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。

Java和c#中的异常性能还有待改进。

作为程序员，这迫使我们遵循“异常应该很少引起”的规则，仅仅是出于实际性能的考虑。

However, as computer scientists, we should rebel against this problematic state. The person authoring a function often has no idea how often it will be called, or whether success or failure is more likely. Only the caller has this information. Trying to avoid exceptions leads to unclear API idoms where in some cases we have only clean-but-slow exception versions, and in other cases we have fast-but-clunky return-value errors, and in still other cases we end up with both. The library implementor may have to write and maintain two versions of APIs, and the caller has to decide which of two versions to use in each situation.

这里有点乱。如果异常具有更好的性能，我们就可以避免这些笨拙的习惯用法，并按照它们应该使用的方式使用异常……作为结构化错误返回工具。

我真的希望看到异常机制使用更接近返回值的技术来实现，这样我们的性能就能更接近返回值。因为这是我们在性能敏感代码中恢复的内容。

下面是一个比较异常性能和错误返回值性能的代码示例。

公共类test {

int value;


public int getValue() {
    return value;
}

public void reset() {
    value = 0;
}

public boolean baseline_null(boolean shouldfail, int recurse_depth) {
    if (recurse_depth <= 0) {
        return shouldfail;
    } else {
        return baseline_null(shouldfail,recurse_depth-1);
    }
}

public boolean retval_error(boolean shouldfail, int recurse_depth) {
    if (recurse_depth <= 0) {
        if (shouldfail) {
            return false;
        } else {
            return true;
        }
    } else {
        boolean nested_error = retval_error(shouldfail,recurse_depth-1);
        if (nested_error) {
            return true;
        } else {
            return false;
        }
    }
}

public void exception_error(boolean shouldfail, int recurse_depth) throws Exception {
    if (recurse_depth <= 0) {
        if (shouldfail) {
            throw new Exception();
        }
    } else {
        exception_error(shouldfail,recurse_depth-1);
    }

}

public static void main(String[] args) {
    int i;
    long l;
    TestIt t = new TestIt();
    int failures;

    int ITERATION_COUNT = 100000000;


    // (0) baseline null workload
    for (int recurse_depth = 2; recurse_depth <= 10; recurse_depth+=3) {
        for (float exception_freq = 0.0f; exception_freq <= 1.0f; exception_freq += 0.25f) {            
            int EXCEPTION_MOD = (exception_freq == 0.0f) ? ITERATION_COUNT+1 : (int)(1.0f / exception_freq);            

            failures = 0;
            long start_time = System.currentTimeMillis();
            t.reset();              
            for (i = 1; i < ITERATION_COUNT; i++) {
                boolean shoulderror = (i % EXCEPTION_MOD) == 0;
                t.baseline_null(shoulderror,recurse_depth);
            }
            long elapsed_time = System.currentTimeMillis() - start_time;
            System.out.format("baseline: recurse_depth %s, exception_freqeuncy %s (%s), time elapsed %s ms\n",
                    recurse_depth, exception_freq, failures,elapsed_time);
        }
    }


    // (1) retval_error
    for (int recurse_depth = 2; recurse_depth <= 10; recurse_depth+=3) {
        for (float exception_freq = 0.0f; exception_freq <= 1.0f; exception_freq += 0.25f) {            
            int EXCEPTION_MOD = (exception_freq == 0.0f) ? ITERATION_COUNT+1 : (int)(1.0f / exception_freq);            

            failures = 0;
            long start_time = System.currentTimeMillis();
            t.reset();              
            for (i = 1; i < ITERATION_COUNT; i++) {
                boolean shoulderror = (i % EXCEPTION_MOD) == 0;
                if (!t.retval_error(shoulderror,recurse_depth)) {
                    failures++;
                }
            }
            long elapsed_time = System.currentTimeMillis() - start_time;
            System.out.format("retval_error: recurse_depth %s, exception_freqeuncy %s (%s), time elapsed %s ms\n",
                    recurse_depth, exception_freq, failures,elapsed_time);
        }
    }

