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[ HOME SHELL ]
Name
Size
Permission
Action
7za
41
B
-rwxr-xr-x
GET
15.84
KB
-rwxr-xr-x
HEAD
15.84
KB
-rwxr-xr-x
POST
15.84
KB
-rwxr-xr-x
[
53.57
KB
-rwxr-xr-x
ab
56.96
KB
-rwxr-xr-x
ac
33.07
KB
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aclocal
35.62
KB
-rwxr-xr-x
aclocal-1.16
35.62
KB
-rwxr-xr-x
acyclic
12.37
KB
-rwxr-xr-x
addr2line
33.28
KB
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alias
29
B
-rwxr-xr-x
appstream-compose
33.21
KB
-rwxr-xr-x
appstream-util
114.34
KB
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apropos
54.04
KB
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ar
61.95
KB
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arch
37.33
KB
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arpaname
11.81
KB
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as
889.89
KB
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aspell
159.5
KB
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at
57.33
KB
-rwsr-xr-x
atq
57.33
KB
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atrm
57.33
KB
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attr
13.06
KB
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audit2allow
14.85
KB
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audit2why
14.85
KB
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aulast
20.56
KB
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aulastlog
12.43
KB
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ausyscall
12.27
KB
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authselect
41.06
KB
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autoconf
14.42
KB
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autoheader
8.33
KB
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autom4te
31.43
KB
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automake
251.9
KB
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automake-1.16
251.9
KB
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autoreconf
20.57
KB
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autoscan
16.72
KB
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autoupdate
33.08
KB
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auvirt
32.71
KB
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awk
669.77
KB
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b2sum
57.68
KB
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base32
41.46
KB
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base64
41.48
KB
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basename
37.41
KB
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bash
1.1
MB
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bashbug
7.18
KB
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bashbug-64
7.18
KB
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batch
137
B
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bc
94.98
KB
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bcomps
20.59
KB
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bdftopcf
45.4
KB
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bg
26
B
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bison
437.72
KB
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blkiomon
30.51
KB
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blkparse
61.48
KB
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18.7
KB
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blktrace
49.66
KB
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bmon
122.1
KB
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bno_plot.py
3.47
KB
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bond2team
22.74
KB
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bootctl
45.14
KB
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brotli
739.2
KB
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891
B
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btrecord
19.94
KB
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btreplay
32.45
KB
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btt
144.79
KB
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bunzip2
37.57
KB
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busctl
77.25
KB
-rwxr-xr-x
bzcat
37.57
KB
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bzcmp
2.08
KB
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bzdiff
2.08
KB
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bzegrep
1.64
KB
-rwxr-xr-x
bzfgrep
1.64
KB
-rwxr-xr-x
bzgrep
1.64
KB
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bzip2
37.57
KB
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bzip2recover
17.15
KB
-rwxr-xr-x
bzless
1.23
KB
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bzmore
1.23
KB
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c++
1.21
MB
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c++filt
28.89
KB
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c89
224
B
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c99
215
B
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ca-legacy
1.61
KB
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cairo-sphinx
69.71
KB
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cal
65.98
KB
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captoinfo
85.23
KB
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cat
37.45
KB
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catchsegv
3.21
KB
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catman
41.45
KB
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cc
1.21
MB
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ccomps
24.68
KB
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cd
26
B
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centrino-decode
11.26
KB
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certbot
387
B
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certbot-3
387
B
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chacl
17.08
KB
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chage
77.67
KB
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chardetect
400
B
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16.4
KB
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chcat
13.57
KB
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chcon
70.34
KB
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427.01
KB
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checkpolicy
488.64
KB
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chfn
32.89
KB
-rws--x--x
chgrp
66.27
KB
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chmem
45.47
KB
-rwxr-xr-x
chmod
62.22
KB
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chown
70.3
KB
-rwxr-xr-x
chronyc
119.37
KB
-rwxr-xr-x
chrt
37.18
KB
-rwxr-xr-x
chsh
24.71
KB
-rws--x--x
chvt
12.66
KB
-rwxr-xr-x
circo
12.23
KB
-rwxr-xr-x
cksum
37.38
KB
-rwxr-xr-x
clear
12.49
KB
-rwxr-xr-x
cluster
518.3
KB
-rwxr-xr-x
cmp
103.76
KB
-rwxr-xr-x
cockpit-bridge
496.18
KB
-rwxr-xr-x
col
29
KB
-rwxr-xr-x
colcrt
16.47
KB
-rwxr-xr-x
colrm
24.88
KB
-rwxr-xr-x
column
49.46
KB
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comm
41.54
KB
-rwxr-xr-x
command
31
B
-rwxr-xr-x
compile_et
1.31
KB
-rwxr-xr-x
config_data
6.97
KB
-rwxr-xr-x
coredumpctl
44.9
KB
-rwxr-xr-x
corelist
14.64
KB
-rwxr-xr-x
cp
147.99
KB
-rwxr-xr-x
cpan
7.87
KB
-rwxr-xr-x
cpan-mirrors
4.19
KB
-rwxr-xr-x
cpio
159.8
KB
-rwxr-xr-x
cpp
1.21
MB
-rwxr-xr-x
cpupower
66.9
KB
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crb
2.58
KB
-rwxr--r--
crc32
1.02
KB
-rwxr-xr-x
cronnext
49.8
KB
-rwxr-xr-x
crontab
61.66
KB
-rwsr-xr-x
csplit
53.66
KB
-rwxr-xr-x
csslint-0.6
24.56
KB
-rwxr-xr-x
cstool
4.58
MB
-rwxr-xr-x
curl
230.08
KB
-rwxr-xr-x
cut
49.51
KB
-rwxr-xr-x
cvtsudoers
263.95
KB
-rwxr-xr-x
date
105.95
KB
-rwxr-xr-x
db_archive
12.45
KB
-rwxr-xr-x
db_checkpoint
16.48
KB
-rwxr-xr-x
db_deadlock
16.48
KB
-rwxr-xr-x
db_dump
16.51
KB
-rwxr-xr-x
db_dump185
69.55
KB
-rwxr-xr-x
db_hotbackup
20.49
KB
-rwxr-xr-x
db_load
28.61
KB
-rwxr-xr-x
db_log_verify
16.52
KB
-rwxr-xr-x
db_printlog
33.4
KB
-rwxr-xr-x
db_recover
16.51
KB
-rwxr-xr-x
db_replicate
16.49
KB
-rwxr-xr-x
db_stat
16.48
KB
-rwxr-xr-x
db_tuner
24.55
KB
-rwxr-xr-x
db_upgrade
12.45
KB
-rwxr-xr-x
db_verify
16.46
KB
-rwxr-xr-x
dbilogstrip
1.35
KB
-rwxr-xr-x
dbinfo
4.5
KB
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dbiprof
6.06
KB
-rwxr-xr-x
dbpmda
93.96
KB
-rwxr-xr-x
dbus-binding-tool
110.43
KB
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dbus-cleanup-sockets
16.38
KB
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dbus-daemon
239.71
KB
-rwxr-xr-x
dbus-monitor
28.57
KB
-rwxr-xr-x
dbus-run-session
15.88
KB
-rwxr-xr-x
dbus-send
28.54
KB
-rwxr-xr-x
dbus-test-tool
24.59
KB
-rwxr-xr-x
dbus-update-activation-environ...
