Concurrent garbage collection
Just a few days ago my work on making the Rubinius garbage collector more concurrent has landed in Rubinius master. In this post I’ll describe how this work was done and what the ideas behind it and pitfalls encountered are. Hopefully after reading this post, you’ll better understand what concurrent garbage collection means for your Ruby programs and how they operate. Note that I have explicitly chosen to keep things like benchmark and performance numbers out of this post - it is long enough as is.
In the beginning there were long pauses
Garbage Collection pauses: anyone with a somewhat complex app usually knows about them. They often result in wildly varying performance of your web requests and cause performance issues in unexpected situations. There has been a lot of effort put towards working around these issues, such as out of band garbage collection done with servers such as Unicorn and Passenger. Honestly, I think these techniques are very useful but in essence still workarounds because of a deeper problem in MRI.
With this work on concurrent garbage collection, long pauses are a thing of the past in Rubinius. You can run a single process application server and see stable and fast performance in highly concurrent scenarios.
So for this post I’ll first introduce some fundamental concepts that we needed to make explicit in Rubinius to support concurrent garbage collection. After that I’ll discuss some of the issues encountered when stress testing and putting Rubinius under load with applications such as Rails and Sidekiq. Often there will be a reference to a commit, this is because commits won’t change, but code can. This makes sure this is still correct and relevant even when things are changing in the future.
The tri-color invariant
For people having some knowledge about garbage collection theory, the term tri-color invariant probably sounds familiar. It is a term that describes a property of the system that is important in being able to garbage collect properly without for example cleaning up used data.
The tri-color invariant defines three different states for an object: white, grey and black. Each of these states describes the state of an object during a garbage collection phase.
White objects are objects that the garbage collector hasn’t seen yet and doesn’t know about yet. It might be found in the future, or it might not if it’s actually garbage and not a reachable object.
Grey objects are objects that the garbage collector has seen, but hasn’t completely handled yet. By handling I mean that this object has not been completely scanned for references to other objects. This means for example that we haven’t visited the class or instance variables table in Rubinius.
Black objects have been seen by the garbage collector and also has been scanned. This means that this object is handled and doesn’t need to be revisited again during the current phase.
Garbage collection works in different phases. The first phase is to start and mark objects known as roots. Roots are objects that we define as always reachable and who should never be cleaned up. One example of these in Rubinius are the built-in classes. We never want to garbage collect Module, Class or Object. Another group of root objects are the objects currently on the stack when we garbage collect. These might be used still after the garbage collection finishes and the application continues to run.
When we start the garbage collection cycle, we make the roots grey. This is done in Rubinius by marking them and adding them to a list of objects that are going to be scanned in the future. This allows us to do garbage collection without having to use recursion here, which could lead to very deep call stacks and potential stack overflows.
When we have done this, we start handling the so called mark stack. We pop off an object and scan it. This makes the object implicitly black, because it is marked and no longer in the mark stack. It is important to realize that the invariant colors are not always explicit states, but sometimes implied by the total system state. They are a tool for reasoning about garbage collection, not a design for how you must write an algorithm.
During the scanning of an object we might encounter new (“white”) objects. We mark them and add them to the mark stack as well, thus making them grey. This process of handling the mark stack continues until the entire mark stack is empty. At that point we know that all the reachable objects are now black and the remaining objects can be cleaned up.
From these steps you can see the following invariant:
“A black object never points to a white object, but always only to grey or other black objects.”
This invariant is important because if it were to be violated, we would clean up a white object if it would be never marked. But that would mean the black object would refer to garbage instead of a valid object.
Tri-color invariant and concurrency
Maintaining the tri-color invariant is important for correctness. If you look at the reasoning in the previous section, you might already realize where violation of the invariant might happen in a concurrent scenario.
The simplest example is the following. When we start to handle the mark stack, we scan objects and make them implicitly black. Now imagine the case where our code (that still runs during GC) writes an unseen white object into that already scanned black object. In this case we can’t guarantee the tri-color invariant because our application might change things behind our back without the garbage collector knowing about it.
