Concretely, what are current tail latencies, worst case?

Ten years ago, “rewrite in C++” was definitely easier than getting the Java GC to stay up under server load.

Most servers I work with run on big machines and are the only process, so figure a 100-250GB heap that lives for months, all async, small requests, so insane amounts of Future and String allocation spam.

Optimizing that stuff away in Java is harder than writing Rust, so assume idiomatic Java.

Also, is there any work on statically enforcing data race freedom in Java? That’s a bigger rust selling point than memory safety for me. I think swift has done some interesting work in that space. It would be nice to get those sorts of safety properties without manually writing borrow checker annotations.

▲

stmw 3 days ago | parent | next [-]

As the OC, I think my view is somewhere in the middle - I am neither as optimistic about it being "great now" nor do I think that "rewrite in C++" 10 years ago was easier.

My reason for disagreeing with the former view is that improvements in physical RAM available and tendency towards smaller workloads have allowed many Java (or other GC runtimes) to essentially "fix their problems because hardware got better". So you can waste more RAM, waste more cycles, but "it doesn't matter", and likely it is fine in many cases - but it's is not the same thing as claiming the GC algorithms are responsivle for that outcome. We have been 3 years away from GC solving memory management for at least 30 years.

My reason for disagreeing with the latter view is that for those who don't have 100-250 GB long-lived heaps (or whatever the contemporary version of that is), the pain level is far lower than rewriting in C++ or Rust, or likely the pain level of hiring enough engineers who can do either. It's a completely different engineering culture.

▲

pron 3 days ago | parent | next [-]

> So you can waste more RAM, waste more cycles

Just to be clear, the main reason for the use of moving collectors in the first place is to waste less cycles on memory management (otherwise we wouldn't use them). They exist to serve as an optimisation.

> We have been 3 years away from GC solving memory management for at least 30 years.

It's now 3 years in the past (since Generational ZGC); e.g. see https://netflixtechblog.com/bending-pause-times-to-your-will.... Of course, it doesn't solve all imaginable memory management issues, but in practice it makes it a non-issue for a large class of interesting and very common programs.

▲

stmw 2 days ago | parent [-]

Maybe I should've mentioned at the start that I've implemented several GCs and worked on several Java VM implementations, so I am generally familiar with the tradeoffs between GC algorithms and other runtime details.

Even in the very positive blog you linked, you see statements like * "ZGC has a fixed overhead 3% of the heap size, requiring more native memory than G1. .." and * "Reference processing is also only performed in major collections with ZGC. We paid particular attention to deallocation of direct byte buffers, but we haven’t seen any impact thus far. This difference in reference processing did cause a performance problem with JSON thread dump support, but that’s a unusual situation caused by a framework accidentally creating an unused ExecutorService instance for every request."

This was my point about how this sort of thing is a type of manual memory management.

As for waste more RAM, waste more cycles wasn't a statemnt about whether a particular GC is better-performing for certain situations, but that the overall improvement likely has more to do with improvements in CPU speeds and RAM size, than the latest GC version (which tends to simply make a different set of engineering tradeoffs).

	▲	pron 2 days ago \| parent [-]
		You're right that managing any resource that isn't just Java heap memory requires manual management, but it isn't what we normally mean by memory management. The two have been somewhat tied together traditionally through reference processing (in the sense of reference queues), which is generally something we now discourage in Java programs, and may be deprecated and removed altogether at some point. It's traditionally been used as a convenience. The framework used in that post is, indeed, based on an old library that relies on manual or reference-processing-assisted management of non-heap resources. > but that the overall improvement likely has more to do with improvements in CPU speeds and RAM size, than the latest GC version (which tends to simply make a different set of engineering tradeoffs). Well, the biggest improvement has been the creation of a new "pauseless" collector, ZGC, with a novel GC algorithm (at least for OpenJDK), which does _zero_ GC work in STW pauses, i.e. no scanning, no marking, no moving. In particular, even roots, including stacks, are processed entirely concurrently with the program. The main practical impact of that has been saying goodbye to GC pauses, and getting low latency, that is perhaps even more predictable than malloc/free (and obviously, still has higher throughputs in a large class of interesting programs). The tradeoff is the usual footprint tradeoff, which is the core of moving algorithms, as well as more CPU cycles compared to STW collectors (but again, still less than malloc/free in many programs). The additional CPU can, of course, be compensated for with an even larger heap. The general idea is to use RAM chips as hardware program accelerators, but in the past latency was also something you had to sacrifice, and this is no longer the case today.

▲

hedora 3 days ago | parent | prev [-]

It definitely was easier for the projects I worked on, but they are exactly the use case where the heap is long lived and most of the machine.

I’ve also worked on systems with lots of small processes, and the operational issues that creates dwarfs GC problems: It takes one middle tier machine, and adds 64-128 network boundaries, and also creates an extremely difficult static memory allocation problem.

I know people do it anyway, but it’s rare that they can articulate a decent technical reason for it, and it wastes something like 90% of the hardware (even in carefully optimized code bases / deployments).

Anyway, I’m not the target market for such stuff.

▲

pron 3 days ago | parent | prev [-]

> Concretely, what are current tail latencies, worst case?

Well under 1ms for ZGC (to the point that OS-caused hiccups are of similar magnitudes).

