hdmap

frb tables: machines, volumes, filenames, metadatas

this application is quality control

physical, digital, virtual

the namespace of the filenames has meaning.

the namespace of the filename paths directories

"mining the gems of my blog"

sort my digital clutter

find all of the _to_sift directories

hard-drive backup

see also blog appmap

commands: $tree -a

the machines datastores have a structure when it comes to the backup flow (and have to monthly culdesacs

commands: $du -hc

define the machines, define the filenames, define the uniquely identifiable feature of a node

command app: tar

the time factor of the situation is that every week there should be a serious backup and this should be recorded to a log

figure out the size of the data storage of all the datastores of all the machines

one unique file type is the database schemas and especially the old ones that have lots of interesting data inside.

commands: $du -ch

store filenames in frb database

do analysis by filenames by file extensions, and then this implies applications.

goal: to find writing

deal with the zipzap (tgz.cpt tgz.tally tgz.hdmap)

consider machines table and inventory the hard-drives on each machines

$ig hdmap exec

have a flash-drive that boots as part of the data

all the tarballs are loaded with filenames, too

big files, search for very big file

list all of the images by extension and then have metadata that shows attention and approved via attention; also need to convert all the gif to png, and then list if published and perhaps move to IPFS

search for symbolic links too

find: inventory.pl, box2box_manifest.pl

also need to consider all of these directories that are the backup of desktops

need to make sure that files are in the correct place (location)

fix the filename (such as the ones with the spaces in the names, also no quotes)

consider the directories and the concept of too many levels

use ig <names> to navigate quickly

blog measurement: count files on all hard-drives

blog measurement: what are the metrics? the hard-drive costs money, how many nodes?, how many files? what are the filetypes?

connect to scoreboard

consider the fields of the file meta data (such as file size and permissions and dates)

consider encryption

be able to search the files

be able to look for duplicate files