tapir, or Typed API descRiptions¶
With tapir you can describe HTTP API endpoints as immutable Scala values. Each endpoint can contain a number of input parameters, error-output parameters, and normal-output parameters. An endpoint specification can be interpreted as:
- a server, given the “business logic”: a function, which computes output parameters based on input parameters. Currently supported:
- a client, which is a function from input parameters to output parameters. Currently supported: sttp.
- documentation. Currently supported: OpenAPI.
Tapir is licensed under Apache2, the source code is available on GitHub.
Depending on how you prefer to explore the library, take a look at one of the examples or read on for a more detailed description of how tapir works!
Other sttp projects¶
sttp is a family of Scala HTTP-related projects, and currently includes:
- sttp client: the Scala HTTP client you always wanted!
- sttp tapir: this project
- sttp model: simple HTTP model classes (used by client & tapir)
Sponsors¶
Development and maintenance of sttp tapir is sponsored by SoftwareMill, a software development and consulting company. We help clients scale their business through software. Our areas of expertise include backends, distributed systems, blockchain, machine learning and data analytics.
Table of contents¶
Quickstart¶
To use tapir, add the following dependency to your project:
"com.softwaremill.sttp.tapir" %% "tapir-core" % "0.16.3"
This will import only the core classes needed to create endpoint descriptions. To generate a server or a client, you will need to add further dependencies.
Most of tapir functionalities are grouped into package objects which provide builder and extensions methods, hence it’s easiest to work with tapir if you import whole packages, e.g.:
import sttp.tapir._
If you don’t have it already, you’ll also need partial unification enabled in the compiler (alternatively, you’ll need to manually provide type arguments in some cases). In sbt, this is:
scalacOptions += "-Ypartial-unification"
Finally, type:
endpoint.
and see where auto-complete gets you!
Examples¶
The examples
sub-project contains a number of runnable tapir usage examples:
- Hello world server, using akka-http
- Hello world server, using http4s
- Separate error & success outputs, using akka-http
- Multiple endpoints, exposing OpenAPI/Swagger documentation, using akka-http
- Multiple endpoints, exposing OpenAPI/Swagger documentation, using http4s
- Multiple endpoints, with the description coupled with server logic, using akka-http
- Reporting errors in a custom format when a query/path/.. parameter cannot be decoded
- Using custom types in endpoint descriptions
- Multipart form upload, using akka-http
- Partial server logic (authentication), using akka-http
- Books example
- ZIO example, using http4s
- ZIO partial server logic example, using http4s
- Streaming body, using akka-http
- Streaming body, using http4s + fs2
Other examples¶
To see an example project using Tapir, check out this Todo-Backend using tapir and http4s.
Blogs, articles¶
Goals of the project¶
- programmer-friendly, human-comprehensible types, that you are not afraid to write down
- (also inferencable by IntelliJ)
- discoverable API through standard auto-complete
- separate “business logic” from endpoint definition & documentation
- as simple as possible to generate a server, client & docs
- based purely on case class-based, immutable and reusable data structures
- first-class OpenAPI support. Provide as much or as little detail as needed.
- reasonably type safe: only, and as much types to safely generate the server/client/docs
Basics¶
An endpoint is represented as a value of type Endpoint[I, E, O, S]
, where:
I
is the type of the input parametersE
is the type of the error-output parametersO
is the type of the output parametersS
is the type of streams that are used by the endpoint’s inputs/outputs
Input/output parameters (I
, E
and O
) can be:
- of type
Unit
, when there’s no input/ouput of the given type - a single type
- a tuple of types
Hence, an empty, initial endpoint (tapir.endpoint
), with no inputs and no outputs, from which all other endpoints are
derived has the type:
val endpoint: Endpoint[Unit, Unit, Unit, Nothing] = ...
An endpoint which accepts two parameters of types UUID
and Int
, upon error returns a String
, and on normal
completion returns a User
, would have the type:
Endpoint[(UUID, Int), String, User, Nothing]
You can think of an endpoint as a function, which takes input parameters of type I
and returns a result of type
Either[E, O]
, where inputs or outputs can contain streaming bodies of type S
.
Infallible endpoints¶
Note that the empty endpoint
description maps no values to either error and success outputs, however errors
are still represented and allowed to occur. If you would prefer to use an endpoint description, where
errors can not happen, use infallibleEndpoint: Endpoint[Unit, Nothing, Unit, Nothing]
. This might be useful when
interpreting endpoints as a client.
Defining an endpoint¶
The description of an endpoint is an immutable case class, which includes a number of methods:
- the
name
,description
, etc. methods allow modifying the endpoint information, which will then be included in the endpoint documentation - the
get
,post
etc. methods specify the HTTP method which the endpoint should support - the
in
,errorOut
andout
methods allow adding a new input/output parameter mapIn
,mapInTo
, … methods allow mapping the current input/output parameters to another value or to a case class
An important note on mapping: in tapir, all mappings are bi-directional. That’s because each mapping can be used to generate a server or a client, as well as in many cases can be used both for input and for output.
Next¶
Read on about describing endpoint inputs/outputs.
Inputs/outputs¶
An input is described by an instance of the EndpointInput
trait, and an output by an instance of the EndpointOutput
trait. Some inputs can be used both as inputs and outputs; then, they additionally implement the EndpointIO
trait.
Each input or output can yield/accept a value (but doesn’t have to).
For example, query[Int]("age"): EndpointInput[Int]
describes an input, which is the age
parameter from the URI’s
query, and which should be coded (using the string-to-integer codec) as an Int
.
The tapir
package contains a number of convenience methods to define an input or an output for an endpoint.
For inputs, these are:
path[T]
, which captures a path segment as an input parameter of typeT
- any string, which will be implicitly converted to a fixed path segment. Path segments can be combined with the
/
method, and don’t map to any values (have typeEndpointInput[Unit]
) paths
, which maps to the whole remaining path as aList[String]
query[T](name)
captures a query parameter with the given namequeryParams
captures all query parameters, represented asQueryParams
cookie[T](name)
captures a cookie from theCookie
header with the given nameextractFromRequest
extracts a value from the request. This input is only used by server interpreters, ignored by documentation interpreters. Client interpreters ignore the provided value.
For both inputs/outputs:
header[T](name)
captures a header with the given nameheaders
captures all headers, represented asList[Header]
cookies
captures cookies from theCookie
header and represents them asList[Cookie]
setCookie(name)
captures the value & metadata of the aSet-Cookie
header with a matching namesetCookies
captures cookies from theSet-Cookie
header and represents them asList[SetCookie]
stringBody
,plainBody[T]
,jsonBody[T]
,rawBinaryBody[R]
,binaryBody[R, T]
,formBody[T]
,multipartBody[T]
captures the bodystreamBody[S]
captures the body as a stream: only a client/server interpreter supporting streams of typeS
can be used with such an endpoint
For outputs:
statusCode
maps to the status code of the responsestatusCode(code)
maps to a fixed status code of the response
Combining inputs and outputs¶
Endpoint inputs/outputs can be combined in two ways. However they are combined, the values they represent always accumulate into tuples of values.
First, inputs/outputs can be combined using the .and
method. Such a combination results in an input/output, which maps
to a tuple of the given types. This combination can be assigned to a value and re-used in multiple endpoints. As all
other values in tapir, endpoint input/output descriptions are immutable. For example, an input specifying two query
parameters, start
(mandatory) and limit
(optional) can be written down as:
val paging: EndpointInput[(UUID, Option[Int])] =
query[UUID]("start").and(query[Option[Int]]("limit"))
// we can now use the value in multiple endpoints, e.g.:
val listUsersEndpoint: Endpoint[(UUID, Option[Int]), Unit, List[User], Nothing] =
endpoint.in("user" / "list").in(paging).out(jsonBody[List[User]])
Second, inputs can be combined by calling the in
, out
and errorOut
methods on Endpoint
multiple times. Each time
such a method is invoked, it extends the list of inputs/outputs. This can be useful to separate different groups of
parameters, but also to define template-endpoints, which can then be further specialized. For example, we can define a
base endpoint for our API, where all paths always start with /api/v1.0
, and errors are always returned as a json:
val baseEndpoint: Endpoint[Unit, ErrorInfo, Unit, Nothing] =
endpoint.in("api" / "v1.0").errorOut(jsonBody[ErrorInfo])
Thanks to the fact that inputs/outputs accumulate, we can use the base endpoint to define more inputs, for example:
val statusEndpoint: Endpoint[Unit, ErrorInfo, Status, Nothing] =
baseEndpoint.in("status").out(jsonBody[Status])
The above endpoint will correspond to the api/v1.0/status
path.
Mapping over input/output values¶
Inputs/outputs can also be mapped over. As noted before, all mappings are bi-directional, so that they can be used both when interpreting an endpoint as a server, and as a client, as well as both in input and output contexts.
There’s a couple of ways to map over an input/output. First, there’s the map[II](f: I => II)(g: II => I)
method,
which accepts functions which provide the mapping in both directions. For example:
case class Paging(from: UUID, limit: Option[Int])
val paging: EndpointInput[Paging] =
query[UUID]("start").and(query[Option[Int]]("limit"))
.map((from, limit) => Paging(from, limit))(paging => (paging.from, paging.limit))
Next, you can use mapDecode[II](f: I => DecodeResult[II])(g: II => I)
, to handle cases where decoding (mapping a
low-level value to a higher-value one) can fail. There’s a couple of failure reasons, captured by the alternatives
of the DecodeResult
trait.
