View Source Spark.Options (spark v2.2.4)
Provides a standard API to handle keyword-list-based options.
This module began its life as a vendored form of NimbleOptions
.
We had various features to add to it, and the spirit of nimble
options is to be as lightweight as possible. With that in mind,
we were advised to vendor NimbleOptions
. We would like to
thank the authors of NimbleOptions
for their excellent work,
and their blessing to transplant their work into Spark.
Spark.Options
allows developers to create schemas using a
pre-defined set of options and types. The main benefits are:
- A single unified way to define simple static options
- Config validation against schemas
- Automatic doc generation
Spark also uses this to power entity and section schemas.
Schema Options
These are the options supported in a schema. They are what defines the validation for the items in the given schema.
:type
- The type of the option item. The default value is:any
.:required
(boolean/0
) - Defines if the option item is required. The default value isfalse
.:default
(term/0
) - The default value for the option item if that option is not specified. This value is validated according to the given:type
. This means that you cannot have, for example,type: :integer
and usedefault: "a string"
.:keys
(keyword/0
) - Available for types:keyword_list
,:non_empty_keyword_list
, and:map
, it defines which set of keys are accepted for the option item. The value of the:keys
option is a schema itself. For example:keys: [foo: [type: :atom]]
. Use:*
as the key to allow multiple arbitrary keys and specify their schema:keys: [*: [type: :integer]]
.:deprecated
(String.t/0
) - Defines a message to indicate that the option item is deprecated. The message will be displayed as a warning when passing the item.:hide
(one or a list ofatom/0
) - A list of keys that should be hidden when generating documentation:as
(atom/0
) - A name to remap the option to when used in DSLs. Not supported in regular option parsing:snippet
(String.t/0
) - A snippet to use when autocompleting DSLs. Not supported in regular option parsing:links
(term/0
) - A keyword list of links to include in DSL documentation for the option item.:doc
(String.t/0
orfalse
) - The documentation for the option item.:subsection
(String.t/0
) - The title of separate subsection of the options' documentation:type_doc
(String.t/0
orfalse
) - The type doc to use in the documentation for the option item. Iffalse
, no type documentation is added to the item. If it's a string, it can be anything. For example, you can use"a list of PIDs"
, or you can use a typespec reference that ExDoc can link to the type definition, such as"`t:binary/0`"
. You can use Markdown in this documentation. If the:type_doc
option is not present, Spark.Options tries to produce a type documentation automatically if it can do it unambiguously. For example, iftype: :integer
, Spark.Options will useinteger/0
as the auto-generated type doc.:type_spec
(Macro.t/0
) - The quoted spec to use in the typespec for the option item. You should use this when the auto-generated spec is not specific enough. For example, if you are performing custom validation on an option (with the{:custom, ...}
type), then the generated type spec for that option will always beterm/0
, but you can use this option to customize that. The value for this option must be a quoted Elixir term. For example, if you have an:exception
option that is validated with a{:custom, ...}
type (based onis_exception/1
), you can override the type spec for that option to bequote(do: Exception.t())
. Available since v1.1.0.
