*[Editor's note: This is part 6 of a series. Check out part 1 , part 2, part 3, part 4, and part 5 first]*

As it turns out, doing work on big data sets is quite hard. To start with, you need to *get*
the data, and it is… well, big. So that takes a while. Instead, I
decided to test my theory on the following scenario. Given 4 GB of random
numbers, let us find how many times we have the number 1.

Because I wanted to ensure a consistent answer, I wrote:

public static IEnumerable<uint> RandomNumbers() { const long count = 1024 * 1024 * 1024L * 1; var random = new MyRand(); for (long i = 0; i < count; i++) { if (i % 1024 == 0) { yield return 1; continue; } var result = random.NextUInt(); while (result == 1) { result = random.NextUInt(); } yield return result; } } /// <summary> /// Based on Marsaglia, George. (2003). Xorshift RNGs. /// http://www.jstatsoft.org/v08/i14/paper /// </summary> public class MyRand { const uint Y = 842502087, Z = 3579807591, W = 273326509; uint _x, _y, _z, _w; public MyRand() { _y = Y; _z = Z; _w = W; _x = 1337; } public uint NextUInt() { uint t = _x ^ (_x << 11); _x = _y; _y = _z; _z = _w; return _w = (_w ^ (_w >> 19)) ^ (t ^ (t >> 8)); } }

I am using a custom Rand function because it is significantly faster than System.Random. This generate 4GB of random numbers, at also ensure that we get exactly 1,048,576 instances of 1. Generating this in an empty loop takes about 30 seconds on my machine.

For fun, I run the external sort routine in 32 bits mode, with a buffer of 256MB. It is currently processing things, but I expect it to take a while. Because the buffer is 256 in size, we flush it every 128 MB (while we still have half the buffer free to do more work). The interesting thing is that even though we generate random number, sorting then compressing the values resulted in about 60% compression rate.

The problem is that for this particular case, I am not sure if that
is a good thing. Because the values are random, we need to select a
pretty high degree of compression just to get a good compression rate.
And because of that, a *significant amount of time *is spent just
compressing the data. I am pretty sure that for real world scenario, it
would be better, but that is something that we’ll probably need to
test. Not compressing the data in the random test is a huge help.

Next, external sort is pretty dependent on the performance of… sort,
of course. And sort isn’t that fast. In this scenario, we are sorting
arrays of about 26 million items. And that takes *time*. Implementing parallel sort cut this down to less than a minute per batch of 26 million.

That let us complete the entire process, but then it halts with the merge. The reason for that is that we push all the values into a heap, and there are 1 billion of them. Now, the heap never exceed 40 items, but those are still 1 billion * O(log 40) or about 5.4 billion comparisons that we have to do, and we do this sequentially, which takes time. I tried thinking about ways to parallel, but I am not sure how that can be done. We have 40 sorted files, and we want to merge all of them.

Obviously we can sort each 10 files set in parallel, then sort the resulting 4, but the cost we have now is the actual sorting cost, not I/O. I am not sure how to approach this.

For what is it worth, you can find the code for this here.

## {{ parent.title || parent.header.title}}

## {{ parent.tldr }}

## {{ parent.linkDescription }}

{{ parent.urlSource.name }}