

Unfortunately, it didn’t leak…

┻━┻ ︵ヽ(`Д´)ノ︵ ┻━┻

┬──┬ ノ(ò_óノ)

Well crap…

That is when Keenan Brock (who I was pairing with at the time) had a brilliant idea, where he suggested that we include a call to require 'config/environment.rb' to load and initialize our Rails app, and then run the script again. What do you know, it leaked! And on a regular basis as well!

From there, we narrowed down what the core components of .processInfo were, and ran each bit overnight and logged the [heap] section of the /proc/<PID>/smaps overtime. The leaky section ended up being a call to MiqSystem.num_cpus, and the code for that method can be seen below:

def self.num_cpus
  return unless Sys::Platform::IMPL == :linux
  require 'linux_admin'
  @num_cpus ||=

Looks completely harmless… what gives!

Well, this method does a no-op if we weren’t testing on linux (we were), and we also tested calls to Sys::Platform::IMPL separately, and that was in the clear. The only time any “work” is done in this method would be the first time this method was called, and that work is short circuited once the cached value is set.

This left the require call. We updated the script above to include require 'config/environment' and replaced MiqProcess.processInfo(pid) with require 'linux_admin' and ran it.

It leaked…


Step 7: Mind blown

After piecing our heads back together, we noticed a few things:

  • Again, without require 'config/environment' at the top, no problems, meaning our environment was at play… at least a bit.
  • No matter what we called require with, it leaked just the same***.
  • We could also recreate this on non-linux operating systems

The second bit should come as no surprise, because after calling require on the same file twice, the code isn’t reloaded if Ruby determines it has already been loaded (hence the difference in return values if you do that in IRB as well: true for the first time, and false every time thereafter).

But still, that means we have a leak in Ruby… right? But with require? How has no one run into this before? Could a c-extension be messing with things here or is Ruby the culprit?

At this point, we tried a few different profiling techniques:

  • I personally used gdb again using a crazy python gdb script I cobbled together
  • some co-workers used the Instruments application to do basically the same thing (but with less upfront work of writing their own script/framework)
  • we even wrapped the require call using good-ole ruby-prof

After a couple of us were trying the above, we noticed that a common thread between them all when the leak was present: Pathname. This was observed in two main ways:

  • when using ruby-prof, require was spending a decent amount of time in Pathname#to_path for each require call
  • in “C land” where rb_get_expanded_load_path was showing up in the stack of the malloc calls of require when it was leaking.

We still weren’t completely sure if this was a Ruby or gem issue. Finally, with a final bit of trial and error (again, props to Joe Rafaniello for this find), we noticed that if we called .to_s on ALL of the places that we added a Pathname value to our autoload_paths in our Rails Application/Engine configs, the leak disappeared! Hooray!

With this, we quickly ruled out Rails being the issue here because we could replicate using a simple Ruby script:

require 'pathname'


dot      = "."
filename = "ostruct"
1000.times { 1000.times { require filename }; print dot; GC.start; }

And we confirmed that Rails even adds autoload_paths to the $LOAD_PATH in an initializer, so this was clearly a Ruby bug. We tested across multiple versions of Ruby and determined that only rubies 2.3.x and 2.4.x were exhibiting the leak (apparently this was fixed in Ruby 2.5.0****).

Other Observations/Conclusions

MIQ is a pretty heavily QA tested application across versions, so we could definitely see from one version to the next that this leak started after our “E release” (we are now on “G”), which was the first release using Ruby 2.3.x.

Also, as mentioned, this is a very rare situation where the programming language, Ruby, was actually at fault here (we can safely admit that the developers of Ruby are WAY smarter than us, so this is generally a safe assumption). Now, there is an assumed asterisk that goes along with this statement for a few reasons:

  • Most developers tend to place require statements at the top of files, so they aren’t triggered constantly like it was in our case. We did this for #Reasons™, but in short, this was mostly used to solve other deficiencies of our monolith application that could have been solved in other ways.
  • The Rails guides tend to show using Strings when setting autoload_paths, and so it is less likely developers will use “deferred requires” like this and also have Pathname entries in their $LOAD_PATH.

There is even a bit of a note in the Ruby source of load.c that even mentions that non-string values in the load path are expanded every time and not cached, so it seems that using Pathname values in the $LOAD_PATH is somewhat discouraged.

Bottom line: Still kinda our fault in the end…

Well, at the very least, hopefully you found this blog post entertaining, and possibly you can learn from our successes, and TONS of failures, if you find yourself trying to unravel your own memory leaks or other weird performance bugs! The key was to continually look at the issue from different perspectives, with various tools, and of course, sleep on it and come back at it with renewed vigor and a clear mind.

A big thanks to Joe Rafaniello, Keenan Brock, Dennis Metzger, and Jason Frey for their contributions in helping to find this leak. I happen to be the one documenting this experience (epic adventure*****), but it was definitely a team effort over many months.

  • * For those unfamiliar with ManageIQ/CFME, these are queue based workers, the first of which “Collects” metrics from an infrastructure provider (VMs, Containers, Instances, etc.) managed by ManageIQ, and sends it to the “Processor” in a generic fashion that it can be imported into our DB schema.
  • ** The GC.start calls here are to avoid typical memory usage of Ruby objects being allocated normally from improperly skewing the results.
  • *** The reason for these “deferred requires” is for base memory savings. Since linux_admin is only needed by a small portion of our code base, but adds around 5 to 10 MB when loading it, we only load it when we need it.
  • **** Note, Ruby 2.5.0 might not leak but Pathnames are much slower in the $LOAD_PATH than strings, perhaps, because they’re not cached as mentioned here.
  • ***** Over selling this quite a bit here… | (• ◡•)|/\(❍ᴥ❍ʋ)