Mappings can also be done given an Mapping[I, II]
instance. More on that in the secion on codecs.
Creating a mapping between a tuple and a case class is a common operation, hence there’s also a
mapTo(CaseClassCompanion)
method, which automatically provides the functions to construct/deconstruct the case class:
case class Paging(from: UUID, limit: Option[Int])
val paging: EndpointInput[Paging] =
query[UUID]("start").and(query[Option[Int]]("limit"))
.mapTo(Paging)
Mapping methods can also be called on an endpoint (which is useful if inputs/outputs are accumulated, for example).
The Endpoint.mapIn
, Endpoint.mapInTo
etc. have the same signatures are the ones above.
Path matching¶
By default (as with all other types of inputs), if no path input/path segments are defined, any path will match.
If any path input/path segment is defined, the path must match exactly - any remaining path segments will cause the
endpoint not to match the request. For example, endpoint.in("api")
will match /api
, /api/
, but won’t match
/
, /api/users
.
To match only the root path, use an empty string: endpoint.in("")
will match http://server.com/
and
http://server.com
.
To match a path prefix, first define inputs which match the path prefix, and then capture any remaining part using
paths
, e.g.: endpoint.in("api" / "download").in(paths)"
.
Streaming support¶
Both input and output bodies can be mapped to a stream, by using streamBody[S]
. The type S
must match the type of
streams that are supported by the interpreter: refer to the documentation of server/client interpreters for the
precise type.
Adding a stream body input/output influences both the type of the input/output, as well as the 4th type parameter
of Endpoint
, which specifies the requirements regarding supported stream types for interpreters.
When using a stream body, the schema (for documentation) and format (media type) of the body must be provided by hand,
as they cannot be inferred from the raw stream type. For example, to specify that the output is an akka-stream, which
is a (presumably large) serialised list of json objects mapping to the Person
class:
endpoint.out(streamBody[Source[ByteString, Any]](schemaFor[List[Person]], CodecFormat.Json()))
See also the runnable streaming example.
Next¶
Read on about status codes.
Status codes¶
To provide a (varying) status code of a server response, use the statusCode
output, which maps to a value of type
sttp.model.StatusCode
. The companion object contains known status
codes as constants. This type of output is used only when interpreting the endpoint as a server. If your endpoint returns varying status codes
which you would like to have listed in documentation use statusCode.description(code1, "code1 description").description(code2, "code2 description")
output.
Alternatively, a fixed status code can be specified using the statusCode(code)
output.
Dynamic status codes¶
It is also possible to specify how status codes map to different outputs. All mappings should have a common supertype, which is also the type of the output. These mappings are used to determine the status code when interpreting an endpoint as a server, as well as when generating documentation and to deserialise client responses to the appropriate type, basing on the status code.
For example, below is a specification for an endpoint where the error output is a sealed trait ErrorInfo
;
such a specification can then be refined and reused for other endpoints:
sealed trait ErrorInfo
case class NotFound(what: String) extends ErrorInfo
case class Unauthorized(realm: String) extends ErrorInfo
case class Unknown(code: Int, msg: String) extends ErrorInfo
case object NoContent extends ErrorInfo
// here we are defining an error output, but the same can be done for regular outputs
val baseEndpoint = endpoint.errorOut(
oneOf[ErrorInfo](
statusMapping(StatusCode.NotFound, jsonBody[NotFound].description("not found")),
statusMapping(StatusCode.Unauthorized, jsonBody[Unauthorized].description("unauthorized")),
statusMapping(StatusCode.NoContent, emptyOutput.map(_ => NoContent)(_ => ())),
statusDefaultMapping(jsonBody[Unknown].description("unknown"))
)
)
Each mapping, defined using the statusMapping
method is a case class, containing the output description as well as
the status code. Moreover, default mappings can be defined using statusDefaultMapping
:
- for servers, the default status code for error outputs is
400
, and for normal outputs200
(unless astatusCode
is used in the nested output) - for clients, a default mapping is a catch-all.
Both statusMapping
and statusDefaultMapping
return a value of type StatusMapping
. A list of these values can be
dynamically assembled (e.g. using a default set of cases, plus endpoint-specific mappings), and provided to oneOf
.
Status mapping and type erasure¶
Sometime at runtime status mapping resolution can not work properly because of type erasure.
For example this code will fail at compile time; because of type erasure Right[NotFound]
and Right[BadRequest]
will
become Right[Any]
, therefore the code would not be able to find the correct mapping for a value:
case class ServerError(what: String)
sealed trait UserError
case class BadRequest(what: String) extends UserError
case class NotFound(what: String) extends UserError
val baseEndpoint = endpoint.errorOut(
oneOf[Either[ServerError, UserError]](
statusMapping(StatusCode.NotFound, jsonBody[Right[ServerError, NotFound]].description("not found")),
statusMapping(StatusCode.BadRequest, jsonBody[Right[ServerError, BadRequest]].description("unauthorized")),
statusMapping(StatusCode.InternalServerError, jsonBody[Left[ServerError, UserError]].description("unauthorized")),
)
)
The solution is therefore to handwrite a function checking that a val
(of type Any
) is of the correct type:
val baseEndpoint = endpoint.errorOut(
oneOf[Either[ServerError, UserError]](
statusMappingValueMatcher(StatusCode.NotFound, jsonBody[Right[ServerError, NotFound]].description("not found")) {
case Right(NotFound(_)) => true
},
statusMappingValueMatcher(StatusCode.BadRequest, jsonBody[Right[ServerError, BadRequest]].description("unauthorized")) {
case Right(BadRequest(_)) => true
},
statusMappingValueMatcher(StatusCode.InternalServerError, jsonBody[Left[ServerError, UserError]].description("unauthorized")) {
case Left(ServerError(_)) => true
}
)
)
Of course you could use statusMappingValueMatcher
to do runtime filtering for other purpose than solving type erasure.
In the case of solving type erasure, writing by hand partial function to match value against composition of case class and sealed trait can be repetitive.
To make that more easy, we provide an experimental typeclass - MatchType
- so you can automatically derive that partial function:
import sttp.tapir.typelevel.MatchType
val baseEndpoint = endpoint.errorOut(
oneOf[Either[ServerError, UserError]](
statusMappingFromMatchType(StatusCode.NotFound, jsonBody[Right[ServerError, NotFound]].description("not found")),
statusMappingFromMatchType(StatusCode.BadRequest, jsonBody[Right[ServerError, BadRequest]].description("unauthorized")),
statusMappingFromMatchType(StatusCode.InternalServerError, jsonBody[Left[ServerError, UserError]].description("unauthorized"))
)
)
Server interpreters¶
Unless specified otherwise, successful responses are returned with the 200 OK
status code, and errors with
400 Bad Request
. For exception and decode failure handling, see error handling.
Codecs¶
Mappings¶
The Mapping[L, H]
trait defines a bi-directional mapping between values of type L
and values of type H
.
Low-level values of type L
can be decoded to a higher-level value of type H
. The decoding can fail; this is
represented by a result of type DecodeResult.Failure
. Failures might occur due to format errors, wrong arity, exceptions,
or validation errors. Validators can be added through the validate
method.
High-level values of type H
can be encoded as a low-level value of type L
.
Mappings can be chained using one of the map
functions.
Codecs¶
A Codec[L, H, CF]
is a Mapping[L, H]
, with additional meta-data: an optional schema and the format of the
low-level value (more on that below).
There are built-in codecs for most common types such as String
, Int
etc. Codecs are usually defined as implicit
values and resolved implicitly when they are referenced. However, they can also be provided explicitly as needed.
For example, a query[Int]("quantity")
specifies an input parameter which corresponds to the quantity
query
parameter and will be mapped as an Int
. There’s an implicit Codec[List[String], Int, TextPlain]
value that is
referenced by the query
method (which is defined in the sttp.tapir
package).
In this example, the low-level value is a List[String]
, as a given query parameter can be absent, have a single or
many values. The high-level value is an Int
. The codec will verify that there’s a single query paramater with the
given name, and parse it as an int. If any of this fails, a failure will be reported.
In a server setting, if the value cannot be parsed as an int, a decoding failure is reported, and the endpoint
won’t match the request, or a 400 Bad Request
response is returned (depending on configuration).
As each codec is also a Mapping
, codecs can be used to map endpoint inputs and outputs. In such cases, the
additional meta-data (schema & format) isn’t taken into account.
Optional and multiple parameters¶
Some inputs/outputs allow optional, or multiple parameters:
- path segments are always required
- query and header values can be optional or multiple (repeated query parameters/headers)
- bodies can be optional, but not multiple
In general, optional parameters are represented as Option
values, and multiple parameters as List
values.
For example, header[Option[String]]("X-Auth-Token")
describes an optional header. An input described as
query[List[String]]("color")
allows multiple occurrences of the color
query parameter, with all values gathered
into a list.
Schemas¶
A codec contains an optional schema, which describes how the high-level value is encoded when sent over the network. This schema information is used when generating documentation.
Schemas consists of the schema type, which is one of the values defined in SchemaType
, such as SString
,
SBinary
, SArray
or SProduct
(for objects). Moreover, a schema contains meta-data: value optionality,
description and low-level format.
For primitive types, the schema values are built-in, and defined in the Schema
companion object.
The schema is left unchanged when mapping a codec or an input/output, as the underlying representation of the value
doesn’t change. However, schemas can be changed for individual inputs/outputs using the .schema(Schema)
method.
When codecs are derived for complex types, e.g. for json mapping, schemas are looked up through implicit
Schema[T]
values. See custom types for more details.