Types
:any
- Any type.:keyword_list
- A keyword list.:non_empty_keyword_list
- A non-empty keyword list.:map
- A map consisting of:atom
keys. Shorthand for{:map, :atom, :any}
. Keys can be specified using thekeys
option.{:map, key_type, value_type}
- A map consisting ofkey_type
keys andvalue_type
values.:atom
- An atom.:string
- A string.:boolean
- A boolean.:integer
- An integer.:non_neg_integer
- A non-negative integer.:pos_integer
- A positive integer.:float
- A float.:timeout
- A non-negative integer or the atom:infinity
.:pid
- A PID (process identifier).:reference
- A reference (seereference/0
).nil
- The valuenil
itself. Available since v1.0.0.:mfa
- A named function in the format{module, function, arity}
wherearity
is a list of arguments. For example,{MyModule, :my_fun, [arg1, arg2]}
.:mod_arg
- A module along with arguments, such as{MyModule, arguments}
. Usually used for process initialization usingstart_link
and similar. The second element of the tuple can be any term.:fun
- Any function.{:fun, arity}
- Any function with the specified arity.{:fun, args_types}
- A function with the specified arguments.{:fun, args_types, return_type}
- A function with the specified arguments and return type.{:in, choices}
or{:one_of, choices}
- A value that is a member of one of thechoices
.choices
should be a list of terms or aRange
. The value is an element in said list of terms, that is,value in choices
istrue
.{:struct, struct_name}
- An instance of the struct type given.:struct
- An instance of any struct{:tagged_tuple, tag, inner_type}
- maps to{tag, type}
{:spark_behaviour, behaviour}
- expects a module that implements the given behaviour, and can be specified with options, i.emod
or{mod, [opt: :val]}
{:spark_behaviour, behaviour, builtin_module}
- Same as the above, but also accepts abuiltin_module
. The builtin_module is used to provide additional options for the elixir_sense plugin.{:spark_function_behaviour, behaviour, {function_mod, arity}}
- expects a module that implements the given behaviour, and can be specified with options, i.emod
or{mod, [opt: :val]}
, that also has a special module that supports being provided an anonymous function or MFA as the:fun
option.{:spark_function_behaviour, behaviour, builtin_module, {function_mod, arity}}
- Same as the above, but also accepts abuiltin_module
. The builtin_module is used to provide additional options for the elixir_sense plugin.{:behaviour, behaviour}
- expects a module that implements a given behaviour.{:protocol, protocol}
- expects a value for which the protocol is implemented.{:spark, dsl_module}
- expects a module that is aSpark.Dsl
{:mfa_or_fun, arity}
- expects a function or MFA of a corresponding arity.{:spark_type, module, builtin_function}
- a behaviour that definesbuiltin_function/0
that returns a list of atoms that map to built in variations of that thing.{:spark_type, module, builtin_function, templates}
- same as the above, but includes additional templates for elixir_sense autocomplete:literal
-> any literal value. Maps to:any
, but is used for documentation.{:literal, value}
-> exactly the value specified.:quoted
-> retains the quoted value of the code provided to the option{:wrap_list, type}
-> Allows a single value or a list of values.{:custom, mod, fun, args}
- A custom type. The related value must be validated bymod.fun(values, ...args)
. The function should return{:ok, value}
or{:error, message}
.{:or, subtypes}
- A value that matches one of the givensubtypes
. The value is matched against the subtypes in the order specified in the list ofsubtypes
. If one of the subtypes matches and updates (casts) the given value, the updated value is used. For example:{:or, [:string, :boolean, {:fun, 2}]}
. If one of the subtypes is a keyword list or map, you won't be able to pass:keys
directly. For this reason,:keyword_list
,:non_empty_keyword_list
, and:map
are special cased and can be used as subtypes with{:keyword_list, keys}
,{:non_empty_keyword_list, keys}
or{:map, keys}
. For example, a type such as{:or, [:boolean, keyword_list: [enabled: [type: :boolean]]]}
would match either a boolean or a keyword list with the:enabled
boolean option in it.{:list, subtype}
- A list where all elements matchsubtype
.subtype
can be any of the accepted types listed here. Empty lists are allowed. The resulting validated list contains the validated (and possibly updated) elements, each as returned after validation throughsubtype
. For example, ifsubtype
is a custom validator function that returns an updated value, then that updated value is used in the resulting list. Validation fails at the first element that is invalid according tosubtype
. Ifsubtype
is a keyword list or map, you won't be able to pass:keys
directly. For this reason,:keyword_list
,:non_empty_keyword_list
, and:map
are special cased and can be used as the subtype by using{:keyword_list, keys}
,{:non_empty_keyword_list, keys}
or{:keyword_list, keys}
. For example, a type such as{:list, {:keyword_list, enabled: [type: :boolean]}}
would a list of keyword lists, where each keyword list in the list could have the:enabled
boolean option in it.{:tuple, list_of_subtypes}
- A tuple as described bytuple_of_subtypes
.list_of_subtypes
must be a list with the same length as the expected tuple. Each of the list's elements must be a subtype that should match the given element in that same position. For example, to describe 3-element tuples with an atom, a string, and a list of integers you would use the type{:tuple, [:atom, :string, {:list, :integer}]}
. Available since v0.4.1.