    // (2) exception_error
    for (int recurse_depth = 2; recurse_depth <= 10; recurse_depth+=3) {
        for (float exception_freq = 0.0f; exception_freq <= 1.0f; exception_freq += 0.25f) {            
            int EXCEPTION_MOD = (exception_freq == 0.0f) ? ITERATION_COUNT+1 : (int)(1.0f / exception_freq);            

            failures = 0;
            long start_time = System.currentTimeMillis();
            t.reset();              
            for (i = 1; i < ITERATION_COUNT; i++) {
                boolean shoulderror = (i % EXCEPTION_MOD) == 0;
                try {
                    t.exception_error(shoulderror,recurse_depth);
                } catch (Exception e) {
                    failures++;
                }
            }
            long elapsed_time = System.currentTimeMillis() - start_time;
            System.out.format("exception_error: recurse_depth %s, exception_freqeuncy %s (%s), time elapsed %s ms\n",
                    recurse_depth, exception_freq, failures,elapsed_time);              
        }
    }
}

}

结果如下:

baseline: recurse_depth 2, exception_freqeuncy 0.0 (0), time elapsed 683 ms
baseline: recurse_depth 2, exception_freqeuncy 0.25 (0), time elapsed 790 ms
baseline: recurse_depth 2, exception_freqeuncy 0.5 (0), time elapsed 768 ms
baseline: recurse_depth 2, exception_freqeuncy 0.75 (0), time elapsed 749 ms
baseline: recurse_depth 2, exception_freqeuncy 1.0 (0), time elapsed 731 ms
baseline: recurse_depth 5, exception_freqeuncy 0.0 (0), time elapsed 923 ms
baseline: recurse_depth 5, exception_freqeuncy 0.25 (0), time elapsed 971 ms
baseline: recurse_depth 5, exception_freqeuncy 0.5 (0), time elapsed 982 ms
baseline: recurse_depth 5, exception_freqeuncy 0.75 (0), time elapsed 947 ms
baseline: recurse_depth 5, exception_freqeuncy 1.0 (0), time elapsed 937 ms
baseline: recurse_depth 8, exception_freqeuncy 0.0 (0), time elapsed 1154 ms
baseline: recurse_depth 8, exception_freqeuncy 0.25 (0), time elapsed 1149 ms
baseline: recurse_depth 8, exception_freqeuncy 0.5 (0), time elapsed 1133 ms
baseline: recurse_depth 8, exception_freqeuncy 0.75 (0), time elapsed 1117 ms
baseline: recurse_depth 8, exception_freqeuncy 1.0 (0), time elapsed 1116 ms
retval_error: recurse_depth 2, exception_freqeuncy 0.0 (0), time elapsed 742 ms
retval_error: recurse_depth 2, exception_freqeuncy 0.25 (24999999), time elapsed 743 ms
retval_error: recurse_depth 2, exception_freqeuncy 0.5 (49999999), time elapsed 734 ms
retval_error: recurse_depth 2, exception_freqeuncy 0.75 (99999999), time elapsed 723 ms
retval_error: recurse_depth 2, exception_freqeuncy 1.0 (99999999), time elapsed 728 ms
retval_error: recurse_depth 5, exception_freqeuncy 0.0 (0), time elapsed 920 ms
retval_error: recurse_depth 5, exception_freqeuncy 0.25 (24999999), time elapsed 1121   ms
retval_error: recurse_depth 5, exception_freqeuncy 0.5 (49999999), time elapsed 1037 ms
retval_error: recurse_depth 5, exception_freqeuncy 0.75 (99999999), time elapsed 1141   ms
retval_error: recurse_depth 5, exception_freqeuncy 1.0 (99999999), time elapsed 1130 ms
retval_error: recurse_depth 8, exception_freqeuncy 0.0 (0), time elapsed 1218 ms
retval_error: recurse_depth 8, exception_freqeuncy 0.25 (24999999), time elapsed 1334  ms
retval_error: recurse_depth 8, exception_freqeuncy 0.5 (49999999), time elapsed 1478 ms
retval_error: recurse_depth 8, exception_freqeuncy 0.75 (99999999), time elapsed 1637 ms
retval_error: recurse_depth 8, exception_freqeuncy 1.0 (99999999), time elapsed 1655 ms
exception_error: recurse_depth 2, exception_freqeuncy 0.0 (0), time elapsed 726 ms
exception_error: recurse_depth 2, exception_freqeuncy 0.25 (24999999), time elapsed 17487   ms
exception_error: recurse_depth 2, exception_freqeuncy 0.5 (49999999), time elapsed 33763   ms
exception_error: recurse_depth 2, exception_freqeuncy 0.75 (99999999), time elapsed 67367   ms
exception_error: recurse_depth 2, exception_freqeuncy 1.0 (99999999), time elapsed 66990 ms
exception_error: recurse_depth 5, exception_freqeuncy 0.0 (0), time elapsed 924 ms
exception_error: recurse_depth 5, exception_freqeuncy 0.25 (24999999), time elapsed 23775  ms
exception_error: recurse_depth 5, exception_freqeuncy 0.5 (49999999), time elapsed 46326 ms
exception_error: recurse_depth 5, exception_freqeuncy 0.75 (99999999), time elapsed 91707 ms
exception_error: recurse_depth 5, exception_freqeuncy 1.0 (99999999), time elapsed 91580 ms
exception_error: recurse_depth 8, exception_freqeuncy 0.0 (0), time elapsed 1144 ms
exception_error: recurse_depth 8, exception_freqeuncy 0.25 (24999999), time elapsed 30440 ms
exception_error: recurse_depth 8, exception_freqeuncy 0.5 (49999999), time elapsed 59116   ms
exception_error: recurse_depth 8, exception_freqeuncy 0.75 (99999999), time elapsed 116678 ms
exception_error: recurse_depth 8, exception_freqeuncy 1.0 (99999999), time elapsed 116477 ms

