In principle it's possible to have a company with no employees and all work done by vendors / contractors. But in practice this usually turns out to be less optimized than having at least some regular employees due to transaction costs. Organizational optimization is always a balancing act.
There is a difference between freedom and oppression yes. Are you saying we can't figure out where to put buildings if we let people celebrate the 4th of July?
I don't feel this way. I have just been tackling more and larger problems, I think? This week one of many things for example is switching a multi-master KV store for tracking views on individual objects to tiered hyperloglogs that periodically merge. I could do this without AI, but it would take me a week instead of a day.
I think, if you're not feeling challenged, you're probably just doing the same work but faster. You should try to tackle harder problems, too!
The main benefit of python to me is that while slow, it's predictable. I do think they're going to get a lot more resistance to adding JITs, moving GCs, etc. it will become java with a million knobs to tune. If people want a JIT'd python just use pypy, right?
Java lost almost all those knobs a while ago (I mean they're there, but you're better off relying on the defaults). The modern GCs have one or at most two knobs remaining, and even that will become unnecessary next year. As to predictablity, you get maximal pause time of well under 1ms for heaps up to 16TB.
Yes, on any hardware that ZGC supports and that your program otherwise runs with acceptable performance you won't get a long pause time, including (hypothetically) Pentium 3. The reason is that there is no work done inside the pause (no marking, no compacting, not even root scanning). It's just used to signal all threads that a new "epoch" is starting. On small hardware you have a small number of active threads, and so the pause will also be very short.
> Compared to Python's, all of them are beyond perfect.
I somehow understand the situation less after reading this.
Is Python's GC bad, or are there cyclic reference issues? Is it possible to detect cyclic references perfectly? What does beyond perfect mean? If we have 7 and 0.1% of the time you need one of the 6 that is non-default, how do we choose? Is the understated version of "Compared to Python's, all of them are beyond perfect" "I think Java's are great"? If not, what about Python's impl makes it so lackluster to any of 7 of Java's?
3. ZGC: low latency (<<1ms maximal pause, i.e. effectively pauseless)
4. G1 (the current default): A balanced mix of throughput and latency.
These are all the standard GCs (the seven you mentioned include a GC similar to Go's that was removed years ago, an "no op" GC for benchmarking hidden behind a development flag, and alternative implementations by different companies to some of the ones above).
It's possible that either Serial or Parallel will be removed when G1 is able to fully replace them.
Now, why do users need options? Because Java runs most of the world's finance, manufacturing, shipping and logistics, telecommunication, travel, healthcare, retail, defence, and government. We're talking large, complex software that handles huge workloads, and the needs vary. What works well enough for a CLI dev tool or a simple website is often not good enough to handle the world's credit card transaction processing or mobile phone networks.
> If not, what about Python's impl makes it so lackluster to any of 7 of Java's?
Java's GCs are moving collectors, which offer advantages not just compared to Python's GC but to all memory management strategies. Memory management (even in C) imposes a CPU/RAM tradeoff. Moving collectors (used in Java, .NET, and V8) give you a knob for controlling the tradeoff, i.e. they're able to convert RAM to CPU (i.e. use RAM chips as a hardware accelerator) and vice-versa.
> Is Python's GC bad, or are there cyclic reference issues?
Unless you're being pedantic and including reference counting without cycle detection as GC, if your GC has cyclic reference issues, your GC is bad.
> Is it possible to detect cyclic references perfectly?
Yes? That's the entire point of tracing GC. You have some set of root objects that you start with (globals, objects on thread stacks, etc.) and then you mark every object that's reachable from them. Anything that's not reachable is garbage, even if there are cycles within them.
>Is Python's GC bad, or are there cyclic reference issues?
Both can be true. The first can even be wholly or partly due to the second.
On addition, the way it does it via RC causes fragmentation, poor locality for caches, and general slowness for mass allocations. And it's one-size-fits-all.
Java has a much larger selection to pick to finetune specific use cases, which each being far greater for that use case. And the default no-need-to-think one (G1 iirc), is already faster and better than Python's.
Are you not confusing GC (freeing memory) with the memory allocator ?
Memory allocator: tcmalloc, jemalloc, they are concerned with fetching (and releasing) pages of memory from the OS and allocating objects for the program
GC is only responsible for saying to the memory allocator "this object is no longer used"
> GC is only responsible for saying to the memory allocator "this object is no longer used"
This is simply not true. Not only are Java's GCs responsible for allocation (which they do simply by bumping a pointer, similar to stack allocation), unlike Python's refcounting collector or Go's nonmoving tracing collector, they have no free operation of any kind and never free objects. Moving collectors don't even know when an object is freed. The way they work is that they compact the live objects, and because the dead objects are invisible to them, they happen to write over them when compacting the live ones. Refcounting collectors and nonmoving tracing collectors do use a free-list-based allocator that they use for allocation and deallocation, but moving collectors work completely differently.
