But every large Go project I've worked on has eventually invested a lot of time into minimizing allocations, rewriting critical path sections to use pools, and various other techniques to improve performance by taking more control over memory allocation. Go's GC is very good, but it still has limitations.
Honestly it's not a bad tradeoff most of the time. Write code that works as quickly as possible, profile it as load scales, and apply further optimization as hot paths are identified. On the other hand, I've been working with Rust long enough that I can almost write the same thing in Rust as fast as I could in Go (Rust is inherently more verbose and requires more cognitive overhead). I've been writing prototypes in Rust more and more lately and haven't really been missing Go as much as I did when I first started with Rust.
- Greenspun's tenth rule
Joking aside, Java VM can be as fast as C++, not C, by using profiling and JIT optimization if tuned to a specific server and only measuring after running for a while. If you are really thinking about your allocators and not just calling malloc, nothing is faster. Half of this is due to less allocator calls and half is due to laying out the memory in a cache-friendly manner.
https://blog.discord.com/why-discord-is-switching-from-go-to...
Am I correct that redis does not offer a native API and so forces you to craft commands as strings? Just having to run snprintf 500k per second wastes a considerable amount of CPU. Combined with the normal overhead of communicating over a socket, using redis is going to be considerably slower than just keeping that memory in your own process. That's assuming the overhead of the redis internals is 0.
>The service we switched from Go to Rust is the “Read States” service. Its sole purpose is to keep track of which channels and messages you have read
>On cache key eviction, we commit your Read States to the database. We also schedule a database commit for 30 seconds in the future whenever a Read State is updated.
So it's a separate service whose only purpose is to store data in-memory with occasional commits to persisted DB, which sounded a lot like Redis. But of course, a special optimized service is always going to outperform a general purpose tool such as Redis.
They also replaced HashMaps with BTreeMaps, reduced the number of memory copies they were doing, etc. -- which has nothing to do with Go, but also contributed to a performance increase
It's not a library interface if you mean that - but that always applies for distributed systems.
> There are millions of Users in each cache. There are tens of millions of Read States in each cache. There are hundreds of thousands of cache updates per second.
This is something that is probably never going to be hit locally in the development toolchain. You can certainly prefer the Rust memory system to Go and have that be a valid reason to use Rust over Go for something like dev toolchains, but you're not going to hit scale problems like those mentioned in the article.
I think more engineers are becoming skeptical of optimizing compilers with that sort of implicit, potential "spooky action at a distance" where nonlocal code can cause performance regressions.
In Haskell, laziness and iterator fusion can do the same thing. Nonlocal code can cause local expressions to compile differently, resulting in - from an engineer's POV - nondeterministic performance, memory usage, GC pressure. "Space leaks" can also occur, again, due to nonlocal effects.
In JavaScript, the JIT can similarly cause local regressions due to nonlocal code. A single expression deep in a call stack mutating an object can result in it no longer fitting an optimized "shape", resulting in function deoptimization.
Just say no to spooky action at a distance in programming languages, in my opinion. It leads to tremendously difficult to debug regressions.
Because it's not the case with Rust or any compiled language? LLVM has shown regressions wich is what Rust uses to compile code.
2sec search on google: https://github.com/rust-lang/rust/issues/24194
I think there may be some edge cases (constant folding, dead code removal) of course, but a change to LLVM cannot cause the 3 sorts of changes I identified in Go, Haskell, and JavaScript.
I thought heap allocations are only triggered by taking pointers, i.e. using reference semantics (including casting to interface and calling a method whose receiver is by-ref)? If I'm careful to only use variables by-value (for types that can afford it, i.e. excluding map/array), what else can trigger heap allocation?
package main
import "fmt"
func main() {
closure := make_closure()
fmt.Println("closure():", closure())
}
func make_closure() func() int {
x := 1
return func() int { return x }
}
$ go run main.go
closure(): 1
* They incur the pain even when nothing needs doing. This is really terrible. If you spend ten minutes furiously reading data from a network device into a fixed RAM buffer, twiddling it, and writing it out again, in Rust (or C, C++ and various other explicit allocation languages) you neither allocated nor de-allocated anything, therefore you pay exactly zero for this work you didn't do. In Go you will incur the cost of the GC verifying that yup, everything is as it was, there's nothing to do, apparently five times.
* The pain is swallowed in lumps, you can't spread it out. So you must either size hardware for lump swallowing, even though as we saw above we don't even want to swallow any lumps, or accept poor performance each time a lump is swallowed. With explicit allocation you can choose to swallow bigger lumps (e.g. arena allocation) to reduce overheads elsewhere, but you decide on the size of lumps you want for your allocation.
Memory allocation is painful in Go?