Example
iex> schema = [
...> producer: [
...> type: :non_empty_keyword_list,
...> required: true,
...> keys: [
...> module: [required: true, type: :mod_arg],
...> concurrency: [
...> type: :pos_integer,
...> ]
...> ]
...> ]
...> ]
...>
...> config = [
...> producer: [
...> concurrency: 1,
...> ]
...> ]
...>
...> {:error, %Spark.Options.ValidationError{} = error} = Spark.Options.validate(config, schema)
...> Exception.message(error)
"required :module option not found, received options: [:concurrency] (in options [:producer])"
Nested Option Items
Spark.Options
allows option items to be nested so you can recursively validate
any item down the options tree.
Example
iex> schema = [
...> producer: [
...> required: true,
...> type: :non_empty_keyword_list,
...> keys: [
...> rate_limiting: [
...> type: :non_empty_keyword_list,
...> keys: [
...> interval: [required: true, type: :pos_integer]
...> ]
...> ]
...> ]
...> ]
...> ]
...>
...> config = [
...> producer: [
...> rate_limiting: [
...> interval: :oops!
...> ]
...> ]
...> ]
...>
...> {:error, %Spark.Options.ValidationError{} = error} = Spark.Options.validate(config, schema)
...> Exception.message(error)
"invalid value for :interval option: expected positive integer, got: :oops! (in options [:producer, :rate_limiting])"
Validating Schemas
Each time validate/2
is called, the given schema itself will be validated before validating
the options.
In most applications the schema will never change but validating options will be done repeatedly.
To avoid the extra cost of validating the schema, it is possible to validate the schema once,
and then use that valid schema directly. This is done by using the new!/1
function first, and
then passing the returned schema to validate/2
.
Create the Schema at Compile Time
If your option schema doesn't include any runtime-only terms in it (such as anonymous functions), you can call
new!/1
to validate the schema and returned a compiled schema at compile time. This is an efficient way to avoid doing any unnecessary work at runtime. See the example below for more information.
Example
iex> raw_schema = [
...> hostname: [
...> required: true,
...> type: :string
...> ]
...> ]
...>
...> schema = Spark.Options.new!(raw_schema)
...> Spark.Options.validate([hostname: "elixir-lang.org"], schema)
{:ok, hostname: "elixir-lang.org"}
Calling new!/1
from a function that receives options will still validate the schema each time
that function is called. Declaring the schema as a module attribute is supported:
@options_schema Spark.Options.new!([...])
This schema will be validated at compile time. Calling docs/1
on that schema is also
supported.
Summary
Functions
Returns documentation for the given schema.
Merges two schemas, and sets the subsection
option on all options on the right side.
Validates the given schema
and returns a wrapped schema to be used with validate/2
.
Returns the quoted typespec for any option described by the given schema.
Validates the given options
with the given schema
.
Validates the given options
with the given schema
and raises if they're not valid.
Types
@type schema() :: [{atom(), option_schema()}]
A schema.
See the module documentation for more information.
@type t() :: %Spark.Options{schema: schema()}
The Spark.Options
struct embedding a validated schema.
See the Validating Schemas section in the module documentation.
