A blog and website by Peter Bengtsson

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I, multiple times per day, find myself wanting to find out what headers I get back on a URL but I don't care about the response payload. The command to use then is:

curl -v > /dev/null

That'll print out all the headers sent and received. Nice and crips.

So because I type this every day I made it into a shortcut script

cd ~/bin
echo '#!/bin/bash
> set -x
> curl -v "$@" > /dev/null
> ' > c
chmod +x c

If it's not clear what the code looks like, it's this:

set -x
curl -v "$@" > /dev/null

Now I can just type:


Or if I want to add some extra request headers for example:

c -H 'User-Agent: foobar'

I just learned a really good bash trick which is something I've wanted to have but didn't really appreciate that it was possible so I never even searched for it.

set -ex

Ok, one thing at a time.

set -e

What this does, at the top of your bash script is that it exits as soon as any line in the bash script fails.
Suppose you have a script like this:

git pull origin master
find . | grep '\.pyc$' | xargs rm

If the first line fails you don't want the second line to execute and you don't want the third line to execute either. The naive solution is to "and" them:

git pull origin master && find . | grep '\.pyc$' | xargs rm && ./

but now it's just getting silly. (and is it even working?)

What set -e does is that it exists if any of the lines fail.

set -x

What this does is that it prints each command that is going to be executed with a little plus.
The output can look something like this:

+ rm -f pg_all.sql pg_all.sql.gz
+ pg_dumpall
+ apack pg_all.sql.gz pg_all.sql
++ date +%A
+ s3cmd put --reduced-redundancy pg_all.sql.gz s3://db-backups-peterbe/Sunday/
pg_all.sql.gz -> s3://db-backups-peterbe/Sunday/pg_all.sql.gz  [part 1 of 2, 15MB]
 15728640 of 15728640   100% in    0s    21.22 MB/s  done
pg_all.sql.gz -> s3://db-backups-peterbe/Sunday/pg_all.sql.gz  [part 2 of 2, 14MB]
 14729510 of 14729510   100% in    0s    21.50 MB/s  done
+ rm pg_all.sql pg_all.sql.gz

...when the script looks like this:

set -ex
rm -f pg_all.sql pg_all.sql.gz
pg_dumpall > pg_all.sql
apack pg_all.sql.gz pg_all.sql
s3cmd put --reduced-redundancy pg_all.sql.gz s3://db-backups-peterbe/`date +%A`/
rm pg_all.sql pg_all.sql.gz

And to combine these two gems you simply put set -ex at the top of your bash script.

Thanks @bramwelt for showing me this one.

I have and have had many sites that I run. They're all some form of side-project.

What they almost all have in common is two things

  1. They have very little traffic (thus not particularly mission critical)
  2. I run everything on one server (no need for "spinning up" new VMs here and there)

Many many years ago, when current interns I work with were mere babies, I started a very simple "procedure".

  1. On the server, in the user directory where the site is deployed, I write a script called something like which is executable and does what the name of the script is: it upgrades the site.

  2. In the server's root home directory I write a script called which also does exactly what the name of the script is: it restarts the service.

  3. On my laptop, in my ~/bin directory I create a script called (*) which runs on the server and runs also on the server.

And here is, if I may say so, the cleverness of this; I use ssh to execute these scripts remotely by simply piping the commands to ssh. For example:

echo "./" | ssh -A
echo "./" | ssh

That's an example I use for Wish List Granted.

This works so darn well, and has done for years, that this is why I've never really learned to use more advanced tools like Fabric, Salt, Puppet, Chef or <insert latest deployment tool name>.

This means that all I need to do run a deployment is just type[ENTER] and the simple little bash scripts takes care of everything else.

The reason I keep these on the server and not on my laptop is simply because that's where they naturally belong and if I'm ssh'ed in and mess around I don't have to exit out to re-run them.

Here's an example of the I use for Wish List Granted:

cd generousfriends
source venv/bin/activate
git pull origin master
find . | grep '\.pyc$' | xargs rm -f
pip install -r requirements/prod.txt
./ syncdb --noinput
./ migrate webapp.main
./ collectstatic --noinput
./ compress --force
echo "Restart must be done by root"

I hope that, by blogging about this, that someone else sees that it doesn't really have to be that complicated. It's not rocket science and most complex tools are only really needed when you have a significant bigger scale in terms of people- and skill-complexity.

In conclusion

Keep it simple.

(*) The reason for the capitalization of my scripts is also an old habit. I use that habit to differentiate my scripts for stuff I install from any third parties.

As of moving over to my new EC2 server I now have all my working sites all under one server.

