DataSchema

Data schemas are declarative descriptions of how to create a struct from some input data. You can set up different schemas to handle different kinds of input data. By default we assume the incoming data is a map, but you can configure schemas to work with any arbitrary data input including XML and json.

Data Schemas really shine when working with API responses - converting the response into trusted internal data easily and efficiently.

This library has no dependencies.

Creating A Simple Schema

Let's think of creating a struct as taking some source data and turning it into the desired struct. To do this we need to know at least three things:

The keys of the desired struct
The types of the values for each of the keys
Where / how to get the data for each value from the source data.

Turning the source data into the correct type defined by the schema will often require casting, so to cater for that the type definitions are casting functions. Let's look at a simple field example

field {:content, "text", &cast_string/1}
#       ^          ^                ^
# struct field     |                |
#     path to data in the source    |
#                            casting function

This says in the source data there will be a field called "text". When creating a struct we should get the data under that field and pass it too cast_string/1. The result of that function will be put in the resultant struct under the key :content.

There are 5 kinds of struct fields we could want:

field - The value will be a casted value from the source data.
list_of - The value will be a list of casted values created from the source data.
has_one - The value will be created from a nested data schema (so will be a struct)
has_many - The value will be created by casting a list of values into a data schema. (You end up with a list of structs defined by the provided schema). Similar to has_many in ecto
aggregate - The value will be a casted value formed from multiple bits of data in the source.

Available options are:

:optional? - specifies whether or not the field in the struct should be included in the @enforce_keys for the struct. By default all fields are required but you can mark them as optional by setting this to true. This will also be checked when creating a struct with DataSchema.to_struct/2 returning an error if the required field is null.
:empty_values - allows you to define what values should be used as "empty" for a given field. If either the value returned from the data accessor or the casted value are equivalent to any element in this list, that field is deemed to be empty. Defaults to [nil], meaning nil is always considered "empty".
:default - specifies a 0 arity function that will be called to produce a default value for a field when casting. This function will only be called if a field is found to be empty AND optional. If it's empty and not optional we will error.

For example:

defmodule Sandwich do
  require DataSchema

  DataSchema.data_schema([
    field: {:type, "the_type", &{:ok, String.upcase(&1)}, optional?: true, empty_values: [nil]},
  ])
end

And:

defmodule Sandwich do
  require DataSchema

  DataSchema.data_schema([
    field: {:list, "list", &{:ok, &1}, optional?: true, empty_values: [[]]},
  ])
end

And:

defmodule Sandwich do
  require DataSchema

  @options [optional?: true, empty_values: [nil], default: &DateTime.utc_now/0]
  DataSchema.data_schema([
    field: {:created_at, "inserted_at", &{:ok, &1}, @options},
  ])
end

To see this better let's look at a very simple example. Assume our input data looks like this:

source_data = %{
  "content" => "This is a blog post",
  "comments" => [%{"text" => "This is a comment"},%{"text" => "This is another comment"}],
  "draft" => %{"content" => "This is a draft blog post"},
  "date" => "2021-11-11",
  "time" => "14:00:00",
  "metadata" => %{ "rating" => 0}
}

And now let's assume the struct we wish to make is this one:

%BlogPost{
  content: "This is a blog post",
  comments: [%Comment{text: "This is a comment"}, %Comment{text: "This is another comment"}],
  draft: %DraftPost{content: "This is a draft blog post"},
  post_datetime: ~N[2020-11-11 14:00:00]
}

We can describe the following schemas to enable this:

defmodule DraftPost do
  import DataSchema, only: [data_schema: 1]

  data_schema([
    field: {:content, "content", &{:ok, to_string(&1)}}
  ])
end

defmodule Comment do
  import DataSchema, only: [data_schema: 1]

  data_schema([
    field: {:text, "text", &{:ok, to_string(&1)}}
  ])
end

defmodule BlogPost do
  import DataSchema, only: [data_schema: 1]

  @date_time_fields [
    field: {:date, "date", {Date, :from_iso8601, []}},
    field: {:time, "time", &Time.from_iso8601/1}
  ]
  data_schema([
    field: {:content, "content", &{:ok, to_string(&1)}},
    has_many: {:comments, "comments", Comment},
    has_one: {:draft, "draft", DraftPost},
    aggregate: {:post_datetime, @date_time_fields, &NaiveDateTime.new(&1.date, &1.time)},
  ])
end

Then to transform some input data into the desired struct we can call DataSchema.to_struct/2 which works recursively to transform the input data into the struct defined by the schema.