16.41
KB
-rwxr-xr-x
dbus-uuidgen
12.37
KB
-rwxr-xr-x
dc
53.02
KB
-rwxr-xr-x
dd
77.96
KB
-rwxr-xr-x
deallocvt
12.66
KB
-rwxr-xr-x
debuginfod-find
16.46
KB
-rwxr-xr-x
delv
42.45
KB
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df
91.08
KB
-rwxr-xr-x
diff
268.01
KB
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diff3
128.6
KB
-rwxr-xr-x
diffimg
12.24
KB
-rwxr-xr-x
dig
162.17
KB
-rwxr-xr-x
dijkstra
16.7
KB
-rwxr-xr-x
dir
139.88
KB
-rwxr-xr-x
dircolors
49.55
KB
-rwxr-xr-x
dirmngr
580.16
KB
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dirmngr-client
120.12
KB
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dirname
33.36
KB
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distro
389
B
-rwxr-xr-x
dltest
13.05
KB
-rwxr-xr-x
dm_date
5.38
KB
-rwxr-xr-x
dm_zdump
4.99
KB
-rwxr-xr-x
dmesg
77.9
KB
-rwxr-xr-x
dnf
1.91
KB
-rwxr-xr-x
dnf-3
1.91
KB
-rwxr-xr-x
dnsdomainname
21.16
KB
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dnstap-read
20.42
KB
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domainname
21.16
KB
-rwxr-xr-x
dos2unix
58.54
KB
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dot
12.23
KB
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dot2gxl
41.27
KB
-rwxr-xr-x
dotty
2.04
KB
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doveadm
678.08
KB
-rwxr-xr-x
doveconf
185.02
KB
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dovecot-sysreport
5.63
KB
-rwxr-xr-x
dracut
67.81
KB
-rwxr-xr-x
dstat
70.37
KB
-rwxr-xr-x
dsync
678.08
KB
-rwxr-xr-x
dtrace
17.25
KB
-rwxr-xr-x
du
107.02
KB
-rwxr-xr-x
dumpkeys
170
KB
-rwxr-xr-x
dwp
2.17
MB
-rwxr-xr-x
dwz
167.64
KB
-rwxr-xr-x
easy_install-3
246
B
-rwxr-xr-x
easy_install-3.6
246
B
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37.34
KB
-rwxr-xr-x
ed
57.28
KB
-rwxr-xr-x
edgepaint
420.73
KB
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egrep
28
B
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eject
57.77
KB
-rwxr-xr-x
elfedit
33.27
KB
-rwxr-xr-x
enc2xs
40.97
KB
-rwxr-xr-x
encguess
2.91
KB
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enchant
21.08
KB
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enchant-lsmod
13.09
KB
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env
41.34
KB
-rwxr-xr-x
envml
4.1
KB
-rwxr-xr-x
envsubst
48.99
KB
-rwxr-xr-x
eqn
232.16
KB
-rwxr-xr-x
erb
4.72
KB
-rwxr-xr-x
evmctl
62.54
KB
-rwxr-xr-x
ex
1.13
MB
-rwxr-xr-x
expand
41.58
KB
-rwxr-xr-x
expr
49.56
KB
-rwxr-xr-x
facter
145.19
KB
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factor
85.97
KB
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fail2ban-client
1.39
KB
-rwxr-xr-x
fail2ban-python
11.59
KB
-rwxr-xr-x
fail2ban-regex
1.25
KB
-rwxr-xr-x
fail2ban-server
1.39
KB
-rwxr-xr-x
fallocate
28.95
KB
-rwxr-xr-x
false
33.3
KB
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fc
26
B
-rwxr-xr-x
fc-cache
132
B
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fc-cache-64
20.35
KB
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fc-cat
16.35
KB
-rwxr-xr-x
fc-conflist
12.25
KB
-rwxr-xr-x
fc-list
12.25
KB
-rwxr-xr-x
fc-match
16.26
KB
-rwxr-xr-x
fc-pattern
12.26
KB
-rwxr-xr-x
fc-query
12.24
KB
-rwxr-xr-x
fc-scan
12.26
KB
-rwxr-xr-x
fc-validate
16.26
KB
-rwxr-xr-x
fdp
12.23
KB
-rwxr-xr-x
fg
26
B
-rwxr-xr-x
fgconsole
12.67
KB
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fgrep
28
B
-rwxr-xr-x
file
24.68
KB
-rwxr-xr-x
fincore
33.02
KB
-rwxr-xr-x
find
255.85
KB
-rwxr-xr-x
findmnt
70.6
KB
-rwxr-xr-x
fips-finish-install
1.29
KB
-rwxr-xr-x
fips-mode-setup
3.91
KB
-rwxr-xr-x
firewall-cmd
139.61
KB
-rwxr-xr-x
firewall-offline-cmd
120.73
KB
-rwxr-xr-x
flex
428.45
KB
-rwxr-xr-x
flex++
428.45
KB
-rwxr-xr-x
flock
33.19
KB
-rwxr-xr-x
fmt
45.48
KB
-rwxr-xr-x
fold
41.4
KB
-rwxr-xr-x
fonttosfnt
41.18
KB
-rwxr-xr-x
formail
48.22
KB
-rwxr-xr-x
free
20.66
KB
-rwxr-xr-x
fribidi
21.14
KB
-rwxr-xr-x
ftpdctl
98.2
KB
-rwxr-xr-x
funzip
36.74
KB
-rwxr-xr-x
g++
1.21
MB
-rwxr-xr-x
g13
212.02
KB
-rwxr-xr-x
gapplication
20.44
KB
-rwxr-xr-x
gawk
669.77
KB
-rwxr-xr-x
gc
16.61
KB
-rwxr-xr-x
gcc
1.21
MB
-rwxr-xr-x
gcc-ar
36.64
KB
-rwxr-xr-x
gcc-nm
36.65
KB
-rwxr-xr-x
gcc-ranlib
36.65
KB
-rwxr-xr-x
gcov
1.31
MB
-rwxr-xr-x
gcov-dump
566.9
KB
-rwxr-xr-x
gcov-tool
603.88
KB
-rwxr-xr-x
gdbm_dump
21.05
KB
-rwxr-xr-x
gdbm_load
25.31
KB
-rwxr-xr-x
gdbmtool
111.57
KB
-rwxr-xr-x
gdbus
48.7
KB
-rwxr-xr-x
gdk-pixbuf-query-loaders-64
18.7
KB
-rwxr-xr-x
gdk-pixbuf-thumbnailer
28.66
KB
-rwxr-xr-x
gem
542
B
-rwxr-xr-x
gencat
24.82
KB
-rwxr-xr-x
genl-ctrl-list
12.04
KB
-rwxr-xr-x
geoiplookup
21.89
KB
-rwxr-xr-x
geoiplookup6
21.65
KB
-rwxr-xr-x
geqn
232.16
KB
-rwxr-xr-x
getconf
32.45
KB
-rwxr-xr-x
getent
33.12
KB
-rwxr-xr-x
getfacl
27.99
KB
-rwxr-xr-x
getfattr
23.03
KB
-rwxr-xr-x
getkeycodes
12.66
KB
-rwxr-xr-x
getopt
20.52
KB
-rwxr-xr-x
getopts
31
B
-rwxr-xr-x
gettext
48.97
KB
-rwxr-xr-x
gettext.sh
4.52
KB
-rwxr-xr-x
gio
85.2
KB
-rwxr-xr-x
gio-querymodules-64
16.3
KB
-rwxr-xr-x
glib-compile-schemas
48.84
KB
-rwxr-xr-x
gmake
235.32
KB
-rwxr-xr-x
gml2gv
41.2
KB
-rwxr-xr-x
gneqn
908
B
-rwxr-xr-x
gnroff
3.23
KB
-rwxr-xr-x
gpasswd
82.17
KB
-rwsr-xr-x
gpg
1.04
MB
-rwxr-xr-x
gpg-agent
419.29
KB
-rwxr-xr-x
gpg-connect-agent
165.3
KB
-rwxr-xr-x
gpg-error
34.16
KB
-rwxr-xr-x
gpg-wks-server
206.69
KB
-rwxr-xr-x
gpg-zip
3.44
KB
-rwxr-xr-x
gpg2
1.04
MB
-rwxr-xr-x
gpgconf
176.09
KB
-rwxr-xr-x
gpgme-json
85.8
KB
-rwxr-xr-x
gpgparsemail
28.74
KB
-rwxr-xr-x
gpgsm
514.45
KB
-rwxr-xr-x
gpgsplit
87.02
KB
-rwxr-xr-x
gpgv
451.58
KB
-rwxr-xr-x
gpgv2
451.58
KB
-rwxr-xr-x
gpic
293.84
KB
-rwxr-xr-x
gpio-event-mon
14.96
KB
-rwxr-xr-x
gpio-hammer
14.95
KB
-rwxr-xr-x
gprof
103.34
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-rwxr-xr-x
gr2fonttest
29.95
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-rwxr-xr-x
graphml2gv
20.62
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grep
193.63
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groff
124.92
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grops
191.14
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grotty
141.9
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groups
37.38
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-rwxr-xr-x
grub2-editenv
444.02
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grub2-file
914.98
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grub2-fstest
1.14
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grub2-glue-efi
279.27
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grub2-kbdcomp
1.63
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grub2-menulst2cfg
262.57
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grub2-mkfont
308.02
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grub2-mkimage
427.29
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-rwxr-xr-x
grub2-mklayout
285.4
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grub2-mknetdir
481.71
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grub2-mkpasswd-pbkdf2
287.74
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grub2-mkrelpath
279.16
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-rwxr-xr-x
grub2-mkrescue
1.1
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grub2-mkstandalone
590.36
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grub2-render-label
919.53
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grub2-script-check
311.89
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-rwxr-xr-x
grub2-syslinux2cfg
844.16
KB
-rwxr-xr-x
gsettings
28.6
KB
-rwxr-xr-x
gsoelim
42.55
KB
-rwxr-xr-x
gss-client
24.55
KB
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gtar
449
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gtbl
154.61
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-rwxr-xr-x
gtk-query-immodules-2.0-64
16.29
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gtk-update-icon-cache
33.02
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-rwxr-xr-x
gtroff
805.02
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-rwxr-xr-x
gunzip
2.29
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-rwxr-xr-x
gv2gml
24.66
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-rwxr-xr-x
gv2gxl
41.27
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gvcolor
46.87
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gvgen
24.77
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-rwxr-xr-x
gvmap
526.3
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-rwxr-xr-x
gvmap.sh
2.14
KB
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gvpack
28.87
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-rwxr-xr-x
gvpr
7.8
KB
-rwxr-xr-x
gxl2dot
41.27
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-rwxr-xr-x
gxl2gv
41.27
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-rwxr-xr-x
gzexe
6.23
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-rwxr-xr-x
gzip
94.67
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h2ph
28.54
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h2xs
59.44
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hash
28
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head
45.49
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hexdump
57.5
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host
142.29
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-rwxr-xr-x
hostid
33.32
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-rwxr-xr-x
hostname
21.16
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-rwxr-xr-x
hostnamectl
20.82
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htdbm
24.66
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htdigest
16.53
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htop
304.73
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htpasswd
24.66
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httxt2dbm
16.47
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-rwxr-xr-x
hunspell
144.7
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-rwxr-xr-x
i386
20.75
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-rwxr-xr-x
ibd2sdi
313.23
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-rwxr-xr-x
ibdev2netdev
5.59
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-rwxr-xr-x
iceauth
41.87
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-rwxr-xr-x
iconv
61.43
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id
45.43
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-rwxr-xr-x
idiag-socket-details
12.09
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idn
39.41
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-rwxr-xr-x
ifnames
4.03
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-rwxr-xr-x
iio_event_monitor
22.97
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iio_generic_buffer
26.98
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-rwxr-xr-x
info
249.89
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infocmp
60.98
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-rwxr-xr-x
infotocap
85.23
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-rwxr-xr-x
innochecksum
219.7
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-rwxr-xr-x
install
156.14
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-rwxr-xr-x
instmodsh
4.1
KB
-rwxr-xr-x
intel-speed-select
93.02
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-rwxr-xr-x
ionice
28.98
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-rwxr-xr-x
ipcalc
46.08
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-rwxr-xr-x
ipcmk
29.13
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-rwxr-xr-x
ipcount
3.55
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-rwxr-xr-x
ipcrm
28.98
KB
-rwxr-xr-x
ipcs
53.4
KB
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iptab
897
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irb
186
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isosize
24.87
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-rwxr-xr-x
ispell
988
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-rwxr-xr-x
isql
37.29
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iusql
29.68
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-rwxr-xr-x
jk_uchroot
28.85
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jobs
28
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join
53.68
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-rwxr-xr-x
journalctl
76.98
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-rwxr-xr-x
json_pp
4.19
KB
-rwxr-xr-x
json_xs
6.84
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jws
373
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kbd_mode
12.67
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-rwxr-xr-x
kbdinfo
16.69
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-rwxr-xr-x
kbdrate
16.64
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-rwxr-xr-x
kbxutil
177.32
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-rwxr-xr-x
kdumpctl
33.18
KB
-rwxr-xr-x
kernel-install
4.41
KB
-rwxr-xr-x
kill
37.27
KB
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killall
29.77
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-rwxr-xr-x
kmod
159.46
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-rwxr-xr-x
krb5-config
6.98
KB
-rwxr-xr-x
kvm_stat
60.85
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-rwxr-xr-x
l4p-tmpl
1.8
KB
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last
49.27
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lastb
49.27
KB
-rwxr-xr-x
lastcomm
37.8
KB
-rwxr-xr-x
lastlog
20.61
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-rwxr-xr-x
lchfn
20.33
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-rwxr-xr-x
lchsh
16.34
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-rwxr-xr-x
ld
1.71
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-rwxr-xr-x
ld.bfd
1.71
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-rwxr-xr-x
ld.gold
2.39
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-rwxr-xr-x
ld.so
1.07
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ldd
5.31
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lefty
304.54
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less
187.54
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-rwxr-xr-x
lessecho
13.52
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-rwxr-xr-x
lesskey
23.11
KB
-rwxr-xr-x
lesspipe.sh
3.07
KB
-rwxr-xr-x
letsencrypt
387
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lex
428.45
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-rwxr-xr-x
lexgrog
93.7
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-rwxr-xr-x
libnetcfg
15.41
KB
-rwxr-xr-x
libtool
359.11
KB
-rwxr-xr-x
libtoolize
126.17
KB
-rwxr-xr-x
link
33.32
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-rwxr-xr-x
linux-boot-prober
5.85
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-rwxr-xr-x
linux32
20.75
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-rwxr-xr-x
linux64
20.75
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-rwxr-xr-x
ln
70.48
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-rwxr-xr-x
lneato
1.51
KB
-rwxr-xr-x
loadkeys
210.65
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-rwxr-xr-x
loadunimap
29.13
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-rwxr-xr-x
locale
56.43
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-rwxr-xr-x
localectl
28.84
KB
-rwxr-xr-x
localedef
307.45
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-rwxr-xr-x
locate
47.41
KB
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lockfile
23.91
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logger
49.97
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-rwxr-xr-x
login
40.95
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-rwxr-xr-x
loginctl
57.28
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-rwxr-xr-x
logname
33.34
KB
-rwxr-xr-x
logresolve
16.39
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-rwxr-xr-x
look
16.45
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-rwxr-xr-x
ls
139.88
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-rwxr-xr-x
lsattr
11.93
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-rwxr-xr-x
lsblk
90.13
KB
-rwxr-xr-x
lscpu
77.68
KB
-rwxr-xr-x
lsgpio
15.05
KB
-rwxr-xr-x
lsiio
22.98
KB
-rwxr-xr-x
lsinitrd
8.68
KB
-rwxr-xr-x
lsipc
73.77
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-rwxr-xr-x
lslocks
37.52
KB
-rwxr-xr-x
lslogins
69.63
KB
-rwxr-xr-x
lsmcli
954
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lsmd
24.87
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lsmem
45.32
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-rwxr-xr-x
lsns
49.27
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-rwxr-xr-x
lsof
175.4
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-rwxr-xr-x
lsscsi
86.01
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-rwxr-xr-x
lsusb
244.14
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-rwxr-xr-x
lsusb.py
14.89
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-rwxr-xr-x
lwp-download
10.05
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lwp-dump
2.65
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lwp-mirror
2.36
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lwp-request
15.84
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m4
185.56
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mac2unix
58.54
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mailq
28.48
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mailq.postfix
28.48
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mailstat
5.72
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make
235.32
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make-dummy-cert
610
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makedb
24.83
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man
112.52
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mandb
134.52
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manpath
33.42
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mapscrn
24.97
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mcookie
33.26
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-rwxr-xr-x
mcpp
9.02
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md5sum
45.52
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mdig
48.51
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mecab
7.84
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-rwxr-xr-x
memstrack
83.72
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mesg
16.36
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-rwxr-xr-x
miltertest
56.8
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mkdir
82.7
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mkfifo
66.48
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mkfontdir
65
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mkfontscale
41.59
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mkinitrd
6.43
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-rwxr-xr-x
mknod
70.47
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-rwxr-xr-x
mktemp
45.66
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-rwxr-xr-x
mm2gv
90.51
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mmdblookup
16.8
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modulecmd
384.46
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modulemd-validator
24.96
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more
44.94
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mount
49.16
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mountpoint
16.47
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msgattrib
25.58
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msgcat
25.55
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msgcmp
26.12
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msgcomm
25.55
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msgconv
21.55
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msgen
21.55
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msgexec
21.55
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msgfilter
34.53
KB
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msgfmt
90.3
KB
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msggrep
43.6
KB
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msginit
67.85
KB
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msgmerge
71.48
KB
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msgunfmt
35.8
KB
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msguniq
25.56
KB
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mv
143.96
KB
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my_print_defaults
195.91
KB
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myisam_ftdump
6.38
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myisamchk
6.59
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-rwxr-xr-x
myisamlog
6.4
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-rwxr-xr-x
myisampack
6.44
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-rwxr-xr-x
mysql
7.27
MB
-rwxr-xr-x
mysql_config_editor
181.27
KB
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mysql_migrate_keyring
7.18
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-rwxr-xr-x
mysql_secure_installation
7.09
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-rwxr-xr-x
mysql_ssl_rsa_setup
232.91
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-rwxr-xr-x
mysql_tzinfo_to_sql
123.82
KB
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mysql_upgrade
7.18
MB
-rwxr-xr-x
mysqladmin
7.11
MB
-rwxr-xr-x
mysqlbinlog
7.44
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-rwxr-xr-x
mysqlcheck
7.12
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-rwxr-xr-x
mysqld_pre_systemd
4.27
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mysqldump
7.19
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-rwxr-xr-x
mysqldumpslow
7.49
KB
-rwxr-xr-x
mysqlimport
7.1
MB
-rwxr-xr-x
mysqlpump
7.52
MB
-rwxr-xr-x
mysqlshow
7.1
MB
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mysqlslap
7.12
MB
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named-rrchecker
19.87
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namei
33.09
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nano
254.73
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-rwxr-xr-x
ncurses6-config
5.87
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ncursesw6-config
5.88
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ndptool
24.41
KB
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neato
12.23
KB
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neqn
908
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net-server
3.34
KB
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netstat
158.68
KB
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newaliases
28.48
KB
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newaliases.postfix
28.48
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newgidmap
47.79
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newgrp
42.45
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newuidmap
47.75
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nf-ct-add
16.46
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nf-ct-events
12.38
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nf-ct-list
16.49
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nf-exp-add
16.87
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nf-exp-delete
16.66
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nf-exp-list
16.49
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nf-log
12.35
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nf-monitor
12.36
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-rwxr-xr-x
nf-queue
16.35
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ngettext
48.97
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nice
37.32
KB
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nisdomainname
21.16
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nl
45.54
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-rwxr-xr-x
nl-addr-add
12.34
KB
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nl-addr-delete
16.77
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nl-addr-list
16.88
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nl-class-add
16.73
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nl-class-delete
12.63
KB
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nl-class-list
12.59
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nl-classid-lookup
12.47
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nl-cls-add
16.77
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nl-cls-delete
16.77
KB
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nl-cls-list
12.73
KB
-rwxr-xr-x
nl-fib-lookup
12.5
KB
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nl-link-enslave
11.87
KB
-rwxr-xr-x
nl-link-ifindex2name
11.87
KB
-rwxr-xr-x
nl-link-list
12.23
KB
-rwxr-xr-x
nl-link-name2ifindex
11.86
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-rwxr-xr-x
nl-link-release
11.86
KB
-rwxr-xr-x
nl-link-set
12.77
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-rwxr-xr-x
nl-link-stats
12.59
KB
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nl-list-caches
12.27
KB
-rwxr-xr-x
nl-list-sockets
11.87
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-rwxr-xr-x
nl-monitor
12.52
KB
-rwxr-xr-x
nl-neigh-add
12.63
KB
-rwxr-xr-x
nl-neigh-delete
12.66
KB
-rwxr-xr-x
nl-neigh-list
12.19
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-rwxr-xr-x
nl-neightbl-list
12.01
KB
-rwxr-xr-x
nl-pktloc-lookup
12.56
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nl-qdisc-add
12.65
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nl-qdisc-delete
12.63
KB
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nl-qdisc-list
16.74
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nl-route-add
16.39
KB
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nl-route-delete
16.88
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nl-route-get
12.35
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nl-route-list
16.44
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nl-rule-list
12.05
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nl-tctree-list
12.66
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-rwxr-xr-x
nl-util-addr
11.85
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-rwxr-xr-x
nload
234.35
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nm
50.24
KB
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nm-online
20.83
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nmcli
1008.96
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nmtui
780.12
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nmtui-connect
780.12
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nmtui-edit
780.12
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nmtui-hostname
780.12
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nohup
37.41
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nop
12.47
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nproc
37.4
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nroff
3.23
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nsenter
33.29
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nslookup
146.24
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nsupdate
73.03
KB
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numfmt
65.63
KB
-rwxr-xr-x
objcopy
240.05
KB
-rwxr-xr-x
objdump
415.61
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od
73.8
KB
-rwxr-xr-x
odbc_config
13.05
KB
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odbcinst
37.68
KB
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open
21.09
KB
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openssl
745.94
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openvt
21.09
KB
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os-prober
5.78
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osage
12.23
KB
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p11-kit
37.16
KB
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package-stash-conflicts
842
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10.88
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11.88
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57.44
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13.07
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32.77
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37.37
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12.23
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37.32
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4.53
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5.3
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65.89
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2.09
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1.9
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333
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360
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248
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16.51
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21.4
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12.52
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perl5.26.3
12.52
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perlbug
44.39
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perldoc
118
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perlivp
10.56
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44.39
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129.13
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1.43
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2.57
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28.7
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14.46
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phar.phar
14.46
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6.1
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php-bkp9july
4.84
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4.74
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5.11
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6.09
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4.3
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4.27
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393
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14.5
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4.31
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4.21
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393
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14.5
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4.46
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4.36
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393
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php71-phar
14.48
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4.84
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4.74
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393
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14.48
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4.62
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4.51
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393
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14.48
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6.08
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6.08
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php74-pear
393
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php74-phar
14.55
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6.08
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6.08
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php80-phar
24.5
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6.09
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php81-cgi
6.09
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php81-phar
14.9
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6.09
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php82-cgi
6.09
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php82-phar
14.9
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php83-cgi
6.1
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php83-phar
14.9
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4.65
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293.84
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8.08
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16.55
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125.38
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2.35
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77.89
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66.08
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41.45
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209
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209
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209
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209
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16.38
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24.4
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81.53
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28.4
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40.04
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40.04
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28.7
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25.72
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33.78
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20.9
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20.23
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4.43
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16.73
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11.84
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pmap
32.65
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pmdate
11.83
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7.63
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33.53
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41.9
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21.21
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2.38
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182.95
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2.81
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60.01
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44.39
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42.02
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pmiostat
20.78
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pmjson
12.51
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58.81
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37.18
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54.48
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44.39
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70.02
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143.2
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21.01
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16.91
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16.98
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29.02
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33.59
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21.41
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11.83
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65.89
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pmrepconf
54.48
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pmsearch
21.36
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37.97
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pmsocks
1010
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pmstat
25.55
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pmstore
16.97
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pmtrace
15.84
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pmval
41.9
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4.04
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pod2man
14.68
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pod2text
10.55
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pod2usage
3.86
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3.57
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2.47
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238.73
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10.87
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82.13
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130.55
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5.52
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57.65
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5.52
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5.52
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11.98
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33.3
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53.55
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37.53
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116.16
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8.45
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13.24
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20.51
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16.7
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134.63
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20.77
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20.77
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20.77
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20.77
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12.48
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33.53
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33.53
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3.38
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2.48
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4.2
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4.48
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77.98
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37.41
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12.55
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12.26
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12.26
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89
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89
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89
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11.59
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11.59
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11.59
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446
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446
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91.49
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74.69
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61.95
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16.48
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934
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28
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624.54
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45.88
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49.93
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17.99
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89
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16.48
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725
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16.45
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38.24
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24.72
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20.89
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195.74
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12.45
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184
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70.38
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262
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rmail.postfix
262
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45.46
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rnano
254.73
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17.34
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20.84
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rpm2archive
19.84
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11.84
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16.95
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rpmkeys
16.87
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20.84
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20.84
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506.1
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5.96
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11.84
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1.94
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85
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37.36
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1.13
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1.13
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2.93
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4.9
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14.52
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44.83
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72.6
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17.62
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20.59
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36.95
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258
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1.82
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102.82
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482.46
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36.82
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28.98
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38.24
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8.38
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3.52
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1.3
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1.09
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3.77
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1.25
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1.44
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936
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105.33
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26.97
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25.45
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21.17
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115.48
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273.66
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198.48
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12.27
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12.27
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16.67
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12.28
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53.45
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17.38
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20.75
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41.48
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23.19
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45.3
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12.69
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16.72
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12.76
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45.15
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16.37
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45.12
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1.5
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1.5
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16.77
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12.23
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sftp
159.73
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sg
42.45
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sg_bg_ctl
16.07
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20.9
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sg_copy_results
20.81
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sg_dd
44.58
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20.33
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sg_emc_trespass
12.26
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33.37
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33.31
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sg_get_lba_status
20.46
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16.17
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117.91
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150.02
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24.85
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16.41
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24.87
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43.88
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28.69
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34.13
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12.07
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24.38
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20.84
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15.88
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sg_read
24.41
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sg_read_attr
34.79
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sg_read_block_limits
12.12
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sg_read_buffer
20.96
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16.27
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20.86
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16.22
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16.23
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sg_rep_zones
20.66
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16.3
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16.73
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sg_reset_wp
16.17
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sg_rmsn
12.06
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sg_rtpg
16.16
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sg_safte
20.27
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sg_sanitize
24.58
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sg_sat_identify
16.71
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sg_sat_phy_event
20.59
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sg_sat_read_gplog
16.3
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sg_sat_set_features
16.27
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sg_scan
16.5
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sg_seek
16.85
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sg_senddiag
25.27
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sg_ses
118.23
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sg_ses_microcode
29.48
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sg_start
20.93
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sg_stpg
20.3
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sg_stream_ctl
20.27
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sg_sync
16.22
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sg_test_rwbuf
20.73
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sg_timestamp
20.74
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sg_turs
16.74
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sg_unmap
24.32
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sg_verify
20.46
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sg_vpd
108.75
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sg_wr_mode
20.3
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sg_write_buffer
21.24
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sg_write_long
16.33
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sg_write_same
24.48
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sg_write_verify
20.78
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sg_write_x
53.75
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#!/usr/bin/perl -T -w # <@LICENSE> # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to you under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at: # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # </@LICENSE> use strict; use warnings; # use bytes; use Errno qw(EBADF); use Getopt::Long; use Pod::Usage; use File::Spec; use POSIX qw(locale_h setsid sigprocmask _exit); POSIX::setlocale(LC_TIME,'C'); our ( $spamtest, %opt, $isspam, $forget, $messagecount, $learnedcount, $messagelimit, $progress, $total_messages, $init_results, $start_time, $synconly, $learnprob, @targets, $bayes_override_path ); my $PREFIX = '/usr'; # substituted at 'make' time my $DEF_RULES_DIR = '/usr/share/spamassassin'; # substituted at 'make' time my $LOCAL_RULES_DIR = '/etc/mail/spamassassin'; # substituted at 'make' time use lib '/usr/share/perl5/vendor_perl'; # substituted at 'make' time BEGIN { # see comments in "spamassassin.raw" for doco my @bin = File::Spec->splitpath($0); my $bin = ($bin[0] ? File::Spec->catpath(@bin[0..1], '') : $bin[1]) || File::Spec->curdir; if (-e $bin.'/lib/Mail/SpamAssassin.pm' || !-e '/usr/share/perl5/vendor_perl/Mail/SpamAssassin.pm' ) { my $searchrelative; if ($searchrelative && $bin eq '../' && -e '../blib/lib/Mail/SpamAssassin.pm') { unshift ( @INC, '../blib/lib' ); } else { foreach ( qw(lib ../lib/site_perl ../lib/spamassassin ../share/spamassassin/lib)) { my $dir = File::Spec->catdir( $bin, split ( '/', $_ ) ); if ( -f File::Spec->catfile( $dir, "Mail", "SpamAssassin.pm" ) ) { unshift ( @INC, $dir ); last; } } } } } use Mail::SpamAssassin; use Mail::SpamAssassin::ArchiveIterator; use Mail::SpamAssassin::Message; use Mail::SpamAssassin::PerMsgLearner; use Mail::SpamAssassin::Util::Progress; use Mail::SpamAssassin::Logger; ########################################################################### $SIG{PIPE} = 'IGNORE'; # used to be CmdLearn::cmd_run() ... %opt = ( 'force-expire' => 0, 'use-ignores' => 0, 'nosync' => 0, 'quiet' => 0, 'cf' => [] ); Getopt::Long::Configure( qw(bundling no_getopt_compat permute no_auto_abbrev no_ignore_case) ); GetOptions( 'forget' => \$forget, 'ham|nonspam' => sub { $isspam = 0; }, 'spam' => sub { $isspam = 1; }, 'sync' => \$synconly, 'rebuild' => sub { $synconly = 1; warn "The --rebuild option has been deprecated. Please use --sync instead.\n" }, 'q|quiet' => \$opt{'quiet'}, 'username|u=s' => \$opt{'username'}, 'configpath|config-file|config-dir|c|C=s' => \$opt{'configpath'}, 'prefspath|prefs-file|p=s' => \$opt{'prefspath'}, 'siteconfigpath=s' => \$opt{'siteconfigpath'}, 'cf=s' => \@{$opt{'cf'}}, 'folders|f=s' => \$opt{'folders'}, 'force-expire|expire' => \$opt{'force-expire'}, 'local|L' => \$opt{'local'}, 'no-sync|nosync' => \$opt{'nosync'}, 'showdots' => \$opt{'showdots'}, 'progress' => \$opt{'progress'}, 'use-ignores' => \$opt{'use-ignores'}, 'no-rebuild|norebuild' => sub { $opt{'nosync'} = 1; warn "The --no-rebuild option has been deprecated. Please use --no-sync instead.\n" }, 'learnprob=f' => \$opt{'learnprob'}, 'randseed=i' => \$opt{'randseed'}, 'stopafter=i' => \$opt{'stopafter'}, 'max-size=i' => \$opt{'max-size'}, 'debug|debug-level|D:s' => \$opt{'debug'}, 'help|h|?' => \$opt{'help'}, 'version|V' => \$opt{'version'}, 'dump:s' => \$opt{'dump'}, 'import' => \$opt{'import'}, 'backup' => \$opt{'backup'}, 'clear' => \$opt{'clear'}, 'restore=s' => \$opt{'restore'}, 'dir' => sub { $opt{'old_format'} = 'dir'; }, 'file' => sub { $opt{'old_format'} = 'file'; }, 'mbox' => sub { $opt{'format'} = 'mbox'; }, 'mbx' => sub { $opt{'format'} = 'mbx'; }, 'single' => sub { $opt{'old_format'} = 'single'; }, 'db|dbpath=s' => \$bayes_override_path, 're|regexp=s' => \$opt{'regexp'}, '<>' => \&target, ) or usage( 0, "Unknown option!" ); if ( defined $opt{'help'} ) { usage( 0, "For more information read the manual page" ); } if ( defined $opt{'version'} ) { print "SpamAssassin version " . Mail::SpamAssassin::Version() . "\n"; exit 0; } # set debug areas, if any specified (only useful for command-line tools) if (defined $opt{'debug'}) { $opt{'debug'} ||= 'all'; } if ( $opt{'force-expire'} ) { $synconly = 1; } if ($opt{'showdots'} && $opt{'progress'}) { print "--showdots and --progress may not be used together, please select just one\n"; exit 0; } if ( !defined $isspam && !defined $synconly && !defined $forget && !defined $opt{'dump'} && !defined $opt{'import'} && !defined $opt{'clear'} && !defined $opt{'backup'} && !defined $opt{'restore'} && !defined $opt{'folders'} ) { usage( 0, "Please select either --spam, --ham, --folders, --forget, --sync, --import,\n--dump, --clear, --backup or --restore" ); } # We need to make sure the journal syncs pre-forget... if ( defined $forget && $opt{'nosync'} ) { $opt{'nosync'} = 0; warn "sa-learn warning: --forget requires read/write access to the database, and is incompatible with --no-sync\n"; } if ( defined $opt{'old_format'} ) { #Format specified in the 2.5x form of --dir, --file, --mbox, --mbx or --single. #Convert it to the new behavior: if ( $opt{'old_format'} eq 'single' ) { push ( @ARGV, '-' ); } } my $post_config = ''; # kluge to support old check_bayes_db operation # bug 3799: init() will go r/o with the configured DB, and then dbpath needs # to override. Just access the dbpath version via post_config_text. if ( defined $bayes_override_path ) { # Add a default prefix if the path is a directory if ( -d $bayes_override_path ) { $bayes_override_path = File::Spec->catfile( $bayes_override_path, 'bayes' ); } $post_config .= "bayes_path $bayes_override_path\n"; } # These options require bayes_scanner, which requires "use_bayes 1", but # that's not necessary for these commands. if (defined $opt{'dump'} || defined $opt{'import'} || defined $opt{'clear'} || defined $opt{'backup'} || defined $opt{'restore'}) { $post_config .= "use_bayes 1\n"; } $post_config .= join("\n", @{$opt{'cf'}})."\n"; # create the tester factory $spamtest = new Mail::SpamAssassin( { rules_filename => $opt{'configpath'}, site_rules_filename => $opt{'siteconfigpath'}, userprefs_filename => $opt{'prefspath'}, username => $opt{'username'}, debug => $opt{'debug'}, local_tests_only => $opt{'local'}, dont_copy_prefs => 1, PREFIX => $PREFIX, DEF_RULES_DIR => $DEF_RULES_DIR, LOCAL_RULES_DIR => $LOCAL_RULES_DIR, post_config_text => $post_config, } ); $spamtest->init(1); dbg("sa-learn: spamtest initialized"); # Bug 6228 hack: bridge the transition gap of moving Bayes.pm into a plugin; # To be resolved more cleanly!!! if ($spamtest->{bayes_scanner}) { foreach my $plugin ( @{ $spamtest->{plugins}->{plugins} } ) { if ($plugin->isa('Mail::SpamAssassin::Plugin::Bayes')) { # copy plugin's "store" object ref one level up! $spamtest->{bayes_scanner}->{store} = $plugin->{store}; } } } if (Mail::SpamAssassin::Util::am_running_on_windows()) { binmode(STDIN) or die "cannot set binmode on STDIN: $!"; # bug 4363 binmode(STDOUT) or die "cannot set binmode on STDOUT: $!"; } if ( defined $opt{'dump'} ) { my ( $magic, $toks ); if ( $opt{'dump'} eq 'all' || $opt{'dump'} eq '' ) { # show us all tokens! ( $magic, $toks ) = ( 1, 1 ); } elsif ( $opt{'dump'} eq 'magic' ) { # show us magic tokens only ( $magic, $toks ) = ( 1, 0 ); } elsif ( $opt{'dump'} eq 'data' ) { # show us data tokens only ( $magic, $toks ) = ( 0, 1 ); } else { # unknown option warn "Unknown dump option '" . $opt{'dump'} . "'\n"; $spamtest->finish_learner(); exit 1; } if (!$spamtest->dump_bayes_db( $magic, $toks, $opt{'regexp'}) ) { $spamtest->finish_learner(); die "ERROR: Bayes dump returned an error, please re-run with -D for more information\n"; } $spamtest->finish_learner(); # make sure we notice any write errors while flushing output buffer close STDOUT or die "error closing STDOUT: $!"; close STDIN or die "error closing STDIN: $!"; exit 0; } if ( defined $opt{'import'} ) { my $ret = $spamtest->{bayes_scanner}->{store}->perform_upgrade(); $spamtest->finish_learner(); # make sure we notice any write errors while flushing output buffer close STDOUT or die "error closing STDOUT: $!"; close STDIN or die "error closing STDIN: $!"; exit( !$ret ); } if (defined $opt{'clear'}) { unless ($spamtest->{bayes_scanner}->{store}->clear_database()) { $spamtest->finish_learner(); die "ERROR: Bayes clear returned an error, please re-run with -D for more information\n"; } $spamtest->finish_learner(); # make sure we notice any write errors while flushing output buffer close STDOUT or die "error closing STDOUT: $!"; close STDIN or die "error closing STDIN: $!"; exit 0; } if (defined $opt{'backup'}) { unless ($spamtest->{bayes_scanner}->{store}->backup_database()) { $spamtest->finish_learner(); die "ERROR: Bayes backup returned an error, please re-run with -D for more information\n"; } $spamtest->finish_learner(); # make sure we notice any write errors while flushing output buffer close STDOUT or die "error closing STDOUT: $!"; close STDIN or die "error closing STDIN: $!"; exit 0; } if (defined $opt{'restore'}) { my $filename = $opt{'restore'}; unless ($filename) { $spamtest->finish_learner(); die "ERROR: You must specify a filename to restore.\n"; } unless ($spamtest->{bayes_scanner}->{store}->restore_database($filename, $opt{'showdots'})) { $spamtest->finish_learner(); die "ERROR: Bayes restore returned an error, please re-run with -D for more information\n"; } $spamtest->finish_learner(); # make sure we notice any write errors while flushing output buffer close STDOUT or die "error closing STDOUT: $!"; close STDIN or die "error closing STDIN: $!"; exit 0; } if ( !$spamtest->{conf}->{use_bayes} ) { warn "ERROR: configuration specifies 'use_bayes 0', sa-learn disabled\n"; exit 1; } $spamtest->init_learner( { force_expire => $opt{'force-expire'}, learn_to_journal => $opt{'nosync'}, wait_for_lock => 1, caller_will_untie => 1 } ); $spamtest->{bayes_scanner}{use_ignores} = $opt{'use-ignores'}; if ($synconly) { $spamtest->rebuild_learner_caches( { verbose => !$opt{'quiet'}, showdots => $opt{'showdots'} } ); $spamtest->finish_learner(); # make sure we notice any write errors while flushing output buffer close STDOUT or die "error closing STDOUT: $!"; close STDIN or die "error closing STDIN: $!"; exit 0; } $messagelimit = $opt{'stopafter'}; $learnprob = $opt{'learnprob'}; if ( defined $opt{'randseed'} ) { srand( $opt{'randseed'} ); } # sync the journal first if we're going to go r/w so we make sure to # learn everything before doing anything else. # if ( !$opt{nosync} ) { $spamtest->rebuild_learner_caches(); } # what is the result of the run? will end up being the exit code. my $exit_status = 0; # run this lot in an eval block, so we can catch die's and clear # up the dbs. eval { $SIG{HUP} = \&killed; $SIG{INT} = \&killed; $SIG{TERM} = \&killed; if ( $opt{folders} ) { open( F, $opt{folders} ) or die "cannot open $opt{folders}: $!"; for ($!=0; <F>; $!=0) { chomp; next if /^\s*$/; if (/^(ham|spam):(\w*):(.*)/) { my $class = $1; my $format = $2 || "detect"; my $target = $3; push ( @targets, "$class:$format:$target" ); } else { target($_); } } defined $_ || $!==0 or $!==EBADF ? dbg("error reading from $opt{folders}: $!") : die "error reading from $opt{folders}: $!"; close(F) or die "error closing $opt{folders}: $!"; } ########################################################################### # Deal with the target listing, and STDIN -> tempfile my $tempfile; # will be defined if stdin -> tempfile push(@targets, @ARGV); @targets = ('-') unless @targets || $opt{folders}; for(my $elem = 0; $elem <= $#targets; $elem++) { # ArchiveIterator doesn't really like STDIN, so if "-" is specified # as a target, make it a temp file instead. if ( $targets[$elem] =~ /(?:^|:)-$/ ) { if (defined $tempfile) { # uh-oh, stdin specified multiple times? warn "skipping extra stdin target (".$targets[$elem].")\n"; splice @targets, $elem, 1; $elem--; # go back to this element again next; } else { my $handle; ( $tempfile, $handle ) = Mail::SpamAssassin::Util::secure_tmpfile(); binmode $handle or die "cannot set binmode on file $tempfile: $!"; # avoid slurping the whole file into memory, copy chunk by chunk my($inbuf,$nread); while ( $nread=sysread(STDIN,$inbuf,16384) ) { print {$handle} $inbuf or die "error writing to $tempfile: $!" } defined $nread or die "error reading from STDIN: $!"; close $handle or die "error closing $tempfile: $!"; # re-aim the targets at the tempfile instead of STDIN $targets[$elem] =~ s/-$/$tempfile/; } } # make sure the target list is in the normal AI format if ($targets[$elem] !~ /^[^:]*:[a-z]+:/) { my $item = splice @targets, $elem, 1; target($item); # add back to the list $elem--; # go back to this element again next; } } ########################################################################### my $iter = new Mail::SpamAssassin::ArchiveIterator( { # skip messages larger than max-size bytes, # 0 for no limit, undef defaults to 256 KB 'opt_max_size' => $opt{'max-size'}, 'opt_want_date' => 0, 'opt_from_regex' => $spamtest->{conf}->{mbox_format_from_regex}, } ); $iter->set_functions(\&wanted, \&result); $messagecount = 0; $learnedcount = 0; $init_results = 0; $start_time = time; # if exit_status isn't already set to non-zero, set it to the reverse of the # run result (0 is bad, 1+ is good -- the opposite of exit status codes) my $run_ok = eval { $exit_status ||= ! $iter->run(@targets); 1 }; print STDERR "\n" if ($opt{showdots}); $progress->final() if ($opt{progress} && $progress); my $phrase = defined $forget ? "Forgot" : "Learned"; print "$phrase tokens from $learnedcount message(s) ($messagecount message(s) examined)\n" if !$opt{'quiet'}; # If we needed to make a tempfile, go delete it. if (defined $tempfile) { unlink $tempfile or die "cannot unlink temporary file $tempfile: $!"; undef $tempfile; } if (!$run_ok && $@ !~ /HITLIMIT/) { die $@ } 1; } or do { my $eval_stat = $@ ne '' ? $@ : "errno=$!"; chomp $eval_stat; $spamtest->finish_learner(); die $eval_stat; }; $spamtest->finish_learner(); # make sure we notice any write errors while flushing output buffer close STDOUT or die "error closing STDOUT: $!"; close STDIN or die "error closing STDIN: $!"; exit $exit_status; ########################################################################### sub killed { $spamtest->finish_learner(); die "interrupted"; } sub target { my ($target) = @_; my $class = ( $isspam ? "spam" : "ham" ); my $format = ( defined( $opt{'format'} ) ? $opt{'format'} : "detect" ); push ( @targets, "$class:$format:$target" ); } ########################################################################### sub init_results { $init_results = 1; return unless $opt{'progress'}; $total_messages = $Mail::SpamAssassin::ArchiveIterator::MESSAGES; $progress = Mail::SpamAssassin::Util::Progress->new({total => $total_messages,}); } ########################################################################### sub result { my ($class, $result, $time) = @_; # don't open results files until we get here to avoid overwriting files &init_results if !$init_results; $progress->update($messagecount) if ($opt{progress} && $progress); } ########################################################################### sub wanted { my ( $class, $id, $time, $dataref ) = @_; my $spam = $class eq "s" ? 1 : 0; if ( defined($learnprob) ) { if ( int( rand( 1 / $learnprob ) ) != 0 ) { print STDERR '_' if ( $opt{showdots} ); return 1; } } if ( defined($messagelimit) && $learnedcount > $messagelimit ) { $progress->final() if ($opt{progress} && $progress); die 'HITLIMIT'; } $messagecount++; my $ma = $spamtest->parse($dataref); if ( $ma->get_header("X-Spam-Checker-Version") ) { my $new_ma = $spamtest->parse($spamtest->remove_spamassassin_markup($ma), 1); $ma->finish(); $ma = $new_ma; } my $status = $spamtest->learn( $ma, undef, $spam, $forget ); my $learned = $status->did_learn(); if ( !defined $learned ) { # undef=learning unavailable die "ERROR: the Bayes learn function returned an error, please re-run with -D for more information\n"; } elsif ( $learned == 1 ) { # 1=message was learned. 0=message wasn't learned $learnedcount++; } # Do cleanup ... $status->finish(); undef $status; $ma->finish(); undef $ma; print STDERR '.' if ( $opt{showdots} ); return 1; } ########################################################################### sub usage { my ( $verbose, $message ) = @_; my $ver = Mail::SpamAssassin::Version(); print "SpamAssassin version $ver\n"; pod2usage( -verbose => $verbose, -message => $message, -exitval => 64 ); } # --------------------------------------------------------------------------- =head1 NAME sa-learn - train SpamAssassin's Bayesian classifier =head1 SYNOPSIS B<sa-learn> [options] [file]... B<sa-learn> [options] --dump [ all | data | magic ] Options: --ham Learn messages as ham (non-spam) --spam Learn messages as spam --forget Forget a message --use-ignores Use bayes_ignore_from and bayes_ignore_to --sync Synchronize the database and the journal if needed --force-expire Force a database sync and expiry run --dbpath <path> Allows commandline override (in bayes_path form) for where to read the Bayes DB from --dump [all|data|magic] Display the contents of the Bayes database Takes optional argument for what to display --regexp <re> For dump only, specifies which tokens to dump based on a regular expression. -f file, --folders=file Read list of files/directories from file --dir Ignored; historical compatibility --file Ignored; historical compatibility --mbox Input sources are in mbox format --mbx Input sources are in mbx format --max-size <b> Skip messages larger than b bytes; defaults to 256 KB, 0 implies no limit --showdots Show progress using dots --progress Show progress using progress bar --no-sync Skip synchronizing the database and journal after learning -L, --local Operate locally, no network accesses --import Migrate data from older version/non DB_File based databases --clear Wipe out existing database --backup Backup, to STDOUT, existing database --restore <filename> Restore a database from filename -u username, --username=username Override username taken from the runtime environment, used with SQL -C path, --configpath=path, --config-file=path Path to standard configuration dir -p prefs, --prefspath=file, --prefs-file=file Set user preferences file --siteconfigpath=path Path for site configs (default: /usr/etc/mail/spamassassin) --cf='config line' Additional line of configuration -D, --debug [area=n,...] Print debugging messages -V, --version Print version -h, --help Print usage message =head1 DESCRIPTION Given a typical selection of your incoming mail classified as spam or ham (non-spam), this tool will feed each mail to SpamAssassin, allowing it to 'learn' what signs are likely to mean spam, and which are likely to mean ham. Simply run this command once for each of your mail folders, and it will ''learn'' from the mail therein. Note that csh-style I<globbing> in the mail folder names is supported; in other words, listing a folder name as C<*> will scan every folder that matches. See C<Mail::SpamAssassin::ArchiveIterator> for more details. If you are using mail boxes in format other than maildir you should use the B<--mbox> or B<--mbx> parameters. SpamAssassin remembers which mail messages it has learnt already, and will not re-learn those messages again, unless you use the B<--forget> option. Messages learnt as spam will have SpamAssassin markup removed, on the fly. If you make a mistake and scan a mail as ham when it is spam, or vice versa, simply rerun this command with the correct classification, and the mistake will be corrected. SpamAssassin will automatically 'forget' the previous indications. Users of C<spamd> who wish to perform training remotely, over a network, should investigate the C<spamc -L> switch. =head1 OPTIONS =over 4 =item B<--ham> Learn the input message(s) as ham. If you have previously learnt any of the messages as spam, SpamAssassin will forget them first, then re-learn them as ham. Alternatively, if you have previously learnt them as ham, it'll skip them this time around. If the messages have already been filtered through SpamAssassin, the learner will ignore any modifications SpamAssassin may have made. =item B<--spam> Learn the input message(s) as spam. If you have previously learnt any of the messages as ham, SpamAssassin will forget them first, then re-learn them as spam. Alternatively, if you have previously learnt them as spam, it'll skip them this time around. If the messages have already been filtered through SpamAssassin, the learner will ignore any modifications SpamAssassin may have made. =item B<--folders>=I<filename>, B<-f> I<filename> sa-learn will read in the list of folders from the specified file, one folder per line in the file. If the folder is prefixed with C<ham:type:> or C<spam:type:>, sa-learn will learn that folder appropriately, otherwise the folders will be assumed to be of the type specified by B<--ham> or B<--spam>. C<type> above is optional, but is the same as the standard for ArchiveIterator: mbox, mbx, dir, file, or detect (the default if not specified). =item B<--mbox> sa-learn will read in the file(s) containing the emails to be learned, and will process them in mbox format (one or more emails per file). =item B<--mbx> sa-learn will read in the file(s) containing the emails to be learned, and will process them in mbx format (one or more emails per file). =item B<--use-ignores> Don't learn the message if a from address matches configuration file item C<bayes_ignore_from> or a to address matches C<bayes_ignore_to>. The option might be used when learning from a large file of messages from which the hammy spam messages or spammy ham messages have not been removed. =item B<--sync> Synchronize the journal and databases. Upon successfully syncing the database with the entries in the journal, the journal file is removed. =item B<--force-expire> Forces an expiry attempt, regardless of whether it may be necessary or not. Note: This doesn't mean any tokens will actually expire. Please see the EXPIRATION section below. Note: C<--force-expire> also causes the journal data to be synchronized into the Bayes databases. =item B<--forget> Forget a given message previously learnt. =item B<--dbpath> Allows a commandline override of the I<bayes_path> configuration option. =item B<--dump> I<option> Display the contents of the Bayes database. Without an option or with the I<all> option, all magic tokens and data tokens will be displayed. I<magic> will only display magic tokens, and I<data> will only display the data tokens. Can also use the B<--regexp> I<RE> option to specify which tokens to display based on a regular expression. =item B<--clear> Clear an existing Bayes database by removing all traces of the database. WARNING: This is destructive and should be used with care. =item B<--backup> Performs a dump of the Bayes database in machine/human readable format. The dump will include token and seen data. It is suitable for input back into the --restore command. =item B<--restore>=I<filename> Performs a restore of the Bayes database defined by I<filename>. WARNING: This is a destructive operation, previous Bayes data will be wiped out. =item B<-h>, B<--help> Print help message and exit. =item B<-u> I<username>, B<--username>=I<username> If specified this username will override the username taken from the runtime environment. You can use this option to specify users in a virtual user configuration when using SQL as the Bayes backend. NOTE: This option will not change to the given I<username>, it will only attempt to act on behalf of that user. Because of this you will need to have proper permissions to be able to change files owned by I<username>. In the case of SQL this generally is not a problem. =item B<-C> I<path>, B<--configpath>=I<path>, B<--config-file>=I<path> Use the specified path for locating the distributed configuration files. Ignore the default directories (usually C</usr/share/spamassassin> or similar). =item B<--siteconfigpath>=I<path> Use the specified path for locating site-specific configuration files. Ignore the default directories (usually C</etc/mail/spamassassin> or similar). =item B<--cf='config line'> Add additional lines of configuration directly from the command-line, parsed after the configuration files are read. Multiple B<--cf> arguments can be used, and each will be considered a separate line of configuration. =item B<-p> I<prefs>, B<--prefspath>=I<prefs>, B<--prefs-file>=I<prefs> Read user score preferences from I<prefs> (usually C<$HOME/.spamassassin/user_prefs>). =item B<--progress> Prints a progress bar (to STDERR) showing the current progress. In the case where no valid terminal is found this option will behave very much like the --showdots option. =item B<-D> [I<area,...>], B<--debug> [I<area,...>] Produce debugging output. If no areas are listed, all debugging information is printed. Diagnostic output can also be enabled for each area individually; I<area> is the area of the code to instrument. For example, to produce diagnostic output on bayes, learn, and dns, use: spamassassin -D bayes,learn,dns For more information about which areas (also known as channels) are available, please see the documentation at: C<http://wiki.apache.org/spamassassin/DebugChannels> Higher priority informational messages that are suitable for logging in normal circumstances are available with an area of "info". =item B<--no-sync> Skip the slow synchronization step which normally takes place after changing database entries. If you plan to learn from many folders in a batch, or to learn many individual messages one-by-one, it is faster to use this switch and run C<sa-learn --sync> once all the folders have been scanned. Clarification: The state of I<--no-sync> overrides the I<bayes_learn_to_journal> configuration option. If not specified, sa-learn will learn to the database directly. If specified, sa-learn will learn to the journal file. Note: I<--sync> and I<--no-sync> can be specified on the same commandline, which is slightly confusing. In this case, the I<--no-sync> option is ignored since there is no learn operation. =item B<-L>, B<--local> Do not perform any network accesses while learning details about the mail messages. This will speed up the learning process, but may result in a slightly lower accuracy. Note that this is currently ignored, as current versions of SpamAssassin will not perform network access while learning; but future versions may. =item B<--import> If you previously used SpamAssassin's Bayesian learner without the C<DB_File> module installed, it will have created files in other formats, such as C<GDBM_File>, C<NDBM_File>, or C<SDBM_File>. This switch allows you to migrate that old data into the C<DB_File> format. It will overwrite any data currently in the C<DB_File>. Can also be used with the B<--dbpath> I<path> option to specify the location of the Bayes files to use. =back =head1 MIGRATION There are now multiple backend storage modules available for storing user's bayesian data. As such you might want to migrate from one backend to another. Here is a simple procedure for migrating from one backend to another. Note that if you have individual user databases you will have to perform a similar procedure for each one of them. =over 4 =item sa-learn --sync This will sync any outstanding journal entries =item sa-learn --backup > backup.txt This will save all your Bayes data to a plain text file. =item sa-learn --clear This is optional, but good to do to clear out the old database. =item Repeat! At this point, if you have multiple databases, you should perform the procedure above for each of them. (i.e. each user's database needs to be backed up before continuing.) =item Switch backends Once you have backed up all databases you can update your configuration for the new database backend. This will involve at least the bayes_store_module config option and may involve some additional config options depending on what is required by the module. (For example, you may need to configure an SQL database.) =item sa-learn --restore backup.txt Again, you need to do this for every database. =back If you are migrating to SQL you can make use of the -u <username> option in sa-learn to populate each user's database. Otherwise, you must run sa-learn as the user who database you are restoring. =head1 INTRODUCTION TO BAYESIAN FILTERING (Thanks to Michael Bell for this section!) For a more lengthy description of how this works, go to http://www.paulgraham.com/ and see "A Plan for Spam". It's reasonably readable, even if statistics make me break out in hives. The short semi-inaccurate version: Given training, a spam heuristics engine can take the most "spammy" and "hammy" words and apply probabilistic analysis. Furthermore, once given a basis for the analysis, the engine can continue to learn iteratively by applying both the non-Bayesian and Bayesian rulesets together to create evolving "intelligence". SpamAssassin 2.50 and later supports Bayesian spam analysis, in the form of the BAYES rules. This is a new feature, quite powerful, and is disabled until enough messages have been learnt. The pros of Bayesian spam analysis: =over 4 =item Can greatly reduce false positives and false negatives. It learns from your mail, so it is tailored to your unique e-mail flow. =item Once it starts learning, it can continue to learn from SpamAssassin and improve over time. =back And the cons: =over 4 =item A decent number of messages are required before results are useful for ham/spam determination. =item It's hard to explain why a message is or isn't marked as spam. i.e.: a straightforward rule, that matches, say, "VIAGRA" is easy to understand. If it generates a false positive or false negative, it is fairly easy to understand why. With Bayesian analysis, it's all probabilities - "because the past says it is likely as this falls into a probabilistic distribution common to past spam in your systems". Tell that to your users! Tell that to the client when he asks "what can I do to change this". (By the way, the answer in this case is "use whitelisting".) =item It will take disk space and memory. The databases it maintains take quite a lot of resources to store and use. =back =head1 GETTING STARTED Still interested? Ok, here's the guidelines for getting this working. First a high-level overview: =over 4 =item Build a significant sample of both ham and spam. I suggest several thousand of each, placed in SPAM and HAM directories or mailboxes. Yes, you MUST hand-sort this - otherwise the results won't be much better than SpamAssassin on its own. Verify the spamminess/haminess of EVERY message. You're urged to avoid using a publicly available corpus (sample) - this must be taken from YOUR mail server, if it is to be statistically useful. Otherwise, the results may be pretty skewed. =item Use this tool to teach SpamAssassin about these samples, like so: sa-learn --spam /path/to/spam/folder sa-learn --ham /path/to/ham/folder ... Let SpamAssassin proceed, learning stuff. When it finds ham and spam it will add the "interesting tokens" to the database. =item If you need SpamAssassin to forget about specific messages, use the B<--forget> option. This can be applied to either ham or spam that has run through the B<sa-learn> processes. It's a bit of a hammer, really, lowering the weighting of the specific tokens in that message (only if that message has been processed before). =item Learning from single messages uses a command like this: sa-learn --ham --no-sync mailmessage This is handy for binding to a key in your mail user agent. It's very fast, as all the time-consuming stuff is deferred until you run with the C<--sync> option. =item Autolearning is enabled by default If you don't have a corpus of mail saved to learn, you can let SpamAssassin automatically learn the mail that you receive. If you are autolearning from scratch, the amount of mail you receive will determine how long until the BAYES_* rules are activated. =back =head1 EFFECTIVE TRAINING Learning filters require training to be effective. If you don't train them, they won't work. In addition, you need to train them with new messages regularly to keep them up-to-date, or their data will become stale and impact accuracy. You need to train with both spam I<and> ham mails. One type of mail alone will not have any effect. Note that if your mail folders contain things like forwarded spam, discussions of spam-catching rules, etc., this will cause trouble. You should avoid scanning those messages if possible. (An easy way to do this is to move them aside, into a folder which is not scanned.) If the messages you are learning from have already been filtered through SpamAssassin, the learner will compensate for this. In effect, it learns what each message would look like if you had run C<spamassassin -d> over it in advance. Another thing to be aware of, is that typically you should aim to train with at least 1000 messages of spam, and 1000 ham messages, if possible. More is better, but anything over about 5000 messages does not improve accuracy significantly in our tests. Be careful that you train from the same source -- for example, if you train on old spam, but new ham mail, then the classifier will think that a mail with an old date stamp is likely to be spam. It's also worth noting that training with a very small quantity of ham, will produce atrocious results. You should aim to train with at least the same amount (or more if possible!) of ham data than spam. On an on-going basis, it is best to keep training the filter to make sure it has fresh data to work from. There are various ways to do this: =over 4 =item 1. Supervised learning This means keeping a copy of all or most of your mail, separated into spam and ham piles, and periodically re-training using those. It produces the best results, but requires more work from you, the user. (An easy way to do this, by the way, is to create a new folder for 'deleted' messages, and instead of deleting them from other folders, simply move them in there instead. Then keep all spam in a separate folder and never delete it. As long as you remember to move misclassified mails into the correct folder set, it is easy enough to keep up to date.) =item 2. Unsupervised learning from Bayesian classification Another way to train is to chain the results of the Bayesian classifier back into the training, so it reinforces its own decisions. This is only safe if you then retrain it based on any errors you discover. SpamAssassin does not support this method, due to experimental results which strongly indicate that it does not work well, and since Bayes is only one part of the resulting score presented to the user (while Bayes may have made the wrong decision about a mail, it may have been overridden by another system). =item 3. Unsupervised learning from SpamAssassin rules Also called 'auto-learning' in SpamAssassin. Based on statistical analysis of the SpamAssassin success rates, we can automatically train the Bayesian database with a certain degree of confidence that our training data is accurate. It should be supplemented with some supervised training in addition, if possible. This is the default, but can be turned off by setting the SpamAssassin configuration parameter C<bayes_auto_learn> to 0. =item 4. Mistake-based training This means training on a small number of mails, then only training on messages that SpamAssassin classifies incorrectly. This works, but it takes longer to get it right than a full training session would. =back =head1 FILES B<sa-learn> and the other parts of SpamAssassin's Bayesian learner, use a set of persistent database files to store the learnt tokens, as follows. =over 4 =item bayes_toks The database of tokens, containing the tokens learnt, their count of occurrences in ham and spam, and the timestamp when the token was last seen in a message. This database also contains some 'magic' tokens, as follows: the version number of the database, the number of ham and spam messages learnt, the number of tokens in the database, and timestamps of: the last journal sync, the last expiry run, the last expiry token reduction count, the last expiry timestamp delta, the oldest token timestamp in the database, and the newest token timestamp in the database. This is a database file, using C<DB_File>. The database 'version number' is 0 for databases from 2.5x, 1 for databases from certain 2.6x development releases, 2 for 2.6x, and 3 for 3.0 and later releases. =item bayes_seen A map of Message-Id and some data from headers and body to what that message was learnt as. This is used so that SpamAssassin can avoid re-learning a message it has already seen, and so it can reverse the training if you later decide that message was learnt incorrectly. This is a database file, using C<DB_File>. =item bayes_journal While SpamAssassin is scanning mails, it needs to track which tokens it uses in its calculations. To avoid the contention of having each SpamAssassin process attempting to gain write access to the Bayes DB, the token timestamps are written to a 'journal' file which will later (either automatically or via C<sa-learn --sync>) be used to synchronize the Bayes DB. Also, through the use of C<bayes_learn_to_journal>, or when using the C<--no-sync> option with sa-learn, the actual learning data will take be placed into the journal for later synchronization. This is typically useful for high-traffic sites to avoid the same contention as stated above. =back =head1 EXPIRATION Since SpamAssassin can auto-learn messages, the Bayes database files could increase perpetually until they fill your disk. To control this, SpamAssassin performs journal synchronization and bayes expiration periodically when certain criteria (listed below) are met. SpamAssassin can sync the journal and expire the DB tokens either manually or opportunistically. A journal sync is due if I<--sync> is passed to sa-learn (manual), or if the following is true (opportunistic): =over 4 =item - bayes_journal_max_size does not equal 0 (means don't sync) =item - the journal file exists =back and either: =over 4 =item - the journal file has a size greater than bayes_journal_max_size =back or =over 4 =item - a journal sync has previously occurred, and at least 1 day has passed since that sync =back Expiry is due if I<--force-expire> is passed to sa-learn (manual), or if all of the following are true (opportunistic): =over 4 =item - the last expire was attempted at least 12hrs ago =item - bayes_auto_expire does not equal 0 =item - the number of tokens in the DB is > 100,000 =item - the number of tokens in the DB is > bayes_expiry_max_db_size =item - there is at least a 12 hr difference between the oldest and newest token atimes =back =head2 EXPIRE LOGIC If either the manual or opportunistic method causes an expire run to start, here is the logic that is used: =over 4 =item - figure out how many tokens to keep. take the larger of either bayes_expiry_max_db_size * 75% or 100,000 tokens. therefore, the goal reduction is number of tokens - number of tokens to keep. =item - if the reduction number is < 1000 tokens, abort (not worth the effort). =item - if an expire has been done before, guesstimate the new atime delta based on the old atime delta. (new_atime_delta = old_atime_delta * old_reduction_count / goal) =item - if no expire has been done before, or the last expire looks "weird", do an estimation pass. The definition of "weird" is: =over 8 =item - last expire over 30 days ago =item - last atime delta was < 12 hrs =item - last reduction count was < 1000 tokens =item - estimated new atime delta is < 12 hrs =item - the difference between the last reduction count and the goal reduction count is > 50% =back =back =head2 ESTIMATION PASS LOGIC Go through each of the DB's tokens. Starting at 12hrs, calculate whether or not the token would be expired (based on the difference between the token's atime and the db's newest token atime) and keep the count. Work out from 12hrs exponentially by powers of 2. ie: 12hrs * 1, 12hrs * 2, 12hrs * 4, 12hrs * 8, and so on, up to 12hrs * 512 (6144hrs, or 256 days). The larger the delta, the smaller the number of tokens that will be expired. Conversely, the number of tokens goes up as the delta gets smaller. So starting at the largest atime delta, figure out which delta will expire the most tokens without going above the goal expiration count. Use this to choose the atime delta to use, unless one of the following occurs: =over 8 =item - the largest atime (smallest reduction count) would expire too many tokens. this means the learned tokens are mostly old and there needs to be new tokens learned before an expire can occur. =item - all of the atime choices result in 0 tokens being removed. this means the tokens are all newer than 12 hours and there needs to be new tokens learned before an expire can occur. =item - the number of tokens that would be removed is < 1000. the benefit isn't worth the effort. more tokens need to be learned. =back If the expire run gets past this point, it will continue to the end. A new DB is created since the majority of DB libraries don't shrink the DB file when tokens are removed. So we do the "create new, migrate old to new, remove old, rename new" shuffle. =head2 EXPIRY RELATED CONFIGURATION SETTINGS =over 4 =item C<bayes_auto_expire> is used to specify whether or not SpamAssassin ought to opportunistically attempt to expire the Bayes database. The default is 1 (yes). =item C<bayes_expiry_max_db_size> specifies both the auto-expire token count point, as well as the resulting number of tokens after expiry as described above. The default value is 150,000, which is roughly equivalent to a 6Mb database file if you're using DB_File. =item C<bayes_journal_max_size> specifies how large the Bayes journal will grow before it is opportunistically synced. The default value is 102400. =back =head1 INSTALLATION The B<sa-learn> command is part of the B<Mail::SpamAssassin> Perl module. Install this as a normal Perl module, using C<perl -MCPAN -e shell>, or by hand. =head1 SEE ALSO spamassassin(1) spamc(1) Mail::SpamAssassin(3) Mail::SpamAssassin::ArchiveIterator(3) E<lt>http://www.paulgraham.com/E<gt> Paul Graham's "A Plan For Spam" paper E<lt>http://www.linuxjournal.com/article/6467E<gt> Gary Robinson's f(x) and combining algorithms, as used in SpamAssassin E<lt>http://www.bgl.nu/~glouis/bogofilter/E<gt> 'Training on error' page. A discussion of various Bayes training regimes, including 'train on error' and unsupervised training. =head1 PREREQUISITES C<Mail::SpamAssassin> =head1 AUTHORS The SpamAssassin(tm) Project E<lt>https://spamassassin.apache.org/E<gt> =cut
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