In my opinion they need to invest a lot more time and money into it for that.
The development experience on VSCode was pretty bad (I think the LSP has a memory leak), and some important (for me) libraries aren't tuned very well yet (a Vapor webserver can sit around 100 MiB memory, whereas putting a bunch of load on the grpc implementation balloons the memory usage to >1 GiB).
I haven't followed swift too closely, but ref counting is not a good fit for typical server applications. Sure, value types and such take off a lot of load from the GC (yes, ref counting is a GC), but still, tracing GCs have much better performance on server workloads. (Reference counting when an object is shared between multiple cores require atomic increments/decrements and that is very expensive).
> but still, tracing GCs have much better performance on server workloads
Good performance with traditional tracing GC's involves a lot of memory overhead. Golang improves on this quite a bit with its concurrent GC, and maybe Java will achieve similarly in the future with ZGC, but reference counting has very little memory overhead in most cases.
> Reference counting when an object is shared between multiple cores require atomic increments/decrements and that is very expensive
Reference counting with a language like Rust only requires atomic inc/dec when independently "owning" references (i.e. references that can keep the object around and extend its lifecycle) are added or removed, which should be a rare operation. It's not really performing an atomic op on every access.
And memory is cheap, especially when we talk about backend workloads.
A tracing GCs can do the job concurrently, without slowing down the actual, work-bearing threads, so throughput will be much better.
> Golang improves on this quite a bit with its concurrent GC
Not sure what does it have to do with memory overhead. Java's GCs are at least generation ahead on every count, Go can just get away with a slower GC due to value types.
Sure, though RC can't get away from pauses either - ever seen a C++ program seemingly hang at termination? That's a large object graph recursively running its destructors. And the worst thing is that it runs on the mutator thread (the thread doing the actual work).
Also, Java has ZGC that basically solved the pause time issue, though it does come at the expense of some throughput (compared to their default GC).
In my opinion they need to invest a lot more time and money into it for that. The development experience on VSCode was pretty bad (I think the LSP has a memory leak), and some important (for me) libraries aren't tuned very well yet (a Vapor webserver can sit around 100 MiB memory, whereas putting a bunch of load on the grpc implementation balloons the memory usage to >1 GiB).
I haven't followed swift too closely, but ref counting is not a good fit for typical server applications. Sure, value types and such take off a lot of load from the GC (yes, ref counting is a GC), but still, tracing GCs have much better performance on server workloads. (Reference counting when an object is shared between multiple cores require atomic increments/decrements and that is very expensive).
> but still, tracing GCs have much better performance on server workloads
Good performance with traditional tracing GC's involves a lot of memory overhead. Golang improves on this quite a bit with its concurrent GC, and maybe Java will achieve similarly in the future with ZGC, but reference counting has very little memory overhead in most cases.
> Reference counting when an object is shared between multiple cores require atomic increments/decrements and that is very expensive
Reference counting with a language like Rust only requires atomic inc/dec when independently "owning" references (i.e. references that can keep the object around and extend its lifecycle) are added or removed, which should be a rare operation. It's not really performing an atomic op on every access.
And memory is cheap, especially when we talk about backend workloads.
A tracing GCs can do the job concurrently, without slowing down the actual, work-bearing threads, so throughput will be much better.
> Golang improves on this quite a bit with its concurrent GC
Not sure what does it have to do with memory overhead. Java's GCs are at least generation ahead on every count, Go can just get away with a slower GC due to value types.
Tracing GC and their pauses on server workload is another tradeoff. They all have a tradeoff. You make a fair point.
Sure, though RC can't get away from pauses either - ever seen a C++ program seemingly hang at termination? That's a large object graph recursively running its destructors. And the worst thing is that it runs on the mutator thread (the thread doing the actual work).
Also, Java has ZGC that basically solved the pause time issue, though it does come at the expense of some throughput (compared to their default GC).