Generating a Rust client library for ZIO Http endpoints

Learn how to use several ZIO libraries together to get Rust clients for your HTTP endpoints!

Tech Trends
08 September 2023





Learn how to use several ZIO libraries together to get Rust clients for your HTTP endpoints!

We at Golem Cloud built our first developer preview on top of the ZIO ecosystem, including ZIO Http for defining and implementing our server's REST API. By using ZIO Http we immediately had the ability to call our endpoints using endpoint clients, which allowed us to develop the first version of Golem's CLI tool very rapidly.

Although very convenient for development, using a CLI tool built with Scala for the JVM is not a pleasant experience for the users due to the slow startup time. One possible solution is to compile to native using GraalVM Native Image but it is very hard to set up and even when it works, it is extremely fragile - further changes to the code or updated dependencies can break it causing unexpected extra maintenance cost. After some initial experiments we dropped this idea - and instead chose to reimplement the CLI using Rust - a language being a much better fit for command line tools, and also already an important technology in our Golem stack.

ZIO Http

If we rewrite golem-cli to Rust, we lose the convenience of using endpoint definitions (written in Scala with ZIO Http, the ones we have for implementing the server) for calling our API, and we would also lose all the types used in these APIs as they are all defined as Scala case classes and enums. Just to have more context, let's take a look at one of the endpoints!

A ZIO Http endpoint is just a definition of a single endpoint of a HTTP API, describing the routing as well the inputs and outputs of it:

val getWorkerMetadata =
    Endpoint(GET / "v1" / "templates" / rawTemplateId / "workers" / workerName)
      .out[WorkerMetadata] ?? Doc.p("Get the current worker status and metadata")

Let's see what we have here:

the endpoint is reached by sending a GET request

the request path consists of some static segments as well as the template id and the worker name

it also requires an authorization header

we define the kind of errors it can return

and finally it defines that the response's body will contain a JSON representation (default in ZIO Http) of a type called WorkerMetadata

What are rawTemplateId and workerName? These are so called path codecs, defined in a common place so they can be reused in multiple endpoints. They allow us to have dynamic parts of the request path mapped to specific types - so when we implement the endpoint (or call it in a client) we don't have to pass strings and we can directly work with the business domain types, in this case RawTemplateId and WorkerName.

The simplest way to define path codecs is to transform an existing one:

val workerName: PathCodec[WorkerName] =
  string("worker-name").transformOrFailLeft(WorkerName.make(_).toErrorEither, _.value)

Here the make function is a ZIO Prelude Validation which we have to convert to an Either for the transform function. Validations can contain more than one failures, as opposed to Eithers, which allows us to compose them in a way that we can keep multiple errors instead of immediately returning with the first failure.

The tokenSecret is similar, but it is a HeaderCodec describing what type of header it is and how the value of the given header should be mapped to a specific type (a token, in this case).

What is WorkerMetadata and how does ZIO Http know how to produce a JSON from it?

It's just a simple case class:

final case class WorkerMetadata(
  workerId: ComponentInstanceId,
  accountId: AccountId,
  args: Chunk[String],
  env: Map[String, String],
  status: InstanceStatus,
  templateVersion: Int,
  retryCount: Int

But with an implicit derived ZIO Schema:

object WorkerMetadata {
  implicit val schema: Schema[WorkerMetadata] = DeriveSchema.gen[WorkerMetadata]

We will talk more about ZIO Schema below - for now all we need to know is it describes the structure of Scala types, and this information can be used to serialize data into various formats, including JSON.

Once we have our endpoints defined like this, we can do several things with them - they are just data describing what an endpoint looks like!

Implementing an endpoint

When developing a server, the most important thing to do with an endpoint is to implement it. Implementing an endpoint looks like the following:

val getWorkerMetadataImpl =
    getWorkerMetadata.implement {
      Handler.fromFunctionZIO { (rawTemplateId, workerName, authTokenId) =>
        // ... ZIO program returning a WorkerMetadata

The type of getWorkerMetadataImpl is Route - it is no longer just a description of what an endpoint looks like, it defines a specific HTTP route and its associated request handler, implemented by a ZIO effect (remember that ZIO effects are also values - we describe what we need to do when a request comes in, but executing it will be the responsibility of the server implementation).

