Serious question: when should one use Java for greenfield projects in 2026?
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 :)
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!)
This is really, really silly. Java is many times beyond the position where its developers need to desperately convince people to use it. This is a person who has unique technical expertise in the area whose credentials are smack dab on their profile, not hidden from you. Their closeness to the domain at hand should make you less skeptical of what they are saying.
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.
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'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.
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.
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
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.
It's usually the build systems that add quite some overhead.
Nope, not yet. It's a good question given that up to now we used to deliver our product only on-premises and Windows Server-only, but this year we are now finally going with the Cloud, which means Docker containers and Linux.
If I remember correctly Leyden required some sort of warm-up and training data collection before being able to effectively execute AOT, right? I need to freshen up my info on that.
I did try GraalVM-compiled Java executables a couple of years ago and they were not bad, but the binaries were quite big (not a showstopper though) and the class-loading issues were kind of a PITA.
Which specific optimizations are you referring to?
In my experience, this is largely a myth; compared to Rust, you actually get even faster code right away.
JIT is effective for languages where the source code lacks sufficient information (dynamic typing, where anything can be null).
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.
Do you mind a reasoned discussion?
> 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.
What you're describing isn't a stack, but an automatic arena, and this optimisation is easier to do in Java. It's easier to do in Java because it requires setting a "current arena" or inlining, both of which Java can do more easily, and then either the arena will be heap allocated (which will be slower in Rust) or associated with the thread, which is not something low-level languages tend to do.
> You don't need GC if you allocate data on stack. You also do not need to dereference the pointer.
Moving collectors don't need to dereference anything (they don't know and don't want to know when an object is "dead"), and stack allocation works in both languages, only, as you pointed out, is not quite general (not every data structure with a known lifetime can be allocated on the stack).
> 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.
Sure, except Java does this automatically, and it can do it more aggressively. Dynamic dispatch is rare in low-level languages because it's expensive in those languages. But it's not easy to avoid as programs get larger. That is exactly one of the problems in large programs that the JVM set out to solve.
> 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.
I agree that whether it has downsides is outside the scope of this discussion, but the point is that as programs evolve and grow, the abstractions tend to be more general, and low-level languages suffer from "abstraction cost", where the more general abstraction (which becomes more common over time) is more expensive. Again, this is exactly why large C++ programs suffered from performance issues and what the JVM tried to address.
> Yes, monomorphization is the default solution in Rust.
... and in C++. But it is viral, and Java monomorphises without suffering from "zero overhead abstractions".
The ability to move pointers, both to data and to code, opens up the possibility of using JITs and moving GCs, which are very powerful optimisations. A JIT does impose two further tradeoffs (aside from the need for an FFI layer), though, which are warmup and the possibility of deoptimisation. We can now cache the generated machine code from one execution to another (https://openjdk.org/jeps/544), but the possibility of deoptimisation remains (in fact, it's what enables the aggressive speculative optimisations), which means you gain average (or even amortised) performance at the cost of the worst case.
Anyway, the JVM was designed as a solution for the performance issues low-level languages suffer from as programs grow and/or evolve. It comes with tradeoffs, but those most affect small or short-lived programs.
The thing to remember is that low-level languages are not optimised for performance but for low-level control (i.e. pointers are direct addresses etc.). Such control can translate to good performance when programs are small (see next) but it becomes a practical hindrance to performance when they're large.
> 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
That advantage is a performance advantage. The question isn't "does there exist (in the mathematical sense) some program that is fast?" but "how fast is the program we can write within the budget we have?" When programs are small, manual optimisation is practical; when they grow large - not so much. And that's excluding the matter of a moving collector, which is just hard to compete with on speed regardless of program size, unless you use areans, but they're not at all easy to use in most low-level languages except Zig.
> and the Rust code will definitely be faster.
This is true only in the abstract mathematical sense. The reason we don't write programs that we want to be fast in Assembly (which is faster than anything in the same sense: for any program in any language, there exists and Assembly program that's at least as fast) is not because other languages are fast enough, but because in practice the programs we can actually write in the budget we have will be faster than the Assembly programs we could write. Of course, that could change when AI is able to generate perfect low-level code, but when that happens, it might as well generate machine code directly.
At least you aren't claiming that the JVM is ~1.5 faster than perfectly written assembly :)
I disagree with a lot of what you’re writing. However, we’ve reached the point where we need to run benchmarks and analyze the generated code (this is easy to do for compiled languages using https://godbolt.org/, but for the JVM, it can be a bit more complex, given the warm-up factor).
So, there is one fundamental point I started with:
> JIT is effective for languages where the source code lacks sufficient information (dynamic typing, where anything can be null)
And your answer is:
> 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.
Essentially, you are saying that the compiler can apply aggressive optimizations when it knows what is happening in the code.
But I say that JIT is needed so the compiler can figure out what is happening in the code and perform aggressive optimizations.
There are many things that can be inferred from the code without needing to execute it. The question is how difficult it is to make such an inference: in one scenario, the compiler might attempt to track whether specific data changes-and, if it can prove this, mark the data as immutable and apply certain optimizations-whereas in another, it might already possess the information that the data is immutable.
Moreover, information about immutability is useful not only to the compiler but also to the programmer. Just like information about types: it benefits both the compiler and the programmer. Imagine a fan of JS or Python joining our conversation and claiming that both Java and Rust are low-level languages because you have to specify types - something they view as complex and a hindrance to development speed.
The same applies to the GC: the compiler can perform more optimizations when it knows when memory needs to be cleared (move it to stack or even place the data on registers). The JVM attempts to do this (via escape analysis), but there are limitations; consequently, data ends up on the heap, and GC operations come at a cost (due to data movement).
Rust simply makes it easy to obtain far more information, enabling aggressive optimizations that are both immediate and guaranteed.
There remain a small number of cases, such as `switch` statements - where one branch executes 99% of the time, while the other 99 branches execute only 1% of the time. In such instances, the JIT could indeed perform further optimizations; however, I am not even sure if the overhead of monitoring wouldn't outweigh the benefits. And the question is when and how to perform PGO, or whether to perform it at all.
