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。

我对异常速度和以编程方式检查数据的看法。

许多类都有字符串到值的转换器(扫描器/解析器)，也有受人尊敬和知名的库;)

通常有形式

class Example {
public static Example Parse(String input) throws AnyRuntimeParsigException
...
}

异常名称只是例子，通常是未选中的(运行时)，所以抛出声明只是我的图片

有时存在第二种形式:

public static Example Parse(String input, Example defaultValue)

不扔

当第二个文件不可用时(或者程序员读的文档太少，只使用第一个文件)，用正则表达式编写这样的代码。正则表达式很酷，政治正确等:

Xxxxx.regex(".....pattern", src);
if(ImTotallySure)
{
  Example v = Example.Parse(src);
}

使用这段代码，程序员没有异常成本。BUT具有相当高的代价的正则表达式ALWAYS与小的代价异常有时。

我几乎总是在这种情况下使用

try { parse } catch(ParsingException ) // concrete exception from javadoc
{
}

没有分析堆栈跟踪等，我相信在你的讲座后相当快。

不要害怕例外情况

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。

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。

比较一下，假设是Integer。将parseInt转换为以下方法，该方法在不可解析数据的情况下只返回默认值，而不会抛出异常:

  public static int parseUnsignedInt(String s, int defaultValue) {
    final int strLength = s.length();
    if (strLength == 0)
      return defaultValue;
    int value = 0;
    for (int i=strLength-1; i>=0; i--) {
      int c = s.charAt(i);
      if (c > 47 && c < 58) {
        c -= 48;
        for (int j=strLength-i; j!=1; j--)
          c *= 10;
        value += c;
      } else {
        return defaultValue;
      }
    }
    return value < 0 ? /* übergebener wert > Integer.MAX_VALUE? */ defaultValue : value;
  }

只要您将这两种方法应用于“有效”数据，它们将以大致相同的速率工作(即使Integer。parseInt设法处理更复杂的数据)。但是当您试图解析无效数据时(例如解析“abc”1.000.000次)，性能上的差异应该是至关重要的。