Codec format¶
Codecs contain an additional type parameter, which specifies the codec format. Each format corresponds to a media type,
which describes the low-level format of the raw value (to which the codec encodes). Some built-in formats include
text/plain
, application/json
and multipart/form-data
. Custom formats can be added by creating an
implementation of the sttp.tapir.CodecFormat
trait.
Thanks to codecs being parametrised by codec formats, it is possible to have a Codec[String, MyCaseClass, TextPlain]
which
specifies how to serialize a case class to plain text, and a different Codec[String, MyCaseClass, Json]
, which specifies
how to serialize a case class to json. Both can be implicitly available without implicit resolution conflicts.
Different codec formats can be used in different contexts. When defining a path, query or header parameter, only a codec
with the TextPlain
media type can be used. However, for bodies, any media types is allowed. For example, the
input/output described by jsonBody[T]
requires a json codec.
Next¶
Read on about custom types.
Custom types¶
To support a custom type, you’ll need to provide an implicit Codec
for that type.
This can be done by writing a codec from scratch, mapping over an existing codec, or automatically deriving one. Which of these approaches can be taken, depends on the context in which the codec will be used.
Providing an implicit codec¶
To create a custom codec, you can either directly implement the Codec
trait, which requires to provide the following
information:
encode
andrawDecode
methods- optional schema (for documentation)
- optional validator
- codec format (
text/plain
,application/json
etc.)
This might be quite a lot of work, that’s why it’s usually easier to map over an existing codec. To do that, you’ll need to provide two mappings:
- a
decode
method which decodes the lower-level type into the custom type, optionally reporting decode failures (the return type is aDecodeResult
) - an
encode
method which encodes the custom type into the lower-level type
For example, to support a custom id type:
def decode(s: String): DecodeResult[MyId] = MyId.parse(s) match {
case Success(v) => DecodeResult.Value(v)
case Failure(f) => DecodeResult.Error(s, f)
}
def encode(id: MyId): String = id.toString
implicit val myIdCodec: Codec[String, MyId, TextPlain] =
Codec.string.mapDecode(decode)(encode)
// or, using the type alias for codecs in the TextPlain format and String as the raw value:
implicit val myIdCodec: PlainCodec[MyId] = Codec.string.mapDecode(decode)(encode)
Note
Note that inputs/outputs can also be mapped over. In some cases, it’s enough to create an input/output corresponding to one of the existing types, and then map over them. However, if you have a type that’s used multiple times, it’s usually better to define a codec for that type.
Then, you can use the new codec e.g. to obtain an id from a query parameter or a path segment:
endpoint.in(query[MyId]("myId"))
// or
endpoint.in(path[MyId])
Automatically deriving codecs¶
In some cases, codecs can be automatically derived:
Automatic codec derivation usually requires other implicits, such as:
- json encoders/decoders from the json library
- codecs for individual form fields
- schema of the custom type, through the
Schema[T]
implicit
Schema derivation¶
For case classes types, Schema[_]
values are derived automatically using Magnolia, given
that schemas are defined for all of the case class’s fields. It is possible to configure the automatic derivation to use
snake-case, kebab-case or a custom field naming policy, by providing an implicit tapir.generic.Configuration
value:
implicit val customConfiguration: Configuration =
Configuration.default.withSnakeCaseMemberNames
Alternatively, Schema[_]
values can be defined by hand, either for whole case classes, or only for some of its fields.
For example, here we state that the schema for MyCustomType
is a String
:
implicit val schemaForMyCustomType: Schema[MyCustomType] = Schema(SchemaType.SString)
If you have a case class which contains some non-standard types (other than strings, number, other case classes, collections), you only need to provide the schema for the non-standard types. Using these schemas, the rest will be derived automatically.
Sealed traits / coproducts¶
Tapir supports schema generation for coproduct types (sealed trait hierarchies) of the box, but they need to be defined by hand (as implicit values). To properly reflect the schema in OpenAPI documentation, a discriminator object can be specified.
For example, given following coproduct:
sealed trait Entity{
def kind: String
}
case class Person(firstName:String, lastName:String) extends Entity {
def kind: String = "person"
}
case class Organization(name: String) extends Entity {
def kind: String = "org"
}
The schema may look like this:
val sPerson = implicitly[Schema[Person]]
val sOrganization = implicitly[Schema[Organization]]
implicit val sEntity: Schema[Entity] =
Schema.oneOf[Entity, String](_.kind, _.toString)("person" -> sPerson, "org" -> sOrganization)
Customising derived schemas¶
In some cases, it might be desirable to customise the derived schemas, e.g. to add a description to a particular
field of a case class. This can be done by looking up an implicit instance of the Derived[Schema[T]]
type,
and assigning it to an implicit schema. When such an implicit Schema[T]
is in scope will have higher priority
than the built-in low-priority conversion from Derived[Schema[T]]
to Schema[T]
.
Schemas for products/coproducts (case classes and case class families) can be traversed and modified using
.modify
method. To traverse collections, use .each
.
For example:
case class Basket(fruits: List[FruitAmount])
case class FruitAmount(fruit: String, amount: Int)
implicit val customBasketSchema: Schema[Basket] = implicitly[Derived[Schema[Basket]]].value
.modify(_.fruits.each.amount)(_.description("How many fruits?"))
There is also an unsafe variant of this method, but it should be avoided in most cases. The “unsafe” prefix comes from the fact that the method takes a list of strings, which represent fields, and the correctness of this specification is not checked.
Non-standard collections can be unwrapped in the modification path by providing an implicit value of ModifyFunctor
.
Next¶
Read on about validation.
Validation¶
Tapir supports validation for primitive/base types. Validation of composite values, whole data structures, business
rules enforcement etc. should be done as part of the server logic of the endpoint, using the
dedicated error output (the E
in Endpoint[I, E, O, S]
) to report errors.
Single type validation¶
Validation rules are part of the codec for a given type. They can be specified when creating the codec
(using the Codec.validate()
method):
case class MyId(id: String)
implicit val myIdCodec: Codec[String, MyId, TextPlain] = Codec.string
.mapDecode(decode)(encode)
.validate(Validator.pattern("^[A-Z].*").contramap(_.id))
Validators can also be added to individual inputs/outputs, in addition to whatever the codec provides:
val e = endpoint.in(
query[Int]("amount")
.validate(Validator.min(0))
.validate(Validator.max(100)))
Validation rules added using the built-in validators are translated to OpenAPI documentation.
Validation rules and automatic codec derivation¶
When a codec is automatically derived for a type (see custom types), validators for all types
(for json this is a recursive process) are looked up through implicit Validator[T]
values.
Note
Implicit Validator[T]
values are used only when automatically deriving codecs. They are not used
when the codec is defined by hand.
Note that to validate a nested member of a case class, it needs to have a unique type (that is, not an Int
, as
providing an implicit Validator[Int]
would validate all ints in the hierarchy), as validator lookup is type-driven.
To introduce unique types for primitive values, you can use value classes or type tagging.
For example, to support an integer wrapped in a value type in a json body, we need to provide Circe encoders and decoders (if that’s the json library that we are using), schema information and a validator:
case class Amount(v: Int) extends AnyVal
case class FruitAmount(fruit: String, amount: Amount)
val e: Endpoint[FruitAmount, Unit, Unit, Nothing] = {
implicit val schemaForAmount: SchemaFor[Amount] = SchemaFor(Schema.SInteger)
implicit val encoder: Encoder[Amount] = Encoder.encodeInt.contramap(_.v)
implicit val decode: Decoder[Amount] = Decoder.decodeInt.map(Amount.apply)
implicit val v: Validator[Amount] = Validator.min(1).contramap(_.v)
endpoint.in(jsonBody[FruitAmount])
}
Decode failures¶
Codecs support reporting decoding failures, by returning a DecodeResult
from the Codec.decode
method. However, this
is meant for input/output values which are in an incorrect low-level format, when parsing a “raw value” fails. In other
words, decoding failures should be reported for format failures, not business validation errors.
To customise error messages that are returned upon validation/decode failures by the server, see error handling.
Enum validators¶
Validators for enumerations can be created using the Validator.enum
method, which either:
- takes a type parameter, which should be an abstract, sealed base type, and using a macro determines the possible implementations
- takes the list of possible values
To properly represent possible values in documentation, the enum validator additionally needs an encode
method, which
converts the enum value to a raw type (typically a string). This method is inferred only if the validator is directly
added to a codec (without any mapping etc.), for example:
sealed trait Color
case object Blue extends Color
case object Red extends Color
implicit def plainCodecForColor: PlainCodec[Color] = {
Codec.string
.map[Color]((_: String) match {
case "red" => Red
case "blue" => Blue
})(_.toString.toLowerCase)
.validate(Validator.enum)
}
If the enum is nested within an object, regardless of whether the codec for that object is defined by hand or derived, we need to specify the encode function by hand:
implicit def colorValidator: Validator[Color] = Validator.enum.encode(_.toString.toLowerCase)
Like other validators, enum validators need to be added to a codec, or through an implicit value, if the codec and validator is automatically derived.
Next¶
Read on about json support.
Working with JSON¶
Json values are supported through codecs, which encode/decode values to json strings. Most often, you’ll be using a third-party library to perform the actual json parsing/printing. Currently, Circe, µPickle, Spray JSON and Play JSON are supported.
All of the integrations, when imported into scope, define a jsonBody[T]
method. This method depends on
library-specific implicits being in scope, and derives from them a json codec. The derivation also requires implicit
Schema[T]
and Validator[T]
instances, which should be automatically derived. For more details see documentation
on supporting custom types.
