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.
The max pause time thing is a meme :) I have gotten multi second pause times with ZGC. It depends on what hardware you run it on.
The new generational ZGC? I'm sceptical.
Have a reproducer?
As far as I know, java has 7 GC implementations, none of which are perfect, all of which have drawbacks
Lately, they seems to work with CRIU, various heuristics, multi-stage in-process bytecode compilation ..
Java is a mess, they are working hard to avoid fixing their issue (that nobody else have, so fixes are available)
>As far as I know, java has 7 GC implementations, none of which are perfect, all of which have drawbacks
Compared to Python's, all of them are beyond perfect. And 99.9% of the time you don't even need to use anything but the default.
> 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?
> 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 it possible to detect cyclic references perfectly?
Yes. The GCs in Java, .NET, V8, and Go do it.
> If we have 7 and 0.1% of the time you need one of the 6 that is non-default, how do we choose?
Java's GC are optimised for different workloads and environments, and when the choice matters, they're easy to choose among:
1. Parallel GC: Maximal throughput when latency doesn't matter (batch processing).
2. Serial GC: Very small machines.
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?
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"
(please stay focused on java)
>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.
Please read up some more about Java and GCs. Memory allocation and GC are heavily intertwined.
> 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.
Next year? Do tell
https://openjdk.org/jeps/8377305
As Python using SRE and supporting Python Flask apps, most of us would love JIT in Python assuming it pretty much drop in replacement.
PyPy doesn't have the support it needs and is stuck on 3.11.
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.
Why not just use Go? It has a proper concurrent, non-moving GC that, AIUI, has not been associated with sudden memory spikes.
For a new project, teams can decide whether to use Go, but there are many millions of lines of existing Python servers out there.
Not to mention that there are differences in ecosystem, familiarity, and ergonomics that may make a team want to stick with Python.
“Just use Go” is not really actionable advice in most cases.
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.
It's a tradeoff. Go programs are extremely slow at starting up for example.
A do nothing C program (int main() { return; }):
A do-nothing Go program: 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:
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.)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).
So are Python and Java programs.
That doesn't matter for anything other then CLIs.
Some people are writing CLIs
It is the same for me. Predicability is better than any optimization.
And if people want python with java, there's always Jython.
Graal vm has support for python 3 unfortunately it’s funded by oracle.
If it makes you feel any better (it probably doesn't), the development of OpenJDK and the Java language itself is also mostly funded by Oracle
Java is funded by Oracle, all of it.
People parrot to use OpenJDK without understanding it is mostly Oracle employees working on it.
And if you dislike Oracle, the other minor contributors are Red-Hat, IBM, SAP, Microsoft, Alibaba, Azul,... which for many HNers are the same.
jython has been basically unmaintained for quite some time
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.
Jython is unmaintained, I'd recommend Clojure. Use python libraries and code while seamlessly targeting the JVM.
jpype and graalpy are life.
jython went EOL.with python 2 going EOL.
In what way do you feel Python is predictable, especially in comparison to other languages one would build a backend system in?
It's predictable vs Rust, C#, F#, Elixir, Go, etc.?
Resistance from anyone who matters to the developers?