source_data = %{
  "content" => "This is a blog post",
  "comments" => [%{"text" => "This is a comment"},%{"text" => "This is another comment"}],
  "draft" => %{"content" => "This is a draft blog post"},
  "date" => "2021-11-11",
  "time" => "14:00:00",
  "metadata" => %{ "rating" => 0}
}

DataSchema.to_struct(source_data, BlogPost)
# This will output the following:

%BlogPost{
  content: "This is a blog post",
  comments: [%Comment{text: "This is a comment"}, %Comment{text: "This is another comment"}],
  draft: %DraftPost{content: "This is a draft blog post"},
  post_datetime: ~N[2020-11-11 14:00:00]
}

So imagine the input data came from an API response:

with {:ok, %{status: 200, response_body: body}} <- Http.get("https://www.my_thing.example.com"),
     {:ok, decoded} <- Jason.decode(body) do
  DataSchema.to_struct(source_data, BlogPost)
end

Different Source Data Types

As we mentioned before we want to be able to handle multiple different kinds of source data in our schemas. For each type of source data we want to be able to specify how you access the data for each field type. We do that by providing a "data accessor" (a module that implements the DataSchema.DataAccessBehaviour) when we create the schema. We do this by providing a @data_accessor on the schema. By default if you do not provide this module attribute we use DataSchema.MapAccessor. That means the above example is equivalent to doing the following:

defmodule DraftPost do
  import DataSchema, only: [data_schema: 1]

  @data_accessor DataSchema.MapAccessor
  data_schema([
    field: {:content, "content", &{:ok, to_string(&1)}}
  ])
end

defmodule Comment do
  import DataSchema, only: [data_schema: 1]

  @data_accessor DataSchema.MapAccessor
  data_schema([
    field: {:text, "text", &{:ok, to_string(&1)}}
  ])
end

defmodule BlogPost do
  import DataSchema, only: [data_schema: 1]
  @data_accessor DataSchema.MapAccessor

  @date_time_fields [
    field: {:date, "date", &Date.from_iso8601/1},
    field: {:time, "time", &Time.from_iso8601/1}
  ]
  data_schema([
    field: {:content, "content", &{:ok, to_string(&1)}},
    has_many: {:comments, "comments", Comment},
    has_one: {:draft, "draft", DraftPost},
    aggregate: {:post_datetime, @date_time_fields, &NaiveDateTime.new(&1.date, &1.time)},
  ])
end

When creating the struct DataSchema will call the relevant function for the field we are creating, passing it the source data and the path to the value(s) in the source. Our DataSchema.MapAccessor looks like this:

defmodule DataSchema.MapAccessor do
  @behaviour DataSchema.DataAccessBehaviour

  @impl true
  def field(data = %{}, field) do
    Map.get(data, field)
  end

  @impl true
  def list_of(data = %{}, field) do
    Map.get(data, field)
  end

  @impl true
  def has_one(data = %{}, field) do
    Map.get(data, field)
  end

  @impl true
  def has_many(data = %{}, field) do
    Map.get(data, field)
  end
end

To save repeating @data_accessor DataSchema.MapAccessor on all of your schemas you could use a __using__ macro like so:

defmodule MapSchema do
  defmacro __using__(_) do
    quote do
      import DataSchema, only: [data_schema: 1]
      @data_accessor DataSchema.MapAccessor
    end
  end
end

Then use it like so:

defmodule DraftPost do
  use MapSchema

  data_schema([
    field: {:content, "content", &{:ok, to_string(&1)}}
  ])
end

This means should we want to change how we access data (say we wanted to use Map.fetch! instead of Map.get) we would only need to change the accessor used in one place - inside the __using__ macro. It also gives you a handy place to provide other functions for the structs that get created, perhaps implementing a default Inspect protocol implementation for example:

defmodule MapSchema do
  defmacro __using__(opts) do
    skip_inspect_impl = Keyword.get(opts, :skip_inspect_impl, false)

    quote bind_quoted: [skip_inspect_impl: skip_inspect_impl] do
      import DataSchema, only: [data_schema: 1]
      @data_accessor DataSchema.MapAccessor

      unless skip_inspect_impl do
        defimpl Inspect do
          def inspect(struct, _opts) do
            "<" <> "#{struct.__struct__}" <> ">"
          end
        end
      end
    end
  end
end

This could help ensure you never log sensitive fields by requiring you to explicitly implement an inspect protocol for a struct in order to see the fields in it.