If you have a custom, implicit Codec[String, T, Json]
instance, you should use the anyJsonBody[T]
method instead.
This description of endpoint input/output, instead of deriving a codec basing on other library-specific implicits, uses
the json codec that is in scope.
Circe¶
To use Circe add the following dependency to your project:
"com.softwaremill.sttp.tapir" %% "tapir-json-circe" % "0.16.3"
Next, import the package (or extend the TapirJsonCirce
trait, see MyTapir):
import sttp.tapir.json.circe._
This will allow automatically deriving Codec
s which, given an in-scope circe Encoder
/Decoder
and a Schema
,
will create a codec using the json media type. Circe includes a couple of approaches to generating encoders/decoders
(manual, semi-auto and auto), so you may choose whatever suits you.
Note that when using Circe’s auto derivation, any encoders/decoders for custom types must be in scope as well.
Additionally, the above import brings into scope the jsonBody[T]
body input/output description, which uses the above
codec.
For example, to automatically generate a JSON codec for a case class:
import sttp.tapir._
import sttp.tapir.json.circe._
import io.circe.generic.auto._
case class Book(author: String, title: String, year: Int)
val bookInput: EndpointIO[Book] = jsonBody[Book]
Circe lets you select an instance of io.circe.Printer
to configure the way JSON objects are rendered. By default
Tapir uses Printer.nospaces
, which would render:
Json.obj(
"key1" -> Json.fromString("present"),
"key2" -> Json.Null
)
as
{"key1":"present","key2":null}
Suppose we would instead want to omit null
-values from the object and pretty-print it. You can configure this by
overriding the jsonPrinter
in tapir.circe.json.TapirJsonCirce
:
object MyTapirJsonCirce extends TapirJsonCirce {
override def jsonPrinter: Printer = Printer.spaces2.copy(dropNullValues = true)
}
import MyTapirJsonCirce._
Now the above JSON object will render as
{"key1":"present"}
µPickle¶
To use µPickle add the following dependency to your project:
"com.softwaremill.sttp.tapir" %% "tapir-json-upickle" % "0.16.3"
Next, import the package (or extend the TapirJsonuPickle
trait, see MyTapir and add TapirJsonuPickle
not TapirCirceJson
):
import sttp.tapir.json.upickle._
µPickle requires a ReadWriter in scope for each type you want to serialize. In order to provide one use the macroRW
macro in the companion object as follows:
import sttp.tapir._
import upickle.default._
import sttp.tapir.json.upickle._
case class Book(author: String, title: String, year: Int)
object Book {
implicit val rw: ReadWriter[Book] = macroRW
}
val bookInput: EndpointIO[Book] = jsonBody[Book]
Like Circe, µPickle allows you to control the rendered json output. Please see the Custom Configuration of the manual for details.
For more examples, including making a custom encoder/decoder, see TapirJsonuPickleTests.scala
Play JSON¶
To use Play JSON add the following dependency to your project:
"com.softwaremill.sttp.tapir" %% "tapir-json-play" % "0.16.3"
Next, import the package (or extend the TapirJsonPlay
trait, see MyTapir and add TapirJsonPlay
not TapirCirceJson
):
import sttp.tapir.json.play._
Play JSON requires Reads
and Writes
implicit values in scope for each type you want to serialize.
Spray JSON¶
To use Spray JSON add the following dependency to your project:
"com.softwaremill.sttp.tapir" %% "tapir-json-spray" % "0.16.3"
Next, import the package (or extend the TapirJsonSpray
trait, see MyTapir and add TapirJsonSpray
not TapirCirceJson
):
import sttp.tapir.json.spray._
Spray JSON requires a JsonFormat
implicit value in scope for each type you want to serialize.
Tethys JSON¶
To use Tethys JSON add the following dependency to your project:
"com.softwaremill.sttp.tapir" %% "tapir-json-tethys" % "0.16.3"
Next, import the package (or extend the TapirJsonTethys
trait, see MyTapir and add TapirJsonTethys
not TapirCirceJson
):
import sttp.tapir.json.tethysjson._
Tethys JSON requires JsonReader
and JsonWriter
implicit values in scope for each type you want to serialize.
Jsoniter Scala¶
To use Jsoniter-scala add the following dependency to your project:
"com.softwaremill.sttp.tapir" %% "tapir-jsoniter-scala" % "0.16.3"
Next, import the package (or extend the TapirJsonJsoniter
trait, see MyTapir and add TapirJsonJsoniter
not TapirCirceJson
):
import sttp.tapir.json.jsoniter._
Jsoniter Scala requires JsonValueCodec
implicit value in scope for each type you want to serialize.
Other JSON libraries¶
To add support for additional JSON libraries, see the sources for the Circe codec (which is just a couple of lines of code).
Schemas¶
To derive json codecs automatically, not only implicits from the base library are needed (e.g. a circe
Encoder
/Decoder
), but also an implicit SchemaFor[T]
value, which provides a mapping between a type T
and its
schema. A schema-for value contains a single schema: Schema
field.
See custom types for details.
Next¶
Read on about working with forms.
Forms¶
URL-encoded forms¶
An URL-encoded form input/output can be specified in two ways. First, it is possible to map all form fields as a
Seq[(String, String)]
, or Map[String, String]
(which is more convenient if fields can’t have multiple values):
formBody[Seq[(String, String)]]: EndpointIO.Body[String, Seq[(String, String)]]
formBody[Map[String, String]]: EndpointIO.Body[String, Seq[(String, String)]]
Second, form data can be mapped to a case class. The codec for the case class is automatically derived using a macro at compile-time. The fields of the case class should have types, for which there is a plain text codec. For example:
case class RegistrationForm(name: String, age: Int, news: Boolean, city: Option[String])
formBody[RegistrationForm]
Each form-field is named the same as the case-class-field. The names can be transformed to snake or kebab case by
providing an implicit tapir.generic.Configuraton
.
Multipart forms¶
Similarly as above, multipart form input/outputs can be specified in two ways. To map to all parts of a multipart body, use:
multipartBody[Seq[AnyPart]]: EndpointIO.Body[Seq[RawPart], Seq[AnyPart]]
where type AnyPart = Part[_]
. Part
is a case class containing the name
of the part, disposition parameters,
headers, and the body. The bodies will be mapped as byte arrays (Array[Byte]
), unless a custom multipart codec
is defined using the Codec.multipartCodec
method.
As with URL-encoded forms, multipart bodies can be mapped directly to case classes, however without the restriction
on codecs for individual fields. Given a field of type T
, first a plain text codec is looked up, and if one isn’t
found, any codec for any media type (e.g. JSON) is searched for.
Each part is named the same as the case-class-field. The names can be transformed to snake or kebab case by
providing an implicit sttp.tapir.generic.Configuraton
.
Additionally, the case class to which the multipart body is mapped can contain both normal fields, and fields of type
Part[T]
. This is useful, if part metadata (e.g. the filename) is relevant.
For example:
case class RegistrationForm(userData: User, photo: Part[File], news: Boolean)
multipartBody[RegistrationForm]
Next¶
Read on about authentication.
Authentication¶
Inputs which carry authentication data wrap another input can be marked as such by declaring them using members of the
auth
object. Apart from predefined codecs for some authentication methods, such inputs will be treated differently
when generating documentation. Otherwise, they behave as normal inputs which map to the given type.
Currently, the following authentication inputs are available (assuming import sttp.tapir._
):
auth.apiKey(anotherInput)
: wraps any other input and designates it as an api key. The input is typically a header, cookie or a query parameterauth.basic[T]
: reads data from theAuthorization
header, removing theBasic
prefix. To parse the data as a base64-encoded username/password combination, use:basic[UsernamePassword]
.auth.bearer[T]
: reads data from theAuthorization
header, removing theBearer
prefix. To get the string as a token, use:bearer[String]
.auth.oauth2.authorizationCode(authorizationUrl, tokenUrl, scopes, refreshUrl): EndpointInput[String]
: creates an OAuth2 authorization using authorization code - sign in using an auth service (for documentation, requires defining also theoauth2-redirect.html
, see Generating OpenAPI documentation)
Multiple authentication inputs indicate that all of the given authentication values should be provided. Specifying
alternative authentication methods (where only one value out of many needs to be provided) is currently not supported.
However, optional authentication can be described by mapping to optional types, e.g. bearer[Option[String]]
.
When interpreting a route as a server, it is useful to define the authentication input first, to be able to share the authentication logic among multiple endpoints easily. See server logic for more details.
Next¶
Read on about datatypes integrations.
Datatypes integrations¶
Cats datatypes integration¶
The tapir-cats
module contains schema, validator and codec instances for some cats
datatypes:
"com.softwaremill.sttp.tapir" %% "tapir-cats" % "0.16.3"
See the sttp.tapir.codec.cats.TapirCodecCats
trait or import sttp.tapir.codec.cats._
to bring the implicit values
into scope.
Refined integration¶
If you use refined, the tapir-refined
module will provide implicit codecs and
validators for T Refined P
as long as a codec for T
already exists:
"com.softwaremill.sttp.tapir" %% "tapir-refined" % "0.16.3"
You’ll need to extend the sttp.tapir.codec.refined.TapirCodecRefined
trait or import sttp.tapir.codec.refined._
to bring the implicit values into scope.
The refined codecs contain a validator which wrap/unwrap the value from/to its refined equivalent.
