In Clojure, Java interoperability or “interop” is a core feature. In
out-of-the-box across optimization modes. Extern files or “externs”
required for advanced optimizations are often hard to find.
To fix this a few newly found friends and I created CLJSJS.
extern files and provide tools to integrate them into your project.
My personal hope is that this will make it easier for newcomers to get
started with Clojurescript.
Also existing solutions like deps.clj (more here) only
can serve as a vehicle to find some “pseudo-standard” for this kind of
Thanks to Juho Teperi, Micha Niskin & Alan Dipert for their
contributions and ideas so far. Now go and check out the
project homepage or jump straight into the
packages repo and learn how you can contribute.
Boot is a build system for Clojure projects. It roughly competes
in the same area as Leiningen but Boot’s new version brings some
interesting features to the table that make it an alternative
Compose Build Steps
If you’ve used Leiningen for more than packaging jars and uberjars
you likely came across plugins like lein-cljsbuild or
lein-garden, both compile your stuff into a target format (i.e. JS, CSS).
Now if you want to run both of these tasks at the same time — which
you probably want during development — you have two options: either
you open two terminals and start them separately or you fall back to
something like below that you run in a dev profile (this is how it’s
done in Chestnut):
Now there are issues with both of these options in my opinion. Opening
two terminals to initiate your development environment is just not
very user friendly and putting code related to building the project
into your codebase is boilerplate that unnecessarily can cause trouble
by getting outdated.
What Boot allows developers to do is to write small composable tasks.
These work somewhat similar to stateful transducers and ring middleware
in that you can just combine them with regular function composition.
A Quick Example
Playing around with Boot, I tried to write a task. To test this task
in an actual project I needed to install it into my local repository
(in Leiningen: lein install). Knowing that I’d need to reinstall
the task constantly as I change it I was looking for something like
Leiningen’s Checkouts so I don’t have to re-install after every
Turns out Boot can solve this problem in a very different way
that illustrates the composing mechanism nicely. Boot defines a
that help with packaging and installing a jar: pom, add-src, jar
We could call all of these these on the command line as follows:
boot pom add-src jar install
Because we’re lazy we’ll define it as a task in our project’s
build.boot file. (Command-line task and their arguments are
symmetric to their Clojure counterparts.)
(require'[boot.core:refer[deftask]]'[boot.task.built-in:refer[pomadd-srcjarinstall]])(deftaskbuild-jar"Build jar and install to local repo."(comp (pom)(add-src)(jar)(install)))
Now boot build-jar is roughly equivalent to lein install. To have
any changes directly reflected on our classpath we can just compose
our newly written build-jar task with another task from the
repertoire of built-in tasks: watch. The watch-task observes the
file system for changes and initiates a new build cycle when they
boot watch build-jar
With that command we just composed our already composed task with
another task. Look at that cohesion!
There Are Side-Effects Everwhere!
Is one concern that has been raised about Boot. Leiningen is
beautifully declarative. It’s one immutable map that describes your
whole project. Boot on the other hand looks a bit different. A usual
boot file might contain a bunch of side-effectful functions and in
general it’s much more a program than it is data.
I understand that this might seem like a step back at first sight, in
fact I looked at it with confusion as well. There are some problems
with Leiningen though that are probably hard to work out in
Leiningen’s declarative manner (think back to
running multiple lein X auto commands.
Looking at Boot’s code it becomes apparent that the authors spent a
great deal of time on isolating the side effects that might occur in
various build steps. I recommend reading the
comments on this Hacker News thread
for more information on that.
When To Use Boot, When To Use Leiningen
Boot is a build tool. That said it’s task composition features only
get to shine when multiple build steps are involved. If you’re
developing a library I’m really not going to try to convince you to
switch to Boot. Leiningen works great for that and is, I’d assume,
more stable than Boot.
If you however develop an application that requires various build
steps (like Clojurescript, Garden, live reloading, browser-repl) you
should totally check out Boot. There are tasks for all of the above
live reloading. I wrote the
Garden task and writing tasks is not hard once you have a basic
understanding of Boot.
If you need help or have questions join the
#hoplon channel on freenode IRC.
I’ll try to help and if I can’t Alan or Micha, the authors of Boot,
S3-Beam — Direct Upload to S3 with Clojure & Clojurescript
I described how to upload files from the browser directly to S3 using
Clojure and Clojurescript. I now packaged this up into a small (tiny,
An interesting note on what changed to the process described in the
earlier post: the code now uses pipeline-async instead of
transducers. After some discussion with Timothy Baldridge this seemed
more appropriate even though there are some aspects about the
transducer approach that I liked but didn’t get to explore further.
Maybe in an upcoming version it will make sense to reevaluate that
decision. If you have any questions, feedback or suggestions I’m happy
to hear them!
Patalyze — An Experiment Exploring Publicly Available Patent Data
For a few months now I’ve been working on and off on a little
“data-project” analyzing patents published by the US Patent &
Trademark Office. Looking at the time I spent on this until now I
think I should start talking about it instead of just hacking away
evening after evening.
It started with a simple observation: there are companies like
Apple that sometimes collaborate with smaller companies building a
small part of Apple’s next device. A contract like this usually gives
the stock of the small company a significant boost. What if you could
foresee those relationships by finding patents that employees from
Apple and from the small company filed?
An API for patent data?
Obviously this isn’t going to change the world for the better but just
the possibility that such predictions or at least indications are
possible kept me curious to look out for APIs offering patent data. I
did not find much. So thinking about something small that could be
“delivered” I thought a patent API would be great. To build the
dataset I’d parse the archives provided on Google’s
USPTO Bulk downloads
I later found out about Enigma and some offerings
by Thomson Reuters. The prices are
high and the sort of analysis we wanted to do would have been hard
with inflexible query APIs.
For what we wanted to do we only required a small subset of the data a
patent contains. We needed the organization, it’s authors, the title
and description, filing- and publication dates and some identifiers.
With such a reduced amount of data that’s almost only useful in
combination with the complete data set I discarded the plan to build
an API. Maybe it will make sense to publish reduced and more easily
parseable versions of the archives Google provides at some point.
Let me know if you would be interested in that.
So far I’ve built up a system to parse, store and query some 4 million patents
that have been filed at the USPTO since beginning of 2001. While it
sure would be great to make some money off of the work I’ve done so
far I’m not sure what product could be built from the technology I created
so far. Maybe I could sell the dataset but the number of potential
customers is probably small and in general I’d much more prefer to
make it public. I’ll continue to explore the possibilities with regards
For now I want to explore the data and share the results of this
exploration. I setup a small site that I’d like to use as home for any
further work on this. By now it only has a newsletter signup form
(just like any other landing page) but I hope to share some
interesting analysis with the subscribers to the list every now and
then in the near future. Check it out at
patalyze.co. There even is a small
chart showing some data.
Now when you build a new image from that Dockerfile it adds the
fetch-and-run.sh script to the image’s filesystem. Note that the
jar is not part of the image but that it will be downloaded whenever
a new container is being started from the image. That way a simple restart
will always fetch the most recent version of the jar. In some
scenarios it might become confusing to not have precise deployment
tracking but in my case it turned out much more convenient than going
through the process of destroying the container, deleting the image,
creating a new image and starting up a new container.