检查和传播返回值与基线空调用相比确实增加了一些成本，而该成本与调用深度成正比。在调用链深度为8时，错误返回值检查版本比不检查返回值的基线版本慢了约27%。

相比之下，异常性能不是调用深度的函数，而是异常频率的函数。然而，随着异常频率的增加，这种退化更为显著。当错误频率只有25%时，代码运行速度变慢了24倍。当错误频率为100%时，异常版本几乎要慢100倍。

这在我看来可能是在我们的异常实现中做出了错误的权衡。异常可以更快，可以避免代价高昂的跟踪遍历，也可以直接将异常转换为编译器支持的返回值检查。在此之前，当我们希望代码运行得更快时，我们不得不避免它们。

It depends how exceptions are implemented. The simplest way is using setjmp and longjmp. That means all registers of the CPU are written to the stack (which already takes some time) and possibly some other data needs to be created... all this already happens in the try statement. The throw statement needs to unwind the stack and restore the values of all registers (and possible other values in the VM). So try and throw are equally slow, and that is pretty slow, however if no exception is thrown, exiting the try block takes no time whatsoever in most cases (as everything is put on the stack which cleans up automatically if the method exists).

Sun和其他人认识到，这可能是次优的，当然随着时间的推移，虚拟机会变得越来越快。还有另一种实现异常的方法，它使try本身闪电般快(实际上try本身根本不会发生任何事情——当类被VM加载时，需要发生的一切都已经完成了)，并且它使throw不那么慢。我不知道哪个JVM使用了这种新的、更好的技术……

.．.但你是在用Java写代码，所以你的代码以后只能在一个特定系统的一个JVM上运行吗?因为如果它可以在任何其他平台或任何其他JVM版本(可能是任何其他供应商的)上运行，谁说他们也使用快速实现呢?速度快的要比速度慢的复杂得多，而且不容易在所有系统上实现。你想要便携吗?那就不要指望异常会很快。

It also makes a big difference what you do within a try block. If you open a try block and never call any method from within this try block, the try block will be ultra fast, as the JIT can then actually treat a throw like a simple goto. It neither needs to save stack-state nor does it need to unwind the stack if an exception is thrown (it only needs to jump to the catch handlers). However, this is not what you usually do. Usually you open a try block and then call a method that might throw an exception, right? And even if you just use the try block within your method, what kind of method will this be, that does not call any other method? Will it just calculate a number? Then what for do you need exceptions? There are much more elegant ways to regulate program flow. For pretty much anything else but simple math, you will have to call an external method and this already destroys the advantage of a local try block.

