As for applications that are huge, memory hogs, complex and non elegant, well, congratulations you just described the majority of enterprise applications.
I see the proliferation of "Java is not slow" articles as a sign that it is --- if it wasn't, why would there be a need for such strong propaganda and carefully constructed microbenchmarks? Sun/Oracle have a deep interest in hiding their "naked emperor". Where are all the "C is not slow" articles, for example? I'm sure anyone who has used a non-trivial Java application, not even an "Enterprise" one, is immediately aware that it seems slower, even those who might not know at all what Java is.
Nobody writes "C is not slow" articles any more, but I remember a time when there were people who believed C++ was inherently slower than C. Maybe some such people still exist! I don't see much talk like that anymore though.
With respect to bloated code, the 1990's and early 2000's also saw things like DCOM and other C++ bloat-zones. It's not really to do with the language. The presence of bloat in enterprise apps is more to do with the lack of competition (bespoke apps are by definition monopolies), the frequent lack of deadline pressure (deadlines are invariably artificial) and the inflexible team sizes (over-engineering is incentivised by the need to retain a nice safe corporate job). The fact that enterprise software and Java go hand in hand speaks more to the lack of competition in the space Java plays in, than anything about the language itself.
Yeah, I remember the days when junior Assembly coders could easily write better code than any C compiler for home computers.
Or the days when the arguments that are now used against Java, Go, Swift and many others where used against Turbo Pascal, C, C++, Modula-2, Basic compilers.
The more things change, the more they stay the same, I guess.
I just don't write code in Java anymore --- I still need to use some Java apps, and the difference remains obvious.
The other thing I've noticed is that a lot of those "Java is actually fast" articles are [1] entirely words about all the fancy optimisations JVMs do with no real numbers, [2] comparing Java with previous versions of itself, [3] comparing Java with highly dynamic languages like JavaScript, Python, or Ruby, or [4] microbenchmarks designed to specifically highlight certain JVM optimisations.
but I remember a time when there were people who believed C++ was inherently slower than C.
The C++ vs C case is slightly different, because C++ is essentially an extension of C. Some features like iostreams vs stdio are objectively slower on the C++ side, and efficient C++ code usually tends to look a lot like C. Those who say C++ is slower are referring to the "overuse" of C++ features that add overhead. As the benchmarks posted below show, even carefully written and optimised Java that looks like C is slower than C.
https://days2011.scala-lang.org/sites/days2011/files/ws3-1-H...
They write the same program in C++, Java, Scala and Go, then compare performance both before and after optimisation.
They key part that shows Java can be as fast as C++ is V.E "Java tunings":
Jeremy Manson brought the performance of Java on par with the original C++ version. This version is kept in the java_pro directory. Note that Jeremy deliberately refused to optimize the code further, many of the C++ optimizations would apply to the Java version as well.
The tunings he applied are not exotic, but would not be used in most code unless it was actually performance sensitive. For instance he chose better starting sizes in ArrayLists and replaced boxing collections with more optimal ones (presumably from GNU Trove).
Of course, that is a comparison against the original C++. Later in the paper it is shown that Google were able to optimize the C++ version heavily and it then beat the Java version again, however, apparently the optimised version relied on data structures so exotic that they're Google proprietary and they chose not to open source them, so I'm not sure that'd be reflective of the experience of the average C++ developer.
Consistently twice as slow, with memory usage between 2 and 400(!) times larger. Java is slow and bloated compared to native.
Your next argument will be something about how micro-benchmarks don't reflect reality for larger programs...
mandelbrot 17% slower
fannkuch-redux 34% slower
fasta 38% slower
fasta-redux 39% slower
pidigits 44% slower
spectral-norm 115% slower
reverse-compliment 121% slower
n-body 136% slower
regex-dna 235% slower
k-nucleotide 253% slower
As for being dishonest, well, you're commenting from a newly minted temp account so it would seem you didn't have the courage/backbone to use your real one.
I'm not seeing the 400x more memory - there is a 40x though. But if you add up the columns for the programs that have both C and Java versions, you get this rough summary...
secs KB gz
Java 76.33 2004048 12775
C gcc 37.32 793652 11651
What do you mean by "native"? C/C++? Yes, given enough effort you can write C code that outperforms Java code, sometimes handily, especially in single threaded or low-contention cases. Yet we did write applications in C/C++ before we switched to Java, we had excellent reasons to make the switch, and there have been precious few companies making the switch back. That shows you that the microbenchmarks don't tell the whole story.