So the question is, what would be a solution for this problem? Well, the obvious thing would be to make sure we run some additional checks when we encounter the scenario of a white object being writing into a black object. This means, however, that we have to make sure we can catch all these cases where this happens. What if somewhere in the virtual machine we would assign a variable and not run this code? It would mean breaking the invariant and that leads to memory corruption down the road.
This situation represents a triumph in the history of Rubinius engineering - because the VM already had a Generational GC and no global interpreter lock, the work that went toward making the GC concurrent was much simpler. Let’s try to understand why.
The write barrier
In generational collection, we have a problem that is somewhat similar to the tri-color invariant problem here under concurrent garbage collection. Generational garbage collection stems from the “weak generational hypothesis” which states that “objects tend to die young.” One obvious example of this can be found in Rails. The objects loaded in your Rails app often consist of two classes of objects.
The first class of objects are all the objects that define your app. For example the class definitions of your controllers and models, but also templates for your views. These objects stick around for the entire lifetime of your application. The second class of objects are the ones allocated when you handle a request. They only live for the lifetime of the request, so if you would only garbage collect those objects after a number of requests, you would already prevent any memory growth.
This is a very simple example of the generational hypothesis and why generational garbage collection works well on workloads like Rails. But I said that this suffers from a similar problem as the tri-color invariant violation, so what is that problem?
The problem occurs if you cross the barrier between young and old generations. For example you store a new object into a long existing array. That array is part of the mature generation. What you don’t want to happen is that when we clean up the young generation, we miss this. If we would, the array would suddenly contain garbage and no longer a valid object. This means that also for this problem we need to run an additional check when writing an object into another object.
The code running these checks is called a write barrier, because it’s a piece of code that is being run on each write of object into another object. What exactly this code is depends on the use case. We have already described two of them now, one for generational and one for concurrent garbage collection.
The remember set
So we’ve just discussed the write barrier. It also stated that we already have a write barrier for generational collection. For some of the issues mentioned later on, the generational concerns are also of significance, so it’s useful to also explain the concept of the remember set.
The remember set is a set of objects that is used to scan for young objects during a generational collection. This is needed for the cases where we store a reference to a young object into a mature object. What we do is store the mature object in the remember set, so we can scan it when we do a young collection. This makes sure we follow all the paths which through a young object is reachable.
Changing the write barrier for concurrent GC
So we need to be able to run some code when we are writing a white object into a black one during generational garbage collection. Because we already have a write barrier, we should have a single place where we can change this.
Well, that was not entirely true. As part of implementing the concurrent garbage collection solution, I also cleaned up some of the internals to unify for example our usage of write barriers. It wasn’t a terribly daunting task, since the logic was already there, just that we had the logic in more than one place. If you want to take a look at what this work was, I’d suggest taking a look at the diff here:
If you read through the commit, you can see it actually removes more code than it adds, because it cleans up and unifies the usage of the write barrier.
So this brings us to the next point of how to change the write barrier. What we need to do is to make sure we guard the case where we store a white object into a black object. If we detect this scenario, there are basically two possible strategies. The first one is the make the white object grey by marking it. This means we will scan it in the future. The other option is to make the black object grey again, so we scan that again in the future.
The first solution has the advantage of moving the collection forward, not backward like the second solution. It does however have the downside that it could keep objects alive longer than the second solution would. In the implementation for Rubinius we’ve chosen the first option, mainly because it proved to be the simplest to implement and it moves the so-called wavefront forward.
The other thing we need for this check is to know whether an object is white, grey or black. As you might remember, I’ve said before that these states might not be explicitly modeled. This was actually the case in Rubinius. There was a way to determine that an object was black, namely when it was marked but not in the mark stack anymore. The first check is easy and cheap, but the second check isn’t. We would have to scan the entire mark stack for an object if we want to write another one into it. This would quickly become really expensive and isn’t a good solution.
Introducing a new garbage collection state
The only way to really tackle this problem is to introduce a new state for an object, so we can easily see that it’s already scanned, not only just marked.
For this I also have to take a sidestep and explain another concept, namely the rotating mark concept. If you look at how to track the state during the mark phase, there are a few solutions. The easiest one is to have a single bit to identify whether we’ve marked an object or not. This comes with a downside though, that we have to make sure we set all the marks back to 0 before we start a garbage collection, or at the end of a garbage collection so it’s 0 for the next cycle.
