They lack in performance, stability (compatibility), observability (telemetry), productivity, or some combination thereof. They are chosen, of course (especially C#; Go and Rust are far behind), but not as much as Java.
Saying that Go lacks in those is just showing how people are making software those days. It’s just terrifying.
As to Rust - we all, hopefully, agree that it’s great language, but not for some startup making websites or Mongo based, boring backends. It’s great for the stable, system level products.
I don't know what compiler and GC quality has to do with how people are making software these days, and I don't think state-of-the-art optimising compilers and GCs are terrifying at all. Go opts for more traditional, simpler algorithms under the assumption that for many purposes they're good enough. That may be so, but sometimes workloads really are very demanding, and you need the best performance.
and the costs they are willing to pay. Go/Rust just kill everything else (except maybe C++) for performance and resource needs. JVM requires so many resources just to run small apps.
Quite the opposite, and the reason is that you can't extrapolate from small programs to large ones. Low-level languages (like C++) incur some significant overheads as they grow large (because of essential constraints of low-level languages that prevent them from doing certain optimisations that matter mostly in large programs), and these are exactly the overheads the JVM is designed to reduce. In small or short-lived programs, the situation is different, because Java does have some warmup costs and some fixed memory overheads that matter when you're small or short-lived. Go's compiler and GC are pretty basic, and are certainly good enough for smaller things, but don't scale as well to high workloads. Just the other day a colleague tested Caffeine, an old and well-established Java caching library, and Moka, a Rust caching library with the same workload. Caffeine had the same latency as Moka across all percentiles at twice the throughput.
I use Java every day but just to point out that your info about Go‘s GC seems out of date. They switched to Green Tea in 1.25 (I think?) - new GC that even has AVX-512 optimizations. Not sure what you mean by basic about the compiler but it‘s very fast and supports a large set of platforms. That‘s not basic to me.
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. I actually love both languages.
> 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.
Go's compiler is fast because it doesn't do as many advanced (read: computationally expensive) optimizations as other compilers do. No clue about Green Tea and how awesome it is :-).
Lower memory pressure is certainly a difficult thing to beat Go at, Java (OpenJDK) is probably never gonna get there. You get a lot of other stuff, like better peak performance, instead.
Btw, have you tried Leyden/AOT for better startup times? Curious about your experiences with that.
> Which specific optimizations are you referring to?
A JIT with speculative optimisation and a moving GC.
There are two constraints in low-level languages that trump any of their performance goals, one technical and one a matter of preference.
The technical limitation is that they must use stable pointers (because they need to be low-level and so having an FFI layer that separates "hardware pointers" from "language pointers", as we have in Java defeats their main purpose). This means that you need to translate data storage or code storage to hardware addresses, and that interferes with both moving collection and with JIT compilation.
The other constraint is that low-level languages value worst-case performance over the average-case and even amortised performance. These languages prefer an operation (e.g. dynamic dispatch) to be slow as long as it's never too slow. With a JIT (and I describe more later), virtual dispatch can be super-fast almost all the time, but occassionally, you'll hit a trap because the speculation was wrong, and then you need to deoptimise and recompile.
> In my experience, this is largely a myth; compared to Rust, you actually get even faster code right away.
We wouldn't be doing it in the first place if it was a myth. In a low-level language, you can get very fast code if you do some manual optimisations, but they don't easily scale as the program grows and evolves, because they're viral. The two most basic examples are dynamic dispatch (which is the most general mechanism, which scales the best in terms of program evolution) and shared heap objects (again, the most general mechanism). These become more common and less easily avoided over time, and they're slow in low-level languages because of the constraints I mentioned.
That low-level languages make it harder and harder to preserve good performance over time as they evolve and grow is a problem familiar to those who've worked for years on large software written in a low level language (as I have). The JVM was designed, among other things, to solve this performance problem in large programs.
> JIT is effective for languages where the source code lacks sufficient information (dynamic typing, where anything can be null).