Yes, and the important point is that when it comes to knowing things statically, abstraction and optimisation are in conflict. The whole point of abstraction is that the implementation details aren't known. So in C++ we always suffer from this problem called "zero overhead abstractions" or "abstraction costs", which means that to give the compiler the information it needs, we have to use less general abstractions, which are viral and harm evolution. What a JIT does is allow the compiler to learn the very things that abstraction hides; yes, it's a virtual call, yes, it could target anything, but I've seen it hit the same target 1000 out of the last 1000 times, so I speculate that this will continue and I'll inline even though I could be wrong.
> The same applies to the GC: the compiler can perform more optimizations when it knows when memory needs to be cleared
I understand why this could be true in theory, but in practice the problem is:
1. not that the compiler knows when an object is unreachable, but that the generated code has to do something at that point, and
2. the most efficient known memory management algorithms - moving collectors and arenas, both work in nearly the same way - are entirely predicated on freeing memory in bulk and on not doing anything when an object becomes unreachable, and so the knowledge of when an object becomes unreachable doesn't help them.
So it is true that C and C++ and Rust always statically know when an object is dead, and you could say that hypothetically they don't need to do anything with that information, but in practice they all act on that information immediately and that's inefficient.
> There remain a small number of cases, such as `switch` statements - where one branch executes 99% of the time, while the other 99 branches execute only 1% of the time.
So the main practical benefit of a JIT isn't that at all, but that it can do the "mother of all optimisations" - inlining - far more aggressively. Inlining is important because it cracks open the abstraction boundary of the inlined subroutine, and allows the compiler to further specialise and optimise things, now with the appropriate context.
Anyway, all of these fundamental questions and differences between languages with more statically known information and figuring out "unprovable" information in practice were very well known before the JVM was built to address the performance problems we had suffered from in large C++ programs. So we can argue over which workloads are helped by this and which aren't, but there is no way to say which is usually faster in the absract (because, again, these considerations were known and taken into account). It's merely an empirical question, and not one that's easy to settle. After more than 25 years of working with C++ and almost 20 years of working with Java, my default is that low-level wins on performance (if written by experts) in smaller programs, and Java wins on performance in larger programs, but of course, there are many caveats in either direction.
I disagree with that phrasing; it is better to say that abstractions allow a programmer to ignore unimportant details. For example, when developing two modules (possibly even by different teams), all they know about each other is a lean interface, without any implementation details. However, the compiler might know everything.
> So in C++ we always suffer from this problem called "zero overhead abstractions" or "abstraction costs"
This is another odd term. In Rust, the term used instead is "zero-cost abstractions," referring to cases where the compiler can generate instructions for higher-level code just as efficiently.
> So the main practical benefit of a JIT isn't that at all, but that it can do the "mother of all optimisations" - inlining - far more aggressively.
I’ll reiterate that I disagree with this: inlining is performed very efficiently during monomorphization. And monomorphization is used very frequently in Rust.
> After more than 25 years of working with C++
I don't have much experience with C++; I mostly use Rust. I can only assume that the C++ development experience is far worse than Rust - especially when trying to write software that is both reliable and fast. This may be particularly relevant to older C++.
So, a Dog, a Cat, and an Abstract Mammal walk into a bar...
https://godbolt.org/z/dEsW1sfM8
I didn't want to do this, but I went ahead and created a small example showing that monomorphization and inlining work remarkably well. (Obviously, this example does not address memory management)
You're talking about abstraction at the code level; I'm talking about abstraction at the language level. A virtual call means "the implementation is unknowable here", and it is, indeed, rarely knowable to an AOT compiler.
> This is another odd term. In Rust, the term used instead is "zero-cost abstractions," referring to cases where the compiler can generate instructions for higher-level code just as efficiently.
Rust took that term from C++ (and it had slightly different ones over the years). What it means that the language offers different mechanisms - chosen statically - with different abstraction levels (i.e. different generality) and different costs, some of which are zero, but often similar or identical-looking code at the use site, because the mechanism choice depends on some non-local information, typically associated with the type. I call it "writes like a low-level language, reads like a high-level one". This is different from C (or Zig), which usually makes the selected mechanism explicit at the use site, or from Java, which chooses the cheapest applicable mechanism at every use-site for a single general construct.
The problem is that, because the mechanims is chosen statically, you need to choose the cheapest applicable mechanism, usually virally, yourself, and that over time this gets harder or things drift toward the more general and costly mechanisms.
That's what Java tried to solve, but there are, of course, tradeoffs. The obvious one (which has solutions) is warmup time, because the compiler needs to wait to learn what optimisations can be applied even if they're unprovable, e.g. to learn that a polymorphic application is actually monomorphic in practice at a particular call-site (the solution is to cache the optimised machine code from one run to the next). The more fundamental tradeoffs are 1. you're not guaranteed which mechanism will be chosen, 2. there can be a bad, though amortised, worst-case due to deoptimisation (this is what happens when the compiler optimises too aggressively and then finds out it was wrong, e.g. it inlined a virtual call under the assumption it's the only target at the use site, but after a while, another target appears (in Rust/C++, you'll always pay the higher price, but there's no point at which deoptimisation occurs), and 3. you need an FFI layer, as you can't take the machine address of a compiled subroutine (as it may be re-compiled multiple times).
Tradeoffs 2 and 3 are the main reasons low-level languages don't do this optimisation, and 3 is particularly important. Low-level languages are designed, first and foremost, to be low level. To do its sophisticated optimisations, Java needs to move around pointers to both code and data, which requires a clear FFI layer between Java code and anything external. Having such an FFI layer in a low-level language (and I'm not talking about Rust/C++'s thin extern FFI) defeats the very purpose of a low-level language, which is to talk directly to the hardware and OS. That is the chief goal of all low-level languages, and they sacrifice everything for it. Not only safety (Rust's unsafe is used relatively pervasively) but also performance.
> I can only assume that the C++ development experience is far worse than Rust
Actually, the experience in the two languages is remarkably similar, and not by accident. Rust certainly improves some details, but the overall experience "in the large" is very close. But note that the performance problem is not because of "zero cost abstractions" but because of the low-levelness and focus on the worst-case. Even in Zig, which tries hard to avoid zero cost abstractions to keep use sites explicit, the choice between a specific-and-cheap and a general-and-expensive mechanism means that for best performance you need to pick a less general mechanism, and that gets trickier and trickier to maintain as the program evolves over the years, and especially if it's large.