To prevent this overhead, there is a concept called a rotating mark. It’s actually very simple, instead of having a mark of 0 or 1, we have 0, 1 and 2. When an object is new, it still has a mark of 0. The first time when we garbage collect, we use 1 as the mark. This means we can clean up all the objects afterwards which don’t have mark 1.
The second time we swap the mark to 2 and do the same thing. We can remove anything with a mark that is not 2 after the collection cycle. This is a concept that the garbage collection in Rubinius already used before these changes. So what we did was extend the mark bits to also include the scanned state. Rubinius uses the object header to store this information inside the object and we had a few more bits of room to store additional information.
We could have solved this basically in two ways, one would be to just use a single bit as the scanned state. This would work fine, but it would make updating the header with this bit more expensive in certain concurrent scenarios, where we would end up doing a compare and swap operation twice instead of once.
Therefore we opted for merging it with the mark bits. This means that instead of 2 bits to store 0, 1 or 2, we now use 3 bits to store 0, 2, 3, 4 or 5. Why is 1 not used you might ask? Well, that is a side effect of the combination of a rotating mark and the scanned state.
The scanned state is actually represented in the last bit. So a value of 1 would mean the object is scanned, but not marked. Since we always first mark an object before scanning, this scenario can’t happen. The values 2 and 4 mean the object is only marked, the values 3 and 5 mean the object is marked and scanned. These values also make the operations for checking if a mark is set and setting the scan state simple.
You can find the code that introduces this new state in the following commit:
A new version of the write barrier
So with this new addition, we actually have the tools at hand to change the write barrier so we can handle this new case for the concurrent garbage collection. So besides checking the generations of the objects, we now also have to check the newly introduced scanned state, which represents the black state in the tri-color invariant. If the object we write into is already black and the object to be written is white, we store this object in a separate set.
This set is then in the finalization phase of the concurrent garbage collector used. How this works exactly will be explained later, for now it’s important to remember that objects in this set will be marked in the future.
The new version of the write barrier can be found here in this commit:
As you see, it also incorporates more changes, mainly the addition of the before mentioned set and changes to the JIT. The changes to the JIT are necessary because the JIT basically emits the assembly code for the write barrier directly, so it also needs to emit the new version of the code.
There is one thing here that might strike some as surprising. That is the fact that we actually set the scanned state before scanning the object. This is not a bug, but deliberate, since otherwise there is a race condition possible. The race condition would be that a scan of on object is in progress while another thread runs the write barrier for that same object. In that case it could see the object as not scanned, and not store the new reference. The other thread would then mark the object as scanned, but it would still have a white object stored. This would violate the tri-color invariant and cause corruption.
By setting the scanned state as the first operation, the only risk is perhaps adding an element unnecessary due to a race condition, but this isn’t problematic but just a minor increment in the amount of work. Since this only happens with this race condition, this case is so small it doesn’t cause any performance issues.
The actual concurrent garbage collection
So with these pieces put into place, I was kind of surprised. It only had taken very little work to add these new concepts, a testament I think to the design that Evan and Brian originally started. Most of you probably know the feeling where you start to implement a seemingly daunting feature, but after starting it everything falls into place so easily you feel something must be wrong. Of course at this point it did take me longer to finish the work, but luckily not because of fundamental problems with the approach, but just with bugs for certain edge cases.
So with this initial surprise out of the way, let’s see how the basic algorithm works. The first thing that is important to realize, is that this is a mostly concurrent garbage collection, so it’s not 100% concurrent. There are still stop the world pauses, but they are much smaller than with a complete stop the world collection.
These stop the world pauses are needed to get a consistent view of the roots to start collection from and to finish it all up to get another consistent snapshot of the system. So what we do is trade one big stop the world pause for two much smaller ones.
During the first stop the world, we do the same thing we did before to setup the initial state to start collection with. After this is done, we signal all the threads that they can continue to do their work, while a separate thread will start the mark phase. This mark phase will happen concurrently with the other threads running. When the mark thread has marked everything in the mark stack, we request the second stop the world phase. In this second phase we rescan all the roots, because they might have changed. This means also rescanning the thread state so we can see what is on the stack at that point.
