I'm working on a simulation model, where I want to determine when the storage IOPS capacity becomes a bottleneck (e.g. and HDD has ~150 IOPS, while an SSD can have 150,000). So I'm trying to come up with a way to benchmark IOPS in a command (git) for some of it's different operations (push, pull, merge, clone).
So far, I have found tools like iostat, however, I am not sure how to limit the report to what a single command does.
The best idea I can come up with is to determine my HDD IOPS capacity, use time on the actual command, see how long it lasts, multiply that by IOPS and those are my IOPS:
HDD ->150 IOPS
time df -h
real 0m0.032s
150 * .032 = 4.8 IOPS
But, this is of course very stupid, because the duration of the execution may have been related to CPU usage rather than HDD usage, so unless usage of HDD was 100% for that time, it makes no sense to measure things like that.
So, how can I measure the IOPS for a command?
There are multiple time(1) commands on a typical Linux system; the default is a bash(1) builtin which is somewhat basic. There is also /usr/bin/time which you can run by either calling it exactly like that, or telling bash(1) to not use aliases and builtins by prefixing it with a backslash thus: \time. Debian has it in the "time" package which is installed by default, Ubuntu is likely identical, and other distributions will be quite similar.
Invoking it in a similar fashion to the shell builtin is already more verbose and informative, albeit perhaps more opaque unless you're already familiar with what the numbers really mean:
$ \time df
[output elided]
0.00user 0.00system 0:00.01elapsed 66%CPU (0avgtext+0avgdata 864maxresident)k
0inputs+0outputs (0major+261minor)pagefaults 0swaps
However, I'd like to draw your attention to the man page which lists the -f option to customise the output format, and in particular the %w format which counts the number of times the process gave up its CPU timeslice for I/O:
$ \time -f 'ios=%w' du Maildir >/dev/null
ios=184
$ \time -f 'ios=%w' du Maildir >/dev/null
ios=1
Note that the first run stopped for I/O 184 times, but the second run stopped just once. The first figure is credible, as there are 124 directories in my ~/Maildir: the reading of the directory and the inode gives roughly two IOPS per directory, less a bit because some inodes were likely next to each other and read in one operation, plus some extra again for mapping in the du(1) binary, shared libraries, and so on.
The second figure is of course lower due to Linux's disk cache. So the final piece is to flush the cache. sync(1) is a familiar command which flushes dirty writes to disk, but doesn't flush the read cache. You can flush that one by writing 3 to /proc/sys/vm/drop_caches. (Other values are also occasionally useful, but you want 3 here.) As a non-root user, the simplest way to do this is:
echo 3 | sudo tee /proc/sys/vm/drop_caches
Combining that with /usr/bin/time should allow you to build the scripts you need to benchmark the commands you're interested in.
As a minor aside, tee(1) is used because this won't work:
sudo echo 3 >/proc/sys/vm/drop_caches
The reason? Although the echo(1) runs as root, the redirection is as your normal user account, which doesn't have write permissions to drop_caches. tee(1) effectively does the redirection as root.
The iotop command collects I/O usage information about processes on Linux. By default, it is an interactive command but you can run it in batch mode with -b / --batch. Also, you can a list of processes with -p / --pid. Thus, you can monitor the activity of a git command with:
$ sudo iotop -p $(pidof git) -b
You can change the delay with -d / --delay.
You can use pidstat:
pidstat -d 2
More specifically pidstat -d 2 | grep COMMAND or pidstat -C COMMANDNAME -d 2
The pidstat command is used for monitoring individual tasks currently being managed by the Linux kernel. It writes to standard output activities for every task selected with option -p or for every task managed by the Linux kernel if option -p ALL has been used. Not selecting any tasks is equivalent to specifying -p ALL but only active tasks (tasks with non-zero statistics values) will appear in the report.
The pidstat command can also be used for monitoring the child processes of selected tasks.
-C commDisplay only tasks whose command name includes the stringcomm. This string can be a regular expression.
Related
Frequently facing the issue of the kswapd0 running in one of the linux machines, what could be the reason for that, by looking more at the issue, understood that it will be because of the less memory, I tried the below options to avoid it:
echo 1 > /proc/sys/vm/drop_caches
cat /proc/sys/vm/drop_caches
sudo cat /proc/sys/vm/swappiness
sudo sysctl vm.swappiness=60
but it does not yield fruitful results, what could be the best method to avoid it, or its something some action needs to be taken on the RAM memory of the machine, Any suggestions on this ?