Some predicates will bind correctly to the vanilla tapir Validator, while others will bind to a custom validator that
might not be very clear when reading the generated documentation. Correctly bound predicates can be found in
integration/refined/src/main/scala/sttp/tapir/codec/refined/TapirCodecRefined.scala
.
If you are not satisfied with the validator generated by tapir-refined
, you can provide an implicit
ValidatorForPredicate[T, P]
in scope using `ValidatorForPredicate.fromPrimitiveValidator’ to build it (do not
hesitate to contribute your work!).
Enumeratum integration¶
The tapir-enumeratum
module provides schemas, validators and codecs for Enumeratum
enumerations. To use, add the following dependency:
"com.softwaremill.sttp.tapir" %% "tapir-enumeratum" % "0.16.3"
Then, import sttp.tapir.codec.enumeratum
, or extends the sttp.tapir.codec.enumeratum.TapirCodecEnumeratum
trait.
This will bring into scope implicit values for values extending *EnumEntry
.
Enumeration integration¶
There is no library for the use of the build in scala Enumeration
, but it can be implemented by hand.
The example code below will generate enums to the open-api documentation.
trait EnumHelper { e: Enumeration =>
import io.circe._
implicit val enumDecoder: Decoder[e.Value] = Decoder.decodeEnumeration(e)
implicit val enumEncoder: Encoder[e.Value] = Encoder.encodeEnumeration(e)
implicit val schemaForEnum: Schema[e.Value] = Schema(SchemaType.SString)
implicit def validatorForEnum: Validator[e.Value] = Validator.`enum`(e.values.toList, v => Option(v))
}
object Color extends Enumeration with EnumHelper {
type Color = Value
val Blue = Value("blue")
val Red = Value("red")
}
ZIO integration¶
The tapir-zio
module defines type aliases and extension methods which make it more ergonomic to work with
ZIO and tapir. Moreover, the tapir-zio-http4s-server
contains similar extensions useful when
exposing the endpoints using the http4s server.
You’ll need the following dependencies:
"com.softwaremill.sttp.tapir" %% "tapir-zio" % "0.16.3"
"com.softwaremill.sttp.tapir" %% "tapir-zio-http4s-server" % "0.16.3"
Next, instead of the usual import sttp.tapir._
, you should import:
import sttp.tapir.ztapir._
This brings into scope all of the basic input/output descriptions, which can be used to define an endpoint.
Additionally, it defines the ZEndpoint
type alias, which should be used instead of Endpoint
.
Note
You should have only one of these imports in your source file. Otherwise, you’ll get naming conflicts. The
import sttp.tapir.ztapir._
import is meant as a complete replacement of import sttp.tapir._
.
Server logic¶
When defining the business logic for an endpoint, the following methods are available, which replace the standard ones:
def zServerLogic(logic: I => ZIO[R, E, O]): ZServerEndpoint[R, I, E, O]
def zServerLogicPart(logicPart: T => ZIO[R, E, U])
def zServerLogicForCurrent(logicPart: I => ZIO[R, E, U])
The first defines complete server logic, while the second and third allow defining server logic in parts.
Exposing endpoints using the http4s server¶
To bring into scope the extension methods used to interpret a ZServerEndpoint
as a http4s server, add the following
import:
import sttp.tapir.server.http4s.ztapir._
This adds the following methods on ZEndpoint
:
def toRoutes(logic: I => ZIO[Any, E, O]): HttpRoutes[Task]
def toRoutesR(logic: I => ZIO[R, E, O]): URIO[R, HttpRoutes[Task]]
(when the logic requires an environment)
And the following methods on ZServerEndpoint
or List[ZServerEndpoint]
:
def toRoutes: HttpRoutes[Task]
def toRoutesR: URIO[R, HttpRoutes[Task]]
(when the logic requires an environment)
Example¶
Two examples of using the ZIO integration are available. The first showcases basic functionality, while the second shows how to use partial server logic methods:
Running as an akka-http server¶
To expose an endpoint as an akka-http server, first add the following dependency:
"com.softwaremill.sttp.tapir" %% "tapir-akka-http-server" % "0.16.3"
This will transitively pull some Akka modules in version 2.6. If you want to force your own Akka version (for example 2.5), use sbt exclusion. Mind the Scala version in artifact name:
"com.softwaremill.sttp.tapir" %% "tapir-akka-http-server" % "0.16.3" exclude("com.typesafe.akka", "akka-stream_2.12")
Now import the package:
import sttp.tapir.server.akkahttp._
This adds extension methods to the Endpoint
type: toDirective
, toRoute
and toRouteRecoverErrors
. The first two
require the logic of the endpoint to be given as a function of type:
I => Future[Either[E, O]]
The third recovers errors from failed futures, and hence requires that E
is a subclass of Throwable
(an exception);
it expects a function of type I => Future[O]
.
For example:
import sttp.tapir._
import sttp.tapir.server.akkahttp._
import scala.concurrent.Future
import akka.http.scaladsl.server.Route
def countCharacters(s: String): Future[Either[Unit, Int]] =
Future.successful(Right[Unit, Int](s.length))
val countCharactersEndpoint: Endpoint[String, Unit, Int, Nothing] =
endpoint.in(stringBody).out(plainBody[Int])
val countCharactersRoute: Route = countCharactersEndpoint.toRoute(countCharacters)
Note that these functions take one argument, which is a tuple of type I
. This means that functions which take multiple
arguments need to be converted to a function using a single argument using .tupled
:
def logic(s: String, i: Int): Future[Either[Unit, String]] = ???
val anEndpoint: Endpoint[(String, Int), Unit, String, Nothing] = ???
val aRoute: Route = anEndpoint.toRoute((logic _).tupled)
The created Route
/Directive
can then be further combined with other akka-http directives, for example nested within
other routes. The tapir-generated Route
/Directive
captures from the request only what is described by the endpoint.
It’s completely feasible that some part of the input is read using akka-http directives, and the rest using tapir endpoint descriptions; or, that the tapir-generated route is wrapped in e.g. a metrics route. Moreover, “edge-case endpoints”, which require some special logic not expressible using tapir, can be always implemented directly using akka-http. For example:
val myRoute: Route = metricsDirective {
securityDirective { user =>
tapirEndpoint.toRoute(input => /* here we can use both `user` and `input` values */)
}
}
Streaming¶
The akka-http interpreter accepts streaming bodies of type Source[ByteString, Any]
, which can be used both for sending
response bodies and reading request bodies. Usage: streamBody[Source[ByteString, Any]](schema, mediaType)
.
Configuration¶
The interpreter can be configured by providing an implicit AkkaHttpServerOptions
value and status mappers, see
server options for details.
Defining an endpoint together with the server logic¶
It’s also possible to define an endpoint together with the server logic in a single, more concise step. See server logic for details.
Running as an http4s server¶
To expose an endpoint as an http4s server, first add the following dependency:
"com.softwaremill.sttp.tapir" %% "tapir-http4s-server" % "0.16.3"
and import the package:
import sttp.tapir.server.http4s._
This adds two extension methods to the Endpoint
type: toRoutes
and toRoutesRecoverErrors
. This first requires the
logic of the endpoint to be given as a function of type:
I => F[Either[E, O]]
where F[_]
is the chosen effect type. The second recovers errors from failed effects, and hence requires that E
is
a subclass of Throwable
(an exception); it expects a function of type I => F[O]
. For example:
import sttp.tapir._
import sttp.tapir.server.http4s._
import cats.effect.IO
import org.http4s.HttpRoutes
import cats.effect.ContextShift
// will probably come from somewhere else
implicit val cs: ContextShift[IO] =
IO.contextShift(scala.concurrent.ExecutionContext.global)
def countCharacters(s: String): IO[Either[Unit, Int]] =
IO.pure(Right[Unit, Int](s.length))
val countCharactersEndpoint: Endpoint[String, Unit, Int, Nothing] =
endpoint.in(stringBody).out(plainBody[Int])
val countCharactersRoutes: HttpRoutes[IO] =
countCharactersEndpoint.toRoutes(countCharacters _)
Note that these functions take one argument, which is a tuple of type I
. This means that functions which take multiple
arguments need to be converted to a function using a single argument using .tupled
:
def logic(s: String, i: Int): IO[Either[Unit, String]] = ???
val anEndpoint: Endpoint[(String, Int), Unit, String, Nothing] = ???
val aRoute: Route = anEndpoint.toRoute((logic _).tupled)
The created HttpRoutes
are the usual http4s Kleisli
-based transformation of a Request
to a Response
, and can
be further composed using http4s middlewares or request-transforming functions. The tapir-generated HttpRoutes
captures from the request only what is described by the endpoint.
It’s completely feasible that some part of the input is read using a http4s wrapper function, which is then composed with the tapir endpoint descriptions. Moreover, “edge-case endpoints”, which require some special logic not expressible using tapir, can be always implemented directly using http4s.
Streaming¶
The http4s interpreter accepts streaming bodies of type Stream[F, Byte]
, which can be used both for sending
response bodies and reading request bodies. Usage: streamBody[Stream[F, Byte]](schema, mediaType)
.
Configuration¶
The interpreter can be configured by providing an implicit Http4sServerOptions
value and status mappers, see
server options for details.
The http4s options also includes configuration for the blocking execution context to use, and the io chunk size.
Defining an endpoint together with the server logic¶
It’s also possible to define an endpoint together with the server logic in a single, more concise step. See server logic for details.