If I list all sites in /etc/nginx/sites-enabled/ I count 14 sites. This blog being one of many. More listed here.

All but one of these services are Python. One is a Node server. About half of the Python services are Django and the other half is Tornado. There are four persistant databases (Postgres, Redis, Memcache, MongoDB) and two message queues (RabbitMQ and Python RQ).

I have this little script called which does a decent job summorizing how much memory all of these take. Its output currently looks like this:

 Private  +   Shared  =  RAM used   Program
  6.5 MiB +  27.3 MiB =  33.7 MiB   postgres (5)
 40.1 MiB +  58.0 KiB =  40.1 MiB   memcached
 54.7 MiB +  37.5 KiB =  54.7 MiB   redis-server
 72.2 MiB + 849.0 KiB =  73.1 MiB   mongod
 82.4 MiB +   1.5 MiB =  83.9 MiB   rqworker (10)
605.6 MiB + 350.9 MiB = 956.5 MiB   python (61)
  1.9 GiB +  51.2 MiB =   2.0 GiB   uwsgi-core (26)
                          3.3 GiB                       

It's sorted by "RAM used" and I just showed here the bottom 7 ones.
Anyway, 3.3 Gb to run 14 sites isn't bad. All through one Nginx (which only uses 10Mb by the way).

The server is a Debian 7 on a reserved Large instance. I'll try to post an update later about this server with more details. I have a lot of work to do to set up all monitoring and backups for all these things.

(if you're wondering what you're doing here, jed is a hardcore text based editor for programmers)

Thanks to fellow Jed user and hacker Ullrich Horlacher I can now have local settings per directory.

I personally prefer 2 spaces in my Javascript. And thankfully most projects I work on agrees with that standard. However, I have one Mozilla project I work on which uses 4 spaces for indentation. So, what I've had to get used to to is to edit my ~/.jedrc every time I switch to work on that particular project. I change: variable C_INDENT = 2; to variable C_INDENT = 4; and then back again when switching to another project.

No more of that. Now I just add a file into the project root like this:

$ cd dev/airmozilla
$ cat
variable C_INDENT = 4;

And whenever I work on any file in that tree it applies the local override setting.

Here's how you can do that too:

First, put this code into your <your jed lib>/ (on my OSX, the jed lib is /usr/local/Cellar/jed/0.99-19/jed/lib/)

% load from current or parent directories
% but only if the user is the same
define load_local_config() {
  variable dir = getcwd();
  variable uid = getuid;
  variable jsl,st;
  while (dir != "/" and strlen(dir) > 1) {
    st = stat_file(dir);
    if (st == NULL) return;
    if (st.st_uid != uid) return;
    jsl = dir + "/";
    st = stat_file(jsl);
    if (st != NULL) {
      if (st.st_uid == uid) {
    dir = path_dirname(dir);

Then add this to the bottom of your ~/.jedrc:

define startup_hook() {
  load_local_config(); %

Now, go into a directory where you want to make local settings, create a file called and fill it to your hearts content!

I'm quite fond of It's fast. It's reliable. And it's got nice keyboard shortcuts that work for my taste.

So, I created a little program to quickly throw things into hastebin. You can have one too:

First create ~/bin/hastebinit and paste in:


import urllib2
import os
import json

URL = ''

def run(*args):
    if args:
        content = [open(x).read() for x in args]
        extensions = [os.path.splitext(x)[1] for x in args]
        content = []
        extensions = [None]

    for i, each in enumerate(content):
        req = urllib2.Request(URL, each)
        response = urllib2.urlopen(req)
        the_page =
        key = json.loads(the_page)['key']
        url = "" % key
        if extensions[i]:
            url += extensions[i]
        print url

if __name__ == '__main__':
    import sys

Then run: chmod +x ~/bin/hastebinit

Now you can do things like:

$ cat ~/myfile | hastebinit
$ hastebinit < ~/myfile
$ hastebinit ~/myfile myotherfile

Hopefully it'll one day help at least one more soul out there!

This is how you check if a command (with or without any output) exited successfully or if it exited with something other than 0, in bash:

if [ "$WORKED" != 0 ]; then
  echo "FAILED"
  echo "WORKED"

But how do you inspect this on the command line? I actually don't know, until it hit me. The simplest possible solution:

$ ./someprogram && echo worked || echo failed

What a great low-tech solution. I just works. If you're on OSX, you can nerd it up a bit more:

$ ./someprogram && say worked || say failed

pgFouine in action on my server
pgFouine is a PostgreSQL log analyzer. You basically, configure your Postgres server to be very verbose about all statements. Then, you simply run the pgfouine.php command against the log file and it spits out a page like this:

Running all this verbose logging will obviously slow down the database server a bit so I'm only going to be running this temporarily. The overhead is actually pretty small but it's also piling on quite a few bytes in terms of the size of the log file.