@type type() :: :any | :keyword_list | :non_empty_keyword_list | :map | {:map, key_type :: type(), value_type :: type()} | :atom | :string | :boolean | :integer | :non_neg_integer | :pos_integer | :float | :timeout | :pid | :reference | :mfa | :mod_arg | :fun | {:fun, arity :: non_neg_integer()} | {:fun, [type()]} | {:fun, [type()], type()} | {:in, [any()] | Range.t()} | {:or, [ type() | {:keyword_list, schema()} | {:non_empty_keyword_list, schema()} | {:map, schema()} ]} | {:list, type() | {:keyword_list, schema()} | {:non_empty_keyword_list, schema()} | {:map, schema()}} | {:tuple, [type()]} | {:one_of, [any()] | Range.t()} | {:tagged_tuple, tag :: atom(), inner_type :: type()} | {:spark_behaviour, module()} | {:spark_behaviour, module(), module()} | {:spark_function_behaviour, module(), {module(), integer()}} | {:spark_function_behaviour, module(), module(), {module(), integer()}} | {:behaviour, module()} | {:protocol, module()} | {:spark, module()} | {:mfa_or_fun, non_neg_integer()} | {:spark_type, module(), builtin_function :: atom()} | {:spark_type, module(), builtin_function :: atom(), templates :: [String.t()]} | {:struct, module()} | {:wrap_list, type()} | :literal | {:literal, any()} | :quoted | {:custom, module(), function :: atom(), args :: [any()]}
Functions
Returns documentation for the given schema.
You can use this to inject documentation in your docstrings. For example, say you have your schema in a module attribute:
@options_schema [...]
With this, you can use docs/1
to inject documentation:
@doc "Supported options:\n#{Spark.Options.docs(@options_schema)}"
Options
:nest_level
- an integer deciding the "nest level" of the generated docs. This is useful when, for example, you usedocs/2
inside the:doc
option of another schema. For example, if you have the following nested schema:nested_schema = [ allowed_messages: [type: :pos_integer, doc: "Allowed messages."], interval: [type: :pos_integer, doc: "Interval."] ]
then you can document it inside another schema with its nesting level increased:
schema = [ producer: [ type: {:or, [:string, keyword_list: nested_schema]}, doc: "Either a string or a keyword list with the following keys:\n\n" <> Spark.Options.docs(nested_schema, nest_level: 1) ], other_key: [type: :string] ]
Merges two schemas, and sets the subsection
option on all options on the right side.
Validates the given schema
and returns a wrapped schema to be used with validate/2
.
If the given schema is not valid, raises a Spark.Options.ValidationError
.
Returns the quoted typespec for any option described by the given schema.
The returned quoted code represents the type union for all possible
keys in the schema, alongside their type. Nested keyword lists are
spec'ed as keyword/0
.
Usage
Because of how typespecs are treated by the Elixir compiler, you have
to use unquote/1
on the return value of this function to use it
in a typespec:
@type option() :: unquote(Spark.Options.option_typespec(my_schema))
This function returns the type union for a single option: to give you flexibility to combine it and use it in your own typespecs. For example, if you only validate part of the options through Spark.Options, you could write a spec like this:
@type my_option() ::
{:my_opt1, integer()}
| {:my_opt2, boolean()}
| unquote(Spark.Options.option_typespec(my_schema))
If you want to spec a whole schema, you could write something like this:
@type options() :: [unquote(Spark.Options.option_typespec(my_schema))]
Example
schema = [
int: [type: :integer],
number: [type: {:or, [:integer, :float]}]
]
@type option() :: unquote(Spark.Options.option_typespec(schema))
The code above would essentially compile to:
@type option() :: {:int, integer()} | {:number, integer() | float()}
@spec validate( keyword(), schema() | t() ) :: {:ok, validated_options :: keyword()} | {:error, Spark.Options.ValidationError.t()}
Validates the given options
with the given schema
.
See the module documentation for what a schema
is.
If the validation is successful, this function returns {:ok, validated_options}
where validated_options
is a keyword list. If the validation fails, this
function returns {:error, validation_error}
where validation_error
is a
Spark.Options.ValidationError
struct explaining what's wrong with the options.
You can use raise/1
with that struct or Exception.message/1
to turn it into a string.
Validates the given options
with the given schema
and raises if they're not valid.
This function behaves exactly like validate/2
, but returns the options directly
if they're valid or raises a Spark.Options.ValidationError
exception otherwise.