请看下面的测试代码:

public class Test {
    int value;


    public int getValue() {
        return value;
    }

    public void reset() {
        value = 0;
    }

    // Calculates without exception
    public void method1(int i) {
        value = ((value + i) / i) << 1;
        // Will never be true
        if ((i & 0xFFFFFFF) == 1000000000) {
            System.out.println("You'll never see this!");
        }
    }

    // Could in theory throw one, but never will
    public void method2(int i) throws Exception {
        value = ((value + i) / i) << 1;
        // Will never be true
        if ((i & 0xFFFFFFF) == 1000000000) {
            throw new Exception();
        }
    }

    // This one will regularly throw one
    public void method3(int i) throws Exception {
        value = ((value + i) / i) << 1;
        // i & 1 is equally fast to calculate as i & 0xFFFFFFF; it is both
        // an AND operation between two integers. The size of the number plays
        // no role. AND on 32 BIT always ANDs all 32 bits
        if ((i & 0x1) == 1) {
            throw new Exception();
        }
    }

    public static void main(String[] args) {
        int i;
        long l;
        Test t = new Test();

        l = System.currentTimeMillis();
        t.reset();
        for (i = 1; i < 100000000; i++) {
            t.method1(i);
        }
        l = System.currentTimeMillis() - l;
        System.out.println(
            "method1 took " + l + " ms, result was " + t.getValue()
        );

        l = System.currentTimeMillis();
        t.reset();
        for (i = 1; i < 100000000; i++) {
            try {
                t.method2(i);
            } catch (Exception e) {
                System.out.println("You'll never see this!");
            }
        }
        l = System.currentTimeMillis() - l;
        System.out.println(
            "method2 took " + l + " ms, result was " + t.getValue()
        );

        l = System.currentTimeMillis();
        t.reset();
        for (i = 1; i < 100000000; i++) {
            try {
                t.method3(i);
            } catch (Exception e) {
                // Do nothing here, as we will get here
            }
        }
        l = System.currentTimeMillis() - l;
        System.out.println(
            "method3 took " + l + " ms, result was " + t.getValue()
        );
    }
}

结果:

method1 took 972 ms, result was 2
method2 took 1003 ms, result was 2
method3 took 66716 ms, result was 2

try块的减速太小，无法排除后台进程等混杂因素。但是catch block杀死了一切，让它慢了66倍!

正如我所说，如果将try/catch和throw都放在同一个方法(method3)中，结果不会那么糟糕，但这是我不依赖的特殊JIT优化。即使使用这种优化，抛出仍然非常慢。我不知道你们想做什么，但肯定有比try/catch/throw更好的方法。

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。

使用附带的代码，在JDK 15上，@Mecki测试用例得到了完全不同的结果。这基本上是在5个循环中运行代码，第一个循环稍微短一些，给VM一些时间来热身。

结果:

Loop 1 10000 cycles
method1 took 1 ms, result was 2
method2 took 0 ms, result was 2
method3 took 22 ms, result was 2
method4 took 22 ms, result was 2
method5 took 24 ms, result was 2
Loop 2 10000000 cycles
method1 took 39 ms, result was 2
method2 took 39 ms, result was 2
method3 took 1558 ms, result was 2
method4 took 1640 ms, result was 2
method5 took 1717 ms, result was 2
Loop 3 10000000 cycles
method1 took 49 ms, result was 2
method2 took 48 ms, result was 2
method3 took 126 ms, result was 2
method4 took 88 ms, result was 2
method5 took 87 ms, result was 2
Loop 4 10000000 cycles
method1 took 34 ms, result was 2
method2 took 34 ms, result was 2
method3 took 33 ms, result was 2
method4 took 98 ms, result was 2
method5 took 58 ms, result was 2
Loop 5 10000000 cycles
method1 took 34 ms, result was 2
method2 took 33 ms, result was 2
method3 took 33 ms, result was 2
method4 took 48 ms, result was 2
method5 took 49 ms, result was 2

package hs.jfx.eventstream.api;

public class Snippet {
  int value;


  public int getValue() {
      return value;
  }

  public void reset() {
      value = 0;
  }

  // Calculates without exception
  public void method1(int i) {
      value = ((value + i) / i) << 1;
      // Will never be true
      if ((i & 0xFFFFFFF) == 1000000000) {
          System.out.println("You'll never see this!");
      }
  }

  // Could in theory throw one, but never will
  public void method2(int i) throws Exception {
      value = ((value + i) / i) << 1;
      // Will never be true
      if ((i & 0xFFFFFFF) == 1000000000) {
          throw new Exception();
      }
  }

  private static final NoStackTraceRuntimeException E = new NoStackTraceRuntimeException();

  // This one will regularly throw one
  public void method3(int i) throws NoStackTraceRuntimeException {
      value = ((value + i) / i) << 1;
      // i & 1 is equally fast to calculate as i & 0xFFFFFFF; it is both
      // an AND operation between two integers. The size of the number plays
      // no role. AND on 32 BIT always ANDs all 32 bits
      if ((i & 0x1) == 1) {
          throw E;
      }
  }

