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。


当前回答

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。

其他回答

我已经扩展了@Mecki和@incarnate给出的答案,没有为Java填充stacktrace。

在Java 7+中,我们可以使用Throwable(String message, Throwable cause, boolean enableSuppression,boolean writableStackTrace)。但是对于Java6,请参阅我对这个问题的回答

// 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) {
    int i;
    long l;
    Test t = new Test();

    l = System.currentTimeMillis();
    t.reset();
    for (i = 1; i < 100000000; i++) {
        try {
            t.method4(i);
        } catch (NoStackTraceThrowable e) {
            // Do nothing here, as we will get 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 < 100000000; i++) {
        try {
            t.method5(i);
        } catch (RuntimeException e) {
            // Do nothing here, as we will get here
        }
    }
    l = System.currentTimeMillis() - l;
    System.out.println( "method5 took " + l + " ms, result was " + t.getValue() );
}

输出与Java 1.6.0_45,在Core i7, 8GB RAM:

method1 took 883 ms, result was 2
method2 took 882 ms, result was 2
method3 took 32270 ms, result was 2 // throws Exception
method4 took 8114 ms, result was 2 // throws NoStackTraceThrowable
method5 took 8086 ms, result was 2 // throws NoStackTraceRuntimeException

因此,返回值的方法仍然比引发异常的方法更快。恕我直言,我们不能仅仅为成功流和错误流使用返回类型来设计一个清晰的API。在没有stacktrace的情况下抛出异常的方法比普通异常快4-5倍。

谢谢@Greg

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

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。

前段时间,我写了一个类来测试将字符串转换为整数的相对性能,使用两种方法:(1)调用Integer.parseInt()并捕获异常,或者(2)用正则表达式匹配字符串并仅在匹配成功时调用parseInt()。我以最有效的方式使用正则表达式(即,在终止循环之前创建Pattern和Matcher对象),并且我没有打印或保存异常的堆栈跟踪。

对于一个包含10,000个字符串的列表,如果它们都是有效数字,那么parseInt()方法的速度是regex方法的四倍。但如果只有80%的字符串是有效的,则regex的速度是parseInt()的两倍。如果20%是有效的,这意味着异常在80%的时间内被抛出和捕获,则regex的速度大约是parseInt()的20倍。

我对结果感到惊讶,因为regex方法处理了两次有效字符串:一次用于匹配,另一次用于parseInt()。但是抛出和捕获异常完全弥补了这一点。这种情况在现实世界中不太可能经常发生,但如果发生了,您绝对不应该使用异常捕获技术。但如果您只是验证用户输入或类似的东西,务必使用parseInt()方法。

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

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。