Separation Anxiety: A Tutorial for Isolating Your System with Linux Namespaces

2017-02-06 18 min read GuestPost Linux Vurtualization

With the advent of tools like Docker, Linux Containers, and others, it has become super easy to isolate Linux processes into their own little system environments. This makes it possible to run a whole range of applications on a single real Linux machine and ensure no two of them can interfere with each other, without having to resort to using virtual machines. These tools have been a huge boon to PaaS providers. But what exactly happens under the hood?

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journalctl command to see kernel messages

2017-01-30 1 min read Fedora

Sometimes, there are just too many messages in journalctl output and it becomes a mystery game to search for the messages you are looking for. But luckily you do not need to use grep to find the right message. Here is example of what I had to do when I was looking for kernel messages.

journalctl _TRANSPORT=kernel
# To see all the fields, you can use the verbose mode
journalctl _TRANSPORT=kernel -o verbose

# And the filter on priority if needed to get the messages you need
journalctl _TRANSPORT=kernel PRIORITY=4

# and follow
journalctl _TRANSPORT=kernel PRIORITY=4 -f -l

Get disk usage for all the containers with python script

2017-01-16 1 min read Python

With my increasing love for python, here is my attempt to get the disk usage of all the containers on some host. Well, since the requirements vary for everyone, so this script is far from complete.

import docker
import json

# We will connect to 192.168.122.1 for docker daemon. If that is not the case,
# then change the below.

client = docker.DockerClient(base_url="tcp://192.168.122.1:4243")

# Get list of all containers.
cls=client.containers.list()
stats={}

# And now we will iterate over that list to get stats for all the containers.
for val in cls:
    print (val.name)
    stats[val.name] = val.stats(stream=False)
    # Get the disk usage for root and /tmp from containers with docker.exec
    stats[val.name]['df-root'] = ( str(val.exec_run(r'df -kh --output="size,used,avail,pcent" /', stream=False).splitlines()[1]).replace("'","").split()[1:] )
    stats[val.name]['df-tmp'] = ( str((val.exec_run(r'df -kh --output="size,used,avail,pcent" /tmp ', stream=False).splitlines()[1:]+[''])[0]).replace("'","").split()[1:] )

# Now if you want, we have dict of all the data and we can process the
# way we like it, for example create a html table for disk usage only.
print ('<table>')
for st in stats:
    print ('<tr>')
    print ("<td>Root-%s</td>"%(st))
    for i in stats[st]['df-root']:
        print ('<td>%s</td>'%(i) )
    print ('</tr>')
    print ('<tr>')
    print ("<td>tmp-%s</td>"%(st))
    for i in stats[st]['df-tmp']:
        print ('<td>%s</td>'%(i) )
    print ('</tr>')

print ('</table>')
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