After we do this, we also schedule the special set that the write barrier has tracked during execution. We add that as well to the mark stack. We then scan the remaining mark stack during this stop the world phase. This is in general a much smaller stack, because we already scanned the majority of the system.
After we’ve finished scanning the mark stack, we do the same finalization as we did before in the stop the world collector. We have to handle things like finalizers, C-API handles etc. and also sweep up the garbage. If this final phase has finished, we can continue running everything and garbage collection for this iteration is done.
The commit introducing this new thread can be found in:
Most of the changes are for updating the gathering of statistics. We of course want to know how much time is spent in the stop the world pauses and the concurrent mark phase, so we can easily see the benefits of this strategy. Another large amount of the code is actually dedicated to supporting another feature, which is described in the next section.
Running young collections while concurrently marking
One thing that is of concern when doing a concurrent garbage collection of the whole program is what happens to the young generation. As explained earlier, Rubinius has a generational garbage collector, so we run different styles of collection at different times. Only when a young collection can’t satisfy memory release, or we have allocated a lot in mature space, we trigger a full concurrent collection.
The easiest way to prevent this issue is of course preventing a young collection from happening while we’re concurrently marking. This however, introduces a significant problem. The problem is that when this happens, any allocation that would normally create a young object can’t succeed. This means those objects would be allocated as mature objects, increasing the memory pressure for the total system.
In a system that for example runs Rails with reasonably complex pages, it’s not that strange a situation that we would want to run multiple young collections during the runtime of the concurrent mark phase. If we don’t solve this problem, this would mean megabytes more of mature objects being allocated.
The young collection still happens in a stop the world phase. This stop is not that problematic, since it only takes a short time to collect the young space. This is because it only copies over live objects and it’s limited in size. This means that collection times are usually in the order of a few milliseconds.
So we can use the fact that we stop the world in this cases to make sure we update all the structures at that point for the concurrent mark phase. The biggest thing here is that we need to update the mark stack and other similar structures, such as the set of weak ref objects and the finalizers.
Since the concurrent mark phase marks all objects, it also marks young objects. It needs to see the copied objects if they are still alive. This also applies to the set that is tracked by the write barrier.
Inflated headers and code resources
Another solution to the problem of writing white objects into black ones is to always make all objects grey if they are allocated during a concurrent garbage collection cycle. This approach is similar to how we solved problems with other managed resources that aren’t Ruby objects.
There are two types of those objects worth mentioning, inflated headers and code resources. The first one is a place where we store information if it doesn’t fit in a normal object header, or when it requires non moving memory. This last required applies for example to using an object as a Mutex, which is possible with Rubinius much like how the JVM also works. We can’t just move memory if an object is locked, so we keep that information separate.
The inflated headers also keep a mark. This is done so we can clean them up after a garbage collection cycle and deallocate space for objects that are not longer alive. This means we now have two marks which implies a possible race condition. What if an object is marked by one thread and concurrently inflated by another? We would need to make sure we don’t lose information in that case about the inflated header mark.
Since we don’t inflate objects often, we choose a very conservative approach here. What that means is that if an inflated header is allocated, it always gets the current mark set as it’s mark. It means that the inflated will possibly not be reclaimed during the first garbage collection if the object is already out of reach. We don’t view this as a problem, since inflated headers often are allocated for objects that will stick around longer, for example because they are used as a lock.
The second case is code resources. Code resources are things like code compiled into the virtual machine representations of bytecode, or native code created for jitted functions.
Here we use the same approach: always allocate them with the current mark. Code resources are also very likely to stay around, since often code gets executed more than once. This approach here is also the simplest for fixing issues related to this problem.
These changes where made in the following two commits:
Fibers and how they affect stack scanning
Fibers are somewhat special. They have a part that works like a normal Ruby object, for example where we store the return value of a fiber. The other parts of a fiber is the actual stack that is executing.
In the past we tracked these two things separately, but that wasn’t actually needed. The problem is that with concurrent collection, we have to separate them again. This is needed because we can’t concurrently scan the stack parts of a fiber, since those stacks could be executing and become invalid at any moment.
