Sorry, I don't understand the above comment at all. Would you mind explaining that?
Let's say you have 100 files to process:
- 2,250,000 lines
- 500,000 lines
- And the rest are all 10,000 lines long
Let's say you have enough memory to handle 1,000,000 lines. Let's ignore byte size for simplicity, but you'll I placed a limit on that as well in the split example.
You have two problems:
- You have many files to merge.
- One of your files is too big.
The first problem is easy to fix: Just concatenate all the files. Then you're left with one file that's too big.
- 3,730,000 lines
The second problem is easy to fix as well: Just split the file into smaller pieces. Just keep those pieces as big as possible.
You end up with:
- 1,000,000 lines
- 1,000,000 lines
- 1,000,000 lines
- 730,000 lines
If all you had done was split the large file, you would have 101 files to sort and merge, resulting in 100 merges. By concatenating first, all you had to do is 4 (long) merges. It cuts down on overhead a little.
| [reply] [d/l] |
This cannot be guaranteed, what if the file is too big
Can't be. It sorts the split chunks, not the original files.
| [reply] |
Yes, that's right.
Sorting on the split chunks isn't free as well as there is additional 'n-1' open and close involved with the extra 'n-1' files when working with split chunks of a file
| [reply] |
What extra open and closes? Grouping files greatly cuts down on the number of opens and closes.
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In the case of a few small files, say 10 files, 100 of which fit in memory at a time.
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In the case of lots of small files, say 100 files, 10 of which fit in memory at a time.
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In the case of big files, it simply won't work without splitting.
You could cut down on the number of opens and closes by merging more than two files at a time, but it's not relevant here because it would apply to both grouping and non-grouping algorithms.
| [reply] [d/l] |