# Tutorial

## Making a Data.toml file

Suppose you had some data which you wanted to access located in your Julia home directory at ~/.julia/datasets (or joinpath(homedir(), ".julia", "datasets") on windows). For this tutorial we'll use the tutorial data from the DataSets docs directory at https://github.com/JuliaComputing/DataSets.jl/tree/master/docs/src/data..

To make DataSets aware of the data, let's create a Data.toml file in joinpath(homedir(), ".julia", "datasets", "Data.toml") and add the following content:

# Version of the data TOML format.
data_config_version=1

[[datasets]]
# Some alphanumeric name (can include spaces and underscores)
name="a_text_file"
# Optional description
description="A text file containing the standard greeting"
# A unique ID. Generate with UUIDs.uuid4()
uuid="b498f769-a7f6-4f67-8d74-40b770398f26"

# Storage location
[datasets.storage]
# The name of the storage driver.
driver="FileSystem"
# Data stored in FileSystem is either Blob (a file) or BlobTree (a directory/folder)
type="Blob"
# Path with posix / separators.
# Use @__DIR__ for paths relative to Data.toml
path="@__DIR__/data/file.txt"

# A second example
[[datasets]]
name="a_tree_example"
description="A very simple directory tree"
uuid="e7fd7080-e346-4a68-9ca9-98593a99266a"

[datasets.storage]
driver="FileSystem"
type="BlobTree"
path="@__DIR__/data/csvset"

# Further datasets can be added as desired
# [[datasets]]
# ...

Because we've written the Data.toml into a default location which is searched for by DataSets.PROJECT, it will automatically become accessible in the default global data project:

julia> DataSets.PROJECT
DataSets.StackedDataProject:
DataSets.ActiveDataProject:
(empty)
DataSets.TomlFileDataProject [~/.julia/datasets/Data.toml]:
a_text_file    => b498f769-a7f6-4f67-8d74-40b770398f26
a_tree_example => e7fd7080-e346-4a68-9ca9-98593a99266a

The dataset function can then be used to load metadata for a particular dataset:

julia> dataset("a_text_file")
name = "a_text_file"
uuid = "b498f769-a7f6-4f67-8d74-40b770398f26"
description = "A text file containing the standard greeting"

[storage]
driver = "FileSystem"
type = "Blob"
path = ".../DataSets/docs/src/data/file.txt"

If you prefer to pass around the data project explicitly rather than relying on global configuration this is also possible:

DataSets.DataProject:
a_text_file    => b498f769-a7f6-4f67-8d74-40b770398f26
a_tree_example => e7fd7080-e346-4a68-9ca9-98593a99266a

julia> dataset(project, "a_text_file")
name = "a_text_file"
uuid = "b498f769-a7f6-4f67-8d74-40b770398f26"
description = "A text file containing the standard greeting"

[storage]
driver = "FileSystem"
type = "Blob"
path = ".../DataSets/docs/src/data/file.txt"

You can call open() on a DataSet to inspect the data inside. open() will return the Blob and BlobTree types for local files and directories on disk. For example,

julia> open(dataset("a_text_file"))
📄  @ .../DataSets/docs/src/data/file.txt

julia> open(dataset("a_tree_example"))
📂 Tree  @ .../DataSets/docs/src/data/csvset
📄 1.csv
📄 2.csv

Use the form open(T, dataset) to read the data as a specific type. Blob data can be opened as String, IO, or Vector{UInt8}, depending on your needs:

julia> io = open(IO, dataset("a_text_file"))
IOStream(<file .../DataSets/docs/src/data/file.txt>)

"Hello world!\n"

julia> buf = open(Vector{UInt8}, dataset("a_text_file"));

julia> String(buf)
"Hello world!\n"

julia> open(String, dataset("a_text_file"))
"Hello world!\n"

To ensure the dataset is closed again in a timely way (freeing any resources such as file handles), you should use the scoped form, for example:

julia> open(IO, dataset("a_text_file")) do io
@show content
nothing
end
content = "Hello world!\n"

Let's look at some tree-like data which is represented on local disk as a folder or directory. Tree data is opened in Julia as the BlobTree type and can be indexed with path components to get at the file Blobs inside. In turn, we can open() one of the file blobs and look at the data contained within.

julia> tree = open(BlobTree, dataset("a_tree_example"))
📂 Tree  @ .../DataSets/docs/src/data/csvset
📄 1.csv
📄 2.csv

julia> tree["1.csv"]
📄 1.csv @ .../DataSets/docs/src/data/csvset

julia> open(String, tree["1.csv"]) |> Text
Name,Age
"Aaron",23
"Harry",42

## Program Entry Points

Rather than manually using the open() functions as shown above, the @datafunc macro lets you define entry points where DataSets will be mapped into your program.

For example, here we define an entry point called main which takes

• DataSet type Blob, presenting it as a String within the program
• DataSet type BlobTree, presenting it as a BlobTree within the program

The @datarun macro allows you to call such program entry points, extracting named data sets from a given project.

julia> @datafunc function main(x::Blob=>String, t::BlobTree=>BlobTree)
@show x
open(String, t["1.csv"]) do csv_data
@show csv_data
end
end
main (generic function with 2 methods)

julia> @datarun main("a_text_file", "a_tree_example");
x = "Hello world!\n"
csv_data = "Name,Age\n\"Aaron\",23\n\"Harry\",42\n"

In a given program it's possible to have multiple entry points by simply defining multiple @datafunc implementations. In this case @datarun will dispatch to the entry point with the matching DataSet type.