> monomorphization and inlining work remarkably well.
Of course it does, which is why the optimising JIT was invented: to make it work more broadly!
This wasn't done just on principle, but to solve a very real problem. What we used to do in C++ is architect a solution and write code that monomorphises in all the right places - because that's what one does - and the result was good and fast. And then, five years later, we had to add some feature and were faced with the choice of either undoing some core optimisation or re-architecting some 10,000 LOC. The problems didn't arise when first writing the program, when everything was known. It arose when some change - that hadn't been foreseen when the program was first written - had to be done. Java didn't make the first step substantially cheaper; it made all the following work - five, ten, fifteen years down the line - substantially cheaper.
An important caveat is that HotSpot currently misses many auto-specialisation opportunities that it could take advantage of, but that's one of the things that make working on such a cutting-edge compiler so interesting :) The problem, as always, isn't just the work required, but also determining which optimisations actually make a difference in real programs (and not just in specific benchmarks).
Of course, now there's this hypothesis that AI could do this costly rearchitecting for you, even in large programs, but it doesn't do it well (at all!) today, and I think that when we get to a point where it can do it well, it will also be smart enough to do it in machine code directly (or at least in C), at which point all programming languages will be over. What I don't think is likely is that AI will be able to do extremely complex semantics-preserving large-scale transformations correctly, yet still need the help of a sophisticated compiler for much more local transformations and far simpler correctness checks.
I have seen it mentioned everywhere, but is this actually true?
I mean, of course it is faster than random cold memory, but is it actually faster than a hot, in-cache part of the heap? It is not special in any other way, AFAIK.
And for what it's worth, what pron mentioned, Java uses a pretty similar structure for initial allocation, a thread local buffer where you just pointer bump. Another thread can then in the background copy still alive objects from this "arena" and then reset the whole thing.
Yes, it just adding or subtraction int to stack pointer register. I’m not certain, but the only thing that might be faster is accessing data at a fixed address - that is, global variables.
Stack is fast because it is frequently "touched" staying in cache. If you were to continuously read write a small segment of the heap, I don't think it would fair any worse than "the stack". This was my point
Caffeine's next release has roughly 25% higher read throughput, with unchanged write throughput, thanks to fixing a false sharing mistake. That won't be visible in real workloads, but is fun nonetheless (500M reads/s on 8 cores).
I'm not following. Saying Go lacks in X shows how poor other software is? Can you connect the dots?
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.
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).
30-50% of what?
Right, so one case (which I certainly believe is possible) is very different from “time and time again.”
As for the caching test, it's just technically interesting, because the JVM was designed to address the performance issues we suffered from in large C++ programs (all the JVM engineers are, of course, C++ people), both due to compilation and to memory management, and we regularly compare both our compilation and memory management algorithms to other approaches, and it just so happens that last week one of our GC engineers compared Caffeine to Moka and saw how CPU-intensive the memory management work is compared to ZGC (he was particularly interested in this because caching is one of the more challenging workloads for generational moving GCs because a cache deals with many old objects, whereas generational GCs tend to focus more on young objects, and he wanted to make sure that our GCs help reduce the high memory-management overheads associated with low-level languages even in this challenging scenario).
Generally, efficient memory management is orthogonal to object oriented design. Meaning, as your complexity grows and your business logic changes, it often means the optimal memory management changes because the lifecycle and relationship between objects change.
For a web server for instance, you have both request/response as well as various transactional memory requirements. In Java, the role of the garbage collector is to adapt to whatever the best memory policy is based on runtime behavior, rather than statically defined rules. One could say that the evolutionary and revolutionary changes in garbage collectors as well as the multitude of tuning parameters comes from this being a really hard task.
If you have a services architecture, the runtime advantages of Java go down significantly.
> I wasn't talking about "runtime compatibility" but of overall version compatibility. Java has an unmatched compatibility record.
I would say both matter significantly more again in a monolithic architecture. It matters a lot more when you are trying to deploy your software into a single application server, or trying to avoid version incompatibilities when integrating large amounts of code into a single executable.
> Time and again we see Rust or C++ programs spend 30-50% on memory management.
I've seen plenty of Java applications spend 30 seconds or longer because they had to do a full garbage collection back in the day. I even had one customer who maxed out Java to utilize all the memory in their server and hit a 13 minute production pause due to otherwise unoptimized GC (promoting many temporary transactional objects to the mature generation until it eventually exhausted memory).
The different strategies for memory management (static vs dynamic) ultimately still require recognizing, diagnosing and correcting issues. GC provides unique challenges because the tuning mechanism is decoupled from the actual code. GC challenges can also often go undiagnosed until staging/production workloads hit them, precisely because they are dynamic behaviors.
Java's current GCs are quite different from the earlier revisions. ZGC for instance guarantees < 1ms pause times even for TB heaps as far as I'm aware.
Java also breaks very few things. Breaking binary compatibility is a no-go since it's a core promise of the platform. The only thing in the surface language that has ever been changed is the meaning of the underscore as an identifier, as well as the behavior of == in upcoming Project Valhalla.
> As far as runtime compatibility goes, Rust and Go apps ship with the runtime.
Java applications can also be shipped together with the runtime.
> Productivity is something that is dead if you are AI-heavy.
Nevertheless, making constructs available to express intent more clearly should also help LLMs to not go off the rails.
It felt like every dev that worked on our Java behemoth at a previous job was elated to switch to Go.
I'm not sure what kind of tools you mean, but unless you're looking for something that just works exactly the way EJBs do for some mysterious reasons, I don't see why you can't do most "enterprisey" things with Rust or Go. Or Python or TypeScript for that matter.
Yes and that's exactly what modern tooling is missing. Try to develop for node.js 0.2.12 on today's update of Visual Studio Code. See? No enterprise-level collaboration for ya.
Exactly. Never updated, never discussed, never challenged. Set in stone.
Rust is too low-level for typical enterprise app where requirements changes twice a day. You end up spending time and tokens fighting with borrow checker.