Running as a Finatra server¶
To expose an endpoint as an finatra server, first add the following dependency:
"com.softwaremill.sttp.tapir" %% "tapir-finatra-server" % "0.16.3"
and import the package:
import sttp.tapir.server.finatra._
or if you would like to use cats-effect project, you can add the following dependency:
"com.softwaremill.sttp.tapir" %% "tapir-finatra-server-cats" % "0.16.3"
and import the packate:
import sttp.tapir.server.finatra.cats._
This adds extension methods to the Endpoint
type: toRoute
and toRouteRecoverErrors
. The first one
requires the logic of the endpoint to be given as a function of type (note this is a Twitter Future
or a cats-effect project’s Effect
type):
I => Future[Either[E, O]]
or
An effect type that supports cats-effect’s Effect[F]
type, for example, project Catbird’s Rerunnbale
.
I => Rerunnable[Either[E, O]]
The second recovers errors from failed futures, and hence requires that E
is a subclass of Throwable
(an exception);
it expects a function of type I => Future[O]
or type I => F[O]
if F
supports cat-effect’s Effect
type.
For example:
import sttp.tapir._
import sttp.tapir.server.finatra._
import com.twitter.util.Future
def countCharacters(s: String): Future[Either[Unit, Int]] =
Future.value(Right[Unit, Int](s.length))
val countCharactersEndpoint: Endpoint[String, Unit, Int, Nothing] =
endpoint.in(stringBody).out(plainBody[Int])
val countCharactersRoute: FinatraRoute = countCharactersEndpoint.toRoute(countCharacters)
or a cats-effect’s example:
import cats.effect.IO
import sttp.tapir._
import sttp.tapir.server.finatra.cats._
def countCharacters(s: String): IO[Either[Unit, Int]] =
IO.pure(Right[Unit, Int](s.length))
val countCharactersEndpoint: Endpoint[String, Unit, Int, Nothing] =
endpoint.in(stringBody).out(plainBody[Int])
val countCharactersRoute: FinatraRoute = countCharactersEndpoint.toRoute(countCharacters)
Note that these functions take one argument, which is a tuple of type I
. This means that functions which take multiple
arguments need to be converted to a function using a single argument using .tupled
:
def logic(s: String, i: Int): Future[Either[Unit, String]] = ???
val anEndpoint: Endpoint[(String, Int), Unit, String, Nothing] = ???
val aRoute: FinatraRoute = anEndpoint.toRoute((logic _).tupled)
Now that you’ve created the FinatraRoute
, add TapirController
as a trait to your Controller
. You can then
add the created route with addTapirRoute
.
class MyController extends Controller with TapirController {
addTapirRoute(endpoint.toRoute { (s: String, i: Int) => ??? })
}
Defining an endpoint together with the server logic¶
It’s also possible to define an endpoint together with the server logic in a single, more concise step. See server logic for details.
Running as a Play server¶
To expose endpoint as a play-server first add the following dependencies:
"com.softwaremill.sttp.tapir" %% "tapir-play-server" % "0.16.3"
and
"com.typesafe.play" %% "play-akka-http-server" % "2.8.1"
or
"com.typesafe.play" %% "play-netty-http-server" % "2.8.1"
depending on whether you want to use netty or akka based http-server under the hood.
Then import the package:
import sttp.tapir.server.play._
This adds two extension methods to the Endpoint
type: toRoutes
and toRoutesRecoverErrors
. This first requires the
logic of the endpoint to be given as a function of type:
I => Future[Either[E, O]]
The second recovers errors from failed effects, and hence requires that E
is
a subclass of Throwable
(an exception); it expects a function of type I => Future[O]
. For example:
import sttp.tapir._
import sttp.tapir.server.play._
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.Future
import play.api.routing.Router.Routes
def countCharacters(s: String): Future[Either[Unit, Int]] =
Future(Right[Unit, Int](s.length))
val countCharactersEndpoint: Endpoint[String, Unit, Int, Nothing] =
endpoint.in(stringBody).out(plainBody[Int])
val countCharactersRoutes: Routes =
countCharactersEndpoint.toRoutes(countCharacters _)
Note that these functions take one argument, which is a tuple of type I
. This means that functions which take multiple
arguments need to be converted to a function using a single argument using .tupled
:
def logic(s: String, i: Int): Future[Either[Unit, String]] = ???
val anEndpoint: Endpoint[(String, Int), Unit, String, Nothing] = ???
val aRoute: Routes = anEndpoint.toRoute((logic _).tupled)
In practice, the routes are put in a class taking an endpoints.play.server.PlayComponents
parameter.
An HTTP server can then be started as in the following example:
object Main {
// JVM entry point that starts the HTTP server
def main(args: Array[String]): Unit = {
val playConfig = ServerConfig(port =
sys.props.get("http.port").map(_.toInt).orElse(Some(9000))
)
NettyServer.fromRouterWithComponents(playConfig) { components =>
val playComponents = PlayComponents.fromBuiltInComponents(components)
new CounterServer(playComponents).routes orElse new DocumentationServer(
playComponents
).routes
}
}
}
Configuration¶
The interpreter can be configured by providing an implicit PlayServerOptions
value and status mappers, see
server options for details.
Defining an endpoint together with the server logic¶
It’s also possible to define an endpoint together with the server logic in a single, more concise step. See server logic for details.
Running as a Vert.X server¶
Endpoints can be mounted as Vert.x Route
s on top of a Vert.x Router
.
Use the following dependency
"com.softwaremill.sttp.tapir" %% "tapir-vertx-server" % "0.16.3"
Then import the package:
import sttp.tapir.server.vertx._
This adds extension methods to the Endpoint
type:
route(logic: I => Future[Either[E, O]])
: returns a functionRouter => Route
that will create a route matching the endpoint definition, and attach yourlogic
as an handler. Errors will be recovered automatically (but generically)routeRecoverErrors(logic: I => Future[O])
: returns a functionRouter => Route
that will create a route matching the endpoint definition, and attach yourlogic
as an handler. You’re providing your own way to deal with errors happening in thelogic
function.blockingRoute(logic: I => Future[Either[E, O]])
: returns a functionRouter => Route
that will create a route matching the endpoint definition, and attach yourlogic
as an blocking handler. Errors will be recovered automatically (but generically)blockingRouteRecoverErrors(logic: I => Future[O])
: returns a functionRouter => Route
that will create a route matching the endpoint definition, and attach yourlogic
as a blocking handler. You’re providing your own way to deal with errors happening in thelogic
function.
The methods recovering errors from failed effects, require E
to be a subclass of Throwable
(an exception); and expect a function of type I => Future[O]
. For example:
Note that these functions take one argument, which is a tuple of type I
. This means that functions which take multiple
arguments need to be converted to a function using a single argument using .tupled
:
def logic(s: String, i: Int): Future[Either[Unit, String]] = ???
val anEndpoint: Endpoint[(String, Int), Unit, String, Nothing] = ???
val aRoute: Router => Route = anEndpoint.route(router, (logic _).tupled)
In practice, routes will be mounted on a router, this router can then be used as a request handler for your http server. An HTTP server can then be started as in the following example:
object Main {
// JVM entry point that starts the HTTP server
def main(args: Array[String]): Unit = {
val vertx = Vertx.vertx()
val server = vertx.createHttpServer()
val router = Router.router(vertx)
val anEndpoint: Endpoint[(String, Int), Unit, String, Nothing] = ??? // your definition here
def logic(s: String, i: Int): Future[Either[Unit, String]] = ??? // your logic here
anEndpoint.route(router, (logic _).tupled) // your endpoint is now attached to the router, and the route has been created
server.requestHandler(router).listenFuture(9000)
}
}
Configuration¶
Every endpoint can be configured by providing an implicit VertxEndpointOptions
, see server options for details.
You can also provide your own ExecutionContext
to execute the logic.
Defining an endpoint together with the server logic¶
It’s also possible to define an endpoint together with the server logic in a single, more concise step. See server logic for details.
Server options¶
Each interpreter accepts an implicit options value, which contains configuration values for:
- how to create a file (when receiving a response that is mapped to a file, or when reading a file-mapped multipart part)
- how to handle decode failures (see also error handling)
- debug-logging of request handling
To customise the server options, define an implicit value, which will be visible when converting an endpoint or multiple
endpoints to a route/routes. For example, for AkkaHttpServerOptions
:
implicit val customServerOptions: AkkaHttpServerOptions =
AkkaHttpServerOptions.default.copy(...)
Server logic¶
The logic that should be run when an endpoint is invoked can be passed when interpreting the endpoint as a server, and converting to a server-implementation-specific route. However, there’s also the option to define an endpoint coupled with the server logic - either given entirely or gradually, in parts. This might make it easier to work with endpoints and their collections in a server setting.
Defining an endpoint together with the server logic¶
It’s possible to combine an endpoint description with the server logic in a single object,
ServerEndpoint[I, E, O, S, F]
. Such an endpoint contains not only an endpoint of type Endpoint[I, E, O, S]
, but
also a logic function I => F[Either[E, O]]
, for some effect F
.
The book example can be more concisely written as follows:
import sttp.tapir._
import sttp.tapir.server.akkahttp._
import scala.concurrent.Future
import akka.http.scaladsl.server.Route
val countCharactersServerEndpoint: ServerEndpoint[String, Unit, Int, Nothing, Future] =
endpoint.in(stringBody).out(plainBody[Int]).serverLogic { s =>
Future.successful(Right[Unit, Int](s.length))
}
val countCharactersRoute: Route = countCharactersServerEndpoint.toRoute
A ServerEndpoint
can then be converted to a route using .toRoute
/.toRoutes
methods (without any additional
parameters; the exact method name depends on the server interpreter), or to documentation.
