问:Java中的异常处理真的很慢吗?
传统观点以及大量谷歌结果表明，不应该将异常逻辑用于Java中的正常程序流。通常会给出两个原因，
它真的很慢——甚至比普通代码慢一个数量级(给出的原因各不相同)，
and
它很混乱，因为人们只希望在异常代码中处理错误。
这个问题是关于第一条的。
As an example, this page describes Java exception handling as "very slow" and relates the slowness to the creation of the exception message string - "this string is then used in creating the exception object that is thrown. This is not fast." The article Effective Exception Handling in Java says that "the reason for this is due to the object creation aspect of exception handling, which thereby makes throwing exceptions inherently slow". Another reason out there is that the stack trace generation is what slows it down.
My testing (using Java 1.6.0_07, Java HotSpot 10.0, on 32 bit Linux), indicates that exception handling is no slower than regular code. I tried running a method in a loop that executes some code. At the end of the method, I use a boolean to indicate whether to return or throw. This way the actual processing is the same. I tried running the methods in different orders and averaging my test times, thinking it may have been the JVM warming up. In all my tests, the throw was at least as fast as the return, if not faster (up to 3.1% faster). I am completely open to the possibility that my tests were wrong, but I haven't seen anything out there in the way of the code sample, test comparisons, or results in the last year or two that show exception handling in Java to actually be slow.
引导我走上这条路的是我需要使用的一个API，它将抛出异常作为正常控制逻辑的一部分。我想纠正它们的用法，但现在我可能做不到。我是否应该赞美他们的前瞻思维?
在论文《即时编译中的高效Java异常处理》中，作者建议，即使没有抛出异常，仅异常处理程序的存在就足以阻止JIT编译器正确优化代码，从而降低代码的速度。我还没有测试过这个理论。
Aleksey Shipilëv做了一个非常彻底的分析，他在各种条件组合下对Java异常进行了基准测试:
新创建的异常vs预先创建的异常
启用与禁用堆栈跟踪
请求的堆栈跟踪vs从未请求的堆栈跟踪
在顶层捕获vs在每一层重新抛出vs在每一层被链接/包裹
不同级别的Java调用堆栈深度
无内联优化vs极端内联vs默认设置
用户定义字段读与不读
他还将它们与在不同错误频率级别检查错误代码的性能进行了比较。
结论(逐字摘自他的帖子)如下:
Truly exceptional exceptions are beautifully performant. If you use them as designed, and only communicate the truly exceptional cases among the overwhelmingly large number of non-exceptional cases handled by regular code, then using exceptions is the performance win.
The performance costs of exceptions have two major components: stack trace construction when Exception is instantiated and stack unwinding during Exception throw.
Stack trace construction costs are proportional to stack depth at the moment of exception instantiation. That is already bad because who on Earth knows the stack depth at which this throwing method would be called? Even if you turn off the stack trace generation and/or cache the exceptions, you can only get rid of this part of the performance cost.
Stack unwinding costs depend on how lucky we are with bringing the exception handler closer in the compiled code. Carefully structuring the code to avoid deep exception handlers lookup is probably helping us get luckier.
Should we eliminate both effects, the performance cost of exceptions is that of the local branch. No matter how beautiful it sounds, that does not mean you should use Exceptions as the usual control flow, because in that case you are at the mercy of optimizing compiler! You should only use them in truly exceptional cases, where the exception frequency amortizes the possible unlucky cost of raising the actual exception.
The optimistic rule-of-thumb seems to be 10^-4 frequency for exceptions is exceptional enough. That, of course, depends on the heavy-weights of the exceptions themselves, the exact actions taken in exception handlers, etc.