So, at the time of writing, it's been about 1 day running and it has captured about 70,000 queries (by the time you look at the file it might have gone up significantly). I haven't started actually looking at it in detail yet but it's clear that there's some use of the LIKE operator that Postgres spends most of its time on.

You can configure your pgFouine to filter on specific databases. I have not done so because I'm at the moment just interested in what the whole database server is getting up to. Most of these guilty queries comes from the Crosstips site. Maybe it's time to optimize the worst performing queries there a bit.


After running for 24 hours, I did some low-hanging fruit optimization to the biggest culprits and reset the logs. The first 24 hours report is still here:


I've stopped logging all queries now. The results are still there. I'm quite pleased with the results so far.

This is part 2. Part 1 is here about how I managed to make this site fast.

The web framework powering this site is Django and in front of that is Nginx which serves all the static content (once before Amazon CloudFront CDN takes over) and all non-static traffic is passed on to a uWSGI daemon which is running 6 worker processes. The database that stores the content is PostgreSQL and all caching is done in Redis. Actually another Redis database is used for other things such as maintaining a quick look-up index of keywords to primary keys so that I can quickly mesh together blog posts by keywords.

However, as we all know the deciding factor of a web sites server-side speed is effectively the speed of the database or any other disk-bound I/O device. To remedy this I've set up some practical caching strategies which I'm quite happy with.

So, how fast is it? Here's an ab stress test against home page with 10,000 requests spread across 10 concurrent users:

Document Path:          /
Document Length:        73272 bytes

Concurrency Level:      10
Time taken for tests:   4.426 seconds
Complete requests:      10000
Failed requests:        0
Write errors:           0
Total transferred:      734250000 bytes
HTML transferred:       732720000 bytes
Requests per second:    2259.59 [#/sec] (mean)
Time per request:       4.426 [ms] (mean)
Time per request:       0.443 [ms] (mean, across all concurrent requests)
Transfer rate:          162022.11 [Kbytes/sec] received

I could probably make that 2,300 requests/second to 3,000 or 4,000 if I just increase the number of workers. However, that costs memory and since I'm currently running 19 other uWSGI workers on this server that all (all 25) in total take up a steady 1.4 Gb I don't feel like increasing that number much more. Besides since this site doesn't really get any traffic, I'm not so concerned about massive throughput on concurrent benchmarks but more about serving each and every page as fast as possible the few times it's called.

Every single page on this site is behind some sort of internal cache. The only time the PostgreSQL is involved is in rendering a page is when it's first requested after a comment has been entered or I've added (or edited) a new post. Thing is, I don't want to be inconvenienced by a stupid cache that forces me to wait an hour every time I change something. No, instead lots of Django database model signals are put in place that fire off cache invalidation when certain pieces of data is changed. You can see the code for that here.

So, for the home page for example: For each request, a small piece of Python code checks the Redis for what the latest comment add-date is and based on that tells the Django page_cache decorator to either render the page as normal or to serve the whole HTML payload from Redis. In other words, on a successful cache "hit" it actually needs two Redis look-ups. Even that could be improved and blindly just spare these look-ups by serving from the workers allocated Python memory instead but that would make things fragile, hard to unit test and it would only make the benchmarks faster which is not necessary.

The most important thing to optimize on a web site is the static content. Well, there's little point in serving the static content fast if it takes 3 seconds to say what static content to serve. Also, a fast website is likely to appear more favorable on the Google bot which effectively makes the site appear higher on Google searches.

In the next part, I'll try to share more in-depth technical bits and pieces of what I actually did although they're no secrets I think some of them are best practice and even senior web developers sometimes get them wrong.

Short answer: about 5%

I had a few minutes and wanted to see if changing from Apache + mod_wsgi to Nginx + gunicorn would make the otherwise slow site any faster. It's not this site but another Django site for work (which, by the way, doesn't have to be fast). It's slow because it doesn't cache any of the SQL queries.

# with Apache + mod_wsgi
$ ab -n 1000 -c 10 http://thelocaldomain/
Requests per second:    39 [#/sec] (mean)
# Uses about 110 Mb

That's after running multiple times and roughly averaging the requests per seconds.

# with Nginx + guncorn --workers=4
$ ab -n 1000 -c 10 http://thelocaldomain/
Requests per second:    41 [#/sec] (mean)
# uses about 70 Mb

So, if you want to make a site fast forget about how the code is being served until all the slow db I/O is taken care of properly.