Comment by pron | Hacker News Reader

Comment by pron | Hacker News Reader

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> I use Java every day but just to point out that your info about Go‘s GC seems out of date.

I'm well aware that Go's GC has improved, but the moving algorithm was designed not just to be fast for a GC, but to be faster than no GC. So Go's new GC is good - for a mark and sweep collector. But it can't compete with a moving collector (the only thing that can is arenas, which are user-friendly only in Zig).

> We are using JDK25 and are considering rewriting parts of our product to Go because of lower memory pressure and faster startup time, i.e., cloud friendly.

Java probably will never have perfect warmup, but it's getting very good - https://openjdk.org/jeps/544 - probably in JDK 28.

As for memory, I think Java's memory strategy is generally misunderstood and I've given a talk about it: https://youtu.be/xr73mR7ii9M The footprint overhead exists to compensate for CPU utilisation when the CPU utilisation is more disruptive than memory usage. The problem is that many Java developers - and I'm not blaming them - don't understand this tradeoff and how to configure the JVM for optimal resource usage, but the great news is that a solution is coming soon, too - https://openjdk.org/jeps/8377305 - also possibly in JDK 28.

So it's very likely that both of these issues will be resolved six months from today, and you'd still get to enjoy better performance and telemetry than all alternatives.

A couple data points, I like Java but I've seen metrics of container fleets at multiple companies that were memory constrained with low CPU usage sitting around underutilized. The reason in both cases was a bunch of memory-heavy yet CPU-efficient Java processes.

When CPU utilisation is low, the heap can be set much smaller. Many don't know that, so in the next year we'll have the VM do it automatically: https://openjdk.org/jeps/8377305

The amount of memory a Java program uses is whatever the setting is, not how much it "needs", because the need depends on the preference of the CPU/RAM tradeoff. But again, not many understand that, so we're making that automatic.

I'm sure both of the cases I'm thinking of could have been tuned better. Just saying that it's a default case that I've seen 2 places land, both of which had a lot of smart engineers following best practices. Maybe its food for thought for you in your position

Yes, this is why we're doing automatic heap sizing :)

CPU utilization is a red herring. Unless you're doing heavy number crunching (which these days heavily favors GPUs) the practical bottleneck on CPU utilization for large general purpose programs (especially when spanning multiple cores) is memory bandwidth. And moving GC is terrible for memory bandwidth compared to both Go-style concurrent GC (which doesn't have to do bulk moves) and manual memory management.

> And moving GC is terrible for memory bandwidth compared to both Go-style concurrent GC (which doesn't have to do bulk moves) and manual memory management.

This is not true. The whole point of the algorithm - the reason it was designed - is that the amount of moving is well below what's required in a non-moving collector. The downside is that the algorithm is more complicated and requires an FFI layer for FFI, but even though non-moving collectors are far simpler to implement, every language/runtime that can use moving collectors uses them (and all of those can also use non-moving collectors, too, as Java did earlier on; concurrent mark-and-sweep collectors like Go's or Java's old CMS are easier to make). Whatever you say about the complexity of moving collectors or their impact to latency before the recent invention of pauseless moving collectors, they are widely recognised fact that as the most efficient general purpose memory management solution (but also the most elaborate).

You could argue about certain workloads, but it is ridiculous to claim that the world's top memory management researchers worked for years to come up with an algorithm to be more efficient than mark-and-sweep collectors and malloc/free failed to notice that it has to move objects around a lot (the whole point of the algorithm is that it does not), and then every language that can use the algorithm chooses to use it because they also failed to notice that the algorithm that is so much more costly to implement is so obviously worse.

BTW, Go's reason for using a simpler, older style mark-and-sweep collector isn't that it's better (Google's larger V8 team opted for a moving collector), but that Go can get away with a simpler, less efficient GC because the allocation rate is lower (and we can argue over that, but at least that would be an argument over something that could actually be controversial).

Anyway, if you're interested to know how moving collectors really work, and how they were created to be more efficient than any non-moving general memory management strategy, I go through the basics in a recent talk I gave: https://youtu.be/xr73mR7ii9M

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