Julia language: A letter of recommendation

After spending quite some time using Julia (a programming language for technical computing) during the last few months, I am confident enough to provide kind of a “letter of recommendation” by now. Hence, I decided to list some of the features that make Julia appealing to me, while also interspersing some resources on Julia that I found helpful and worth sharing.

1 It is free

Julia language is develop under the MIT open source license and hence can be used free of charge. Open source is a highly desirable feature to me, especially in research, as it promotes cooperation and interchange between researchers. That being said, Julia easily stands up to any comparison with proprietary software (like MATLAB) as well, and I hope this will become clear in the following.

2 It is fast

Although I did not make any formal performance comparisons with other programming languages so far, I can at least assure you that Julia feels quite fast on a day-to-day standard usage to me, especially in comparison to R. On the homepage, however, there are some formal benchmarks listed that indicate a really good performance compared to other languages. Of course, these are just some made-up test cases. Objective comparison in real applications, however, is quite hard to achieve, since languages like R make substantial use of C code in almost any computationally intensive package under the hood. For the sake of both efficiency and reliability, however, I think that researchers should generally avoid usage of low-level software languages like C. As most researchers did never get a true and deep training in software development, such low-level languages simply are too error-prone, especially if you refrain from any thought out extensive and well-structured software testing. So, excluding factoring out code parts into C, I am quite confident that Julia truly is faster than R, and at least equally fast as Matlab. Furthermore, Julia allegedly was designed to enable things like parallel computing and big data handling from scratch.

Nothing comes without a price, however, and hence truly leveraging performance capabilities forces you to deal with data types more explicitly. This happens in Julia to a far lower degree than in C, but it still can be un-intuitive and cumbersome at some points, and it especially complicates the learning process in the beginning. But, dealing with types more explicitly also allows some additional benefits like multiple dispatch, where the behavior of a function can be defined across many combinations of argument types.

3 It is expressive

The next selling point is probably a bit underestimated in general, since it is harder to understand its true benefit than, for example, when we are talking just about speed. The point is that the syntax of Julia is very rich and expressive. Having my roots in MATLAB, I myself generally favor the syntax of Julia (which is fundamentally similar to MATLAB) over the syntax of R, as it appears to be cleaner to me (of course: this is a matter of taste!). More generally, however, the syntax of Julia is much richer, such that it allows a high level of customization. For example, Julia is able to mimic R formula syntax as it is done in the GLM package. Furthermore, you can build your own types that behave exactly the way you want. For example, one of my first projects in Julia was to create a type that is especially suited for time series data (I wrapped it up in the package TimeData). Thereby, I could specify, for example, the way that objects are displayed, that mathematical functions do not apply on the time index column but to numeric data only, and that entries can be accessed and indexed through date strings. Also, the rich syntax includes meta-programming capabilities (generating code through code), and macros, making unit testing in Julia as straightforward as it could be (yes, I really think that software testing is indispensable when software is used and disclosed in research!). In order to get a feeling of the richness of the syntax and the infinite possibilities that it provides you could exemplarily check out a use case of iterators in Julia shown in this post. Or, for an impression on compactness and intuitive appeal of the syntax, there is a blog post on Econometrics by Simulation that compares it to R.

4 It is transparent

What I really like about Julia is that it was based on open source software development practices right away, such that it is tightly integrated with version control through git and github. This way, the complete code base is easily accessible and readable, and the github platform provides the best environment for further contributions and cooperation. For example, code improvements from any third person can easily be integrated into the code base, ultimately promoting cooperation. The times where you had to provide your code extension to some package author via email are gone. Also, github allows usage of automated testing services like Travis, such that code ultimately becomes more robust with less bugs.

5 It is illustrative

Meanwhile, there already is a bunch of graphics packages out there, for both on-the-fly visualizations and publication-ready graphics, as well as for graphics that suit for html formats:

Even more, there also exists an interface to Python’s interactive graphical notebook (IJulia), which allows to combine code, formatted text, math, and multimedia in a single document.

