5. Creating Packages

Generating files for a package

To generate the bare minimum files for a new package, use pkg> generate.

(@v1.8) pkg> generate HelloWorld

This creates a new project HelloWorld in a subdirectory by the same name, with the following files (visualized with the external tree command):

shell> tree HelloWorld/
HelloWorld/
├── Project.toml
└── src
    └── HelloWorld.jl

2 directories, 2 files

The Project.toml file contains the name of the package, its unique UUID, its version, the authors and potential dependencies:

name = "HelloWorld"
uuid = "b4cd1eb8-1e24-11e8-3319-93036a3eb9f3"
version = "0.1.0"
authors = ["Some One <someone@email.com>"]

[deps]

The content of src/HelloWorld.jl is:

module HelloWorld

greet() = print("Hello World!")

end # module

We can now activate the project by using the path to the directory where it is installed, and load the package:

pkg> activate ./HelloWorld

julia> import HelloWorld

julia> HelloWorld.greet()
Hello World!

For the rest of the tutorial we enter inside the directory of the project, for convenience:

julia> cd("HelloWorld")

Adding dependencies to the project

Let’s say we want to use the standard library package Random and the registered package JSON in our project. We simply add these packages (note how the prompt now shows the name of the newly generated project, since we activated it):

(HelloWorld) pkg> add Random JSON
   Resolving package versions...
    Updating `~/HelloWorld/Project.toml`
  [682c06a0] + JSON v0.21.3
  [9a3f8284] + Random
    Updating `~/HelloWorld/Manifest.toml`
  [682c06a0] + JSON v0.21.3
  [69de0a69] + Parsers v2.4.0
  [ade2ca70] + Dates
 ...

Both Random and JSON got added to the project’s Project.toml file, and the resulting dependencies got added to the Manifest.toml file. The resolver has installed each package with the highest possible version, while still respecting the compatibility that each package enforces on its dependencies.

We can now use both Random and JSON in our project. Changing src/HelloWorld.jl to

module HelloWorld

import Random
import JSON

greet() = print("Hello World!")
greet_alien() = print("Hello ", Random.randstring(8))

end # module

and reloading the package, the new greet_alien function that uses Random can be called:

julia> HelloWorld.greet_alien()
Hello aT157rHV

Adding a build step to the package

The build step is executed the first time a package is installed or when explicitly invoked with build. A package is built by executing the file deps/build.jl.

julia> mkpath("deps");

julia> write("deps/build.jl",
             """
             println("I am being built...")
             """);

(HelloWorld) pkg> build
  Building HelloWorld → `deps/build.log`
 Resolving package versions...

julia> print(readchomp("deps/build.log"))
I am being built...

If the build step fails, the output of the build step is printed to the console

julia> write("deps/build.jl",
             """
             error("Ooops")
             """);

(HelloWorld) pkg> build
    Building HelloWorld → `~/HelloWorld/deps/build.log`
ERROR: Error building `HelloWorld`:
ERROR: LoadError: Ooops
Stacktrace:
 [1] error(s::String)
   @ Base ./error.jl:35
 [2] top-level scope
   @ ~/HelloWorld/deps/build.jl:1
 [3] include(fname::String)
   @ Base.MainInclude ./client.jl:476
 [4] top-level scope
   @ none:5
in expression starting at /home/kc/HelloWorld/deps/build.jl:1

Adding tests to the package

When a package is tested the file test/runtests.jl is executed:

julia> mkpath("test");

julia> write("test/runtests.jl",
             """
             println("Testing...")
             """);

(HelloWorld) pkg> test
   Testing HelloWorld
 Resolving package versions...
Testing...
   Testing HelloWorld tests passed

Tests are run in a new Julia process, where the package itself, and any test-specific dependencies, are available, see below.

Test-specific dependencies

There are two ways of adding test-specific dependencies (dependencies that are not dependencies of the package but will still be available to load when the package is tested).

target based test specific dependencies

Using this method of adding test-specific dependencies, the packages are added under an [extras] section and to a test target, e.g. to add Markdown and Test as test dependencies, add the following:

[extras]
Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[targets]
test = ["Markdown", "Test"]

to the Project.toml file. There are no other "targets" than test.

test/Project.toml file test specific dependencies

is given by test/Project.toml. Thus, when running tests, this will be the active project, and only dependencies to the test/Project.toml project can be used. Note that Pkg will add the tested package itself implicitly.

To add a test-specific dependency, i.e. a dependency that is available only when testing, it is thus enough to add this dependency to the test/Project.toml project. This can be done from the Pkg REPL by activating this environment, and then use add as one normally does. Let's add the Test standard library as a test dependency:

(HelloWorld) pkg> activate ./test
[ Info: activating environment at `~/HelloWorld/test/Project.toml`.

(test) pkg> add Test
 Resolving package versions...
  Updating `~/HelloWorld/test/Project.toml`
  [8dfed614] + Test
  Updating `~/HelloWorld/test/Manifest.toml`
  [...]