C# is MS product, which is no-go for some folks.
Kotlin probably would be the answer.
Which Java famously does not have.
> Rust is too low-level for typical enterprise app where requirements changes twice a day. You end up spending time and tokens fighting with borrow checker.
In my experience, you do not spend tokens fighting with the borrow checker anymore, newer models are smarter. But it might not be ideal for a lot of CRUD applications.
> C# is MS product, which is no-go for some folks.
This is 2026, it's not 1996 anymore. .Net works on Linux and Microsoft is as friendly towards open source and open standards as a Big Tech company can be.
If anything, it was Oracle which more recently sued another company for using a JDK alternative. And this was a lawsuit that, if accepted, could have put the entire idea of API compatibility in danger and deal a severe blow to the Open Source movement.
Anyone who is morally bothered by MS but is unfazed by this is probably just mentally stuck in the 1990s.
> Kotlin probably would be the answer.
I love Kotlin, but I'm afraid that's not the case. The conservative organizations that choose Java out of inertia, would keep choosing Java over Kotlin, even if Kotlin is a better JVM language which is facing no downside.
For anyone who doesn't need to be on the JVM or work with JVM tooling, Kotlin doesn't cut it. It doesn't have null pointer dereference problem in theory... Only it does in practice if you're using any Java API that may return null (all these bang-decorated "Platform types"). Generic type erasure can only be overcome in inline functions with reified types. And building and deploying artifacts without docker is still a mess.
I found Kotlin extremely publishing for Java shops in the past, and I've converted multiple departments totaling over hundreds of employees to use Kotlin. But that was before AI. The rationale was simple: Java is an entrenched language that leads to bloated code, slow development cycles and way too many avoidable bugs in productions. Kotlin solves some if these issues, and it's very easy to learn for a Java engineer, while still letting you keep all of your tools and libraries. And as a language (putting ecosystem aside), I find it better than either Go or Typescript, and far more ergonomic than Rust[1].
But all of these arguments die with AI. Rust is just as ergonomic as any other popular language today if you're using an agent, and the fact that an engineer spent their lifetime writing Spring Boot programs in Java you don't have time to let them learn a new stack from scratch doesn't matter anymore.
Sure, there are many companies where letting AI write the code is still not acceptable, but most of these workplaces will accept AI agents sooner than they accept Kotlin.
I feel a bit sad since I like many ideas about Kotlin (especially how amenable it is for making DSLs) but we've lost that opportunity
--
[1] Unless you have to write highly concurrent code without any data races.
Have you worked on large (>500KLOC) codebases with an agent? Not only do you have to be an expert at the language, but even if you're lucky and everything is fine, Java code is likely to be particularly fast by comparison, because the agents aren't very good at manual optimisation, especially as the code grows (they're even worse than humans at that, and humans aren't great at manual optimisation of large codebases, either, which is one of the problems the JVM set out to solve; in fact, agent-written code in a low-level language gets pretty slow well below that size). Oh, and the long build times certainly don't help.
Yes. But keep in mind KLOCs are not easily comparable across languages. Java is notoriously verbose. A 500KLOC codebase in Java would usually be half that size in Rust. If your argument is that large codebases makes life harder for agents, you should go with a less verbose language.
I'm not sure what "manual optimization" means (isn't it a bit of an oxymoron when the agent does it?), but if your agent has the proper tools (e.g. ast-grep, rg, semble) it can deal with large codebases. Would the agent create slop? Yes. But it wouldn't be worse on the slop that humans created on every moderately-sized Java project I've worked on.
> in fact, agent-written code in a low-level language gets pretty slow well below that size
I've never seen this happening. I've seen agents writing suboptimal Rust code (e.g. copies instead of Cow). But while this occassionally happens with Rust, I've never seen an agent optimizing for Java where necessary (e.g. using object pools to avoid GC churn). Java is not magic.
Lol, no way. Especially that rust is pretty verbose all things together (which makes sense, given it's a low level language - ergo you have to literally express more things about the code)
That isn't really a comparison of the languages as much as the standard runtimes and ecosystems. It is important to consider that each have comparable components.
So you aren't comparing a no_std rust project against a comparable JavaCard, but say Diesel vs Hybernate code examples around ORM.
Meanwhile in java it's a constant "Arc<JavaType>", and scopes don't mark "drop points"
Ok, so you barely know either Java or Rust.
> If your argument is that large codebases makes life harder for agents, you should go with a less verbose language.
You mean, like Rust??? But no, that's not my argument. Agents have a hard time keeping up the architecture in large software (and the differences between verbose languages like Java, Go, Rust, and C++ vs less verbose ones like Python and JS don't make much of a difference). So they either make a mess or they do the simple thing, and the simple thing in low-level languages is often slow.
> But it wouldn't be worse on the slop that humans created on every moderately-sized Java project I've worked on.
Yeah, I don't think you've actually tried it.
> I've never seen an agent optimizing for Java where necessary (e.g. using object pools to avoid GC churn). Java is not magic.
Object pools are far less efficient than Java's GCs, but while state-of-the-art compiler and memory management technology is certainly not magic, I suggest you learn more about these things if you want to make informed decisions.
With Java's advanced GC's, object pooling may very well be an anti-pattern (always measure though).
Hmm? Java gets null dereferences all the time, that's what a NPE is. The VM takes on the extra plumbing to surface a dereference of a null pointer in a recoverable way to code. On Windows this is done using SEH, on Unix it is handling SIGFAULT - but each NPE corresponds to a null pointer dereference that java then tries to clean up.
That the language does not have a way to have compiler enforced "never null" is actually a huge productivity drain, specifically because you have to do your own defensive measures against null or attempt cleanup/recovery when it happens.
Even languages like Swift which use Optional (e.g. a maybe monad) to provide a concept of nilability still internally will hit null pointer dereferences on occasion with faulty bridged code/bindings. However, they treat this as a non-recoverable violation of invariants - a developer shouldn't be trying to recover from incorrect code at runtime.
medtech - .NET and TypeScript.
Everything that was done on the backend side was done in Java, although there were some exceptions for .NET deployments.
So we ended up with mixed skills teams where depending on the ticket, you would be coding Java or C#.