The nice thing about ZIO Http endpoints is that they are completely type safe. I've hidden the type signature in the previous code snippets but actually getWorkerMetadata has the type:

    (RawTemplateId, WorkerName),
    (RawTemplateId, WorkerName, TokenSecret),

Here the second type parameter defines the input of the request handler and the forth type parameter defines the output the server constructs the response from.

With these types, we really just have to implement a (ZIO) function from the input to the output:

(RawTemplateId, WorkerName, TokenSecret) => ZIO[Any, WorkerEndpointError, WorkerMetadata]

and this is exactly what we pass to Handler.fromFunctionZIO in the above example.

Calling an endpoint

The same endpoint values can also be used to make requests to our API from clients such as golem-cli. Taking advantage of the same type safe representation we can just call apply on the endpoint definition passing its input as a parameter to get an invocation:

val invocation = getInstanceMetadata(rawTemplateId, workerName, token)

this invocation can be executed to perform the actual request using an EndpointExecutor which can be easily constructed from a ZIO Http Client and some other parameters like the URL of the remote server:

executor(invocation).flatMap { workerMetadata => 
  // ...

The task

So can we do anything to keep this convenient way of calling our endpoints when migrating the CLI to Rust? At the time of writing we already had more than 60 endpoints, with many complex types used in them - defining them by hand in Rust, and keeping the Scala and Rust code in sync sounds like a nightmare.

The ideal case would be to have something like this in Rust:

pub trait Worker {
  // ...
  async fn get_worker_metadata(&self, template_id: &TemplateId, worker_name: &WorkerName, authorization: &Token) -> Result;

with an implementation that just requires the same amount of configuration as the Scala endpoint executor (server URL, etc), and all the referenced types like WorkerMetadata would be an exact clone of the Scala types just in Rust.

Fortunately we can have (almost) this by taking advantage of the declarative nature of ZIO Http and ZIO Schema!

In the rest of this post we will see how we can generate Rust code using a combination of ZIO libraries to automatically have all our type definitions and client implementation ready to use from the Rust version of golem-cli.

The building blocks

We want to generate from an arbitrary set of ZIO Http Endpoint definitions a Rust crate ready to be compiled, published and used. We will take advantage of the following libraries:

ZIO Http as the source of endpoint definitions

ZIO Schema for observing the type definitions

ZIO Parser because it has a composable printer concept

ZIO NIO for working with the filesystem

ZIO Prelude for implementing the stateful endpoint/type discovery in a purely functional way

Generating Rust code

Let's start with the actual source code generation. This is something that can be done in many different ways - one extreme could be to just concatenate strings (or use a StringBuilder)) while the other is to build a full real Rust AST and pretty print that. I had a talk on Function Scala 2021 about the topic.

For this task I chose a technique which is somewhere in the middle and provides some extent of composability while also allowing use to do just the amount of abstraction we want to. The idea is that we define a Rust code generator model which does not have to strictly follow the actual generated language's concepts, and then define a pretty printer for this model. This way we only have to model the subset of the language we need for the code generator, and we can keep simplifications or even complete string fragments in it if that makes our life easier.

Let's see how this works with some examples!

We will have to generate type definitions so we can define a Scala enum describing what kind of type definitions we want to generate:

enum RustDef:
  case TypeAlias(name: Name, typ: RustType, derives: Chunk[RustType])
  case Newtype(name: Name, typ: RustType, derives: Chunk[RustType])
  case Struct(name: Name, fields: Chunk[RustDef.Field], derives: Chunk[RustType], isPublic: Boolean)
  case Enum(name: Name, cases: Chunk[RustDef], derives: Chunk[RustType])
  case Impl(tpe: RustType, functions: Chunk[RustDef])
  case ImplTrait(implemented: RustType, forType: RustType, functions: Chunk[RustDef])
  case Function(name: Name, parameters: Chunk[RustDef.Parameter], returnType: RustType, body: String, isPublic: Boolean)

We can make this as convenient to use as we want, for example adding constructors like:

def struct(name: Name, fields: Field*): RustDef

The Name is an opaque string type with extension methods to convert between various cases like pascal case, snake case, etc. RustType is a similar enum to RustDef, containing all the different type descriptions we will have to use. But it is definitely not how a proper Rust parser would define what a type is - for example we can have a RustType.Option as a shortcut for wrapping a Rust type in Rust's own option type, just because it makes our code generator simpler to write.