We can now use Test in the test script and we can see that it gets installed when testing:

julia> write("test/runtests.jl",
             """
             using Test
             @test 1 == 1
             """);

(test) pkg> activate .

(HelloWorld) pkg> test
   Testing HelloWorld
 Resolving package versions...
  Updating `/var/folders/64/76tk_g152sg6c6t0b4nkn1vw0000gn/T/tmpPzUPPw/Project.toml`
  [d8327f2a] + HelloWorld v0.1.0 [`~/.julia/dev/Pkg/HelloWorld`]
  [8dfed614] + Test
  Updating `/var/folders/64/76tk_g152sg6c6t0b4nkn1vw0000gn/T/tmpPzUPPw/Manifest.toml`
  [d8327f2a] + HelloWorld v0.1.0 [`~/.julia/dev/Pkg/HelloWorld`]
   Testing HelloWorld tests passed```

Compatibility on dependencies

Every dependency should in general have a compatibility constraint on it. This is an important topic so there is a separate chapter about it: Compatibility.

Weak dependencies

A weak dependency is a dependency that will not automatically install when the package is installed but you can still control what versions of that package are allowed to be installed by setting compatibility on it. These are listed in the project file under the [weakdeps] section:

[weakdeps]
SomePackage = "b3785f31-9d33-4cdf-bc73-f646780f1739"

[compat]
SomePackage = "1.2"

The current usage of this is almost solely limited to "extensions" which is described in the next section.

Conditional loading of code in packages (Extensions)

Sometimes one wants to make two or more packages work well together, but may be reluctant (perhaps due to increased load times) to make one an unconditional dependency of the other. A package extension is a module in a file (similar to a package) that is automatically loaded when some other set of packages are loaded into the Julia session. This is very similar to functionality that the external package Requires.jl provides, but which is now available directly through Julia, and provides added benefits such as being able to precompile the extension.

A useful application of extensions could be for a plotting package that should be able to plot objects from a wide variety of different Julia packages. Adding all those different Julia packages as dependencies of the plotting package could be expensive since they would end up getting loaded even if they were never used. Instead, the code required to plot objects for specific packages can be put into separate files (extensions) and these are loaded only when the packages that define the type(s) we want to plot are loaded.

Below is an example of how the code can be structured for a use case in which a Plotting package wants to be able to display objects defined in the external package Contour. The file and folder structure shown below is found in the Plotting package.

Project.toml:

name = "Plotting"
version = "0.1.0"
uuid = "..."

[weakdeps]
Contour = "d38c429a-6771-53c6-b99e-75d170b6e991"

[extensions]
# name of extension to the left
# extension dependencies required to load the extension to the right
# use a list for multiple extension dependencies
PlottingContourExt = "Contour"

[compat]
Contour = "0.6.2"

src/Plotting.jl:

module Plotting

function plot(x::Vector)
    # Some functionality for plotting a vector here
end

end # module

ext/PlottingContourExt.jl (can also be in ext/PlottingContourExt/PlottingContourExt.jl):

module PlottingContourExt # Should be same name as the file (just like a normal package)

using Plotting, Contour

function Plotting.plot(c::Contour.ContourCollection)
    # Some functionality for plotting a contour here
end

end # module

Extensions can have any arbitrary name (here PlottingContourExt), but using something similar to the format of this example that makes the extended functionality and dependency of the extension clear is likely a good idea.

A user that depends only on Plotting will not pay the cost of the "extension" inside the PlottingContourExt module. It is only when the Contour package actually gets loaded that the PlottingContourExt extension is loaded and provides the new functionality.

If one considers PlottingContourExt as a completely separate package, it could be argued that defining Plotting.plot(c::Contour.ContourCollection) is type piracy since PlottingContourExt owns neither the method Plotting.plot nor the type Contour.ContourCollection. However, for extensions, it is ok to assume that the extension owns the methods in its parent package. In fact, this form of type piracy is one of the most standard use cases for extensions.

Backwards compatibility

This section discusses various methods for using extensions on Julia versions that support them, while simultaneously providing similar functionality on older Julia versions.

Requires.jl

This section is relevant if you are currently using Requires.jl but want to transition to using extensions (while still having Requires be used on Julia versions that do not support extensions). This is done by making the following changes (using the example above):

• Add the following to the package file. This makes it so that Requires.jl loads and inserts the callback only when extensions are not supported

  # This symbol is only defined on Julia versions that support extensions
  if !isdefined(Base, :get_extension)
  using Requires
  function __init__()
      @require Contour = "d38c429a-6771-53c6-b99e-75d170b6e991" include("../ext/PlottingContourExt.jl")
  end
  end

  or if you have other things in your __init__() function:

  if !isdefined(Base, :get_extension)
  using Requires
  end
  
  function __init__()
      # Other init functionality here
  
      @static if !isdefined(Base, :get_extension)
          @require Contour = "d38c429a-6771-53c6-b99e-75d170b6e991" include("../ext/PlottingContourExt.jl")
      end
  end

• Make the following change in the conditionally-loaded code:

  isdefined(Base, :get_extension) ? (using Contour) : (using ..Contour)

The package should now work with Requires.jl on Julia versions before extensions were introduced and with extensions on more recent Julia versions.