Every time we observe , all the running apps are killed automatically and kswapd0 occupies the complete cpu and memory.
I want to run 100 networking (non cpu intense) jobs in parallel and want to understand the best approach.
Specifically is it possible to run 100+ jobs using xargs and what are the drawbacks?
I understand that there is a point where there is more context switching being done then actual packet processing.
How to understand where that point is and what is the best way to minimise it?
For example, are there better tools to use other then xargs, etc?
Better will often be a matter of taste.
Using GNU Parallel you can do something like this to fetch 100 images in parallel:
seq 1000 | parallel -j100 wget https://foo.bar/image{}.jpg
If you want data from 100 servers and you get a full line every time:
parallel -a servers.txt -j0 --line-buffer my_connect {}
Or:
parallel -a servers.txt -j0 --line-buffer --tag my_connect {}
GNU Parallel is a general parallelizer and makes is easy to run jobs in parallel on the same machine or on multiple machines you have ssh access to.
If you have 32 different jobs you want to run on 4 CPUs, a straight forward way to parallelize is to run 8 jobs on each CPU:
GNU Parallel instead spawns a new process when one finishes - keeping the CPUs active and thus saving time:
Installation
For security reasons you should install GNU Parallel with your package manager, but if GNU Parallel is not packaged for your distribution, you can do a personal installation, which does not require root access. It can be done in 10 seconds by doing this:
(wget -O - pi.dk/3 || curl pi.dk/3/ || fetch -o - http://pi.dk/3) | bash
For other installation options see http://git.savannah.gnu.org/cgit/parallel.git/tree/README
Learn more
See more examples: http://www.gnu.org/software/parallel/man.html
Watch the intro videos: https://www.youtube.com/playlist?list=PL284C9FF2488BC6D1
Walk through the tutorial: http://www.gnu.org/software/parallel/parallel_tutorial.html
Sign up for the email list to get support: https://lists.gnu.org/mailman/listinfo/parallel
I would like to keep a process running for a long time (e.g., more than half an hour). My program is gpg. If I encrypt a 500MB file using gpg elgamal encryption, it takes around 1-2 minutes (compression is turned off). To increase the running time, I can only create a file with a few GB, which is not desirable. Is there any other way to make this gpg program run for longer time?
I believe, by default, gpg takes its input from standard input and sends output to standard output.
So you could make it run forever with something like:
gpg --encrypt </dev/urandom >/dev/null
To use that for consuming CPU for an hour (for example), you could create a script like:
gpg --encrypt </dev/urandom >/dev/null &
pid=$!
sleep 3600
kill -9 ${pid}
https://unix.stackexchange.com/questions/18869/slow-down-a-process-without-affecting-other-processes
CPULimit might do what you need without affecting other processes:
http://www.cyberciti.biz/faq/cpu-usage-limiter-for-linux/
You start the program, then run cpulimit against the program name or PID, specifying what percentage you want it limited. Note the percentage is of all cores; so if you have 4 cores, you could use 400%.
I want to check the network bandwidth used by my process.
For this i found that nethogs tool is useful. Using this tool i can see which process is eating up a network bandwidth and process behaviour.
But how do I capture data from nethogs for a my process and store it into log file ?
You can run nethogs in background in tracemode and write output to a file like this:
sudo nethogs -t eth1 &> /var/tmp/nethogs.log &
Download and build the nethogs-parser as described here.
Then after you have accumulated enough data you can run the parser to see the results:
./hogs -type=pretty /var/tmp/nethogs.log
Make sure to kill the running nethogs process when you are done collecting data.
More info here on automating the task.
I dont know when these options got implemented but you can use nethogs -t or nethogs -b, the pid and user are strangely placed at the end of the pid command string, but easy enough to parse.
I think you need to use the latest cvs version 0.8.1-SNAPSHOT
You can use this command to capture output:
nethogs -d 5 | sed 's/[^[:print:][:cntrl:]]//g' > output.txt
The right command of nethogs is
nethogs -d 1 eth0 > output.txt
You need to specify the network interface otherwise, the default interface eth0 will be used. Sometime, nethogs might not show the proper output because of the network interface. It is always better to provide the network interface and generate some traffic during the experimentation. You can print the output to a file by adding > output.txt
-d argument specifies how frequently the output will be shown. Here, I gave 1, this indicates that the output will be shown per second.
Hope this might be useful.