So once we have this model (which in practice evolves together with the code generator, usually starting with a few simple case classes) we can use ZIO Parser's printer feature to define composable elements constructing Rust source code.

We start by defining a module and a type alias for our printer:

object Rust:
  type Rust[-A] = Printer[String, Char, A]

and then just define building blocks - what these building blocks are depends completely on us, and the only thing it affects is how well you can compose them. Having very small building blocks may reduce the readability of the code generator, but using too large chunks reduces their composability and makes it harder to change or refactor.

We can define some short aliases for often used characters or string fragments:

def gt: Rust[Any] = Printer.print('>')
def lt: Rust[Any] = Printer.print('<')
def bracketed[A](inner: Rust[A]): Rust[A] =
  lt ~ inner ~ gt

and we have to define Rust printers for each of our model types. For example for the RustType enum it could be something like this:

def typename: Rust[RustType] = Printer.byValue:
  case RustType.Primitive(name)             => str(name)
  case RustType.Option(inner)               => typename(RustType.Primitive("Option")) ~ bracketed(typename(inner))
  case RustType.Vec(inner)                  => typename(RustType.Primitive("Vec")) ~ bracketed(typename(inner))
  case RustType.SelectFromModule(path, typ) => Printer.anyString.repeatWithSep(dcolon)(path) ~ dcolon ~ typename(typ)
  case RustType.Parametric(name, params) =>
    str(name) ~ bracketed(typename.repeatWithSep(comma)(params))
  // ...

We can see that typename uses itself to recursively generate inner type names, for example when generating type parameters of tuple members. It also demonstrates that we can extract patterns such as bracketed to simplify our printer definitions and eliminate repetition.

Another nice feature we get by using a general purpose printer library like ZIO Parser is that we can use the built-in combinators to get printers for new types. One example is the sequential composition of printers. For example the following fragment:

val p = str("pub ") ~ name ~ str(": ") ~ typename

would have the type Rust[(Name, RustType)] and we can even make that a printer of a case class like:

final case class PublicField(name: Name, typ: RustType)

val p2 = p.from[PublicField]

where p2 will have the type Rust[PublicField].

Another very useful combinator is repetition. For example if we have a printer for an enum's case:

def enumCase: Rust[RustDef] = // ...

we can simply use one of the repetition combinators to make a printer for a list of enum cases:

def enumCases: Rust[Chunk[RustDef]] = enumCase.*

or as in the typename example above:


to have a Rust[Chunk[RustType]] that inserts a comma between each element when printed.

Inspecting the Scala types

As we have seen the endpoint DSL uses ZIO Schema to capture information about the types being used in the endpoints (usually as request or response bodies, serialized into JSON). We can use the same information to generate Rust types from our Scala types!

The core data type defined by the ZIO Schema library is called Schema:

sealed trait Schema[A] {
  // ...

Schema describes the structure of a Scala type A in a way we can inspect it from regular Scala code. Let's imagine we have Schema[WorkerMetadata] coming from our endpoint definition and we have to generate an equivalent Rust struct with the same field names and field types.

The first thing to notice is that type definitions are recursive. Unless WorkerMetadata only contains fields of primitive types such as integer or string, our job does not end with generating a single Rust struct - we need to recursively generate all the other types WorkerMetadata is depending on! To capture this fact let's introduce a type that represents everything we have to extract from a single (or a set of) schemas in order to generate Rust types from them:

final case class RustModel(
  typeRefs: Map[Schema[?], RustType], 
  definitions: Chunk[RustDef], 
  requiredCrates: Set[Crate]

We have typeRefs which associates a RustType with a schema so we can use it in future steps of our code generator to refer to a generated type in our Rust codebase. We have a list of RustDef values which are the generated type definitions, ready to be printed with out Rust pretty printer. And finally we can also gather a set of required extra rust crates, because some of the types considered primitive types by ZIO Schema are not having proper representations in the Rust standard library, only in external crates. Examples are UUIDs and various date/time types.

So our job now is to write a function of

def fromSchemas(schemas: Seq[Schema[?]]): Either[String, RustModel]

The Either result type is used to indicate failures. Even if we write a transformation that can produce from any Schema a proper RustModel, we always have to have an error result when working with ZIO Schema because it has an explicit failure case called Schema.Fail. If we process a schema and end up with a Fail node, we can't do anything else than fail our code generator.