"We are a Java house" is not a Greenfield thing. It's picking what you already use internally.
Banks, telcos, etc. aren’t monoliths either. They use plenty of different languages depending on the team, system, and requirements. Java isn’t inherently the choice for greenfield software just because reliability matters.
Second, I wonder where, roughly, would you put a transition from small programs where low-level langs are fine, to programs large enough to heavily benefit from JVM tradeoffs? And how this transition is affected by a stuff like Graal Native?
if you gonna work in a big team or need a project with lots of devs then yeah go for the JVM.
but if you're doing things on the smaller / small scale side. - just use JS/TS or python. you benefit from cheap runtimes such as Cloudflare workers.
It's stable to the point of boring, and there's no shortage of people who know the language and can work with it, it's got best in class tooling, decades worth of libraries almost all very mature. Most of the language's issues are from legacy code bases coded in a style that isn't really relevant to a greenfield project.
Scala technically allows you to use nulls or throw exceptions pretty much wherever (necessary for Java compatibility), but it's not an issue because people simply don't outside of super niche situations (generally some low-level thing, or a shim). Similar to `unsafe` in Rust. Or casts in all sorts of languages.
I don't understand that logic. I sometimes ask people to explain why they think a certain policy should be implemented by the government after they state their support for it, but I don't have the ability to set government policy. I have trouble imagining you genuinely assume that any time someone asks you why something should be the way you say that you think they have the ability to change it if you convince them.
Like if I think my business should open an hour earlier, and you say "but the employees won't be there yet so who will open the doors!" obviously the solution is to also change the work schedule. When you have closely related policies, generally the same person/people are empowered to make both changes.
> When should one use Java on projects?
> One shouldn't consider Java because it lets you use null.
> That's easily solvable by just not using null.
> But you can't just do that. People will use it.
> You can just do that. Tell them not to.
Like I'm not seeing the issue. This is like saying you can't use Rust because people will use `unsafe` because it lets them do C programmer things, and then claiming it is simply impossible to tell them not to do that (and set tool policies to flag anyone attempting to).
In the real world, if you're in a position to even ask "why use Java for a new project?" then you are presumably also in a position to have "don't use nulls" be a satisfying answer to "what about nulls?" If someone is asked what technology to use for a project in the first place, they are almost certainly also asked about how it will be used. The hypothetical here is not "do you have coding standards" but "are you a decision maker," and when the original question is "when should one decide to do X," you have to accept as a premise that you are placing yourself in the role of a decision maker in the first place.
I'm not seeing how you can seriously claim that the amount of unsafe used in Rust over the past 11 years is anywhere close to the amount of null used in Java for the past three decades. The vast majority of Rust projects don't have any unsafe used directly in them. I have trouble believing that the fraction of Java projects that don't ever touch null is anywhere close to as high.
> In the real world, if you're in a position to even ask "why use Java for a new project?" then you are presumably also in a position to have "don't use nulls" be a satisfying answer to "what about nulls?
You keep framing questions as something people only ever ask if they happen to have the power to make decisions, and that still makes no sense to me.
> The hypothetical here is not "do you have coding standards"
That's almost the exact question I responded to initially! You responded to me responding to the question "Why don't you have any coding standards?". I feel like you're trying to argue against me as if I commented several levels above in the thread than I actually did, and then getting confused by me not accepting that premise.
I'm not framing it as a question one only asks if they happen to have the power to actually decide to use it; obviously a curious student might want to know why large successful companies continue to use the JVM even for new projects. I'm saying that the answer can be given from the perspective of someone who does have that power, and that the question is really "if I can choose a language, why would I choose Java." That doesn't imply that the person asking actually has that power, but it prevents silly answers that are addressed by "don't do that."
Theoretically you don’t need to write AbstractFactoryProvider in Java, but looking at languages mentioned in job offers, I have a pretty good idea of which of them have a high probability of working with such code and which do not, even if all of them say they have the best code ever.
For concrete example, it took me a long time to find a job last year due to only fully remote being viable since my wife's autoimmune condition means I'd be risking her health by commuting, and nowadays most places seem to either expect hybrid if you live near an office (I'm within the geographic limits of NYC despite being nowhere near Manhattan), restrict by time zone (there were quite a few jobs I was interested in where they only would accept remote with Pacific or Mountain Time), or have onerous travel requirements (multiple opportunities I interviewed for didn't work out because they expected me to fly to the west coast every couple of months, which between the time there and jet lag would mean I'm not productive close to a quarter of the time).
I was in a fortunate position to be able to hold out for a while and ended up finding a fully job with my preferred language after around eight months, but I had already come up with a timeline for when I should start relaxing certain constraints if it went on longer. Programming language was literally the first constraint that I was going to drop if it lasted a few more months because prioritizing my wife's health is non-negotiable, and I'd rather work in a language I don't like as much on something that I don't feel is actively making the world a worse place than work in my favorite language on adtech or at some cryptocurrency startup. It's not clear to me why it would be a problem for me to care about using my non-favorite programming language well if I happened to be employed to write it.
I just don't see why nullability is a problem in the first place.
Otherwise, yeah, you're still in for a world of pain.
This is kinda bad practice. Go and Rust panics are similar, they aren't exceptions that you can safely recover from. Say there's a panic in the middle of modifying some global state like a database connection. Hence complaints about the net/http recover like https://github.com/golang/go/issues/25245 . But I guess they decided it's worse to let servers crash from trivial mistakes.
https://github.com/uber-go/nilaway
Not exactly the same solution as JSpecify, since it doesn't rely on annotations, but it's also more ergonomic.
I'm not comparing this to "null-restricted types", since that's a draft JEP that hasn't made it even into a preview feature. Go also had multiple proposals for explicit nilability in types, and while they probably have less prospect of ever seeing the light of day compared to Project Valhalla, as things currently stand, Go is in the same position as Java: They are both extremely prone to NEPs out-of-the-box and they both have external tooling that can help you avoid them.
Java null checkers have more comprehensive coverage potential compared to Go, but Go is the more ergonomic one here. You don't need a single extra annotation on your code.
If you develop a library in Java and use it from Kotlin, the built-in Kotlin null-safety will recognize the JSpecify annotations on the library.