This means we have to separate the handling of the stacks from the normal object. We can then scan all the stacks in the final collection phase for all the fibers that are marked and thus reachable at that point. If we don’t do this, applications like Sidekiq that heavily use Fibers will crash almost instantly on the first garbage collection cycle.
This change was made in commit:
The first more subtle bug
The additional of the concurrent mark thread is also where the first subtle bug was introduced and subsequently fixed. As in the previous section described, we have to update the mark stack during a young collection cycle.
During a young collection cycle, objects can be promoted. This happens when objects are alive for a certain number of young collections and are moved to the mature space. During this time, the allocation mechanism also changes and the object gets allocated in the area where we sweep after a concurrent mark phase is complete.
The bug only happened when a young object was promoted that was already marked in the concurrent mark phase. This means the object header already had the mark set. The tricky thing here is that the mature memory space also as a separate mark table. This is part of the Immix algorithm used.
What would happen if a marked object was promoted is that the mark was retained. This means however, that the underlying Immix memory space was not marked. This means that during the sweep phase the memory was incorrectly seen as not in use and reclaimed. This leads to memory corruption because now a piece of memory is used for two different objects.
The solution to this problem is actually very simple. The only thing we have to do is when an object is promoted, is to remove the mark. This makes sure that when this object is stored into some other object, it would go through the write barrier and will be scheduled for proper marking in the future. This will result in the underlying memory to be also marked and the problem is gone.
Concurrency, forking and deadlock hell
One of the most difficult features to get working correctly in a concurrent virtual machine like Rubinius is forking. I can perfectly understand the reason for the JVM not supporting this, besides it not being cross platform. It requires careful orchestration of all the threads to prevent the child process from ending up in a problematic state.
Of course, since we added a new thread for concurrent marking, this also introduced a deadlock that could be triggered by forking easily. This was evident in a Sidekiq app that relied on forking to spawn subprocesses with the fork and exec pattern.
The problem here was that we still had the lock that is used to
safeguard the internals of the marker thread, while also waiting for a
stop the world pause. This stop the world pause would then be triggered
fork(), which then also requests all the auxiliary threads in the
system to pause at a safe point.
To get to this safe point, the forking thread would try to grab the lock of the Immix thread, which it couldn’t because the thread itself still had the lock. The fix is similar to how other auxiliary threads such as the signal thread and finalizer thread work, by releasing their own lock just before they mark themselves as being dependent on the garbage collector again.
This problem was fixed in commit:
Finalizers are tricky to get right
This last found and fixed bug was the most elusive one. It was really hard to trigger and only happened rarely, but luckily often enough so that finding it wasn’t completely impossible. As always with these bugs, reproducing the bug is really 50% of all the work. 49% is finding what causes it and 1% of the time is actually spent fixing it. Remember though, that 80% of all statistics are made up on the spot.
Here again the intricate play between different threads and the timing dependency of execution caused the issue to only appear rarely. The easiest way to reproduce it, was to run a Sidekiq test application that cpuguy83 graciously provided for finding another bug.
After running at full speed for maybe somewhere in between 5 and 10 minutes it would crash. Such long feedback cycles can be very frustrating and make digging into it slow. However, the rewarding feeling and actually providing people with a stable Ruby runtime is definitely worth the hassle.
So what was at play here? First of all, it is important to know about finalizers. Finalizers are functions you can register that run when the garbage collector determines an object has gone out of scope. Inside Rubinius we use this for a number of classes, such as for IO to close it if it hasn’t been and for Fiber’s to clean up the stack space they might have allocated.
This last category of object is actually what triggered the problem in Rubinius. It looked like a fiber during the finalization had references to no longer valid young objects. These young objects would for example be the value that a fiber stores as the return value. So the question is what was causing these invalid objects? It looked like the fiber was no longer properly scanned during young collections and didn’t see any updated value.
That last sentence might seem easy and straightforward, but it took a few hours to actually consciously realize the implications of it. I sometimes call it “A-ha driven development.” The point where you just poke at code, trying to see through it in a concurrent scenario, until the case where it can go wrong pops into your head. I haven’t been able to really identify the though processes going into it, but it includes a lot of poking around and pondering.
