最快的方法:分而治之。

Assuming your range is 0 to MAX_INT, then you have 1 to 10 digits. You can approach this interval using divide and conquer, with up to 4 comparisons per each input. First, you divide [1..10] into [1..5] and [6..10] with one comparison, and then each length 5 interval you divide using one comparison into one length 3 and one length 2 interval. The length 2 interval requires one more comparison (total 3 comparisons), the length 3 interval can be divided into length 1 interval (solution) and a length 2 interval. So, you need 3 or 4 comparisons.

没有除法，没有浮点运算，没有昂贵的对数，只有整数比较。

代码(长但快):

if (n < 100000) {
    // 5 or less
    if (n < 100){
        // 1 or 2
        if (n < 10)
            return 1;
        else
            return 2;
    } else {
        // 3 or 4 or 5
        if (n < 1000)
            return 3;
        else {
            // 4 or 5
            if (n < 10000)
                return 4;
            else
                return 5;
        }
    }
} else {
    // 6 or more
    if (n < 10000000) {
        // 6 or 7
        if (n < 1000000)
            return 6;
        else
            return 7;
    } else {
        // 8 to 10
        if (n < 100000000)
            return 8;
        else {
            // 9 or 10
            if (n < 1000000000)
                return 9;
            else
                return 10;
        }
    }
}

基准测试(在JVM预热之后)——查看下面的代码以了解基准测试是如何运行的:

基线方法(使用String.length): 2145毫秒 Log10方法:711ms = 3.02次 和基线一样快 重复除:2797ms = 0.77次 和基线一样快 分治:74ms = 28.99 时间和基线一样快

完整的代码:

public static void main(String[] args) throws Exception {
    
    // validate methods:
    for (int i = 0; i < 1000; i++)
        if (method1(i) != method2(i))
            System.out.println(i);
    for (int i = 0; i < 1000; i++)
        if (method1(i) != method3(i))
            System.out.println(i + " " + method1(i) + " " + method3(i));
    for (int i = 333; i < 2000000000; i += 1000)
        if (method1(i) != method3(i))
            System.out.println(i + " " + method1(i) + " " + method3(i));
    for (int i = 0; i < 1000; i++)
        if (method1(i) != method4(i))
            System.out.println(i + " " + method1(i) + " " + method4(i));
    for (int i = 333; i < 2000000000; i += 1000)
        if (method1(i) != method4(i))
            System.out.println(i + " " + method1(i) + " " + method4(i));
    
    // work-up the JVM - make sure everything will be run in hot-spot mode
    allMethod1();
    allMethod2();
    allMethod3();
    allMethod4();
    
    // run benchmark
    Chronometer c;
    
    c = new Chronometer(true);
    allMethod1();
    c.stop();
    long baseline = c.getValue();
    System.out.println(c);
    
    c = new Chronometer(true);
    allMethod2();
    c.stop();
    System.out.println(c + " = " + StringTools.formatDouble((double)baseline / c.getValue() , "0.00") + " times as fast as baseline");
    
    c = new Chronometer(true);
    allMethod3();
    c.stop();
    System.out.println(c + " = " + StringTools.formatDouble((double)baseline / c.getValue() , "0.00") + " times as fast as baseline");
    
    c = new Chronometer(true);
    allMethod4();
    c.stop();
    System.out.println(c + " = " + StringTools.formatDouble((double)baseline / c.getValue() , "0.00") + " times as fast as baseline");
}


private static int method1(int n) {
    return Integer.toString(n).length();
}

private static int method2(int n) {
    if (n == 0)
        return 1;
    return (int)(Math.log10(n) + 1);
}

private static int method3(int n) {
    if (n == 0)
        return 1;
    int l;
    for (l = 0 ; n > 0 ;++l)
        n /= 10;
    return l;
}