Transition from normal dependency to extension

This section is relevant if you have a normal dependency that you want to transition be an extension (while still having the dependency be a normal dependency on Julia versions that do not support extensions). This is done by making the following changes (using the example above):

• Make sure that the package is both in the [deps] and [weakdeps] section. Newer Julia versions will ignore dependencies in [deps] that are also in [weakdeps].
• Add the following to your main package file (typically at the bottom):

  if !isdefined(Base, :get_extension)
    include("../ext/PlottingContourExt.jl")
  end

Using an extension while supporting older Julia versions

In the case where one wants to use an extension (without worrying about the feature of the extension begin available on older Julia versions) while still supporting older Julia versions the packages under [weakdeps] should be duplicated into [extras]. This is an unfortunate duplication, but without doing this the project verifier under older Julia versions will throw an error if it finds packages under [compat] that is not listed in [extras].

Package naming guidelines

Package names should be sensible to most Julia users, even to those who are not domain experts. The following guidelines apply to the General registry but may be useful for other package registries as well.

Since the General registry belongs to the entire community, people may have opinions about your package name when you publish it, especially if it's ambiguous or can be confused with something other than what it is. Usually, you will then get suggestions for a new name that may fit your package better.

1. Avoid jargon. In particular, avoid acronyms unless there is minimal possibility of confusion.

   • It's ok to say USA if you're talking about the USA.
   • It's not ok to say PMA, even if you're talking about positive mental attitude.

2. Avoid using Julia in your package name or prefixing it with Ju.

   • It is usually clear from context and to your users that the package is a Julia package.
   • Package names already have a .jl extension, which communicates to users that Package.jl is a Julia package.
   • Having Julia in the name can imply that the package is connected to, or endorsed by, contributors to the Julia language itself.

3. Packages that provide most of their functionality in association with a new type should have pluralized names.

   • DataFrames provides the DataFrame type.
   • BloomFilters provides the BloomFilter type.
   • In contrast, JuliaParser provides no new type, but instead new functionality in the JuliaParser.parse() function.

4. Err on the side of clarity, even if clarity seems long-winded to you.

   • RandomMatrices is a less ambiguous name than RndMat or RMT, even though the latter are shorter.

5. A less systematic name may suit a package that implements one of several possible approaches to its domain.

   • Julia does not have a single comprehensive plotting package. Instead, Gadfly, PyPlot, Winston and other packages each implement a unique approach based on a particular design philosophy.
   • In contrast, SortingAlgorithms provides a consistent interface to use many well-established sorting algorithms.

6. Packages that wrap external libraries or programs should be named after those libraries or programs.

   • CPLEX.jl wraps the CPLEX library, which can be identified easily in a web search.
   • MATLAB.jl provides an interface to call the MATLAB engine from within Julia.

7. Avoid naming a package closely to an existing package

   • Websocket is too close to WebSockets and can be confusing to users. Rather use a new name such as SimpleWebsockets.

Registering packages

Once a package is ready it can be registered with the General Registry (see also the FAQ). Currently, packages are submitted via Registrator. In addition to Registrator, TagBot helps manage the process of tagging releases.

Best Practices

Packages should avoid mutating their own state (writing to files within their package directory). Packages should, in general, not assume that they are located in a writable location (e.g. if installed as part of a system-wide depot) or even a stable one (e.g. if they are bundled into a system image by PackageCompiler.jl). To support the various use cases in the Julia package ecosystem, the Pkg developers have created a number of auxiliary packages and techniques to help package authors create self-contained, immutable, and relocatable packages:

• Artifacts can be used to bundle chunks of data alongside your package, or even allow them to be downloaded on-demand. Prefer artifacts over attempting to open a file via a path such as joinpath(@__DIR__, "data", "my_dataset.csv") as this is non-relocatable. Once your package has been precompiled, the result of @__DIR__ will have been baked into your precompiled package data, and if you attempt to distribute this package, it will attempt to load files at the wrong location. Artifacts can be bundled and accessed easily using the artifact"name" string macro.

• Scratch.jl provides the notion of "scratch spaces", mutable containers of data for packages. Scratch spaces are designed for data caches that are completely managed by a package and should be removed when the package itself is uninstalled. For important user-generated data, packages should continue to write out to a user-specified path that is not managed by Julia or Pkg.

• Preferences.jl allows packages to read and write preferences to the top-level Project.toml. These preferences can be read at runtime or compile-time, to enable or disable different aspects of package behavior. Packages previously would write out files to their own package directories to record options set by the user or environment, but this is highly discouraged now that Preferences is available.