Monitor Process Resources Usage
===============================

| Bench aims to be a handy tool with these functions:
|  - Monitor CPU time (user time, system time, real time)
|  - Monitor memory usage (virtual memory usage, resident memory usage)
|  - Output to TSV(tab-delimited files)
|  - Output benchmark metrics
|  - Visualize performance metrics (memory consumptions) over time

Examples
========

We showed several examples below. **Please note that all output are
tabularized for demonstration purpose.**

-  Example 1: simple command.

| This will start the process *sleep* for 2 seconds. The tabular output
  below was from the actual command:
  ``monitor.py sleep 2 2>&1 |column -t -s $'\t'``.
| If you simply run ``monitor.py sleep 2``, you will get tab-deliminated
  outputs in standard error (stderr).

::

        $> monitor.py sleep 2
        pid     ppid    utime  stime  rtime         rss     vms      maxRss  maxVms   avgRss    avgVms     cwd                                cmd
        133692  133675  0.0    0.0    1.9368159771  774144  6066176  774144  6066176  774144.0  6066176.0  /home/zhanxw/mycode/bench/scripts  sleep 2

-  Example 2: complex shell commands with sampling interval equaling to
   0.1 second

This example will use shell to start 3 processes: ``sleep 2``,
``sleep 4`` and ``seq 1000000``. You can see bench can monitor all 4
processes all together.

::

        $> monitor.py sh -c 'sleep 2 & sleep 4 & seq 1000000 >/dev/null & wait'
        pid     ppid    utime  stime  rtime            rss     vms      maxRss  maxVms   avgRss    avgVms     cwd                                cmd
        135004  134985  0.0    0.0    3.9532430172     798720  4558848  798720  4558848  798720.0  4558848.0  /home/zhanxw/mycode/bench/scripts  sh -c sleep 2 & sleep 4 & seq 10000000 >/dev/null & wait
        135006  135004  0.0    0.0    3.95348381996    655360  6066176  655360  6066176  655360.0  6066176.0  /home/zhanxw/mycode/bench/scripts  sleep 4
        135005  135004  0.0    0.0    1.83160495758    774144  6066176  774144  6066176  774144.0  6066176.0  /home/zhanxw/mycode/bench/scripts  sleep 2
        135007  135004  0.05   0.0    0.0599648952484  720896  6090752  720896  6090752  720896.0  6090752.0  /home/zhanxw/mycode/bench/scripts  seq 10000000

-  Example 3: generate performance metrics to external file

Here we used a small program, burnCpu. It will keep CPU running for
several seconds. Its source code is under src/.

The option ``-t`` will enable outputting traces. That means at several
time stops, performance metrics of each processes will be outputted to
the standard error as well as a separate comma-separated file,
``$prefix.trace.csv``.

The option ``-g`` will generate a graph which contains several
sub-figures, including timings for each processes, memory consumption
for each processes, and memory consumption over the processing running
time.

The option ``-o`` will specify the output prefix. The default value will
be ``bench``, meaning, you will get ``bench.csv``. You can overwrite
this value by using ``-o`` option.

::

        $> monitor.py -t -g -o burnCpu ./burnCpu
        pid     ppid    utime  stime  rtime            rss      vms       cwd                                cmd
        135471  135454  0.04   0.0    0.0441780090332  1449984  12984320  /home/zhanxw/mycode/bench/scripts  ../src/burnCpu
        135471  135454  0.2    0.0    0.205282926559   1449984  12984320  /home/zhanxw/mycode/bench/scripts  ../src/burnCpu
        135471  135454  0.38   0.0    0.381079912186   1449984  12984320  /home/zhanxw/mycode/bench/scripts  ../src/burnCpu
        ...

Additional result are stored in *burnCpu.csv*, *burnCpu.trace.csv* in
the Comma-separated format (CSV).

*burnCpu.csv* file content

::

    pid,ppid,utime,stime,rtime,rss,vms,maxRss,maxVms,avgRss,avgVms,cwd,cmd
    144433,144416,5.4,0.0,5.40555810928,1404928,12984320,1404928,12984320,1404928.0,12984320.0,/home/zhanxw/mycode/bench/scripts,../src/burnCpu

*burnCpu.trace.csv* file content

::

    pid,ppid,utime,stime,rtime,rss,vms,cwd,cmd
    144433,144416,0.03,0.0,0.0423669815063,1404928,12984320,/home/zhanxw/mycode/bench/scripts,../src/burnCpu
    144433,144416,0.19,0.0,0.20046210289,1404928,12984320,/home/zhanxw/mycode/bench/scripts,../src/burnCpu
    144433,144416,0.36,0.0,0.373480081558,1404928,12984320,/home/zhanxw/mycode/bench/scripts,../src/burnCpu
    ...

When ``-g`` optioned is specified, bench will generate several
performance metrics in the file *burnCpu.trace.csv*:

|image|

Notes
=====

| To benchmark a complex command or combinations of commands, you can
  use shell (sh or bash) . For example, you can use "sh -c 'command arg1
  arg2 ... '" (see Example 2).
| Bench requires `psutil <https://pypi.python.org/pypi/psutil>`__ to
  collect basic performance metrics, and
| requires `numpy <http://www.numpy.org/>`__ and
  `pandas <http://pandas.pydata.org/>`__ for statistical calculations.
| In this release, we used psutil 3.1.1, numpy 1.8.2, pandas 0.16.2 and
  matplotlib 1.4.3.

Contact
=======

| For questions or commend, please visit bench github repo:
| `repo <https://github.com/zhanxw/bench>`__

| or email to:
| Xiaowei Zhan 

.. |image| image:: http://zhanxw.com/bench/burnCpu.png