private static int method4(int n) {
    if (n < 100000) {
        // 5 or less
        if (n < 100) {
            // 1 or 2
            if (n < 10)
                return 1;
            else
                return 2;
        } else {
            // 3 or 4 or 5
            if (n < 1000)
                return 3;
            else {
                // 4 or 5
                if (n < 10000)
                    return 4;
                else
                    return 5;
            }
        }
    } else {
        // 6 or more
        if (n < 10000000) {
            // 6 or 7
            if (n < 1000000)
                return 6;
            else
                return 7;
        } else {
            // 8 to 10
            if (n < 100000000)
                return 8;
            else {
                // 9 or 10
                if (n < 1000000000)
                    return 9;
                else
                    return 10;
            }
        }
    }
}


private static int allMethod1() {
    int x = 0;
    for (int i = 0; i < 1000; i++)
        x = method1(i);
    for (int i = 1000; i < 100000; i += 10)
        x = method1(i);
    for (int i = 100000; i < 1000000; i += 100)
        x = method1(i);
    for (int i = 1000000; i < 2000000000; i += 200)
        x = method1(i);
    
    return x;
}

private static int allMethod2() {
    int x = 0;
    for (int i = 0; i < 1000; i++)
        x = method2(i);
    for (int i = 1000; i < 100000; i += 10)
        x = method2(i);
    for (int i = 100000; i < 1000000; i += 100)
        x = method2(i);
    for (int i = 1000000; i < 2000000000; i += 200)
        x = method2(i);
    
    return x;
}

private static int allMethod3() {
    int x = 0;
    for (int i = 0; i < 1000; i++)
        x = method3(i);
    for (int i = 1000; i < 100000; i += 10)
        x = method3(i);
    for (int i = 100000; i < 1000000; i += 100)
        x = method3(i);
    for (int i = 1000000; i < 2000000000; i += 200)
        x = method3(i);
    
    return x;
}

private static int allMethod4() {
    int x = 0;
    for (int i = 0; i < 1000; i++)
        x = method4(i);
    for (int i = 1000; i < 100000; i += 10)
        x = method4(i);
    for (int i = 100000; i < 1000000; i += 100)
        x = method4(i);
    for (int i = 1000000; i < 2000000000; i += 200)
        x = method4(i);
    
    return x;
}

基准:

基线方法(String.length): 2145ms Log10方法:711ms =基线速度的3.02倍 重复除:2797ms =基线速度的0.77倍 分治:74毫秒= 28.99倍的基线速度

Edit

在我写完基准测试之后，我偷偷地看了一下Integer。toString来自Java 6，我发现它使用:

final static int [] sizeTable = { 9, 99, 999, 9999, 99999, 999999, 9999999,
                                  99999999, 999999999, Integer.MAX_VALUE };

// Requires positive x
static int stringSize(int x) {
    for (int i=0; ; i++)
        if (x <= sizeTable[i])
            return i+1;
}

我以我的分治方案为基准:

分治法:104毫秒 Java 6解决方案-迭代和比较:406ms

我的速度大约是Java 6解决方案的4倍。

我还没有看到基于乘法的解决方案。对数、除法和基于字符串的解决方案将在数百万个测试用例中变得相当笨拙，所以这里有一个int型的解决方案:

/**
 * Returns the number of digits needed to represents an {@code int} value in 
 * the given radix, disregarding any sign.
 */
public static int len(int n, int radix) {
    radixCheck(radix); 
    // if you want to establish some limitation other than radix > 2
    n = Math.abs(n);

    int len = 1;
    long min = radix - 1;

    while (n > min) {
        n -= min;
        min *= radix;
        len++;
    }

    return len;
}

以10为基底，这是可行的，因为n本质上是与9,99,999…因为min是9,90,900…n被减去9,90,900…

不幸的是，仅仅因为溢出而替换int的每个实例是不能移植到long的。另一方面，它恰好适用于2垒和10垒(但对于大多数其他垒来说严重失败)。您将需要一个用于溢出点的查找表(或除法测试……)电子战)