> Ten years ago, “rewrite in C++” was definitely easier than getting the Java GC to stay up under server load.

Both could have been hard in some cases, but open-source "pauseless" GCs are only 3 years old (and all of the JDK's GCs are nothing like what they were ten years ago).

> Optimizing that stuff away in Java is harder than writing Rust, so assume idiomatic Java.

Quite the opposite. Performance issues due to memory management are, in practice, more serious in Rust than they are in modern Java.

> Also, is there any work on statically enforcing data race freedom in Java?

There isn't much demand for that atm. If we see growing demand, we could prioritise it.

▲

hedora 3 days ago | parent [-]

In rust, I usually just make sure stuff is not Box<>, and try to reuse buffers. That generally gets the memory allocator completely out of the way (except for async).

The remaining allocator performance problems are mostly due to it zeroing allocated memory unless I use unsafe. Is java able to stackify most new Object calls and elide default initialization of object members these days?

I’m surprised to hear there is no demand for compiler enforced/facilitated thread safety in Java. That was a major pain point in all the Java code bases I’ve worked with in the past, and is a headline safety feature for rust (which goes even further and enforces aliasing rules) and JS. Could you be seeing selection bias in your user base?

▲

noelwelsh 3 days ago | parent | next [-]

> Is java able to stackify most new Object calls and elide default initialization of object members these days?

Escape analysis in OpenJDK will stack allocate values where it can show it is safe to do so. Project Valhalla is also reducing the memory footprint of objects.

As for thread safety, that is more of a language concern than a runtime one. Amongst JVM languages Scala is leading here AFAIK. Its "capture checking"[1] provides thread safety (e.g. [2]) and actually covers escape analysis as well. On Scala Native (the native code backend for Scala) capture checking can be used for safe stack allocation and safe arena allocation.

[1]: https://docs.scala-lang.org/scala3/reference/experimental/cc... [2]: https://softwaremill.com/understanding-capture-checking-in-s...

▲

pron 3 days ago | parent | prev [-]

> In rust, I usually just make sure stuff is not Box<>, and try to reuse buffers. That generally gets the memory allocator completely out of the way (except for async).

You say "just", but this is easy when programs are small. The problem is that this gets harder and harder and harder as programs grow large (the whole point of the JVM's design was to address the performance issues that plague large C++ programs). E.g. someone who works at one of the world's largest tech companies just told me that they have problems with Rust programs spending 30% of their CPU on memory management even when they're as small as a couple hundreds of thousands of LOC.

> Is java able to stackify most new Object calls and elide default initialization of object members these days?

No, the general idea is to just make memory management efficient (although some objects are "stackified" and the compiler will elide zeroing when non-defaults are passed to a constructor). Now, I say "just", but this used to come at the cost of GC pauses and larger footprint. Now it only comes at the cost of a larger footprint.

But there is a definite choice here when it comes to performance. Low level languages give you control that means performance is attained through manual effort. Java takes away control to improve effort-per-performance. Roughly speaking, these tradeoffs mean that when programs are small and the extra effort is manageable, low-level languages are hard to beat, but when programs are large, it is Java that is hard to beat.

> I’m surprised to hear there is no demand for compiler enforced/facilitated thread safety in Java. That was a major pain point in all the Java code bases I’ve worked with in the past, and is a headline safety feature for rust (which goes even further and enforces aliasing rules) and JS.

This used to be a bigger problem when locks were the main mechanism for sharing data among threads. Now, with the wide selection of concurrent data structures, such problems don't occur as much. I'm not saying they don't occur at all, just not frequently enough to become a major priority.

Also, safe Rust's data-race freedom comes at the cost of requiring unsafe for benign races, which are not uncommon in concurrent algorithms (i.e. it excludes even "good" races). This may be fine in languages whose view on performance is "with enough effort you can get good performance", but, as I said, Java is about making more "naive" programs fast with little effort.

▲

stmw 2 days ago | parent [-]

It is fair that there are many ways to be slow in any number of programming languages. I'm surprised to hear "Rust programs spending 30% of their CPU on memory management even when they're as small as a couple hundreds of thousands of LOC", although I can visualize some unique workloads where that's unavoidable irrespective of language & runtime.

	▲	pron 2 days ago \| parent [-]
		You say unavoidable, but moving collectors are designed to reduce CPU at high allocation rates by increasing the heap size. Generational moving collectors have a pathological case - a high allocation rate of long-lived objects - but that's quite hard to get yourself into by accident. Their main downside (besides the inherent increased footprint) used to be unpredictable long pauses, which could have a very high impact on tail latencies, and that's just gone with ZGC. Generational moving collectors are a very powerful memory management optimisation, but because they necessarily require some "interesting" FFI layer between the ordinary heap and any passing of pointers between the program and the hardware/OS - the very thing low-level languages are designed not to have - this is a powerful, general optimisation (not perfect, but extremely useful in a wide class of programs) that is not available to low-level programming languages. And it's not the only one, BTW. JIT compilers are also designed for "global" average-case optimisations at the cost of precise low-level control over the worst case, which is another thing that low-level languages trade away. In the most simplistic way, I would say that the precise control that low-level languages are all about helps their performance when programs are small (and can be manually optimised globally) and hurts their performance as programs get large. They have to give up on some optimisations that come at the cost of ceding low-level control, and that includes moving collectors.