6 It is growing

Due to its outstanding features, Julia also seems to get increasing attention worldwide (you can find some numbers and graphics on the community in this post). With JuliaStudio, there even exists an integrated development environment similar to RStudio already.

7 It is unfinished

Despite all these positive features, there also are some deficiencies that yet need to be overcome. As a matter of fact, we have not yet reached an official 1.0 release of the language. Hence, code development sometimes can be a little bit more cumbersome than necessary, for example due to the following problems:

  • sometimes Julia still crashes and needs to be re-started
  • variables can not be completely removed from workspace
  • in my opinion, MATLAB still has by far the best debugger – Julia does lag behind here
  • any changes to type definitions require re-starting of Julia

And, of course, as a comparatively new language Julia still lacks some of the extensive libraries that already have been implemented for other languages and yet need to be imported into Julia. (That being said, there already exist quite good interfaces to other languages, in order to make some of these libraries also available in Julia).

However, keep in mind that this list of deficiencies is only a description of the state of Julia at the time of writing, and I am quite confident that it will be outdated rather soon.

8 Resources

Besides the helping material provided by the official homepage, here are some links to additional resources that I found helpful:


Posted on 2014/04/25, in Julia and tagged . Bookmark the permalink. 7 Comments.

  1. thanks for sharing your note in Julia. so let us save time to overview what’s Julia. as a good forecaster ;-), i want to tell you secretly, more taking care of some language who has ability incorporated with WWW. like your section style: It is webing :-)

    • Not sure if I get you right, but what you want is kind of a tighter integration with the internet? I guess what you have in mind is a language like java, which can be used in webpages as well?
      I think what you ultimately want consist of two components: first, we’d like to be able to produce output that naturally fits into webpages, and second, we’d like to be able to access online databases.
      To the first point, the IJulia package already provides a way to actually use your browser as IDE and produce html output with graphics and embedded media files. Furthermore, graphical packages like Gadfly already make use of java libraries to produce interactive graphics, and plotly provides even richer ways in this regards. Also, there already exists a rudimentary way to come up with GUIs, although I am quite sure that Julia needs to improve in this regards. However, I don’t see any reason why Julia should not be able to do so, and I expect it happen in the near future.
      Regarding the second point, I really think that it should be no problem to provide interfaces to web-data through either queries to an sql online database, downloading data as txt or csv files or accessing data from html tables. These functionalities already exist for R, and I do not see any point why Julia should not be able to perform the same tasks even better.
      Still, we should not forget that Julia is designed as technical computing language. Hence, what it ultimately should excel with is data analysis, and that’s what it does. As long as any other functionality is implemented through interfaces to other languages I am quite satisfied.
      However, let me us know if you found a better language out there.

  2. thanks for ur comment. i was spent more than 24 hours (i’m not clever) to finding a way to write my datas from R to MySQL. Since then i never read these datas form MySQL to R. as there are such Quandl exists. also we spent not short time to make such PPT things for representation our purpose. however if one in china asks u how is going on when we take 99% instead of 95% CI for VaR. we must recalucate and update our results. so let part of your functions (such as local arguments) integrate into web (as it is dynamic), and put your results/report as dynamic as possible, you will be the Meister of Data in 22 century :-)

  3. Mohammad Masud Alam, NIU, Dekalb, IL

    Please write a book on Julia for economics students, Like : ” Econometrics with Julia”, or ” A beginners guide of Julia for economics”, or ” Julia for Economics”…would be helpful and will allow to expand its horizon among graduate students.

  4. Nice article :) You mentioned that you need to restart Julia when you change a type. You might like this if you haven’t seen it https://github.com/JunoLab/atom-julia-client/blob/master/manual/workflow.md

  1. Pingback: Julia syntax features | Quantifying Information

  2. Pingback: Downloading SP500 stock price data with Julia | Quantifying Information

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