A JIT can make such languages decently fast, but that's not how it's used in Java. In Java it is used for speculative optimisation, which allows far more aggressive optimisation than an AOT compiler can do. E.g. by default, Java inlines and specialises virtual calls 15 levels deep. An AOT compiler can't do that or its code will explode. We get around it with selective use of templates in C++ (or comptime in Zig), but it has to be selective, and it's viral.
> A JIT with speculative optimisation and a moving GC.
Idiomatic Rust, through its concepts of ownership and borrowing, encourages a pattern where you receive data as an argument or create it directly, perform operations on it, and then discard it via RAII. This bears some resemblance to functional programming. This approach does not apply to buffers of unknown size, which still require heap allocation; unfortunately, Rust lacks automatic buffer reuse. However, such optimization is theoretically possible. The stack is definitely faster than anything else.
> This means that you need to translate data storage or code storage to hardware addresses, and that interferes with both moving collection and with JIT compilation.
You don't need GC if you allocate data on stack. You also do not need to dereference the pointer.
> dynamic dispatch
You mentioned templates. In Rust, traits that are monomorphized - much like templates-are the standard approach; using vtables or `dyn trait` is a relatively rare use case. This stems from the fact that all code is known at compile time and there is no dynamic loading, allowing the compiler to eliminate polymorphism from the code entirely.
> and shared heap objects
This might be considered convenient, but in my view, it also leads to code that is harder to maintain when objects can be modified from multiple places. However, I think that is outside the scope of the current discussion.
> We get around it with selective use of templates in C++ (or comptime in Zig), but it has to be selective, and it's viral.
Yes, monomorphization is the default solution in Rust. It is not always viral either, because when using it, you often define specific types, and they do not spread beyond that scope.
I suppose you could say that the programming style I am talking about is complex, inconvenient, unmaintainable, and so on. What I mean is, assuming this programming style is sufficiently convenient—and perhaps even has its own advantages - then none of the optimizations you listed offer an edge, and the Rust code will definitely be faster.
I think you have a biased view. The number of stuff written in Rust in the last couple of years has absolutely exploded. For example, I see a lot of projects now that provide SDKs in Rust but don’t bother with Java. And I say this as someone who still writes most of my code ( or tell my LLM to write) in Java.
There's a difference between number of programs and number of LOC (the latter is related to the number of people involved). I am not aware of any SDK targeting the industries I mentioned that "doesn't bother with Java". It's not only a popular choice in those industries, it's not only among the top choices, but it's the top choice by a large margin. Look at wanted ads in those industries to see that. Overall, there are only two languages as popular as Java or more, and they are JS and Python: https://www.devjobsscanner.com/blog/top-8-most-demanded-prog...
Yeah I mean he literally works on Java at Oracle, so may just be a little biased.
Doesn't bother to disclose it of course, because what, you don't check everyone's profile in every discussion to make sure they're not biased? What, you don't just know who every user on this site works for? You dummy you :)
It's disclosed right there in my profile (I don't see your professional affiliation disclosed in your comment; or your profile, for that matter). Of course, I, like other runtime and compiler people, joined the Java team because we wanted to work on the most advanced compiler and runtime tech. I perfectly understand people who want to work on smaller, newer, potentially insurgent products, but I took the chance to work on the cutting edge of compiler and runtime engineering, and Java is where it's at these days (I'm not saying it's the only one, but it's a very small club).
GP's snark is unwarranted, but it's probably good practice to disclose your professional affiliation explicitly in comments related to it, even if you have already disclosed it in your profile.
I was reading your comments on Java, nodding my head, upvoting, without checking your profile and realizing that you're a member of the Java team. Knowing that doesn't mean I now suddenly disagree with you or anything. But while in an ideal world it doesn't matter who's saying something when evaluating it, there's some human factors at play - I'd like to turn up my internal sense of skepticism when dealing with someone, effectively, selling something their salary depends on; even if you're being entirely earnest, it's ultimately a sales pitch, and I feel bamboozled for not recognizing it - that'd make me appreciate transparency.
(FWIW, even though I prefer being coy about my place-of-work, I have no professional relation to this conversation. I've never used Java in my 9-5 and I haven't even really used it in earnest since, like, version 5 back in high school. I think it's always been underrated by the hacker crowd, though!)