Null-restricted types are on the roadmap. See: https://openjdk.org/jeps/8303099
I get that some people feel like it ought to be built-in to the language rather than a separate tool... but people's personal feelings are irrelevant to the lived experience of my day-to-day work, where worrying about null is truly a thing of the past.
Unfortunately, no, they don't. Not after your application has been running for a while; newer JVMs arbitrarily decide you don't need the stack trace anymore, and all you see in your logs is "NullPointerException" (unless you still have the logs from several weeks ago, just after the last JVM restart, which might still have the full stack trace). Older JVMs were better, since they always had the full stack trace; debugging NPEs was easier with them.
Unlike C it is trivial to catch a NullPointerException and confine the crash to the unit of work. And unlike C you are not talking about insanely dangerous pointers, you're just talking about an NPE.
I'll admit it's a hassle when something wasn't initialized properly and then you get a null pointer exception at some unrelated code much later. It's not always easy to debug. Catastrophic? No!
There are a lot of third party tools that can check for null safety and a lot of work is being done to make Java's initialization safer but also a little more flexible, there is
https://openjdk.org/jeps/8303099
and there are all sorts of practical answers. Nulls in Java are low on my list of annoyances, way behind front end programmers who pepper my CSS files with "!important" because they don't know about precedence (though maybe they think my .clazz.clazz.clazz selector is brain dead!)
Use NullAway and it basically makes the problem go away. Our application won't build if it detects a potential NPE.
I get it why this seems like a less drastic change, but this saddens me. Kotlin solves more issues with the type system (smart casts, reified types, immutability by default), without sacrificing readability. Unless I can see a solution in Java that makes dealing with NPEs as easier for lazy developers as ignoring them, I don't consider it a solved issue.
You deserve the strawman award of the year.
NullAway and JSpecify encourage making as many types non-nullable as possible, thus they can actually also advice about removing redundant null checks. Nullable types become the painful exception that visibly spreads through the codebase, which discourages writing code that relies on null.
Optional doesn't enter the picture at all. NullAway kills their usecase within ones own code. They are anyway only recommend as return types to force others to check for an emoty case, but I think Optional will become fully optional when the Java platform gets nullable types on its own.
Google Error Prone is a code linting tool that's very useful in its own right, and NullAway is just another plugin.
And that "team" nowadays may also consist of many AI agents. In my experience Claude Code for example works very well with a typed, slightly boring language with lots of framework and library support. Because it doesn't compile when you get something wrong, instead of getting a vague runtime issue that Claude can't always see.
I agree with the rest, but there's definitely a lot of magic in Java. This is from both what features the languages makes available (many) and how the community uses them (often). I've had so many hard-to-debug issues in Java over the years due to reflection, annotations, and bytecode manipulation shenanigans.
And another positive point for Java: checked exceptions. It's verbose, but knowing exactly in which ways a function can fail is extremely helpful for building robust applications.
[1] https://www.javacodegeeks.com/2026/01/javas-checked-exceptio...
[2] https://reflectoring.io/do-not-use-checked-exceptions/
WRT magic, I've generally thought that was a result of frameworks - Spring, for example. In the past, my feeling was that these impose a sort of meta/configuration language that itself is not checkable at compile time, so you'd get weird runtime errors that are somewhat inexplicable. This was like... 2018 though, so perhaps things have improved.
Using them correctly can be great tho.
try {
...
} finally() {
...
}
Occasionally though I have built something really sensitive, like an authentication filter for a web site which has at least 5 ways to log in and in that I have a hierarchy of exceptions and use checked exceptions heavily to document all the ways things can go wrong and felt like "the type system really has my back here" but that is like 5% of the Java I write.
That's a code style and code review issue; each project has so set standards regarding how errors are dealt with and enforce them throughput the codebase.
But from a quality standpoint there are three concerns:
(1) Do you actually do the code review, do you actually enforce the style?
I worked on a Scala project where the dev manager thought it was preferable to handle errors with monads and would be vociferous about what a great practice that was compared to exceptions and that code review was central to how we do things... but if you looked at the code most of the time errors just got dropped silently and that was the same for many practices that the dev manager told me were doing but that we don't. He still posts on LinkedIn complaining about other dev managers who say they do code review but really don't. Practically that code didn't consistently give the right answers and poor error handling was one reason, another was that they never really understood that teardown was just as important as initialization.
(2) Is your documented practice correct? Is it really doing the right thing?
In a lot of cases there really is a right and wrong way to do things (e.g. uv resolves Python dependencies properly, pip doesn't) but it's less clear in error handling, like sometimes things went wrong and there is no way you can make it right and you can do the best that you can.
The global nature of the problem is vexing. Like an IOException might really be a BackhoeCutAFiberSomewhereInWisconsinException and a segmentation fault is occasionally a YouAskedForAOneAndGotAZeroInsteadException and it's not just academic because, given an exception, you want to answer questions like "Should I retry this operation? How long should I wait before I retry this operation?"
(3) Is this practice something you can sustain? How hard is to do? How much cognitive load does it add and how does it interact with other practices? "Throw up as much as you can", "tear down in finally {}", "otherwise handle local consequences of errors and rethrow" and "really catch errors at the drivers of units of work" is a practice that really works in many languages and is pretty easy to do right, even code that is written without a lot of care will do the right thing or something close by default. I've seen a lot of "no plan for error handling" or "bad plan for error handling"... like I was traumatized by the first C program I saw in a 1984 issue of Byte magazine which was using errno to handle errors which vastly complicated very simple code because the error path was intimately wound with the happy path and in cases like that there tend to be bugs in both of them. When I saw Exceptions in Java I remembered that old C program and thought "I love this!"
There are several valid ways to do this, but it's important that one strategy is agreed upon and abided to, at least in new code. It's important that one srrategy becomes the orthodox one, else there will never be a reckoning about its effectiveness, but instead only people clinging to their own standards on their own turf. Legacy code complicates the picture of course.
Can you explain why this is pointless? In my mind, this being a checked exception would hopefully be a hint that I should think about this failure-case and make an explicit decision whether to handle it or not. Network connection failed? Maybe I retry. Maybe I store that data somewhere else as a fall back. Isn't this similar to Go programmers needing to check if err is not nil?