Moreover, a list of server endpoints can be converted to routes or documentation as well:
val endpoint1 = endpoint.in("hello").out(stringBody)
.serverLogic { _ => Future.successful(Right("world")) }
val endpoint2 = endpoint.in("ping").out(stringBody)
.serverLogic { _ => Future.successful(Right("pong")) }
val route: Route = List(endpoint1, endpoint2).toRoute
Note that when dealing with endpoints which have multiple input parameters, the server logic function is a function
of a single argument, which is a tuple; hence you’ll need to pattern-match using case
to extract the parameters:
val echoEndpoint = endpoint
.in(query[Int]("count"))
.in(stringBody)
.out(stringBody)
.serverLogic { case (count, body) =>
Future.successful(Right(body * count))
}
Recovering errors from failed effects¶
If your error type is an exception (extends Throwable
), and if errors that occur in the server logic are represented
as failed effects, you can use a variant of the methods above, which extract the error from the failed effect, and
respond with the error output appropriately.
This can be done with the serverLogicRecoverErrors(f: I => F[O])
method. Note that the E
type parameter isn’t
directly present here; however, the method also contains a requirement that E
is an exception, and will only recover
errors which are subtypes of E
. Any others will be propagated without changes.
For example:
case class MyError(msg: String) extends Exception
val testEndpoint = endpoint
.in(query[Boolean]("fail"))
.errorOut(stringBody.map(MyError)(_.msg))
.out(stringBody)
.serverLogicRecoverErrors { fail =>
if (fail) {
Future.successful("OK") // note: no Right() wrapper
} else {
Future.failed(new MyError("Not OK")) // no Left() wrapper, a failed future
}
}
Extracting common route logic¶
Quite often, especially for authentication, some part of the route logic is shared among multiple endpoints. However, these functions don’t compose in a straightforward way, as authentication usually operates on a single input, which is only a part of the whole logic’s input. That’s why there are two options for providing the server logic in parts.
Defining an extendable, base endpoint with partial server logic¶
First, you might want to define a base endpoint, with some part of the server logic already defined, and later extend it with additional inputs/outputs, and successive logic parts.
When using this method, note that the error type must be fully defined upfront, and cannot be extended later. That’s because all of the partial logic functions can return either an error (of the given type), or a partial result.
To define partial logic for the inputs defined so far, you should use the serverLogicForCurrent
method on an
endpoint. This accepts a method, f: I => F[Either[E, U]]
, which given the entire (current) input defined so far,
returns either an error, or a partial result.
For example, we can create a partial server endpoint given an authentication function, and an endpoint describing the authentication inputs:
case class User(name: String)
def auth(token: String): Future[Either[Int, User]] = Future {
if (token == "secret") Right(User("Spock"))
else Left(1001) // error code
}
val secureEndpoint: PartialServerEndpoint[User, Unit, Int, Unit, Nothing, Future] = endpoint
.in(header[String]("X-AUTH-TOKEN"))
.errorOut(plainBody[Int])
.serverLogicForCurrent(auth)
The result is a value of type PartialServerEndpoint
, which can be extended with further inputs and outputs, just
as a normal endpoint (except for error outputs, which are fixed).
Successive logic parts (again consuming the entire input defined so far, and producing another partial result) can
be given by calling serverLogicForCurrent
again. The logic can be completed - given a tuple of partial results,
and unconsumed inputs - using the serverLogic
method:
val secureHelloWorld1WithLogic = secureEndpoint.get
.in("hello1")
.in(query[String]("salutation"))
.out(stringBody)
.serverLogic { case (user, salutation) => Future(Right(s"$salutation, ${user.name}!")) }
Once the server logic is complete, a ServerEndpoint
is returned, which can be then interpreted as a server.
The methods mentioned also have variants which recover errors from failed effects (as described above), using
serverLogicForCurrentRecoverErrors
and serverLogicRecoverErrors
.
Providing server logic in parts, for an already defined endpoint¶
Secondly, you might already have the entire endpoint defined upfront (e.g. in a separate module, which doesn’t know anything about the server logic), and would like to provide the server logic in parts.
This can be done using the serverLogicPart
method, which takes a function of type f: T => F[Either[E, U]]
as
a parameter. Here T
is some prefix of the endpoint’s input I
. The function then consumes some part of the input,
and produces an error, or a partial result.
Unlike previously, here the endpoint is considered complete, and cannot be later extended: no additional inputs or outputs can be defined.
Note
Note that the partial logic functions should be fully typed, to help with inference. It might not be possible for the compiler to infer, which part of the input should be consumed.
For example, if we have an endpoint:
val secureHelloWorld2: Endpoint[(String, String), Int, String, Nothing] = endpoint
.in(header[String]("X-AUTH-TOKEN"))
.errorOut(plainBody[Int])
.get
.in("hello2")
.in(query[String]("salutation"))
.out(stringBody)
We can provide the server logic in parts (using the same auth
method as above):
val secureHelloWorld2WithLogic = secureHelloWorld2
.serverLogicPart(auth)
.andThen { case (user, salutation) => Future(Right(s"$salutation, ${user.name}!")) }
Here, we first define a single part using serverLogicPart
, and then complete the logic (consuming the remaining
inputs) using andThen
. The result is, like previously, a ServerEndpoint
, which can be interpreted as a server.
Multiple parts can be provided using andThenPart
invocations (consuming successive input parts). There are also
variants of the methods, which recover errors from failed effects: serverLogicPartRecoverErrors
,
andThenRecoverErrors
and andThenPartRecoverErrors
.
Status codes¶
By default, successful responses are returned with the 200 OK
status code, and errors with 400 Bad Request
. However,
this can be customised by specifying how an output maps to the status code.
Error handling¶
Exception handling¶
There’s no exception handling built into tapir. However, tapir contains a more general error handling mechanism, as the endpoints can contain dedicated error outputs.
If the logic function, which is passed to the server interpreter, fails (i.e. throws an exception, which results in
a failed Future
or IO
/Task
), this is propagated to the library (akka-http or http4s).
However, any exceptions can be recovered from and mapped to an error value. For example:
type ErrorInfo = String
def logic(s: String): Future[Int] = ...
def handleErrors[T](f: Future[T]): Future[Either[ErrorInfo, T]] =
f.transform {
case Success(v) => Success(Right(v))
case Failure(e) =>
logger.error("Exception when running endpoint logic", e)
Success(Left(e.getMessage))
}
endpoint
.errorOut(plainBody[ErrorInfo])
.out(plainBody[Int])
.in(query[String]("name"))
.toRoute((logic _).andThen(handleErrors))
In the above example, errors are represented as String
s (aliased to ErrorInfo
for readability). When the
logic completes successfully an Int
is returned. Any exceptions that are raised are logged, and represented as a
value of type ErrorInfo
.
Following the convention, the left side of the Either[ErrorInfo, T]
represents an error, and the right side success.
Alternatively, errors can be recovered from failed effects and mapped to the error output - provided that the E
type
in the endpoint description is itself a subclass of exception. This can be done using the toRouteRecoverErrors
method.
Handling decode failures¶
Quite often user input will be malformed and decoding will fail. Should the request be completed with a
400 Bad Request
response, or should the request be forwarded to another endpoint? By default, tapir follows OpenAPI
conventions, that an endpoint is uniquely identified by the method and served path. That’s why:
- an “endpoint doesn’t match” result is returned if the request method or path doesn’t match. The http library should
attempt to serve this request with the next endpoint. The path doesn’t match if a path segment is missing, there’s
a constant value mismatch or a decoding error (e.g. parsing a segment to an
Int
fails) - otherwise, we assume that this is the correct endpoint to serve the request, but the parameters are somehow
malformed. A
400 Bad Request
response is returned if a query parameter, header or body causes any decode failure, or if the decoding a path capture causes a validation error.
This can be customised by providing an implicit instance of tapir.server.DecodeFailureHandler
, which basing on the
request, failing input and failure description can decide, whether to return a “no match” or a specific response.
Only the first failure is passed to the DecodeFailureHandler
. Inputs are decoded in the following order: method,
path, query, header, body.
Note that the decode failure handler is used only for failures that occur during decoding of path, query, body
and header parameters - while invoking Codec.decode
. It does not handle any failures or exceptions that occur
when invoking the logic of the endpoint.
Default failure handler¶
The default decode failure handler is a case class, consisting of functions which decide whether to respond with an error or return a “no match”, create error messages and create the response.
To reuse the existing default logic, parts of the default behavior can be swapped, e.g. to return responses in a different format (other than textual):
case class MyFailure(msg: String)
def myFailureResponse(statusCode: StatusCode, message: String): DecodeFailureHandling =
DecodeFailureHandling.response(statusCode.and(jsonBody[MyFailure]))(
(statusCode, MyFailure(message))
)
val myDecodeFailureHandler = ServerDefaults.decodeFailureHandler.copy(
response = myFailureResponse
)
implicit val myServerOptions: AkkaHttpServerOptions = AkkaHttpServerOptions.default.copy(
decodeFailureHandler = myDecodeFailureHandler
)
Note that when specifying that a response should be returned upon a failure, we need to provide the endpoint output which should be used to create the response, as well as a value for this output.
The default decode failure handler also has the option to return a 400 Bad Request
, instead of a no-match (ultimately
leading to a 404 Not Found
), when the “shape” of the path matches (that is, the number of segments in the request
and endpoint’s paths are the same), but when decoding some part of the path ends in an error. See the
badRequestOnPathErrorIfPathShapeMatches
in ServerDefaults
.