There are many important details to consider when implementing this function, but let's just see first what the actual Schema type looks like. When we have a value of Schema[?] we can pattern match on it and implement the following cases:

Schema.Primitive describes a primitive type - there are a lot of primitive types defined by ZIO Schema's StandardType enum

Schema.Enum describes a type with multiple cases (a sum type) such as a sealed trait or enum

Schema.Record describes a type with multiple fields (a product type) such as a case class

Schema.Map represents a map with a key and value type

Schema.Sequence represents a sequence of items of a given element type

Schema.Set is a set of items of a given element type

Schema.Optional represents an optional type (like an Option[T])

Schema.Either is a special case of sum types representing either one or the other type (like an Either[A, B])

Schema.Lazy is used to safely encode recursive types, it contains a function that evaluates into an inner Schema

Schema.Dynamic represents a type that is dynamic - like a JSON value

Schema.Transform assigns a transformation function that converts a value of a type represented by the schema to a value of some other type. As we have no way to inspect these functions (they are compiled Scala functions) in our code generator, this is not very interesting for us now. Schema.Fail as already mentioned represents a failure in describing the data type

When traversing a Schema recursively (for any reason), it is important to keep in mind that it can encode recursive types! A simple example is a binary tree:

final case class Tree[A](label: A, left: Option[Tree], right: Option[Tree])

We can construct a Schema[Tree[A]] if we have a Schema[A]. This will be something like (pseudo-code):

lazy val tree: Schema[Tree] =
    Field("label", Schema[A]),
    Field("left", Schema.Optional(Schema.Lazy(() => tree))),
    Field("right", Schema.Optional(Schema.Lazy(() => tree)))

If we are not prepared for recursive types we can easily get into an endless loop (or stack overflow) when processing these schemas.

This is just one example of things to keep track of while converting a schema into a set of Rust definitions. If fields refer to the self type we want to use Box so to put them on the heap. We also need to keep track of if everything within a generated type derives Ord and Hash - and if yes, we should derive an instance for the same type classes for our generated type as well.

My preferred way to implement such recursive stateful transformation functions is to use ZIO Prelude's ZPure type. It's type definition looks a little scary:

sealed trait ZPure[+W, -S1, +S2, -R, +E, +A]

ZPure describes a purely functional computation which can:

Emit log entries of type W

Works with an inital state of type S1

Results in a final state of type S2

Has access to some context of type R

Can fail with a value of E

Or succeed with a value of A

In this case we need the state, failure and result types only, but we could also take advantage of W to log debug information within our schema transformation function.

To make it easier to work with ZPure we can introduce a type alias:

type Fx[+A] = ZPure[Nothing, State, State, Any, String, A]

where State is our own case class containing everything we need:

final case class State(
  typeRefs: Map[Schema[?], RustType],
  definitions: Chunk[RustDef],
  requiredCrates: Set[Crate],
  processed: Set[Schema[?]],
  stack: Chunk[Schema[?]],
  nameTypeIdMap: Map[Name, Set[TypeId]],
  schemaCaps: Map[Schema[?], Capabilities]

We won't get into the details of the state type here, but I'm showing some fragments to get a feeling of working with ZPure values.

Some helper functions to manipulate the state can make our code much easier to read:

private def getState: Fx[State] = ZPure.get[State]
private def updateState(f: State => State): Fx[Unit] = ZPure.update[State, State](f)

For example we can use updateState to manipulate the stack field of the state around another computation - before running it, we add a schema to the stack, after that we remove it:

private def stacked[A, R](schema: Schema[A])(f: => Fx[R]): Fx[R] =
  updateState(s => s.copy(stack = s.stack :+ schema))
    .zipLeft(updateState(s => s.copy(stack = s.stack.dropRight(1))))

This allows us to decide whether we have to wrap a generated field's type in Box in the rust code:

private def boxIfNeeded[A](schema: Schema[A]): Fx[RustType] =
    state <- getState
    backRef = state.stack.contains(schema)
    rustType <- getRustType(schema)
  yield if backRef then else rustType

By looking into state.stack we can decide if we are dealing with a recursive type or not, and make our decision regarding boxing the field.