/**
 * For radices 2 &le r &le Character.MAX_VALUE (36)
 */
private static long[] overflowpt = {-1, -1, 4611686018427387904L,
    8105110306037952534L, 3458764513820540928L, 5960464477539062500L,
    3948651115268014080L, 3351275184499704042L, 8070450532247928832L,
    1200757082375992968L, 9000000000000000000L, 5054470284992937710L,
    2033726847845400576L, 7984999310198158092L, 2022385242251558912L,
    6130514465332031250L, 1080863910568919040L, 2694045224950414864L,
    6371827248895377408L, 756953702320627062L, 1556480000000000000L,
    3089447554782389220L, 5939011215544737792L, 482121737504447062L,
    839967991029301248L, 1430511474609375000L, 2385723916542054400L,
    3902460517721977146L, 6269893157408735232L, 341614273439763212L,
    513726300000000000L, 762254306892144930L, 1116892707587883008L,
    1617347408439258144L, 2316231840055068672L, 3282671350683593750L,
    4606759634479349760L};

public static int len(long n, int radix) {
    radixCheck(radix);
    n = abs(n);

    int len = 1;
    long min = radix - 1;
    while (n > min) {
        len++;
        if (min == overflowpt[radix]) break;
        n -= min;
        min *= radix;

    }

    return len;
}

Two comments on your benchmark: Java is a complex environment, what with just-in-time compiling and garbage collection and so forth, so to get a fair comparison, whenever I run a benchmark, I always: (a) enclose the two tests in a loop that runs them in sequence 5 or 10 times. Quite often the runtime on the second pass through the loop is quite different from the first. And (b) After each "approach", I do a System.gc() to try to trigger a garbage collection. Otherwise, the first approach might generate a bunch of objects, but not quite enough to force a garbage collection, then the second approach creates a few objects, the heap is exhausted, and garbage collection runs. Then the second approach is "charged" for picking up the garbage left by the first approach. Very unfair!

也就是说，上述两种方法在本例中都没有产生显著差异。

不管有没有这些修改，我得到的结果和你完全不同。当我运行这个时，是的，toString方法给出的运行时间为6400到6600 millis，而log方法给出的运行时间为20,000到20,400 millis。对数方法对我来说不是稍微快一点，而是慢了3倍。

请注意，这两种方法涉及非常不同的代价，所以这并不完全令人震惊:toString方法将创建许多必须清理的临时对象，而log方法需要更密集的计算。因此，可能区别在于，在内存较少的机器上，toString需要更多的垃圾收集回合，而在处理器较慢的机器上，额外的log计算将更加痛苦。

我还尝试了第三种方法。我写了这个小函数:

static int numlength(int n)
{
    if (n == 0) return 1;
    int l;
    n=Math.abs(n);
    for (l=0;n>0;++l)
        n/=10;
    return l;           
}

在我的机器上，它运行在1600到1900毫厘之间——不到toString方法的1/3,log方法的1/10。

如果您的数字范围很广，您可以通过开始除以1000或1,000,000来进一步加快速度，以减少循环的次数。我还没玩过。

使用Java

int nDigits = Math.floor(Math.log10(Math.abs(the_integer))) + 1;

使用import java.lang.Math.*;一开始

使用C

int nDigits = floor(log10(abs(the_integer))) + 1;

在开始时使用include math.h

我能试试吗?；）

基于德克的解决方案

final int digits = number==0?1:(1 + (int)Math.floor(Math.log10(Math.abs(number))));

没有字符串API，没有utils，没有类型转换，只是纯java迭代->

public static int getNumberOfDigits(int input) {
    int numOfDigits = 1;
    int base = 1;
    while (input >= base * 10) {
        base = base * 10;
        numOfDigits++;
    }
    return numOfDigits;
 }

如果你愿意，你可以追求更大的价值。