A data validation library for Erlang inspired by Pydantic. Spectra provides type-safe data serialization and deserialization for Erlang records and types. Currently the focus is on JSON.

• Type-safe conversion: Convert typed Erlang values to/from external formats such as JSON, making sure the data conforms to the type.
• OpenAPI documentation: Generate OpenAPI 3.1 specifications from your type definitions
• Detailed errors: Get error messages with location information when validation fails
• Support for complex scenarios: Handles unions, records, atoms, nested structures, ...

Requires Erlang/OTP 27 or later - Spectra uses the native json module introduced in OTP 27.

Add spectra to your rebar.config dependencies:

{deps, [
    {spectra, "~> 0.4.0"}
]}.

Your modules must be compiled with debug_info for spectra to extract type information.

Here's how to use spectra for JSON serialization and deserialization:

-module(demo).

-export([json_to_contacts/1, contacts_to_json/1, json_schema/0, binary_to_quality/1]).

-record(email_contact, {address, verified, domain}).
-record(phone_contact, {number, verified, sms_capable}).

-type quality() :: 1..5.
-type verified() ::
    #{source := one_time_code | gut_feeling,
      quality => quality(),
      binary() => binary()} | undefined.
-type email_contact() ::
    #email_contact{address :: nonempty_binary(),
                   verified :: verified(),
                   domain :: nonempty_binary()}.
-type phone_contact() ::
    #phone_contact{number :: binary(),
                   verified :: verified(),
                   sms_capable :: boolean()}.
-type contacts() :: [email_contact() | phone_contact()].

%% Some helper functions

-spec json_to_contacts(binary()) -> {ok, contacts()} | {error, [spectra:error()]}.
json_to_contacts(Json) ->
    spectra:decode(json, ?MODULE, contacts, Json).

-spec contacts_to_json(contacts()) -> {ok, binary()} | {error, [spectra:error()]}.
contacts_to_json(Contacts) ->
    case spectra:encode(json, ?MODULE, contacts, Contacts) of
        {ok, JsonIoList} -> {ok, iolist_to_binary(JsonIoList)};
        {error, _} = Error -> Error
    end.

-spec binary_to_quality(binary()) -> {ok, quality()} | {error, [spectra:error()]}.
binary_to_quality(Bin) ->
    spectra:decode(binary_string, ?MODULE, quality, Bin).

json_schema() ->
    spectra:schema(json_schema, ?MODULE, contacts).

%% Compile the demo module (note: You need debug info)
c("demo.erl", [debug_info]).

%% Load the record defs into the shell.
rr(demo).

%% Create some data
Contacts = [
    #email_contact{
        address = <<"john.doe@example.com">>,
        verified = #{source => one_time_code, quality => 2, <<"code">> => <<"123456">>},
        domain = <<"example.com">>
    },
    #phone_contact{
        number = <<"+1-555-123-4567">>,
        verified = #{source => gut_feeling, <<"confidence">> => <<"high">>},
        sms_capable = true
    },
    #email_contact{
        address = <<"alice@company.org">>,
        domain = <<"company.org">>
    }
].

%% Convert to JSON
{ok, Json} = demo:contacts_to_json(Contacts).

%% Convert back from JSON
demo:json_to_contacts(Json).

%% If you get quality as a query parameter, you can do:
demo:binary_to_quality(<<"4">>).

%% Generate the json schema
demo:json_schema().

These are the main functions for JSON serialization and deserialization:

spectra:encode(Format, Module, Type, Value) ->
    {ok, iolist()} | {error, [spectra:error()]}.
spectra:decode(Format, Module, Type, JsonBinary) ->
    {ok, Value} | {error, [spectra:error()]}.

Where:

• Format is json, binary_string or string
• Module is the module where the type/record is defined (or a type_info() for advanced usage)
• Type is either:
  • an atom: spectra will look for a type of arity 0 or a record with that name
  • {type, TypeName, Arity} for user-defined types (e.g., {type, my_type, 0})
  • {record, RecordName} for records (e.g., {record, user})
  • An actual sp_type() structure (for advanced usage)

spectra:schema(Format, Module, Type) -> Schema :: iodata().

Where:

• Format is json_schema (for now)

And the rest of the arguments are the same as for the data serialization API.

Spectra can generate complete OpenAPI 3.1 specifications for your REST APIs. This provides interactive documentation, client generation, and API testing tools.

The API for building endpoints is very experimental and will probably change a lot. It is meant to be used by developers of web servers / web frameworks. See elli_openapi for an example of how to use it in a web server.

%% Create a base endpoint
spectra_openapi:endpoint(Method, Path) ->
    endpoint_spec().

%% Add responses
spectra_openapi:with_response(Endpoint, StatusCode, Description, Module, Schema) ->
    endpoint_spec().

%% Add request body
spectra_openapi:with_request_body(Endpoint, Module, Schema) ->
    endpoint_spec().

%% Add parameters (path, query, header, cookie)
spectra_openapi:with_parameter(Endpoint, Module, ParameterSpec) ->
    endpoint_spec().

%% Generate complete OpenAPI spec
spectra_openapi:endpoints_to_openapi(Metadata, Endpoints) ->
    {ok, json:encode_value()} | {error, [spectra:error()]}.

Spectra uses two different error handling strategies depending on the type of error:

Data validation errors are returned as {error, [#sp_error{}]} tuples. These occur when input data doesn't match the expected type during encoding/decoding.