Another example is to guard against infinite recursion when traversing the schema definition, as I explained before. We can define a helper function that just keeps track of all the visited schemas and shortcuts the computation if something has already been seen:

private def ifNotProcessed[A](value: Schema[A])(f: => Fx[Unit]): Fx[Unit] =
  getState.flatMap: state =>
    if state.processed.contains(value) then ZPure.unit
    else updateState(_.copy(processed = state.processed + value)).zipRight(f)

Putting all these smaller combinators together we have an easy-to-read core recursive transformation function for converting the schema:

private def process[A](schema: Schema[A]): Fx[Unit] =
    getRustType(schema).flatMap: typeRef =>
        schema match
          // ...

In the end to run a Fx[A] all we need to do is to provide an initial state:


Inspecting the endpoints

  • Traversing the Endpoint data structure for each endpoint we have
  • Generate some intermediate model
  • Pretty print this model to Rust code

The conversion once again is recursive, can fail, and requires keeping track of various things, so we can use ZPure to implement it. Not repeating the same details, in this section we will talk about what exactly the endpoint descriptions look like and what we have be aware of when trying to process them.

The first problem to solve is that currently ZIO Http does not have a concept of multiple endpoints. We are not composing Endpoint values into an API, instead we first implement them to get Route values and compose those. We can no longer inspect the endpoint definitions from the composed routes, so unfortunately we have to repeat ourselves and somehow compose our set of endpoints for our code generator.

First we can define a RustEndpoint class, similar to the RustModel earlier, containing all the necessary information to generate Rust code for a single endpoint.

We can construct it with a function:

// ...
object RustEndpoint:
  def fromEndpoint[PathInput, Input, Err, Output, Middleware <: EndpointMiddleware](
      name: String,
      endpoint: Endpoint[PathInput, Input, Err, Output, Middleware],
  ): Either[String, RustEndpoint] = // ...

The second thing to notice: endpoints do not have a name! If we look back to our initial example of getWorkerMetadata, it did not have a unique name except the Scala value it was assigned to. But we can't observe that in our code generator (without writing a macro) so here we have chosen to just get a name as a string next to the definition.

Then we can define a collection of RustEndpoints:

final case class RustEndpoints(name: Name, originalEndpoints: Chunk[RustEndpoint])

and define a ++ operator between RustEndpoint and RustEndpoints. In the end we can use these to define APIs like this:

      getDefaultProject <- fromEndpoint("getDefaultProject", ProjectEndpoints.getDefaultProject)
      getProjects       <- fromEndpoint("getProjects", ProjectEndpoints.getProjects)
      postProject       <- fromEndpoint("postProject", ProjectEndpoints.postProject)
      getProject        <- fromEndpoint("getProject", ProjectEndpoints.getProject)
      deleteProject     <- fromEndpoint("deleteProject", ProjectEndpoints.deleteProject)
    yield (getDefaultProject ++ getProjects ++ postProject ++ getProject ++ deleteProject).named("Project")

The collection of endpoints also have a name ("Project"). In the code generator we can use these to have a separate client (trait and implementation) for each of these groups of endpoints.

When processing a single endpoint, we need to process the following parts of data:

  • Inputs (endpoint.input)
  • Outputs (endpoint.output)
  • Errors (endpoint.error)

Everything we need is encoded in one of these three fields of an endpoint, and all three are built on the same abstraction called HttpCodec. Still there is a significant difference in what we want to do with inputs versus what we want to do with outputs and errors, so we can write two different traversals for gathering all the necessary information from them.


When gathering information from the inputs, we are going to run into the following cases:

HttpCodec.Combine means we have two different inputs; we need both, so we have to process both inner codecs sequentially, both extending our conversion function's state. HttpCodec.Content describes a request body. Here we have a Schema of our request body type and we can use the previously generated schema-to-rust type mapping to know how to refer to the generated rust type in our client code. It is important that in case there are multiple content codecs, that means the endpoint receives a multipart/form-data body, while if there is only one codec, it accepts an application/json representation of that. HttpCodec.ContentStream represents a body containing a stream of a given element type. We can model this as just a Vec<A> in the Rust side, but there is one special case here - if the element is a Byte, ZIO Http expects a simple byte stream of type application/octet-stream instead of a JSON-encoded array of bytes. HttpCodec.Fallback this represents the case when we should either use the first codec, or the second. A special case is when the right value of Fallback is HttpCodec.Empty. This is how ZIO Http represents optional inputs! We have to handle this specially in our code generator to mark some of the input parameters of the generated API as optional parameters. We don't support currently the other cases (when right is not empty) as it is not frequently used and was not required for the Golem API. HttpCodec.Header means we need to send a header in the request, which can be a static (value described by the endpoint) or dynamic one (where we need to add an extra parameter to the generated function to get a value of the header). There are a couple of different primitive types supported for the value, such as string, numbers, UUIDs. HttpCodec.Method defines the method to be used for calling the endpoint