Is there any ready-to-use solution to log the memory consumption from the start of the system? I'd like to log the data to simple text file or some database so I can analyze it later.
I'm working on Linux 2.4-based embedded system. I need to debug the problem related to memory consumption. My application automatically start on every system start. I need the way to get the data with timestamps from regular intervals (as often as possible), so I can track down problem.
The symptoms of my problem: when system starts it launched my main application and GUI to visualize the main parameters of the system. GUI based on GTK+ (X server). If I disable GUI and X server then my application works OK. If I enable GUI and X server it does not work when I have 256 MiB or 512 MiB of physical memory installed on the motherboard. If I have 1 GiB of memory installed then everything is OK.
The following script prints time stamps and a header.
#!/bin/bash -e
echo " date time $(free -m | grep total | sed -E 's/^ (.*)/\1/g')"
while true; do
echo "$(date '+%Y-%m-%d %H:%M:%S') $(free -m | grep Mem: | sed 's/Mem://g')"
sleep 1
done
The output looks like this (tested on Ubuntu 15.04, 64-bit).
date time total used free shared buffers cached
2015-08-01 13:57:27 24002 13283 10718 522 693 2308
2015-08-01 13:57:28 24002 13321 10680 522 693 2308
2015-08-01 13:57:29 24002 13355 10646 522 693 2308
2015-08-01 13:57:30 24002 13353 10648 522 693 2308
A small script like
rm memory.log
while true; do free >> memory.log; sleep 1; done
I am a big fan of logging everything and I find it useful to know which processes are using the memory and how much each process is using (as well as sumary statistics). The following command records a top printout ordered by memory consumption every 0.5 seconds:
top -bd0.5 -o +%MEM > memory.log
Just note that the log file will grow a lot faster than if you only store the total memory utilization statistics so be sure you don't run out of disk space.
There's a program called
sar
on *nix systems. You could try to use that to monitor memory usage. It takes measurements at regular intervals. Do a
man sar
for more details. I think the option is -r for taking memory measurements, -i to specify the interval you'd like.
I think adding a crontab entry will be enough
*/5 * * * * free -m >> some_output_file
There are other tools like SeaLion, New Relic, Server Density etc which will almost do the same but are much easier to install and configure. My favorite is SeaLion, as it being free and also it gives a awesome timeline view of raw outputs of common linux commands.
You could put something like
vmstat X >> mylogfile
into a startup script. Since your application is already in startup you could just add this line to the end of the initialization script your application is already using.
(where X is # of seconds between log messages)
To periodically log the memory usage efficiently, I combined another answer here with a method to only retain the top-K memory-using processes.
top -bd 1.5 -o +%MEM | grep "load average" -A 9 > memory_usage.log
This command will record, every 1.5s, the top header information and the 3 highest memory-consuming processes (there's a 6-line offset for top's header information). This saves lots of disk space over recording top's information for every process.
So I know that I am late to this game, but I just came up with this answer, as I needed to do this, and really didn't want the extra fields that vmstat, free, etc... all will seem to output without excess filtering. So here is the answer that I came up with:
top -bd 0.1 | grep 'KiB Mem' | cut -d' ' -f10 > memory.txt
OR:
top -bd 0.1 | grep 'KiB Mem' | cut -d' ' -f10 | tee memory.txt
the standard output from top when grep ing with Kib Mem is:
KiB Mem : 16047368 total, 8708172 free, 6015720 used, 1323476 buff/cache
By running this through cut, we filter down to literally just the number prior to used
The user can indeed modify the 0.1 to another number in order to run different capture sample rates. In my case I wanted to use top also because you can run memory stats faster than 1 second per capture, as you can see here I wanted to capture a stat every 1/10th of a second.
NOTES:
It does turn out that piping through cut cause MASSIVE delay in getting anything out to file. As we later found out, it is much faster to leave out the cut command during data acquisition, then perform the cut command on the output file later.
Also, we had no need for timestamps in our tests.
This thus looks as follows:
Begin Logging:
top -bd 0.1 | grep 'KiB Mem' | tee memory_raw.txt
Exit Logging:
ctrl-z (to exit logging)
Filter:
2 levels of cut (filtering), first by comma, then by space. This is due to the alignment of top and provides much cleaner output:
cut memory_raw -d',' -f3 | tee memory_used_withlabel.txt
cut memory_used_withlabel.txt -d' ' -f3 | tee memory_used.txt