XML Schemas

Now let's imagine instead that our source data was XML. What would it require to enable that? First a new Xpath data accessor:

defmodule XpathAccessor do
  @behaviour DataSchema.DataAccessBehaviour
  import SweetXml, only: [sigil_x: 2]

  @impl true
  def field(data, path) do
    SweetXml.xpath(data, ~x"#{path}"s)
  end

  @impl true
  def list_of(data, path) do
    SweetXml.xpath(data, ~x"#{path}"l)
  end

  @impl true
  def has_one(data, path) do
    SweetXml.xpath(data, ~x"#{path}")
  end

  @impl true
  def has_many(data, path) do
    SweetXml.xpath(data, ~x"#{path}"l)
  end
end

As we can see our accessor uses the library Sweet XML to access the XML. That means if we wanted to change the library later we would only need to alter this one module for all of our schemas to benefit from the change.

Our source data looks like this:

source_data = """
<Blog date="2021-11-11" time="14:00:00">
  <Content>This is a blog post</Content>
  <Comments>
    <Comment>This is a comment</Comment>
    <Comment>This is another comment</Comment>
  </Comments>
  <Draft>
    <Content>This is a draft blog post</Content>
  </Draft>
</Blog>
"""

Let's define our schemas like so:

defmodule DraftPost do
  import DataSchema, only: [data_schema: 1]

  @data_accessor XpathAccessor
  data_schema([
    field: {:content, "./Content/text()", &{:ok, to_string(&1)}}
  ])
end

defmodule Comment do
  import DataSchema, only: [data_schema: 1]

  @data_accessor XpathAccessor
  data_schema([
    field: {:text, "./text()", &{:ok, to_string(&1)}}
  ])
end

defmodule BlogPost do
  import DataSchema, only: [data_schema: 1]

  @data_accessor XpathAccessor
  @datetime_fields [
    field: {:date, "/Blog/@date", &Date.from_iso8601/1},
    field: {:time, "/Blog/@time", &Time.from_iso8601/1},
  ]
  data_schema([
    field: {:content, "/Blog/Content/text()", &{:ok, to_string(&1)}},
    has_many: {:comments, "//Comment", Comment},
    has_one: {:draft, "/Blog/Draft", DraftPost},
    aggregate: {:post_datetime, @datetime_fields, &NaiveDateTime.new(&1.date, &1.time)},
  ])
end

And now we can transform:

source_data = """
<Blog date="2021-11-11" time="14:00:00">
  <Content>This is a blog post</Content>
  <Comments>
    <Comment>This is a comment</Comment>
    <Comment>This is another comment</Comment>
  </Comments>
  <Draft>
    <Content>This is a draft blog post</Content>
  </Draft>
</Blog>
"""

DataSchema.to_struct(source_data, BlogPost)

# This will output:

 %BlogPost{
   comments: [
     %Comment{text: "This is a comment"},
     %Comment{text: "This is another comment"}
   ],
   content: "This is a blog post",
   draft: %DraftPost{content: "This is a draft blog post"},
   post_datetime: ~N[2021-11-11 14:00:00]
 }

JSONPath Schemas

This is left as an exercise for the reader but hopefully you can see how you could extend this idea to allow for json data and JSONPaths pointing to the data in the schemas.

Guides

See the docs or the guides in this repo for more details.

Livebook

There are livebooks available under the livebooks folder in this repo. Find out more about livebook here.

For quick instruction at the root of the repo you can:

mix escript.install hex livebook

# Start the Livebook server
livebook server

If using asdf you should also asdf reshim elixir.

The above will start the livebook where you can navigate to the livebooks repo and load any of the interactive docs from there.

Contributing

NB Set the MIX_ENV to :docs when publishing the package. This will ensure that modules inside test/support wont get their documentation published with the library (as they are included in the :dev env).

MIX_ENV=docs mix hex.publish

You will also have to set that env if you want to run mix docs

MIX_ENV=docs mix docs

Installation

available in Hex, the package can be installed by adding data_schema to your list of dependencies in mix.exs:

def deps do
  [
    {:data_schema, "~> 0.5.0"}
  ]
end

Name		Name	Last commit message	Last commit date
Latest commit History 146 Commits
guides/introduction		guides/introduction
lib		lib
livebooks		livebooks
test		test
.formatter.exs		.formatter.exs
.gitignore		.gitignore
.tool-versions		.tool-versions
CHANGELOG.md		CHANGELOG.md
LICENCE.txt		LICENCE.txt
README.md		README.md
impl_notes.md		impl_notes.md
mix.exs		mix.exs
mix.lock		mix.lock

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