Finally, you can provide custom error messages for validation errors (which optionally describe what failed) and failure errors (which describe the source of the error).
A completely custom implementation of the DecodeFailureHandler
function can also be used.
Debugging¶
When dealing with multiple endpoints, how to find out which endpoint handled a request, or why an endpoint didn’t handle a request?
For this purpose, tapir provides optional logging. The logging options (and messages) can be customised by changing
the default LogRequestHandling
class, which is part of server options.
The following can be customised:
logWhenHandled
: when a request is handled by an endpoint, or when the inputs can’t be decoded, and the decode failure maps to a response (default:true
,DEBUG
log)logAllDecodeFailures
: when the inputs can’t be decoded, and the decode failure doesn’t map to a response (the next endpoint will be tried; default:false
,DEBUG
log)logLogicExceptions
: when there’s an exception during evaluation of the server logic (default:true
,ERROR
log)
Logging all decode failures (2) might be helpful when debugging, but can also produce a large amount of logs, hence it’s disabled by default.
Even if logging for a particular category (as described above) is set to true
, normal logger rules apply - if you
don’t see the logs, please verify your logging levels for the appropriate packages.
Using as an sttp client¶
Add the dependency:
"com.softwaremill.sttp.tapir" %% "tapir-sttp-client" % "0.16.3"
To make requests using an endpoint definition using the sttp client, import:
import sttp.tapir.client.sttp._
This adds the two extension methods to any Endpoint
:
toSttpRequestUnsafe(Uri)
: given the base URI returns a function, which might throw an exception if decoding of the result failsI => Request[Either[E, O], Nothing]
toSttpRequest(Uri)
: given the base URI returns a function, which represents decoding errors as theDecodeResult
classI => Request[DecodeResult[Either[E, O]], Nothing]
Note that these are a one-argument functions, where the single argument is the input of end endpoint. This might be a single type, a tuple, or a case class, depending on the endpoint description.
After providing the input parameters, a description of the request to be made is returned, with the input value encoded as appropriate request parameters: path, query, headers and body. This can be further customised and sent using any sttp backend. The response will then contain the decoded error or success values (note that this can be the body enriched with data from headers/status code).
See the runnable example for example usage.
Generating OpenAPI documentation¶
To use, add the following dependencies:
"com.softwaremill.sttp.tapir" %% "tapir-openapi-docs" % "0.16.3"
"com.softwaremill.sttp.tapir" %% "tapir-openapi-circe-yaml" % "0.16.3"
Tapir contains a case class-based model of the openapi data structures in the openapi/openapi-model
subproject (the
model is independent from all other tapir modules and can be used stand-alone).
An endpoint can be converted to an instance of the model by importing the tapir.docs.openapi._
package and calling
the provided extension method:
import sttp.tapir.openapi.OpenAPI
import sttp.tapir.docs.openapi._
val docs: OpenAPI = booksListing.toOpenAPI("My Bookshop", "1.0")
Such a model can then be refined, by adding details which are not auto-generated. Working with a deeply nested case
class structure such as the OpenAPI
one can be made easier by using a lens library, e.g. Quicklens.
The documentation is generated in a large part basing on schemas. Schemas can be automatically derived and customised.
Quite often, you’ll need to define the servers, through which the API can be reached. To do this, you can modify the
returned OpenAPI
case class either directly or by using a helper method:
val docs: OpenAPI = booksListing.toOpenAPI("My Bookshop", "1.0")
.servers(List(Server("https://api.example.com/v1").description("Production server")))
Multiple endpoints can be converted to an OpenAPI
instance by calling the extension method on a list of endpoints:
List(addBook, booksListing, booksListingByGenre).toOpenAPI("My Bookshop", "1.0")
The openapi case classes can then be serialised, either to JSON or YAML using Circe:
import sttp.tapir.openapi.circe.yaml._
println(docs.toYaml)
Options¶
Options can be customised by providing an implicit instance of OpenAPIDocsOptions
, when calling .toOpenAPI
.
operationIdGenerator
: each endpoint corresponds to an operation in the OpenAPI format and should have a unique operation id. By default, thename
of endpoint is used as the operation id, and if this is not available, the operation id is auto-generated by concatenating (using camel-case) the request method and path.
Exposing OpenAPI documentation¶
Exposing the OpenAPI documentation can be very application-specific. However, tapir contains modules which contain akka-http/http4s routes for exposing documentation using Swagger UI or Redoc:
"com.softwaremill.sttp.tapir" %% "tapir-swagger-ui-akka-http" % "0.16.3"
"com.softwaremill.sttp.tapir" %% "tapir-redoc-akka-http" % "0.16.3"
"com.softwaremill.sttp.tapir" %% "tapir-swagger-ui-http4s" % "0.16.3"
"com.softwaremill.sttp.tapir" %% "tapir-redoc-http4s" % "0.16.3"
Note: tapir-swagger-ui-akka-http
transitively pulls some Akka modules in version 2.6. If you want to force
your own Akka version (for example 2.5), use sbt exclusion. Mind the Scala version in artifact name:
"com.softwaremill.sttp.tapir" %% "tapir-swagger-ui-akka-http" % "0.16.3" exclude("com.typesafe.akka", "akka-stream_2.12")
Usage example for akka-http:
import sttp.tapir.docs.openapi._
import sttp.tapir.openapi.circe.yaml._
import sttp.tapir.swagger.akkahttp.SwaggerAkka
val docsAsYaml: String = myEndpoints.toOpenAPI("My App", "1.0").toYaml
// add to your akka routes
new SwaggerAkka(docsAsYaml).routes
For redoc, use RedocAkkaHttp
.
For http4s, use the SwaggerHttp4s
or RedocHttp4s
classes.
Using with sbt-assembly¶
The tapir-swagger-ui-*
modules rely on a file in the META-INF
directory tree, to determine the version of the Swagger UI.
You need to take additional measures if you package your application with sbt-assembly
because the default merge strategy of the assembly
task discards most artifacts in that directory.
To avoid a NullPointerException
, you need to include the following file explicitly:
assemblyMergeStrategy in assembly := {
case PathList("META-INF", "maven", "org.webjars", "swagger-ui", "pom.properties") =>
MergeStrategy.singleOrError
case x =>
val oldStrategy = (assemblyMergeStrategy in assembly).value
oldStrategy(x)
}
Testing¶
White box testing¶
If you are unit testing your application you should stub all external services.
If you are using sttp client to send HTTP requests, and if the externals apis,
which yours application consumes, are described using tapir, you can create a stub of the service by converting
endpoints to SttpBackendStub
(see the sttp documentation for
details on how the stub works).
Add the dependency:
"com.softwaremill.sttp.tapir" %% "tapir-sttp-stub-server" % "0.16.3"
And the following import:
import sttp.tapir.server.stub._
Then, convert any endpoint to SttpBackendStub
:
val endpoint = sttp.tapir.endpoint
.in("api" / "sometest4")
.in(query[Int]("amount"))
.post
.out(jsonBody[ResponseWrapper])
implicit val backend = SttpBackendStub
.apply(idMonad)
.whenRequestMatchesEndpoint(endpoint)
.thenSuccess(ResponseWrapper(1.0))
Black box testing¶
When testing application as a whole component, running for example in docker, you might want to stub external services with which you application interacts, to steer their behaviors.
To do that you might want to use well-known solutions like e.g. wiremock or mock-server, but if their api is described using tapir you might want to use livestub, which combines nicely with the rest of the sttp ecosystem.
Other interpreters¶
At its core, tapir creates a data structure describing the HTTP endpoints. This data structure can be freely interpreted also by code not included in the library. Below is a list of projects, which provide tapir interpreters.
GraphQL¶
Caliban allows you to easily turn your Tapir endpoints into a GraphQL API. More details in the documentation.
tapir-gen¶
tapir-gen extends tapir to do client code generation. The goal is to auto-generate clients in multiple-languages with multiple libraries.
scala-opentracing contains a module which provides a small integration layer that allows you to create traced http endpoints from tapir Endpoint definitions.
Creating your own Tapir¶
Tapir uses a number of packages which contain either the data classes for describing endpoints or interpreters of this data (turning endpoints into a server or a client). Importing these packages every time you want to use Tapir may be tedious, that’s why each package object inherits all of its functionality from a trait.
Hence, it is possible to create your own object which combines all of the required functionalities and provides a single-import whenever you want to use tapir. For example:
object MyTapir extends Tapir
with TapirAkkaHttpServer
with TapirSttpClient
with TapirJsonCirce
with TapirOpenAPICirceYaml
with TapirAliases
Then, a single import MyTapir._
and all Tapir data types and extensions methods will be in scope!
Troubleshooting¶
StackOverflowException during compilation¶
Sidenote for scala 2.12.4 and higher: if you encounter an issue with compiling your project because of
a StackOverflowException
related to this scala bug,
please increase your stack memory. Example:
sbt -J-Xss4M clean compile
Logging of generated macros code¶
For some cases, it may be helpful to examine how generated macros code looks like. To do that, just set an environmental variable and check compilation logs for details.
export TAPIR_LOG_GENERATED_CODE=true
Contributing¶
Tapir is an early stage project. Everything might change. All suggestions welcome :)
See the list of issues and pick one! Or report your own.
If you are having doubts on the why or how something works, don’t hesitate to ask a question on gitter or via github. This probably means that the documentation, scaladocs or code is unclear and can be improved for the benefit of all.