4 min read

The Julia language, which has been touted as the new fastest-growing programming language, held its 6th Annual JuliaCon 2019, between July 22nd to 26th at Baltimore, USA. On the fourth day of the conference, the co-creator of Julia language and the co-founder of Julia computing, Jeff Bezanson, gave a talk explaining “What’s bad about Julia”.

Firstly, Bezanson states a disclaimer that he’s mentioning only those bad things in Julia which he is currently aware of. Next, he begins by listing many popular issues with the programming language.

What’s wrong with Julia

Compiler latency: Compiler latency has been one of the high priority issues in Julia. It is a lot slower when compared to other languages like Python(~27x slower) or C( ~187x slower).

Static compilation support: Of Course, Julia can be compiled. Unlike the language C which is compiled before execution, Julia is compiled at runtime. Thus Julia provides poor support for static compilation.


Immutable arrays: Many developers have contributed immutable array packages, however,  many of these packages assume mutability by default, resulting in more work for users. Thus Julia users have been requesting better support for immutable arrays.

Mutation issues: This is a common stumbling block for Julia developers as many complain that it is difficult to identify which package is safe to mutate.

Array optimizations: To get good performance, Julia users have to use manually in-place operations to get high performance array code.

Better traits: Users have been requesting more traits in Julia, to avoid the big unions of listing all the examples of a type, instead of adding a declaration. This has been a big issue in array code and linear algebra.

Incomplete notations: Many codes in Julia have incomplete notations. For eg. N-d array

Many members from the audience agreed with Bezanson’s list and appreciated his frank efforts in accepting the problems in Julia.

In this talk, Bezanson opts to explore two not-so-popular Julia issues – modularity and extension. He says that these issues are weird and worrisome to even him.

How to tackle modularity and extension issues in Julia

A typical Julia module extends functions from another module. This helps users in composing many things and getting lots of new functionality for free. However, what if a user wants a separately compiled module, which would be completely sealed, predictable, and will need less  time to compile, like an isolated module.

Bezanson starts illustrating how the two issues of modularity and extension can be avoided in Julia code. Firstly, he starts by using two unrelated packages, which can communicate to each other by using extension in another base package. This scenario, he states, is common when used in a core module, which requires few primitives like any type, int type, and others. The two packages in a core module are called Core.Compiler and base, with each having their own definitions.

The two packages have some codes which are common among them, thus it requires the user to write the same code twice in both the packages, which Bezanson think is “fine”. The more intense problem, Bezanson says is the typeof present in the core module. As both these packages needs to define constructors for their own types, it is not possible to share these constructors. This means that, except for constructors, everything else is isolated among the two packages.

He adds that, “In practice, it doesn’t really matter because the types are different, so they can be distinguished just fine, but I find it annoying that we can’t sort of isolate those method tables of the constructors. I find it kind of unsatisfying that there’s just this one exception.” Bezanson then explains how Types can be described using different representations and extensions.

Later, Bezanson provides two rules to tackle method specificity issues in Julia. The first rule is to be more specific, i.e., if it is a strict subtype (<: of another signature. according to bezanson the second rule is that it cannot be avoided. if methods overlap in arguments and have no specificity relationship then>“users have to give an ambiguity error”. Bezanson says that thus users can be on the safer side and assume that things do overlap. Also, if two signatures are similar, “then it does not matter which signature is called”, adds Bezanson.

Finally, after explaining all the workarounds with regard to the said issues, Bezanson concludes that “Julia is not that bad”. And states that the “Julia language could be alot better and the team is trying their best to tackle all the issues.”

Watch the video below to check out all the illustrations demonstrated by Bezanson during his talk.

Julia users around the world have loved Bezanson’s honest and frank talk at the JuliaCon 2019.

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