As soon as you consider retrying a network failure, you immediately need to start thinking about distributed systems failures, idempotency, and all that good stuff.
As soon as you start thinking about the above, it becomes immediately obvious that low-level calls should not be able to decide to re-run themselves.
I appreciate that there is a _ton_ of different experiences out there when it comes to solving problems, but I _have_ encountered exactly the case I was describing, which is what led me to my original question. Isn't the fact that it was a checked exception that led you to "consider retrying a network failure, you immediately need to start thinking about distributed systems failures, idempotency, and all that good stuff" worth it as opposed to an unchecked exception you may not realize is being thrown?
Many times I've decided to switch from one function to another, or even an entirely new library, because the checked exceptions told me that it was doing far more than I expected, and I was not comfortable introducing those new failure modes.
It's far from perfect, one still has to handle nulls and wrapped/merged exceptions, but overall I like this language feature.
I've worked on Java apps that have failed in mysterious ways that no exception could explain. Meanwhile, the overhead of having to call out certain exceptions but not others in language syntax is a bit excessive.
For example, decoding a byte array (or URL encoded form field) into a UTF-8 string means handling a theoretical UnsupportedEncodingException. What the fuck? How the hell can one have a JVM that doesn't support UTF-8? Why does my code need boilerplate that will never run because there might be some broken-ass JVM out there that that doesn't support UTF-8? How did it launch a web server, safely load all the libraries, and accept a web request, and route it to my code without blowing up? "But the encoding scheme might change..." No, it won't change. It's always going to be UTF-8. It will always be UTF-8. If it's not, let it blow up.
It took me quite a long time to accept that the recommended unit testing library manipulates bytecode so that the exception message for `assert a == b` prints the values for both.
- Annotation processing: if you know Lombok, MapStruct.
- Class loader.
- Reflection.
- Garbage collection.
IMO compile-time annotation processors such as Lombok and MapStruct are far from the most magic part of Java. They're straightforward code generators. Their impacts is localized to where they get applied and you can actually see the code that's generated. They're very good for diminishing boilerplate. They're no worse than Rust's very standard #[derive(xyz)] proc macros.
Having the code being generated on the fly (instead of a one-shot) means it follows the rest of the structure it's derived from i.e. equals() and hashCode() don't risk to be forgotten when adding a field to a class (hello maddening Map<> lookup errors)
Also, yes, Lombok is _funky_ in how it works but there are "pure" alternatives like AutoBuilder and AutoValue if one cares.
Another issue with Lombok is that it requires IDEs and other tools to be aware of it. Missing integration with other annotation processors only causes "definition of external element not found"-style errors.
Class loader and reflection shenanigans can be shut down with the module system.
Garbage collection matters when you stress the JVM to its limits. Don't do that.
Sorry, but no, Java has the worst of both worlds here. It has checked exceptions AND unchecked exceptions, AND errors which are like unchecked exceptions but won't get caught by a normal catch-all (you're not supposed to catch Throwable, but it's the only way to prevent some dynamically loaded plugin code ten layers deep in the stack from breaking your invariants or stopping your periodic scheduled task due to an errant NoSuchMethodError or NoClassDefFoundError).
And you can't easily use checked exceptions with Java8-style functional code, since interfaces like Function aren't generic on the exception type. Which leads to aberrations like UncheckedIOException, which exists only to make IOException usable in the functional world.
> And that "team" nowadays may also consist of many AI agents.
And that's the part where the hireability arguments collapse. Sure Claude Code works pretty well with Java. It also works well with Typescript, Python, Go and Rust. It would use types on all of these languages, and run a type checker or LSP on the dynamic ones. And while Java is statically typed, Rust has a stricter type system that prevents some types of runtime bugs that Java's type system won't like data races and forgetting to release a resource.
Which are completely safe in Java
> Forgetting to release a resource
I know what you mean and it's definitely a tongue in cheek reply but it's almost like that's what the GC is made for :D bit less sarcastically java has Cleaners, and try-with-resources are a poor man's RAII
I guess I'm asking this as an open question: Where are we in the "move fast vs stable" spectrum with Node these days? Definitely not rock solid, but it's moving in that direction I feel.
At the risk of a shameless plug - blogged about it recently at https://mandeepgill.net/2026/08/31/java-for-an-ai-startup/
As for an individual developer doing a side project, you should use Java if you haven't used it before to get exposure to it. It's a fundamental component of enterprise software and if you've never used it before take the time to learn something new.
That part i s true.
> with newer Java versions the only real gap with Kotlin is null-safety
But that part isn't. Kotlin has:
- Structured Concurrency: Coming to Java sometime in the future, but it's been in preview for very long now.
- Standalone functions that don't have to live in classes
- Properties
- Property delegation
- Data classes: more powerful than records. Can be used for large DTOs that you can modify with copy(). Java needs something like Lombok to make records more useful.
- Extension methods
- Context parameters
- Operator overloading
- Implementation delegation
- Inline functions (which can receive returning closures and reified types)
- Block syntax (supports `it` for unnamed arguments)
- Sequence abstractions: more powerful and more efficient than Java streams due to the inlining and block syntax.
This is just a partial list, but Kotlin clearly has a lot of things that Java doesn't. If you only personally care about NPEs that's fine, but that's not the only thing.
I think the two languages mean slightly different things here. In any case, Java's model is so much more simpler that I don't think they are honestly comparable. In kotlin's case you have to be very on top of your game to have a chance of correctly using it - there is concurrency, parallelism, exception handling all combined into a single abstraction in a non-native way - so your stack traces will be useless/swallowed etc on incorrect usage. Of course the usual caveat applies, just use java's abstraction if you need that.
> Standalone function
Don't really see the benefit, if anything it creates place for style disagreements. A SomethingUtil class was just fine (and findable).
> Properties
Difficult topic with both cons and pros.
> Data classes
Exactly because they are "more powerful" they are strictly worse. A design element is just as much about what it is as it isn't. Copy is good though.
> Delegation
Used a couple of times, but it's not the full blown thing (see manifold)
> Extension method
I will be honest, I really dislike these. They can occasionally help a bit with some DSL, but for the most part they just make code very hard to read. I much prefer a normal static method instead.