Example:

BadSourceJson = <<"[{\"number\":\"+1-555-123-4567\",\"verified\":{\"source\":\"a_bad_source\",\"confidence\":\"high\"},\"sms_capable\":true}]">>.

{error, [#sp_error{...}]} = json_to_contacts(BadSourceJson).

#error{} contains:

• location - List showing the path to where the error occurred
• type - Error type: type_mismatch, no_match, missing_data, missing_type, type_not_supported, not_matched_fields, not_implemented
• ctx - Context information about the error

Configuration and structural errors raise exceptions. These occur when:

• Module not found, not loaded, or not compiled with debug_info
• Type or record not found in module (e.g., {type_or_record_not_found, TypeName})
• Unsupported type used (e.g., pid(), port(), tuple())

These errors indicate a problem with your application's configuration or type definitions, not with the data being processed.

When deserializing JSON into Erlang (using spectra:decode/4), extra fields that are not defined in the type are silently ignored for maps, records, and structs. This lenient behavior allows for flexible API evolution and backwards compatibility.

Example:

-type user() :: #{name := binary(), age := integer()}.

%% JSON with extra fields is accepted during deserialization
Json = <<"{\"name\":\"Alice\",\"age\":30,\"extra\":\"ignored\"}">>,
{ok, #{name := <<"Alice">>, age := 30}} = spectra:decode(json, ?MODULE, user, Json).

This behavior was introduced in version 0.2.0. Previously, extra fields during deserialization would cause a not_matched_fields error.

Note: The not_matched_fields error is still raised during serialization (Erlang → JSON) when encoding data with exact typed map fields that don't match the provided data structure.

Future changes: This default behavior may change in future versions of spectra when decode/encode options are introduced, allowing users to configure whether extra fields should be ignored or cause errors.

The atoms undefined and nil have special handling in JSON serialization to represent missing or null values.

Encoding (Erlang → JSON):

• Fields with undefined or nil values are omitted from the JSON output
• Example: #{name => <<"John">>, email => undefined} encodes to {"name":"John"}

Decoding (JSON → Erlang):

The behavior depends on whether fields are mandatory (:=) or optional (=>):

Mandatory fields (:=), record fields, and Elixir struct fields:

• Missing JSON fields decode to undefined or nil if the type includes that literal
• Explicit JSON null values also decode to undefined or nil if the type includes that literal
• Example with type #{email := binary() | undefined}:
  • {} (missing field) → #{email => undefined}
  • {"email": null} → #{email => undefined}
  • {"email": "test@example.com"} → #{email => <<"test@example.com">>}

Optional fields (=>):

• Missing JSON fields result in the key being absent from the map entirely
• Explicit JSON null values decode to undefined or nil if the type includes that literal
• Example with type #{email => binary() | undefined}:
  • {} (missing field) → #{} (key absent)
  • {"email": null} → #{email => undefined} (key present)
  • {"email": "test@example.com"} → #{email => <<"test@example.com">>} (key present)

Note on record and struct fields: Erlang record fields and Elixir struct fields behave the same as mandatory map fields (:=). When a field is missing from JSON, it will be filled with undefined or nil if the field type includes that literal. For example, a record field email :: binary() | undefined will decode {} to a record with email = undefined.

Note: If a union type includes both undefined and nil (e.g., integer() | undefined | nil), the selection of which missing value to use depends on the order they appear in the type definition. The last one encountered will be used. For predictable behavior, include only one missing value literal in your type definitions. The nil atom is primarily for Elixir interoperability.

When using types with term, spectra_json will not reject any data, which means it can return data that json.erl cannot convert to JSON.

Char is currently handled as integer, which is probably not what you want. Try to not use the char type for now. This is documented in test/char_test.erl.

Each format supports a subset of Erlang types. For JSON serialization and schema, the following are not supported:

• maybe_improper_list() - Currently returns an error
• pid(), port(), reference() - Cannot be serialized to JSON
• tuple(), bitstring(), nonempty_bitstring() - Not JSON-compatible
• Function types - Cannot be serialized

It would be interesting to add support for key value lists, but as it isn't a native type in erlang, I haven't gotten around to it yet.

You can configure spectra behavior using application environment variables:

• Type: boolean()
• Default: false
• Description: When set to true, enables caching of extracted type information for modules using persistent terms. This can improve performance when repeatedly processing the same modules.
• Note: When only changing types and not code, the module vsn (used for caching) is not updated, so the types will not be updated.
• Recommendation: Enable this in production systems where no hot code reloading is done.

• Type: boolean()
• Default: false
• Description: When set to true, enables additional Unicode validation for string data. This validates that list-type string data contains valid Unicode characters. When disabled, string conversion still works correctly but skips the additional validation step for better performance.
• Note: Required type conversions (e.g., binary to list, list to binary) always use Unicode functions regardless of this setting.
• Recommendation: Enable this if you need strict Unicode validation, or keep disabled for better performance when Unicode validity is guaranteed by other means.

Example configuration in sys.config:

{spectra, [
    {use_module_types_cache, true},
    {check_unicode, false}
]}.

• elli_openapi - Elli middleware for automatic OpenAPI spec generation and validation using spectra
• exdantic - Elixir port of spectra for data validation and JSON serialization

This library is under active development. APIs and error messages will probably change.

Contributions are welcome! Please feel free to submit issues and pull requests.