Actually often in cases like that I just go and take a walk to ponder things, or if it’s late just go to bed. I’ve literally had eureka moments the next morning in the shower, realizing what the problem was. If anyone has insights or ideas on how to improve this process, I really welcome them. I’d love to get a better handle on it to see if it can be made more consistent in some way.
So let’s go back to the original sentence that is much more important than it seems. It stated that the problem occurred if a mature fiber object would not have it’s young variables updated. This should makes us remember the concept of the write barrier. As explained we use that to make sure that if we add a young object to a mature object, we register that. What happens in that case is that the mature object is stored in a remember set, that is always scanned during a young collection. This ensures these values get updated properly.
So what was happening here? After adding some debug logic (yes, just lots and lots of printf statements), I could see that the Fiber was no longer in the remember set. But it should be, since it had young objects referenced to it!
So the question is why is it removed from the remember set. Normally if a young object is collected and stored back into the mature object, the write barrier gets executed and the object is added to the remember set that is used in the next cycle. Why doesn’t this happen in this case? So after looking for where we active remove objects from the remember set, I found this actually happens during finalization. After an object is finalized, we removed it from the remember set since it couldn’t be referenced anywhere anymore.
This reasoning was perfectly sound until we introduced concurrent garbage collection. Because that suddenly introduces a new place that the object might still be referenced from, namely the current mark stack! This means that this goes wrong in the following scenario. The fiber is in the concurrent mark stack, which could happen because for example it was still active when concurrent collection started. Then the fiber goes out of scope and during a young collection it is scheduled for finalization. Because finalization has to keep objects alive, it has to keep the fiber live. This would then cause the fiber to be promoted and to be a mature object. The concurrent mark stack would be updated with the reference to this now mature object. This would also result in the now mature fiber to be added to the remember set.
So this sets the stage for the bug, what would happen next is that the finalizer would run and after it completed, it would removed the mature fiber from the remember set. Meanwhile, before we actually reach the fiber in the concurrent mark phase, another young collection runs. This young collection then moves the object inside the fiber, for example the return value. Now finally the concurrent mark stack reaches the fiber and crashes, because it sees an invalid object as the fiber’s return value.
So how do we fix this? Well, the fix is actually simple, we just don’t remove a finalized object from the remember set. This means it still is scanned during young collections. The downside is that more objects might end up being promoted than necessary, but that’s better than completely crashing and breaking the guarantees a garbage collector should give.
There is room to improve here for the future though, we could check if a mature collection is in progress and only then not remove the object from the remember set. Since starting a mature mark phase always happens in a stop the world phase, this check shouldn’t be problematic due to race conditions.
You can find the actual fix for this bug in this commit:
For now, these are the issues and problems encountered during the implementation of the concurrent garbage collector. I’m sure there will be more garbage collection related bugs in the future, although I think they will often be bugs that also are present for our non-concurrent collection. Of course there were also issues like just wrong syntax that would cause C++ compile errors etc, but those are of course not that interesting to talk about and part of the normal development progress.
I’ve stress tested the concurrent collector under different load patterns, like running a concurrent Rails application and Sidekiq test applications. Those systems are often pretty good in finding concurrency bugs and race conditions and since those are stable, I’m pretty confident that this is ready for more broad usage. This is of course also why it was merged into master.
I’ve tried to highlight the tricky and more complex issues here, so you hopefully have a better insight into what implementing all this means.
If you’re interested in more background on the things discussed here, these are some pointers to more resources. As a general reference work, there is the Garbage Collection Handbook. So far the best book on garbage collection I’ve seen with a lot of clearly explained content. It provides a really good starting point to learn about garbage collection and contains a lot of references to papers that can be read for even digging deeper into the subject matter at hand.
The original paper in the Immix garbage collector, which Rubinius uses. Note that our version of Immix isn’t compacting, something that made concurrent collection possible. In the future this is something we want to revisit and improve upon.
For finding more resources on garbage collection, you can also check out this bibliography of garbage collection related papers:
I would also like to thank Michael R. Bernstein for reviewing this post. He’s been writing interesting blog posts on garbage collection and gave a presentation at GoRuCo 2013. These articles can be really useful if you want to have a starting point for the basic garbage collection concepts that this article assumes you are somewhat familiar with.