> Context parameters
One of the few useful syntactic sugar.
> Operator overloading
Argued to death already :)
> Inline function
Feels more like a hack to support some of these extra features than something you would want to use yourself
> Block syntax
For the rare DSL usecase it's useful. Everywhere else I really dislike it and the accompanying coding style. These .also and similar implicit receiver thingies are just straight up evil.
All in all, there are a few things that are very elegant in kotlin, but I feel they went the c++ c# way of over abstracting just to have a long feature list.
And if you are starting from scratch, whatever part of the ecosystem you use, I'm not optimist on its situation improving with time.
Nowadays a lot of code is written with mostly procedural style with some functional characteristics, I wouldn't use Java for that.
The whole go team's philosophy tends to also revolve around assuming their users don't know what they're doing, which is annoying. Like an inline keyword: thinking you know better than me doesn't mean I'm not going to inline it; it means I'm going to manually write it inline myself in the code, and then think the language sucks because it's tedious, error-prone, and verbose. Or they tend to mark lots of stuff private for no reason, and e.g. with TLS 1.3 they just ignore your config because they think they know better, etc.
Java tooling taking up a lot of ram was a major motivation for me. I have done a lot of Scala as well. I don't think either Java or Scala in the real world beat go on performance for most cases. I don't doubt that in some cases jvm can do better but at least before Valhalla delivers all the promises, in real world, I am doubtful.
I have been a Java/Scala user almost for the majority of my career. I doubt I would pick jvm over golang going forward though. Also not having to deal with OOP is a plus.
Not sure which dev tools specifically you're referring to.
As for Scala it's pretty much a dead language, no one work with it and it's impossible to find dev for it.
10 years ago I was moving Scala code back to regular Java.
go and kotlin aren't it, gleam scratches the itch but I can't justify writing code that would impossible to hire for.
scala situation is a real shame.
I think it used to be common to just look for smart people and assume they can run with whatever stack. Wasn't that the point of abstract algorithm questions etc. (basically an IQ test)?
Lots of companies where software isn't the focus see it as a cost center, so they'd prefer to hire lower-IQ specialists instead of higher-IQ generalists, because the latter are more expensive/have more options.
I worked on a purely functional Scala codebase (Cats). I joined after the original authors were gone.
Some new comers were not fluent in it after ~a year.
Aside: Scala dev here - but I only talk about how wonderful it is with people I trust (mostly Go and Rust developers I used to work with).
Also, Scala Native means I don’t always have to worry about the JVM depending on the use case.
ScalaJS is fun too.
Why should anyone use it over Java? Ms is hostile towards its developers, it creates new versions of things, deprecates previous versions, uses confusing naming for newer versions.. etc.
I've been a .NET dev for a decade now. It's perfectly serviceable, but I wouldn't say I truly love the language anymore, but I would take it over Java any day. Entity Framework and LINQ are gifts from the Gods. I have never used an ORM that even comes remotely close.
Also, C# is big in the gaming world. I am working on a game right now, and I was not impressed with what many other languages had to offer. It seems like the kings are still C(++) and C#. Of course, Java can create games, but I would argue that is a "could vs. should" kind of decision.
Unity, Godot, Monogame, Raylib, XNA, FNA, etc. all can use or rely on C#. I have not seen Java be compatible with any of those -- except maybe Raylib? I do not know of anyone nor any games that use it though.
Microsoft is a business and will always put their business objectives first. In my opinion, they have a non-zero amount of evilness. I do not support them jamming Copilot into every available crevice. I still think they make dumb choices, like every imperfect organization. However, C# is a powerful and intuitive language, and for Microsoft shops that already run a lot of Windows and SQL Server it makes a lot of sense.
No shade to the JVM. I've mostly enjoyed my time in that space. I do believe the choice between Java and Kotlin, the wide variety of vendor JDK distributions and IDE fragmentation make the JVM stack a bit more difficult for newcomers to break into.
Majority of things provided from MS instead of having to rely on 3rd party, especially nowadays when supply-chain issues are huge concern
When you want to make a game and support modding with pretty much zero development effort.
Kotlin is the obvious replacement, but the tooling isn't as good and the community isn't as large. Java keeps getting better, and in ways that diverge from Kotlin - eg, virtual threads vs async/await/coloring. From the perspective of language design, I prefer Kotlin. But I keep picking Java anyway, and I don't see that changing soon.
Go is openly hostile to functional programming. Dynamic languages aren't even in the running. Rust is too low-level for line-of-business software. C# is too Microsoft. The remaining alternatives are too obscure.
I guess if you use/like Intellij it's ok.
It's not as portable as Swing, as it has some platform specific binary components to it. But it works fine on mainstream platforms. For me Swing portability is not worth giving up the FX model.
Just be aware that if you happen to bundle in the Web view component, you're basically adding WebKit to your distribution. I did this with a small project because I wanted to have a "help" screen with Markdown -> HTML. Easy, but "expensive". It simply adds a big chunk (10-20Mb) to your distribution.
(Now I have a very crude Markdown renderer for this task, which is a 100 lines code, and I'm working on a better one -- but I have yet to pull the trigger on the latest FX with its new Rich Text component, which could change everything.)
One hot tip with cross platform FX, however. Embed your fonts. The font suite is not common across the distributions, and the CSS does not honor the font fall back (i.e. if not XXX font, then YYY font), so if the runtime doesn't have your specific font, it collapses to the System font. So, embedding the fonts you use helps a lot with cross platform stability. Plenty of free fonts, I have not had a real problem with this. But it can be one of those O.o moments when you test on other platforms and encounter it the first time.
C# LINQ also seem to be the best compromise between ORM and raw SQL queries, although I never used it myself.
I have been severely disappointed in all similar solutions for Go at least and I imagine Rust does not have something better given it has a smaller community-base.
Python and NodeJS have some very neat solutions for this stuff too, but both are "slow" dynamic languages. I personally dislike python with a passion and NodeJS stuff is extremely community-driven and therefor often unreliable. Prisma (NodeJS ORM) for example just did a major overhaul and is now pushing a completely different API.
If you are making boring REST API to SQL Database it seems like Java and C# are the best options.