在接收流时执行这些步骤。

首先设置一些合理的块大小

伪代码思想:

The first step would be to find all the duplicates and stick them in a dictionary with its count and remove them. The third step would be to place number that exist in sequence of their algorithmic steps and place them in counters special dictionaries with the first number and their step like n, n+1..., n+2, 2n, 2n+1, 2n+2... Begin to compress in chunks some reasonable ranges of number like every 1000 or ever 10000 the remaining numbers that appear less often to repeat. Uncompress that range if a number is found and add it to the range and leave it uncompressed for a while longer. Otherwise just add that number to a byte[chunkSize]

在接收流时继续执行前4步。最后一步是，如果超出内存，则失败，或者在收集完所有数据后开始输出结果，即开始对范围进行排序，并按顺序输出结果，然后按需要解压缩的顺序解压结果，并在得到它们时对它们进行排序。

如果输入流可以接收几次，这就容易多了(没有关于这方面的信息，想法和时间性能问题)。然后，我们可以数小数。有了计数值，就很容易生成输出流。通过计算值来压缩。 这取决于输入流中的内容。

我认为解决方案是结合视频编码的技术，即离散余弦变换。在数字视频中，不是将视频的亮度或颜色的变化记录为常规值，如110 112 115 116，而是从最后一个中减去每一个(类似于运行长度编码)。110 112 115 116变成110 2 3 1。这些值，2,3 1比原始值需要更少的比特。

So lets say we create a list of the input values as they arrive on the socket. We are storing in each element, not the value, but the offset of the one before it. We sort as we go, so the offsets are only going to be positive. But the offset could be 8 decimal digits wide which this fits in 3 bytes. Each element can't be 3 bytes, so we need to pack these. We could use the top bit of each byte as a "continue bit", indicating that the next byte is part of the number and the lower 7 bits of each byte need to be combined. zero is valid for duplicates.

当列表填满时，数字之间的距离应该越来越近，这意味着平均只有1个字节用于确定到下一个值的距离。7位值和1位偏移(如果方便的话)，但可能存在一个“继续”值需要少于8位的最佳点。

总之，我做了一些实验。我使用随机数生成器，我可以将100万个排序过的8位十进制数字放入大约1279000字节。每个数字之间的平均间隔始终是99…

public class Test {
    public static void main(String[] args) throws IOException {
        // 1 million values
        int[] values = new int[1000000];

        // create random values up to 8 digits lrong
        Random random = new Random();
        for (int x=0;x<values.length;x++) {
            values[x] = random.nextInt(100000000);
        }
        Arrays.sort(values);

        ByteArrayOutputStream baos = new ByteArrayOutputStream();

        int av = 0;    
        writeCompact(baos, values[0]);     // first value
        for (int x=1;x<values.length;x++) {
            int v = values[x] - values[x-1];  // difference
            av += v;
            System.out.println(values[x] + " diff " + v);
            writeCompact(baos, v);
        }

        System.out.println("Average offset " + (av/values.length));
        System.out.println("Fits in " + baos.toByteArray().length);
    }

    public static void writeCompact(OutputStream os, long value) throws IOException {
        do {
            int b = (int) value & 0x7f;
            value = (value & 0x7fffffffffffffffl) >> 7;
            os.write(value == 0 ? b : (b | 0x80));
        } while (value != 0);
    }
}

在接收流时执行这些步骤。

首先设置一些合理的块大小

伪代码思想:

The first step would be to find all the duplicates and stick them in a dictionary with its count and remove them. The third step would be to place number that exist in sequence of their algorithmic steps and place them in counters special dictionaries with the first number and their step like n, n+1..., n+2, 2n, 2n+1, 2n+2... Begin to compress in chunks some reasonable ranges of number like every 1000 or ever 10000 the remaining numbers that appear less often to repeat. Uncompress that range if a number is found and add it to the range and leave it uncompressed for a while longer. Otherwise just add that number to a byte[chunkSize]

在接收流时继续执行前4步。最后一步是，如果超出内存，则失败，或者在收集完所有数据后开始输出结果，即开始对范围进行排序，并按顺序输出结果，然后按需要解压缩的顺序解压结果，并在得到它们时对它们进行排序。

你最多要数到99,999,999，并在沿途标明1,000,000个站点。因此，可以使用位流进行解释，即1表示递增计数器，0表示输出数字。如果流中的前8位是00110010，到目前为止我们将有0,0,2,2,3。

Log (99,999,999 + 1,000,000) / Log(2) = 26.59。你的内存中有2^28位。你只需要用一半!

你用的是哪种电脑?它可能没有任何其他“正常”的本地存储，但它是否有视频RAM，例如?100万像素x每像素32位(比如说)非常接近你所需的数据输入大小。

(我主要是问旧的Acorn RISC PC的内存，如果你选择低分辨率或低颜色深度的屏幕模式，它可以“借用”VRAM来扩展可用的系统RAM !)这在只有几MB普通RAM的机器上非常有用。