Moving collectors can be so efficient that in the eighties, there was a famous paper about them called "Garbage Collection Can Be Faster Than Stack Allocation" [1] showing that the cost of managing an object's lifetime with a moving collector can, in principle, be less than a single machine instruction. That's why the cost of heap memory management in Java (and other runtimes that employ moving collectors) cannot be compared to the cost of memory management in languages using free lists, be it C or Python. Their operation is just too different (e.g. in Java, assigning a value to an object field or setting an array cell could sometimes be more expensive than allocating a brand new object/array).
>Are you not confusing GC (freeing memory) with the memory allocator ?
No, you're missing the fact that the allocation of memory and the GC go hand in hand, because you need it so for optimizations. They are designed together to cooperate in modern runtimes.
> Lately, they seems to work with CRIU, various heuristics, multi-stage in-process bytecode compilation ..
Not sure what you mean by this, as this has nothing to do with GC, and Java has had a multi-tier optimising compiler for 15 years now.
> that nobody else have, so fixes are available
Go has much worse problems with GC than Java does these days, and nobody else is able to achieve similar performance in large programs with heavy workloads. So everyone else lives with less sophisticated compilers and memory management simply by accepting worse performance.
Libraries. I use both languages, and a survey of what libraries are available is part of picking an implementation language when starting a greenfield project.
$ time ./a.out
real 0m0.002s
user 0m0.000s
sys 0m0.002s
A do-nothing Go program:
$ time ./tmp
real 0m0.002s
user 0m0.000s
sys 0m0.003s
I don't believe Go has any optimizations to not start its runtime if it isn't necessary, but when I added spawning a goroutine that immediately blocks on a channel read that will never come the numbers didn't change. That doesn't really time the runtime. Probably the program terminated before the goroutine was scheduled to run anything. It just makes it so there definitely wasn't an early exit because the compiler or the runtime "realized" it didn't need to start the runtime.
I'm sure the Go program is somewhat slower to start and end than C, and that we're running into the limits of how quickly processes can be spawned and other timing overhead which is obscuring the difference. However for practical purposes, "it starts up in less than the overhead for starting a process in the shell" is the same speed for most purposes.
Not even a "do nothing" Python program, no Python program at all:
$ time python3 -c 1
real 0m0.012s
user 0m0.008s
sys 0m0.004s
If you had a Go program that was slow to start up, it was your program, not Go. By contrast, Python, and the dynamic scripting languages in general, can be quite slow to start up, just in the reading and compiling of the code. (Even .pyc files, IIRC, take processing, just less processing than Python source code... it's still nowhere near "memory map it in and go" as it is for statically-compiled languages.)
Yes, of course, but I took the conversation to be centered around backend uses cases. What CLI is experiencing garbage collection issues like that in this discussion?
What? Compared to Python they're like lightning. Typically milliseconds to the start of main() - admittedly they can be slowed down by init() nonsense and terrible generated protobuf code nonsense in deep dependency trees - but with a non-trivial Python program you can look forward to an order of magnitude more. There are techniques to help address that but (1) they're not idiomatic and (2) it still only mitigates it.
I suppose Go programs are slower than the equivalent thing in C or C++, but I'm not sure that's a very relevant comparison in most cases today (how many new things being written would choose those languages).
PyPy is not looking healthy right now - it's several versions behind in support and, while it's not dead, it looks like it might be settling down for a rest.
Obviously it's not easy to move the whole language of a big codebase, but I feel a lot of this stuff (fiddling with GC, JITing, type hints, and I'm dubious about the free-threading stuff) tries to take Python somewhere it isn't really good at, and if that's what you want, you really want a different language.
Well, they never made the jump to Python 3. But shipping 2.7 interpreters in 2024 was quite an achievement on its own. So their users already know this pain. And from my experience in academia, python 2.7 and java 8 will probably be used for another 20 years before the last machine running that stuff burns out.
Claude writes java pretty well, and faster than Rust. It's a great middle ground for some projects. I've switched back from Rust to Java for some things.
I don't know why you would use Python at all except for small iterative projects. If you hate java for some reason, there's Go...
Right sorry, that's not in my wheelhouse so I didn't think of that. I should be more specific. For general backend / data processing/pipeline stuff, API servers ...
I rebuilt the entire fastcomments moderation UI 2yrs ago with webstorm on my 16gb thinkpad. 64gb is nice but not needed. I wonder if every dev didn't use an M4 Pro if software wouldn't be so resource hungry...
Still building https://FastComments.com :) I'm planning on launching a desktop app for it soon with a combined forum. So you could have a community on something like discord, but all the chats are indexed and searchable through a web forum style interface as well. The desktop app is a native C++ app so no electron :)
I'm also working on launching https://watch.ly (network/fs sandbox with human in the loop for ai agents), mostly waiting for the entitlements from apple at this point...
oh and I launched https://dirtforever.net recently to keep Clubs going for Dirt Rally 2 without the EA servers. Learned about the egonet protocol and made a server.
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