[gpfsug-discuss] Parse -Y command output

[Hannappel, Juergen]

Hi,
an example in python 2.7 (for Python 3 you need to add e.g. 
errors='ignore'
to the parameters of the Popen call to get the proper kind of
text stream as output.

import subprocess
import csv
mmlsfileset = subprocess.Popen(['/usr/lpp/mmfs/bin/mmlsfileset',
                                    args.filesystem,
                                    '-Y'],
                                   stdout=subprocess.PIPE)
    reader = csv.DictReader(mmlsfileset.stdout, delimiter=':')
    filesets=[]
    for row in reader:
        if row['status'] == 'Linked':
            filesets.append({'name':row['filesetName'],
                             'linkdir':urllib.unquote(row['path'])})
    mmlsfileset.wait()


----- Original Message -----
> From: "Billich Heinrich Rainer (ID SD)" <heinrich.billich at id.ethz.ch>
> To: "gpfsug main discussion list" <gpfsug-discuss at spectrumscale.org>
> Sent: Wednesday, 27 May, 2020 10:41:11
> Subject: [gpfsug-discuss] Parse -Y command output

> Hello,
> 
> I wonder if any python or bash functions exist to parse the output of
> mm-command's -Y format, i.e. colon-separated with HEADER rows. I would be nice
> to convert the output to a python list of named tuples or even better pandas
> dataframe.  I would like to access the  values by column name and row index.
> This would allow  to do quick scripts or  reports easily. Imagine using a
> jupyter notebook to dig into mmrepquota or mmlsdisk output, even doing charts,
> ..., easily.
> 
> I know the ReST interface does exisit, which I could call to get the data in
> Json format, but to parse the Y-format seems much less cumbersome and allows to
> collect the data at one time and process it later.
> 
> If you have other suggestions please let me know. What I would like to get:
> A list of NSDs which shows the connection to vdisk-set. Vdisk, recovery group
> and ESS system for each NSD. To get this I need to merge at least two tables.
> A list of user of fileset quota values highlighting those having non-default
> vaulues or close to the limit.
> The ability to get the data quickly in an uniform way into some tool, preferably
> python, to do analysis or formatting.
> 
> Cheers,
> 
> Heiner
> 
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