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History of commit frequency

CVS Commit History:


   2013-04-07 22:49:45 by Blue Rats | Files touched by this commit (91)
Log message:
Edited DESCR in the case of:
 File too long (should be no more than 24 lines).
 Line too long (should be no more than 80 characters).
 Trailing empty lines.
 Trailing white-space.
Trucated the long files as best as possible while preserving the most info
contained in them.
   2012-10-23 21:51:39 by Aleksej Saushev | Files touched by this commit (447)
Log message:
Drop superfluous PKG_DESTDIR_SUPPORT, "user-destdir" is default these days.
   2009-06-07 15:48:21 by Thomas Klausner | Files touched by this commit (4) | Imported package
Log message:
Initial import of agedu-8590:

Suppose you're running low on disk space. You need to free some
up, by finding something that's a waste of space and deleting it
(or moving it to an archive medium). How do you find the right
stuff to delete, that saves you the maximum space at the cost of
minimum inconvenience?

Unix provides the standard du utility, which scans your disk and
tells you which directories contain the largest amounts of data.
That can help you narrow your search to the things most worth
deleting.

However, that only tells you what's big. What you really want to
know is what's too big. By itself, du won't let you distinguish
between data that's big because you're doing something that needs
it to be big, and data that's big because you unpacked it once and
forgot about it.

Most Unix file systems, in their default mode, helpfully record
when a file was last accessed. Not just when it was written or
modified, but when it was even read. So if you generated a large
amount of data years ago, forgot to clean it up, and have never
used it since, then it ought in principle to be possible to use
those last-access time stamps to tell the difference between that
and a large amount of data you're still using regularly.

agedu is a program which does this. It does basically the same sort
of disk scan as du, but it also records the last-access times of
everything it scans. Then it builds an index that lets it efficiently
generate reports giving a summary of the results for each subdirectory,
and then it produces those reports on demand.


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