I agree that it matters, but whether and how to do it depends on the standard practice in the relevant forum. On HN, it's rare for people to disclose affiliation even in their profile, so I think I'm already better than the norm here on HN in that regard.
While Java can outperform Go in some cases, the situation is very much the opposite when it comes to Rust.
I also don't see the case for stability. Yes, if you're still on JDK 8, it would probably chug on for a couple of years. But we were talking about greenfield projects and newer JDK go EOL much faster. If you want patches, you'll have to run your app to a newer JDK, which may break a couple of things. Rust (within the same edition) or Go (within the same major version) break less than that.
As far as runtime compatibility goes, Rust and Go apps ship with the runtime. This can be better or worse for you, depending on what is your upgrade story, but I don't see a clear winner here. What I would give to Java over Rust is that you will have far fewer dependencies to take care of if you need to upgrade. But the same goes for Go.
For observability, I feel that with Rust you have a bit less that you need to observe (no GC to worry about). Tokio tracing is great, but observability requires a bit more effort. The go observability story is far worse. So Java probably has an edge here, but not something that ever felt like a game changer. My impression is that for most of the enterprise shops that love Java, observability means collecting unstructured log files through NFS and trying to find a needle in the haystack with primitive tools, but I've been out of touch with this world for a couple of years.
Productivity is something that is dead if you are AI-heavy. Sure, many shops are still wary about AI, and I totally get why, but this is a battle that's already been lost. Without AI, I would say I was about 3 to 4 times more productive in Rust than I was in Java, but ramping up that productivity took at least 1 year of practice. It's not time most companies are willing to spend. With AI, this doesn't matter anymore, for better or worse.
I'm not arguing that Java is not chosen often for greenfield projects. It's clearly extremely popular in many circles, especially outside startups and big tech. But I think the reason Java is chosen have little to do with the reasons you've mentioned above and more with organizational preferences.
> the situation is very much the opposite when it comes to Rust.
It isn't, and the problem isn't Rust specifically, but all low-level languages. They can offer very good performance (often better than Java) when small. But as they evolve over time, or are very large to begin with, they become much harder to keep performant. This is for pretty fundamental constraints of low-level language that I mention in another comment here, and this performance problem with large programs written in low-level languages was well known before Java even existed. The JVM was designed, at least in part, to address it.
One of the things that drew me to Java (from years of C++, even though I still work in C++ when I work on the JVM) is precisely how it addresses those performance issues we ran into with C++ five years into a project.
> As far as runtime compatibility goes, Rust and Go apps ship with the runtime. This can be better or worse for you, depending on what is your upgrade story, but I don't see a clear winner here.
I wasn't talking about "runtime compatibility" but of overall version compatibility. Java has an unmatched compatibility record - not perfect, but better than anything else (with at least a medium-sized standard library).
> For observability, I feel that with Rust you have a bit less that you need to observe (no GC to worry about).
Memory management is very often a bigger issue without a GC than with a moving GC. Time and again we see Rust or C++ programs spend 30-50% on memory management.
> Productivity is something that is dead if you are AI-heavy.
Really? Have you had AI write a good medium-sized (say 100-500 KLOC) program or maintain one over a long period of time without very close reviews? The only people I've seen who don't know about the ticking time-bomb agents leave in the codebase are the people who don't look.
> With AI, this doesn't matter anymore, for better or worse.
You may be talking about small programs. I agree that for small programs, low-level languages can offer excellent performance, and AI can be okayish, and you can get some observability you can live with, but I'm talking about large programs.
> But I think the reason Java is chosen have little to do with the reasons you've mentioned above and more with organizational preferences.
Those organisational preferences are due to a long record of delivering on the things I mentioned. Java has an exceptionally low "regret factor", i.e. people who regret choosing it five, ten, or fifteen years into a project (which is when the problems usually start).
I don't agree. If anything these newer languages have better tooling and new projects are always built from ground up to support open standards like open telemetry
Open telemetry is about how telemetry data is reported, not how it's collected. It's hard to compete with JFR on the breadth and depth of low-overhead, in production telemetry, built into the standard library and the JVM itself.
