Baptiste Fontaine’s Blog  (back to the website)

Plotting the Evolution of Movies Durations (1916-2018)

Two weeks ago I created and put online a box plot of 73k movies durations across the time.

It started with a question: “Are movies getting longer and longer?”. Spoiler: Not really, except maybe in the last couple of years.

I used Wikidata’s online query service to export all movies then filtered those with both a publication date and a duration. This gave me a large JSON which I processed using Python in order to extract a couple numbers for each year: min, max, median, first and third quartiles.

The result fits in a small JSON file, which I then used to build a D3 using a few lines of JS. I used colorbrewer2 to find a colorblind-safe color palette.

You can see the result as well as the JS code on Observable.

To avoid outliers such as “Modern Times Forever” (240 hours) or “The Burning of the Red Lotus Temple”, I used the interquartile range (IQR) to limit the size of the bars: any movie whose duration is below Q1-1.5×IQR or above Q3+1.5×IQR (where Q1 is the first quartile and Q3 the third one) is not shown.


As one can see on the graph, the median duration quickly rises from 50 to 95 minutes from the 1920s to the 1960s, then doesn’t move much except in the last two years.


The first obvious limitation is the data: Wikidata has 200k+ movies but only 73k have both a publication date and a duration. It’s not complete enough to let me filter by movie type; e.g. feature film vs. others.

IMDb lists 5.3M titles (most of which are TV episodes), but there’s no way to export them all.

In the end, there’s no way to know how representative Wikidata’s movies dataset is. It does give a hint, but this graph is not a definitive answer to the original question.

See the Python code.

A Quick Link Previewer for Mattermost

Mattermost is a Slack-like self-hosted and open-source alternative. We use it at work but for some reason link previews don’t work. Before diving into Mattermost’s internals I wanted to see if I could write a quick workaround using the fact that Mattermost does show an image if you post a link ending with .png or .jpg.

The Current Situation

When you post an image link, Mattermost makes a request to show it in the application. It detects those images using a regexp; not by e.g. sending a HEAD request to get the content type. If you have an image URL that doesn’t end with common extentions Mattermost won’t show it.

Mattermost doesn’t serve you a preview of the image; it rather gives you an img with the original URL. That means every single person reading the channel will request the image from its original location. Slack, on the other hand, fetch images, cache them, and serves them from its own domain, Slack uses a custom user-agent for its request so you know where it comes from.

User-Agent: Slackbot-LinkExpanding 1.0 (…)

Mattermost, on the other hand, can’t use a custom user-agent because the request is done by your browser. The only thing distinguishing Mattermost’s request for a preview and any other request is it asks for an image:

Accept: image/webp,image/*,*/*;q=0.8

The header above is Chrome telling the Web server it can deal with WebP images, then images in any format, then anything; in that order. Note it explicitly says it accepts WebP images because some browsers don’t support the format.

Unfortunately not all browsers are explicit. Firefox sends Accept: */* since Firefox 47 and so did IE8 and earlier versions. In those cases we can’t really do anything beside complicated rules based on the user-agent and other headers.

Proxying Requests

If we know how to tell if a request comes from Mattermost asking for an image preview rather than a “normal” user we can serve different contents to them: a link preview as an image to Mattermost, and the normal content to the user.

All we have to do is to make some sort of intelligent proxy. Using Flask we can make something like this:

from flask import Flask

app = Flask(__name__)

def main(path):
    if not request.headers.get("Accept", "").startswith("image/"):
        return redirect(path)

    return "Hello, I'm an image"

This is a small web application that takes any route in the form of /<url>/p.png and either redirects you to that URL if your Accept header doesn’t start with image/; either serves you a Hello, I'm an image page.

All we have to do now is to return an actual image in lieu of that placeholder text. I used Requests and Beautiful Soup to fetch and parse webpages, and Pillow to generate images.

When one requests, the app fetches that URL; parses it to extract its title and some excerpt; write that on a blank image; and serves it.

Extracting the title is as easy as grabbing that title HTML tag. If available, I also try the og:title and twitter:title meta tags. If none of those are available, I fallback on the first h1 or h2 tag.

Getting an usable excerpt is not too hard; here again I search for common meta tags: description, og:description, twitter:description. If I can’t get any of them I take the first p that looks long enough.

You can check that code on GitHub.

Generating Images

The Pillow library makes it easy to write text on an image. I replaced the (ugly) default font with Alegreya. The tricky part is mostly to fit the text on the image. I used a combination of Draw#textsize to get the dimensions of some text as if it were written on the image and Python’s textwrap module to cut the title and excerpt so that they wouldn’t cross the right side of the image.

I used fixed dimensions for all images (400×70) and kept a small padding along their sides. Previews with small or missing excerpts get some unused white space at the bottom; this could be fixed by pre-computing the final size of the image before creating it. preview’s generated preview
Mattermost's GitHub repo preview
GitHub repos have a very small excerpt

On most websites the preview works fine. We could tweak the text size as well as add a favicon or an extracted image.

