8 releases (4 breaking)
Uses old Rust 2015
|0.5.0||Aug 12, 2017|
|0.4.2||Aug 5, 2017|
|0.3.0||Aug 4, 2017|
|0.2.3||Aug 2, 2017|
|0.1.2||May 26, 2017|
#4 in #tfs
conc — An efficient concurrent reclamation system
conc builds upon hazard pointers to create a extremely performant system for concurrently
handling memory. It is a general, convenient, memory light — and sometimes faster — alternative
to epoch-based reclamation (see the trade-off section).
- High-level API
Atomic<T>for an lockless readable and writable container.
syncfor basic datastructures implemented through
Treiber<T>for concurrent stacks.
Stm<T>for a simple implementation of STM.
- Low-level API
add_garbage()for queuing destruction of garbage.
Guard<T>for blocking destruction.
- Runtime control
gc()for collecting garbage to reduce memory.
settingsfor reconfiguring the system on-the-go.
aturon's blog post explains the issues of concurrent memory handling very well, although it take basis in epoch-based reclamation, which this crate is an alternative for.
The gist essentially is that you need to delete objects in most concurrent data structure (otherwise there would be memory leaks), however cannot safely do so, as there is no way to know if another thread is accessing the object in question. This (and other reclamation systems) provides a solution to this problem.
While the low-level API is available, it is generally sufficient to use the
abstraction. This acts much like familiar Rust APIs. It allows the programmer to concurrently
access a value through references, as well as update it, and more. Refer to the respective docs
for more information.
If you are interested in implementing your own structures with
conc, you must learn how to
add_garbage. In short,
conc::add_garbage()adds a destructor with a pointer, which will be run eventually, when no one is reading the data anymore. In other words, it acts as a concurrent counterpart to
Guard<T>"protects" a pointer from being destroyed. That is, it delays destruction (which is planned by
conc::add_garbage()) until the guard is gone.
See their respective API docs for details on usage and behavior.
debug-tools and set environment variable
CONC_DEBUG_MODE. For example,
CONC_DEBUG_MODE=1 cargo test --features debug-tools. To get stacktraces after each message,
set environment variable
sync source code.
Tradeoffs - Why not crossbeam/epochs?
Epochs (EBR) are generally faster than this crate, however the major advantage this has over epochs is that this doesn't suffer from memory blow-up in intense cases.
The issue with most other and faster solutions is that, if there is a non-trivial amount of threads (say 16) constantly reading/storing some pointer, it will never get to a state, where it can be reclaimed.
In other words, given sufficient amount of threads and frequency, the gaps between the reclamation might be very very long, causing very high memory usage, and potentially OOM crashes.
These issues are not hypothetical. It happened to me while testing the caching system of TFS. Essentially, the to-be-destroyed garbage accumulated several gigabytes, without ever being open to a collection cycle.
It reminds of the MongoDB debate. It might very well be the fastest solution¹, but if it can't even ensure consistency in these cases, what is the point?
That being said, there are cases where the other libraries are perfectly fine (e.g. if you have a bounded number of thread and a medium-long interval between accesses) and also faster.
¹If you want a super fast memory reclamation system, you should try NOP™, and not calling destructors.
- The API of
concis - by design - more lightweight interface-wise, as it doesn't require the call side to pin epochs or similar. This is particularly nice when you design more complicated structures.
Guard<T>) is not bound to a lifetime or similar, meaning that it is
future-rscompatible among other.
conc, threads can export garbage while there are active objects, meaning that memory won't accumulate on non-stopping usage.
concis runtime configurable through the
- Better debugging tools.
- More-well tested and well-documented.
- More fine-grained control over the runtime.
- Access to low-level API.
- In many cases, it is slower.
- Fewer pre-implemented data structures (for now).
Design & internals
It based on hazard pointers, although there are several differences. The idea is essentially that the system keeps track of some number of "hazards". As long as a hazard protects some object, the object cannot be deleted.
Once in a while, a thread performs a garbage collection by scanning the hazards and finding the objects not currently protected by any hazard. These objects are then deleted.
To improve performance, we use a layered approach: Both garbage (objects to be deleted eventually) and hazards are cached thread locally. This reduces the amount of atomic operations and cache misses.
For more details, see this blog post.
Garbage collection of the concurrently managed object is done automatically between every
n is chosen from some probability distribution.
Note that a garbage collection cycle might not clear all objects. For example, some objects could be protected by hazards. Others might not have been exported from the thread-local cache yet.
It is worth noting that atomic reads through this library usually requires three atomic CPU instruction, this means that if you are traversing a list or something like that, this library might not be for you.
You can reconfigure the system on-the-go through the
There are also presets. For example, if you experience high memory usage, you can do: