I am experimenting with launching rsync from QProcess and although it runs, it behaves differently when run from QProcess compared to running the exact same command from the command line.
Here is the command and stdout when run from QProcess
/usr/bin/rsync -atv --stats --progress --port=873 --compress-level=9 --recursive --delete --exclude="/etc/*.conf" --exclude="A*" rsync://myhost.com/haast/tmp/mysync/* /tmp/mysync/
receiving incremental file list
created directory /tmp/mysync
A
0 100% 0.00kB/s 0:00:00 (xfer#1, to-check=6/7)
B
0 100% 0.00kB/s 0:00:00 (xfer#2, to-check=5/7)
test.conf
0 100% 0.00kB/s 0:00:00 (xfer#3, to-check=4/7)
subdir/
subdir/A2
0 100% 0.00kB/s 0:00:00 (xfer#4, to-check=2/7)
subdir/C
0 100% 0.00kB/s 0:00:00 (xfer#5, to-check=1/7)
subdir/D
0 100% 0.00kB/s 0:00:00 (xfer#6, to-check=0/7)
Number of files: 7
Number of files transferred: 6
Total file size: 0 bytes
Total transferred file size: 0 bytes
Literal data: 0 bytes
Matched data: 0 bytes
File list size: 105
File list generation time: 0.001 seconds
File list transfer time: 0.000 seconds
Total bytes sent: 174
Total bytes received: 367
sent 174 bytes received 367 bytes 360.67 bytes/sec
total size is 0 speedup is 0.00
Notice that although I excluded 'A*', it still copied them! Now running the exact same command from the command line:
/usr/bin/rsync -atv --stats --progress --port=873 --compress-level=9 --recursive --delete --exclude="/etc/*.conf" --exclude="A*" rsync://myhost.com/haast/tmp/mysync/* /tmp/mysync/
receiving incremental file list
created directory /tmp/mysync
B
0 100% 0.00kB/s 0:00:00 (xfer#1, to-check=4/5)
test.conf
0 100% 0.00kB/s 0:00:00 (xfer#2, to-check=3/5)
subdir/
subdir/C
0 100% 0.00kB/s 0:00:00 (xfer#3, to-check=1/5)
subdir/D
0 100% 0.00kB/s 0:00:00 (xfer#4, to-check=0/5)
Number of files: 5
Number of files transferred: 4
Total file size: 0 bytes
Total transferred file size: 0 bytes
Literal data: 0 bytes
Matched data: 0 bytes
File list size: 83
File list generation time: 0.001 seconds
File list transfer time: 0.000 seconds
Total bytes sent: 132
Total bytes received: 273
sent 132 bytes received 273 bytes 270.00 bytes/sec
total size is 0 speedup is 0.00
Notice that now the 'A*' exclude is respected! Can someone explain why they are performing differently?
A noticed that if I removed the quotes surrounding the excludes, then the QProcess run performs correctly.
In your command-line execution, bash interpreter performs a previous substitution and remove quotes, so they are not passed to rsync arg list.
Next script shows how bash substitution is performed:
[tmp]$ cat printargs.sh
#!/bin/bash
echo $*
[tmp]$ ./printargs.sh --exclude="A*"
--exclude=A*
Related
When I start passenger, multiple processes have the same connection.
bundle exec passenger-status
Requests in queue: 0
* PID: 13830 Sessions: 0 Processed: 107 Uptime: 1h 24m 22s
CPU: 0% Memory : 446M Last used: 41s ago
* PID: 13909 Sessions: 0 Processed: 0 Uptime: 41s
CPU: 0% Memory : 22M Last used: 41s ago
ss -antp4 | grep ':3306 '
ESTAB 0 0 XXX.XXX.XXX.XXX:55488 XXX.XXX.XXX.XXX:3306 users:(("ruby",pid=13909,fd=14),("ruby",pid=13830,fd=14),("ruby",pid=4672,fd=14)) #<= 4672 is preloader process?
ESTAB 0 0 XXX.XXX.XXX.XXX:55550 XXX.XXX.XXX.XXX:3306 users:(("ruby",pid=13830,fd=24))
ESTAB 0 0 XXX.XXX.XXX.XXX:55552 XXX.XXX.XXX.XXX:3306 users:(("ruby",pid=13909,fd=24))
Is the connection using port 55488 correct?
I believe that inconsistencies occur when multiple processes refer to the same connection. But I can't find the problem in my application.
I am using Rails 4.x and passenger 6.0.2
My goal is to pull the key items for my servers that we are tracking for KPIs. My plan is to run this daily via a cron job and then have it email me once a week to be able to be put in an excel sheet to grab the monthly KPIs. Here is what I have so far.
#!/bin/bash
server=server1
ports=({8400..8499})
for l in ${ports[#]}
do
echo "checking on '$l'"
sp=$(curl -k --silent "https://"$server":"$l"/server-status" | grep -E "Apache Server|Total accesses|CPU Usage|second|uptime" | sed 's/<[^>]*>//g')
echo "$l: $sp" >> kpi.tmp
grep -v '^$' kpi.tmp > kpi.out
done
The output shows like this.
8400:
8401: Apache Server Status for server1(via x.x.x.x)
Server uptime: 18 days 4 hours 49 minutes 37 seconds
Total accesses: 545 - Total Traffic: 15.2 MB
CPU Usage: u115.57 s48.17 cu0 cs0 - .0104% CPU load
.000347 requests/sec - 10 B/second - 28.6 kB/request
8402: Apache Server Status for server 1(via x.x.x.x)
Server uptime: 20 days 2 hours 20 minutes 26 seconds
Total accesses: 33 - Total Traffic: 487 kB
CPU Usage: u118.64 s49.41 cu0 cs0 - .00968% CPU load
1.9e-5 requests/sec - 0 B/second - 14.8 kB/request
8403:
8404:
8405: Apache Server Status for server1(via x.x.x.x)
Server uptime: 20 days 2 hours 20 minutes 28 seconds
Total accesses: 35 - Total Traffic: 545 kB
CPU Usage: u133.04 s57.48 cu0 cs0 - .011% CPU load
2.02e-5 requests/sec - 0 B/second - 15.6 kB/request
I am having a hard time figuring out how to filter the out put to the way i would like it. As you can see from my desired output, if it does not have any data to not put it in the file, cut some of the info out of the returned data.
I would like my output to look like this:
8401:server1(via x.x.x.x)
Server uptime: 18 days 4 hours 49 minutes 37 seconds
Total accesses: 545 - Total Traffic: 15.2 MB
CPU Usage: .0104% CPU load
.000347 requests/sec - 10 B/second - 28.6 kB/request
8402: server1(via x.x.x.x)
Server uptime: 20 days 2 hours 20 minutes 26 seconds
Total accesses: 33 - Total Traffic: 487 kB
CPU Usage: .00968% CPU load
1.9e-5 requests/sec - 0 B/second - 14.8 kB/request
8405: server1(via x.x.x.x)
Server uptime: 20 days 2 hours 20 minutes 28 seconds
Total accesses: 35 - Total Traffic: 545 kB
CPU Usage: .011% CPU load
2.02e-5 requests/sec - 0 B/second - 15.6 kB/request
I have got a .dat file with 2 columns and rows between 14000 to 36000 saved in file like below:
0.00 0.00
2.00 1.00
2.03 1.01
2.05 1.07
.
.
.
79.03 23.01
The 1st column is extension, the 2nd is strain. When I want to count gradient to designate Hooks Law of the plot, I use below code.
CCCCCC
Program gradient
REAL S(40000),E(40000),GRAD(40000,1)
open(unit=300, file='Probka1A.dat', status='OLD')
open(unit=321, file='result.out', status='unknown')
write(321,400)
400 format('alfa')
260 DO 200 i=1, 40000
read(300,30) S(i),E(i)
30 format(2F7.2)
GRAD(i,1)=(S(i)-S(i-1))/(E(i)-E(i-1))
write(321,777) GRAD(i,1)
777 Format(F7.2)
200 Continue
END
But after I executed it I got the warning
PGFIO-F-231/formatted read/unit=300/error on data conversion.
File name = Probka1A.dat formatted, sequential access record = 1
In source file gradient1.f, at line number 9
What can I do to count gradient by this or other way in Fortran 77?
You are reading from file without checking for the end of the file. Your code should be like this:
260 DO 200 i=1, 40000
read(300,*,ERR=400,END=400) S(i),E(i)
if (i>1) then
GRAD(i-1,1)=(S(i)-S(i-1))/(E(i)-E(i-1))
write(321,777) GRAD(i-1,1)
end if
777 Format(F7.2)
200 Continue
400 continue
I have this big file 1,000,000+ lines, which includes some memory data. For a certain use I need to convert g to mb, example:
DateAndTime#15/03/15 07:57:07
**********************
top - 07:57:27 up 2 days, 15:28, 18 users, load average: 4.65, 3.15, 2.11
Tasks: 774 total, 2 running, 771 sleeping, 0 stopped, 1 zombie
%Cpu(s): 12.8 us, 2.5 sy, 0.0 ni, 83.5 id, 1.2 wa, 0.0 hi, 0.1 si, 0.0 st
KiB Mem: 16327128 total, 16119192 used, 207936 free, 177868 buffers
KiB Swap: 36060156 total, 78552 used, 35981604 free. 6570548 cached Mem
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
26636 fpd 20 0 9987.4m 6.307g 47728 S 0.0 40.5 192:07.10 AppExe
29019 fpd 20 0 1752832 785848 45652 S 77.0 4.8 17:32.74 python
to:
26636 fpd 20 0 9987.4m 6307m 47728 S 0.0 40.5 192:07.10 AppExe
The problem is that the file is built in a non comfortable to use structure, the x.xxxG needs to be found first and only than replaced, thus taking a whole lot of time (via readline), also the rest of the file should stay the same.
The following works on Linux and OSX/BSD systems:
sed -E 's/(^| )([0-9]).([0-9]{3})g( |$)/\1\2\3m\4/g' infile > outfile
It makes certain assumptions:
any field of the form d.dddg (where d is a decimal digit) should be replaced (possibly even multiple occurrences on a single line - remove the g after the last / to replace at most one per line)
fields are space-delimited
If, by contrast, actual calculations need to be performed, awk is your friend.
I have a Django view which creates 500-5000 new database INSERTS in a loop. Problem is, it is really slow! I'm getting about 100 inserts per minute on Postgres 8.3. We used to use MySQL on lesser hardware (smaller EC2 instance) and never had these types of speed issues.
Details:
Postgres 8.3 on Ubuntu Server 9.04.
Server is a "large" Amazon EC2 with database on EBS (ext3) - 11GB/20GB.
Here is some of my postgresql.conf -- let me know if you need more
shared_buffers = 4000MB
effective_cache_size = 7128MB
My python:
for k in kw:
k = k.lower()
p = ProfileKeyword(profile=self)
logging.debug(k)
p.keyword, created = Keyword.objects.get_or_create(keyword=k, defaults={'keyword':k,})
if not created and ProfileKeyword.objects.filter(profile=self, keyword=p.keyword).count():
#checking created is just a small optimization to save some database hits on new keywords
pass #duplicate entry
else:
p.save()
Some output from top:
top - 16:56:22 up 21 days, 20:55, 4 users, load average: 0.99, 1.01, 0.94
Tasks: 68 total, 1 running, 67 sleeping, 0 stopped, 0 zombie
Cpu(s): 5.8%us, 0.2%sy, 0.0%ni, 90.5%id, 0.7%wa, 0.0%hi, 0.0%si, 2.8%st
Mem: 15736360k total, 12527788k used, 3208572k free, 332188k buffers
Swap: 0k total, 0k used, 0k free, 11322048k cached
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
14767 postgres 25 0 4164m 117m 114m S 22 0.8 2:52.00 postgres
1 root 20 0 4024 700 592 S 0 0.0 0:01.09 init
2 root RT 0 0 0 0 S 0 0.0 0:11.76 migration/0
3 root 34 19 0 0 0 S 0 0.0 0:00.00 ksoftirqd/0
4 root RT 0 0 0 0 S 0 0.0 0:00.00 watchdog/0
5 root 10 -5 0 0 0 S 0 0.0 0:00.08 events/0
6 root 11 -5 0 0 0 S 0 0.0 0:00.00 khelper
7 root 10 -5 0 0 0 S 0 0.0 0:00.00 kthread
9 root 10 -5 0 0 0 S 0 0.0 0:00.00 xenwatch
10 root 10 -5 0 0 0 S 0 0.0 0:00.00 xenbus
18 root RT -5 0 0 0 S 0 0.0 0:11.84 migration/1
19 root 34 19 0 0 0 S 0 0.0 0:00.01 ksoftirqd/1
Let me know if any other details would be helpful.
One common reason for slow bulk operations like this is each insert happening in its own transaction. If you can get all of them to happen in a single transaction, it could go much faster.
Firstly, ORM operations are always going to be slower than pure SQL. I once wrote an update to a large database in ORM code and set it running, but quit it after several hours when it had completed only a tiny fraction. After rewriting it in SQL the whole thing ran in less than a minute.
Secondly, bear in mind that your code here is doing up to four separate database operations for every row in your data set - the get in get_or_create, possibly also the create, the count on the filter, and finally the save. That's a lot of database access.
Bearing in mind that a maximum of 5000 objects is not huge, you should be able to read the whole dataset into memory at the start. Then you can do a single filter to get all the existing Keyword objects in one go, saving a huge number of queries in the Keyword get_or_create and also avoiding the need to instantiate duplicate ProfileKeywords in the first place.