The benchmarks seem to revolve around the invoking of the collector. I am very curious as to how common it would be to do that often enough for it to matter in terms of overall speed?
You'd still want to reach for mmm only after A: verifying that GC time really is your biggest problem via profiling and B: taking more normal steps to minimize overallocation first, because with value types Go has some tools (if not necessarily "a lot" of such tools, but definitely some) for dealing with that. But if you end up backed against the wall, this may be helpful.
You could also arguably add a C: did you really mean to use a language with manual memory in the first place, or perhaps Rust? Or can you factor just the relevant bit out into such a language and interact via some RPC mechanism back to the Go code base? But as the situation becomes arbitrarily complicated there simply ceases to be a silver bullet.
GC pauses can be anywhere between 300ms to 30 seconds or more when it starts becoming an issue.
In this configuration, each incoming request means allocations inside Go's RPC package [1], which in turn means that a GC pass will be triggered if GC_PERCENT [2] has been reached, which in turn means that the GC will have to scan all of those long lived pointers (Go's GC is not generational), which in turn means a huge peak in response time.
This basically leaves me with three possible solutions:
- hack into Go's RPC package to minimize allocations, which is a huge price to pay just to delay the inevitable
- build my caches in a language that offers manual memory management, then query those via RPC from my Go services; but I don't want to add a new language into the mix
- provide a generic solution for manual memory management in Go, which is where we are now
I know many people won't agree with that, and there are definitely good reasons not to; and still, as far as I'm concerned, minimizing the complexity of my software stack means there's one less thing that I'll have to worry about, and at the end of the day, that is really quite the upside.
Again, it's almost certainly premature optimization to start with that design, but if that's where your optimization leads you, it's not that surprising.
FWIW, I wrote something myself that hits a similar problem, but in a completely different dimension: https://github.com/thejerf/gomempool My problem was that I had an otherwise rather placid program (from an allocation perspective) that liked to allocate buffers for messages that were many hundreds of kilobytes to low numbers of megabytes in size. In normal usage, only maybe one or two of these are ever in use at a time, but I use hundreds per second. In my case, each individual GC was actually not that big a deal, but I was triggering them every few seconds. The GC would see a lot of large allocs, and then successfully clean them up, meaning that the next batch of large allocations would be seen as crossing the threshold again. My stats clearly showed that on this system, once I started pooling my []byte I never even filled up the memory pool itself, and my GCs plummeted so far that I wouldn't even particularly care if they took half-a-second apiece anymore, which they don't. Almost everything other than those large message buffers were stack-alloc'ed anyhow.