Right now, the JVM's biggest performance issue is the lack of value types. Once they arrive -- and work is well underway -- beating Java would be harder and harder, especially given new compilers like Graal. But in any event, I just don't see large developers switch back to C++ (or Rust) en masse. Memory usage is hardly an issue, as RAM is very cheap and underutilized in server environments anyway. Spending effort to conserve RAM just doesn't make any sense, unless you're targeting RAM constrained environments.
2. How are Java's value types limited?
3. I find your calculation extremely pessimistic. Lambda expression are widespread a couple of years after their release, and I see no reason why value types will be different. I think that 5 years are a better estimate for wide use of value types.
> Except for pointer equality checks, forbidden operations will throw some sort of exception. How to control pointer equality checks is an open question with several possible answers.
And more:
Rules for permanently locked objects:
- restrictions on classes of locked objects
- all non-static fields must be final
- there must be no finalizer method (no override to `Object.finalize`)
- these restrictions apply to any superclasses as well
- an array can be marked locked, but then (of course) its elements cannot be stored to
- if not an array, the object's class must implement the marker type `PermanentlyLockable` (is this a good idea?)
- restricted operations on locked objects (could be enforced, or else documented as producing undefined results)
- do not use any astore or putfield instructions, nor their reflective equivalents, to change any field
- do not lock (you may get a hang or a LockedObjectException)
- do not test for pointer equality; use Object.equals instead (there may be a test for this)
- do not ask for an identity hash code; use Object.hashCode instead (there may be a test for this)
- do not call wait, notify, or notifyAll methods in Object
- at the time it is marked locked, an object's monitor must not be locked (in fact, should never have been?)
- side effects
- elements of locked arrays are stably available to readers just like final object fields (i.e., there is a memory fence)
- a locked object can be locked again, with no additional effect
- any attempt to mutate a permanently locked object raises java.lang.LockedObjectException
- any attempt to synchronize on a permanently locked object raises java.lang.LockedObjectException
- object lifecycle
- all objects are initially created in a normal (unlocked) state
- an object marked locked cannot be "unlocked" (reverted to a normal state)
- an object marked locked must be unreferenced by any other thread (can we enforce this?)
- the reference returned from the (unsafe) marking primitive must be used for all future accesses
- any previous references (including the one passed to the marking primitive) must be unused
- in practice, this means you must mark an object locked immediately after constructing it
- API
- the method `lockPermanently` is used to lock an object permanently
- there is a predicate `isLockedPermanently` which can test whether an object is locked or not
- for initial experiments, these methods are in `sun.misc.Unsafe`; perhaps they belong on `Object` (cf. `clone`)`
- Cyberduck is written in Java, yet nobody seems to be complaining about how slow and bloated it is.
- There exists a version of Quake 2 written in Java: http://bytonic.de/html/benchmarks.html It seems to be able to push out more frames than native, actually.
Both of those are real software that actually run well on Java, regardless of penalty paid for running in a VM, JIT, and GC.
Also when looking at the amount of memory and time taken when comparing C to Java in the microbenchmarks, always be sure to mentally factor in the base overhead of starting up the JVM both in time and memory. It's pretty easy to see that when a C version takes up something like 100KB of memory and the Java one takes up 30MB, Java consistently takes up at least ~30MB regardless of how much memory is required for that microbenchmark. When the C version takes up 300MB and the Java one takes up 700MB, that's a bit better of a comparison. (though still not perfect, because the Java GC will reserve a lot of memory for itself, even if it isn't using the full 700MB, if it feels it needs that much, etc.)
Lol I just double checked that after you said it, it never felt "Javaish" even the interface is really sane.
Actually they are using JNA for a lot of stuff and they written foundation bindings... (https://g.iterate.ch/projects/ITERATE/repos/cyberduck/browse...) cool stuff I keep that for reference. However he should put the bindings stuff under something like LGPL since most stuff will fall under fair use anyway (simple class Names which you would use anyway even without looking at the Source when making a JNA binding to Cocoa)
The table in your link shows it being 6% faster in one (literally) corner case, while all the other entries show it being slower by varying amounts. Keep in mind that in this benchmark a lot of the "heavy lifting" is being done by the GPU via OpenGL, so it isn't great for benchmarking languages that run on the CPU. It also doesn't mention what the "Original C Code" was compiled with.
Not much for those programs:
http://benchmarksgame.alioth.debian.org/sometimes-people-jus...
>> It's pretty easy to see … memory