My i3 dual screen workflow

Using the i3 tiling window manager on two screens („outputs“) can be challenging. The number of workspaces grows twice as fast than with a single screen setup and it is easy to lose track of the numbers and contents of workspaces. For me personally it is more intuitive to remember a certain sequence of workspaces on each screen, which seemingly extends above and below the workspace presently displayed. (It may be that this intuition has been coined by using the GNOME Shell over extended periods.)

In order to achieve a comparable user experience within i3, I use the following Python script with the keybindings presented below. The script is inspired by an article on the i3 homepage by user captnfab. It has one dependency: ziberna/i3-py, which can be installed with pip3 install i3-py. As is apparent from the keybindings, Ctrl+Alt+Up/Down are used to switch the present workspace on the focused output. With the same keys together with +Shift you can take the focused window with you.

#!/usr/bin/python3
#
# i3-switch-workspace.py
# by Fabian Stanke
#
# Sequentially switch workspaces on present output

import i3
import argparse

parser = argparse.ArgumentParser(
    description='i3 workspace switcher.')

parser.add_argument(
    '--move', action='store_true',
    help='take the focused container with you when moving.')
parser.add_argument(
    'direction', choices=['next', 'prev'],
    help='defines in which direction to switch.')

args = parser.parse_args()

workspaces = i3.get_workspaces()

# Determine focused workspace (and thus the focused output)
focused_ws = next((w for w in workspaces if w['focused']))

# Collect all workspaces of the focused output
ws_names = list(w['name'] 
                for w in workspaces 
                if w['output'] == focused_ws['output'])

# Determine position of focused worspace in that collection
idx = ws_names.index(focused_ws['name'])
target = focused_ws['name']

if args.direction == 'next':
	# Determine next workspace	

	if (idx + 1 < len(ws_names)):
		target = ws_names[idx + 1]
	else:
		# Determine last number used on this output
		maxidx = 1
		# Determine unused numbers 
		used = {}
		for w in workspaces:
			try:
				widx = int(w['name'])
				used[widx] = True
				if w['output'] == focused_ws['output']:
					maxidx = max(widx, maxidx)
			except:
				continue
		# Increment to create new name
		while used.get(maxidx, False):
			print(maxidx)
			maxidx += 1
		target = str(maxidx)


elif args.direction == 'prev':
	# Determine previous workspace

	if (idx - 1 >= 0):
		target = ws_names[idx - 1]
	#else remain at first workspace

if args.move:
	# Move the focused container to the target workspace first
	i3.command('move', 'container to workspace ' + target)

# Switch
#print("switch to " + target)
i3.workspace(target)

My preferred keybindings to actually use the above script are:

bindsym Ctrl+Mod1+Down exec i3-switch-workspace.py next
bindsym Ctrl+Mod1+Up exec i3-switch-workspace.py prev
bindsym Ctrl+Mod1+Shift+Down exec i3-switch-workspace.py --move next
bindsym Ctrl+Mod1+Shift+Up exec i3-switch-workspace.py --move prev

More parrallel programming

As a faithful follower of the Fedora Planet, today I stumbled upon a post about parallel programming in Python. Having made similar experiences myself, I would like to add another alternative for parallel programming in Python. I could have posted this in the comments of the original post, but this way the formatting is nicer.

My point is, that Parallel Python is a really nice library, but the functionality (at least at the level demonstrated here) is also provided by the multiprocessing module included with Python.

Here is my slightly modified implementation of the same program:

#!/usr/bin/python
 
"""
Another asynchronous python example
"""
 
import multiprocessing
import time
 
def background_stuff(num):
  time.sleep(5)
  return "%s I'm done" % num
 
if __name__ == "__main__":
    print "Start at:" , time.asctime(time.localtime())
    pool = multiprocessing.Pool()
 
    print "Start doing something"
    it = pool.imap(background_stuff, [(1,), (2,)])
 
    print "Do something..."
    print " ... do something else..."
 
    print it.next()
    print it.next()
 
    print "End at:", time.asctime(time.localtime())

How-to reduce fail2ban memory usage

This morning, when I did the routinely scan of the server’s resource usage history, I noticed a suspicious network activity between 1 and 5 am. Some reading of the latest log files soon identified the traffic to have been caused by a dictionary attack on my SSH server. I took the opportunity to extend my current setup for the script-kiddie enemy called fail2ban. This program monitors potentially any service’s log file for failed login attempts and if their number exceeds a certain limit, it blocks the issuing host using iptables rules.

Unfortunately the first start of the new service turned out to blow up the memory usage by about 100 MB which is unacceptable regarding the tight resources of my virtual private server. As I found out, others had similar experience and switched to DenyHosts due to this issue. My experience with setting up Trac two weeks ago taught me that a Python application (like fain2ban) might consume a lot of memory only because of the relatively oversized default stack size on Linux.

The means to reduce the default stack size in Linux are widely known to be the limits.conf file and the ulimit command. But how to use those two in my situation? The solution turns out to be a one-liner on Debian Lenny: All I had to do was to append the ulimit command to my /etc/default/fail2ban file.

This is the changed /etc/default/fail2ban file:

# This file is part of Fail2Ban.
#
# Fail2Ban is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# Fail2Ban is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Fail2Ban; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
#
# Author: Cyril Jaquier
#
# $Revision: 1.2 $

# Command line options for Fail2Ban. Refer to "fail2ban-client -h" for
# valid options.

FAIL2BAN_OPTS=""

ulimit -s 256

Using this sets the default stack size for the Python instances running fail2ban to 256 KB and lowers the memory consumption of fail2ban approximately by a factor of 10.