  // This one will regularly throw one
  public void method4(int i) throws NoStackTraceThrowable {
      value = ((value + i) / i) << 1;
      // i & 1 is equally fast to calculate as i & 0xFFFFFFF; it is both
      // an AND operation between two integers. The size of the number plays
      // no role. AND on 32 BIT always ANDs all 32 bits
      if ((i & 0x1) == 1) {
          throw new NoStackTraceThrowable();
      }
  }

  // This one will regularly throw one
  public void method5(int i) throws NoStackTraceRuntimeException {
      value = ((value + i) / i) << 1;
      // i & 1 is equally fast to calculate as i & 0xFFFFFFF; it is both
      // an AND operation between two integers. The size of the number plays
      // no role. AND on 32 BIT always ANDs all 32 bits
      if ((i & 0x1) == 1) {
          throw new NoStackTraceRuntimeException();
      }
  }

  public static void main(String[] args) {
    for(int k = 0; k < 5; k++) {
      int cycles = 10000000;
      if(k == 0) {
        cycles = 10000;
        try {
          Thread.sleep(500);
        }
        catch(InterruptedException e) {
          // TODO Auto-generated catch block
          e.printStackTrace();
        }
      }
      System.out.println("Loop " + (k + 1) + " " + cycles + " cycles");
      int i;
      long l;
      Snippet t = new Snippet();

      l = System.currentTimeMillis();
      t.reset();
      for (i = 1; i < cycles; i++) {
          t.method1(i);
      }
      l = System.currentTimeMillis() - l;
      System.out.println(
          "method1 took " + l + " ms, result was " + t.getValue()
      );

      l = System.currentTimeMillis();
      t.reset();
      for (i = 1; i < cycles; i++) {
          try {
              t.method2(i);
          } catch (Exception e) {
              System.out.println("You'll never see this!");
          }
      }
      l = System.currentTimeMillis() - l;
      System.out.println(
          "method2 took " + l + " ms, result was " + t.getValue()
      );

      l = System.currentTimeMillis();
      t.reset();
      for (i = 1; i < cycles; i++) {
          try {
              t.method3(i);
          } catch (NoStackTraceRuntimeException e) {
            // always comes here
          }
      }
      l = System.currentTimeMillis() - l;
      System.out.println(
          "method3 took " + l + " ms, result was " + t.getValue()
      );


      l = System.currentTimeMillis();
      t.reset();
      for (i = 1; i < cycles; i++) {
          try {
              t.method4(i);
          } catch (NoStackTraceThrowable e) {
            // always comes here
          }
      }
      l = System.currentTimeMillis() - l;
      System.out.println( "method4 took " + l + " ms, result was " + t.getValue() );


      l = System.currentTimeMillis();
      t.reset();
      for (i = 1; i < cycles; i++) {
          try {
              t.method5(i);
          } catch (RuntimeException e) {
            // always comes here
          }
      }
      l = System.currentTimeMillis() - l;
      System.out.println( "method5 took " + l + " ms, result was " + t.getValue() );
    }
  }

  public static class NoStackTraceRuntimeException extends RuntimeException {
    public NoStackTraceRuntimeException() {
        super("my special throwable", null, false, false);
    }
  }

  public static class NoStackTraceThrowable extends Throwable {
    public NoStackTraceThrowable() {
        super("my special throwable", null, false, false);
    }
  }
}

我用JVM 1.5做了一些性能测试，使用异常至少慢了两倍。平均:一个非常小的方法的执行时间超过3倍(3倍)。一个必须捕获异常的小循环的自时间增加了2倍。

我在产品代码和微基准测试中也看到过类似的数字。

异常绝对不应该用于任何频繁调用的东西。每秒抛出数千个异常将导致巨大的瓶颈。

例如，使用“Integer.ParseInt(…)”在一个非常大的文本文件中找到所有错误的值——非常糟糕的想法。(我曾在产品代码上看到过这种实用方法的性能下降)

使用异常在用户GUI表单上报告错误的值，从性能的角度来看可能并不是那么糟糕。

无论这是否是一个好的设计实践，我都会遵循这样的规则:如果错误是正常的/预期的，那么就使用返回值。如果不正常，请使用异常。例如:读取用户输入，错误值是正常的—使用错误代码。将值传递给内部实用程序函数时，应该通过调用代码来过滤坏值——使用异常。