结果是，当没有抛出异常时，您不会付出代价，因此当异常条件足够罕见时，异常处理比每次都使用if更快。这篇文章的全文非常值得一读。
关于异常性能的好文章是:
https://shipilev.net/blog/2014/exceptional-performance/
实例化vs重用现有的，有堆栈跟踪和没有，等等:
Benchmark Mode Samples Mean Mean error Units
dynamicException avgt 25 1901.196 14.572 ns/op
dynamicException_NoStack avgt 25 67.029 0.212 ns/op
dynamicException_NoStack_UsedData avgt 25 68.952 0.441 ns/op
dynamicException_NoStack_UsedStack avgt 25 137.329 1.039 ns/op
dynamicException_UsedData avgt 25 1900.770 9.359 ns/op
dynamicException_UsedStack avgt 25 20033.658 118.600 ns/op
plain avgt 25 1.259 0.002 ns/op
staticException avgt 25 1.510 0.001 ns/op
staticException_NoStack avgt 25 1.514 0.003 ns/op
staticException_NoStack_UsedData avgt 25 4.185 0.015 ns/op
staticException_NoStack_UsedStack avgt 25 19.110 0.051 ns/op
staticException_UsedData avgt 25 4.159 0.007 ns/op
staticException_UsedStack avgt 25 25.144 0.186 ns/op
根据堆栈跟踪的深度:
Benchmark Mode Samples Mean Mean error Units
exception_0000 avgt 25 1959.068 30.783 ns/op
exception_0001 avgt 25 1945.958 12.104 ns/op
exception_0002 avgt 25 2063.575 47.708 ns/op
exception_0004 avgt 25 2211.882 29.417 ns/op
exception_0008 avgt 25 2472.729 57.336 ns/op
exception_0016 avgt 25 2950.847 29.863 ns/op
exception_0032 avgt 25 4416.548 50.340 ns/op
exception_0064 avgt 25 6845.140 40.114 ns/op
exception_0128 avgt 25 11774.758 54.299 ns/op
exception_0256 avgt 25 21617.526 101.379 ns/op
exception_0512 avgt 25 42780.434 144.594 ns/op
exception_1024 avgt 25 82839.358 291.434 ns/op
有关其他详细信息(包括来自JIT的x64汇编程序)，请阅读原始博客文章。
这意味着Hibernate/Spring/etc-EE-shit因为异常(xD)而变慢。
通过重写应用程序控制流，避免异常(返回错误作为返回)，提高应用程序的性能10 -100倍，这取决于你抛出它们的频率))
不知道这些主题是否相关，但我曾经想实现一个依赖于当前线程的堆栈跟踪的技巧:我想发现方法的名称，它触发了实例化类中的实例化(是的，这个想法很疯狂，我完全放弃了它)。所以我发现调用Thread.currentThread(). getstacktrace()是非常慢的(由于本机的dumpThreads方法，它在内部使用)。
相应地，Java Throwable有一个本地方法fillInStackTrace。我认为前面描述的kill -catch块以某种方式触发了该方法的执行。
但让我告诉你另一个故事……
在Scala中，一些函数特性是使用ControlThrowable在JVM中编译的，它扩展了Throwable，并以以下方式覆盖了它的fillInStackTrace:
override def fillInStackTrace(): Throwable = this
所以我调整了上面的测试(循环量减少了十，我的机器有点慢:):
class ControlException extends ControlThrowable
class T {
var value = 0
def reset = {
value = 0
}
def method1(i: Int) = {
value = ((value + i) / i) << 1
if ((i & 0xfffffff) == 1000000000) {
println("You'll never see this!")
}
}
def method2(i: Int) = {
value = ((value + i) / i) << 1
if ((i & 0xfffffff) == 1000000000) {
throw new Exception()
}
}
def method3(i: Int) = {
value = ((value + i) / i) << 1
if ((i & 0x1) == 1) {
throw new Exception()
}
}
def method4(i: Int) = {
value = ((value + i) / i) << 1
if ((i & 0x1) == 1) {
throw new ControlException()
}
}
}
class Main {
var l = System.currentTimeMillis
val t = new T
for (i <- 1 to 10000000)
t.method1(i)
l = System.currentTimeMillis - l
println("method1 took " + l + " ms, result was " + t.value)
t.reset
l = System.currentTimeMillis
for (i <- 1 to 10000000) try {
t.method2(i)
} catch {
case _ => println("You'll never see this")
}
l = System.currentTimeMillis - l
println("method2 took " + l + " ms, result was " + t.value)
t.reset
l = System.currentTimeMillis
for (i <- 1 to 10000000) try {
t.method4(i)
} catch {
case _ => // do nothing
}
l = System.currentTimeMillis - l
println("method4 took " + l + " ms, result was " + t.value)
t.reset
l = System.currentTimeMillis
for (i <- 1 to 10000000) try {
t.method3(i)
} catch {
case _ => // do nothing
}
l = System.currentTimeMillis - l
println("method3 took " + l + " ms, result was " + t.value)
}
所以，结果是:
method1 took 146 ms, result was 2
method2 took 159 ms, result was 2
method4 took 1551 ms, result was 2
method3 took 42492 ms, result was 2
你看，method3和method4之间唯一的区别是它们会抛出不同类型的异常。是的，method4仍然比method1和method2慢，但是差异是可以接受的。
供你参考，我扩展了Mecki做的实验:
method1 took 1733 ms, result was 2
method2 took 1248 ms, result was 2
method3 took 83997 ms, result was 2
method4 took 1692 ms, result was 2
method5 took 60946 ms, result was 2
method6 took 25746 ms, result was 2
前3个和Mecki的一样(我的笔记本电脑明显慢一些)。
method4和method3是一样的，除了它创建了一个新的Integer(1)而不是抛出一个新的Exception()。
method5类似于method3，除了它创建了新的Exception()而不抛出它。
Method6和method3很像，只是它会抛出一个预先创建的异常(一个实例变量)，而不是创建一个新异常。
在Java中，抛出异常的大部分开销是收集堆栈跟踪所花费的时间，这发生在创建异常对象时。抛出异常的实际成本虽然很大，但比创建异常的成本要小得多。