This post's preview
This post’s generated preview


In the end it took a couple hours to have a working prototype. Most of that time was spent dealing with encoding issues and trying various webpages to find edge cases.

The result is acceptable; it has the issue of not being accessible at all but that’s still better than nothing.

The source code is on GitHub.

Add This Directory to Your PATH

You’ve probably read this instruction quite a few times in online tutorials about installing command-line tools. What is this PATH for? Why should we add directories “to” it? This is the subject of this post.

How the Shell Finds Executables

When you type something like ls, your shell has to find what is this ls program you’re trying to run. Typical shells only have a handful predefined commands like cd; most of the commands you use everyday are standalone programs.

In order to find that ls program; your shell looks in a few directories. Those directories are stored in an environment variable called PATH; you can look up its value in most shells using the following command:

echo $PATH

You can see it contains a list of paths separated by colons. Finding a program in these is just a matter of checking each directory to see if it contains an executable with the given name. This lookup is implemented by the which program. The algorithm is pretty simple and goes like this:

cmd = "ls"
for directory in $PATH.split(":") {
    candidate = $directory/$cmd ;
    if candidate exists and is executable {
        return candidate ;

This is pseudo-code, but there are implementations in various languages: C, Go, Ruby, Dart, etc.

As you can see, the order of these directories matters because the first matching candidate is used. That means if you have a custom ls program in /my/path, putting /my/path at the beginning of the PATH variable will cause ls to always refer to your version instead of e.g. /bin/ls because /bin appears after /my/path.

You can perform this lookup using the which command. Add -a to get all matching executables in the order in which they’re found. This is what which python gives on my machine:

$ which python

$ which -a python

You can see I have two python’s but the shell picks the one in /usr/local/bin instead of /usr/bin because it appears first in my PATH. You can bypass this lookup by giving a path to your executable. This is why you run executables in the current directory by prefixing them with ./:


This tells the shell you want to run the program called my-program in the current directory. It won’t search in the PATH for it. It also works with absolute paths. The following command runs my python in /usr/bin regardless of what’s in my PATH variable:


For performance reasons a shell like Bash won’t look executables up all the time. It’ll cache the information for the current session and will hence do this lookup only once per command. This is why you must reload your shell to have your PATH modifications taken into account. You can force Bash to clear its current cache with the hash builtin:

hash -r

Now that we know how our shell find executables; let’s see how this PATH variable is populated.

Where Does That PATH Come From?

This part depends on both your shell and your operating system. Bash reads /etc/profile when it starts. It contains some setup instructions, including initial values for the PATH variable. On macOS, it executes /usr/libexec/path_helper which in turns looks in /etc/paths for the initial paths.

The file looks like this on my machine:

$ cat /etc/paths

The actual code to set the PATH variable (or any variable for that matter) in Bash is below:


By default, Bash doesn’t pass its variables to the child processes. That is, if you set a variable in Bash then try to use it in a program it’ll fail:

$ myvar=foo
$ ruby -e 'puts ENV["myvar"] || "nope :("'
nope :(

Bash allows one to mark a variable as exported to subprocesses with the export builtin command:

$ myvar=foo
$ export myvar
$ ruby -e 'puts ENV["myvar"] || "nope :("'

This is usually done when setting the variable:

$ export myvar=foo
$ ruby -e 'puts ENV["myvar"] || "nope :("'

Technically Bash doesn’t need you to export the PATH variable to use it but it’s better if for example a program you use executes another program; in this case the former must be able to find the latter using the correct PATH.

How Do We Modify It?

Each shell has its own file in the user directory to allow per-user setup scripts. For Bash, it’s ~/.bash_profile, which often sources ~/.bashrc. You can use this file to override the default PATH. It’ll be loaded when starting a session; meaning you have to either reload your shell either re-source this file after modifying it.

We saw in the previous section how to set the PATH variable; but most of the time we don’t want to manually set the whole directories list; we only want to modify its value to add our own directories.

We won’t dive into the details in that post, but Bash has a syntax to get the value of a variable by prefixing it with a dollar symbol:

echo myvar  # prints "myvar"
echo $myvar # prints "foo", i.e. myvar's value

Bash also supports string interpolation using double quotes: You can include the value of a variable in a double-quotes string by just writing $ followed by its name:

echo "hello I'm $myvar"  # prints "hello I'm foo"

We use this feature to append or prepend directories to the PATH variable: prepending means setting the PATH’s value to that directory followed by a colon followed by the previous PATH’s value:


You usually don’t need to re-mark this variable as exported but using export at the beginning of the command doesn’t hurt.

Wrapping Things Up

Modifying the PATH is not something we do very often because most tools are installed in standard locations—already in our PATH. Most package managers install executables in their own location and need the user to modify their PATH. Homebrew, for example, installs them under /usr/local/bin and /usr/local/sbin by default. If those are not already in the PATH, one needs to add them:

# In e.g. ~/.bash_profile
export PATH="/usr/local/bin:/usr/local/sbin:$PATH"

This means the shell will first look in these directories for executables. It allows one to “override” existing tools with more up-to-date ones.