HttpCodec.Path describes the request path, which consists of a sequence of static and dynamic segments - for the dynamic segments the generated API need to have exposed function parameters of the appropriate type HttpCodec.Query similar to the header codec defines query parameters to be sent

HttpCodec.TransformOrFail transforms a value with a Scala function - the same case as with Schema.Transform. We cannot use the Scala function in our code generator so we just need to ignore this and go to the inner codec. HttpCodec.Annotated attaches additional information to the codecs that we are currently not using, but it could be used to get documentation strings and include them in the generated code as comments, for example.


For outputs we are dealing with the same HttpCodec type but there are some significant differences:

We can ignore Path, Method, Query as they have no meaning for outputs

We could look for output headers but currently we ignore them

Fallback on the other hand needs to be properly handled for outputs (errors, especially) because this is how the different error responses are encoded. Status is combined with Content in these Fallback nodes to describe cases. This complicates the code generator because we need to record "possible outputs" which are only added as real output once we are sure we will not get any other piece of information for them.

To understand the error fallback handling better, let's take a look at how it is defined in one of Golem's endpoint groups:

val errorCodec: HttpCodec[HttpCodecType.Status & HttpCodecType.Content, LimitsEndpointError] =

This leads to a series of nested HttpCodec.Fallback, HttpCodec.Combine, HttpCodec.Status and HttpCodec.Content nodes. When processing them we first add values of possible outputs:

final case class PossibleOutput(tpe: RustType, status: Option[Status], isError: Boolean, schema: Schema[?])

and once we have fully processed one branch of a Fallback, we finalize these possible outputs and make them real outputs. The way these different error cases are mapped into different case classes of a a single error type (LimitsEndpointError) also complicates things. When we reach a HttpCodec.Content referencing Schema[LimitsEndpointError.LimitExceeded] for example, all we see is a Schema.Record - and not the parent enum! For this reason in the code generator we are explicitly defining the error ADT type:

val fromEndpoint = RustEndpoint.withKnownErrorAdt[LimitsEndpointError].zio

and we detect if all cases are subtypes of this error ADT and generate the client code according to that.

The Rust client

It is time to take a look at what the output of all this looks like. In this section we will examine some parts of the generated Rust code.

Let's take a look at the Projects API. We have generated a trait for all the endpoints belonging to it:

This is quite close to our original goal! One significant difference is that some type information is lost: project_id was ProjectId in Scala, and authorization was TokenSecret etc. Unfortunately with the current version of ZIO Schema these newtypes (or Scala 3 opaque types) are represented as primitive types transformed by a function. As explained earlier, we can't inspect the transformation function so all we can do is to use the underlying primitive type's schema here. This can be solved by introducing the concept of newtypes into ZIO Schema.

The ProjectError is a client specific generated enum which can represent a mix of internal errors (such as not being able to call the endpoint) as well as the endpoint-specific domain errors:

pub enum ProjectError {
    Status404 {
        message: String,
    Status403 {
        error: String,
    Status400 {
        errors: Vec,
    Status500 {
        error: String,
    Status401 {
        message: String,

So why are these per-status-code error types inlined here instead of generating the error ADT as a Rust enum and using that? The reason is a difference between Scala and Rust: we have a single error ADT in Scala and we can still use its cases directly in the endpoint definition:

sealed trait ProjectEndpointError
object ProjectEndpointError {
  final case class ArgValidation(errors: Chunk[String]) extends ProjectEndpointError
  // ...

// ...

We do generate the corresponding ProjectEndpointError enum in Rust:

#[derive(Debug, Clone, PartialEq, Eq, Hash, Ord, PartialOrd, serde::Serialize, serde::Deserialize)]
pub enum ProjectEndpointError {
    ArgValidation {
        errors: Vec,
    // ...

but we cannot use ProjectEndpointError::ArgValidation as a type in the above ProjectError enum. And we cannot safely do something like Either[ClientError, ProjectEndpointError] because in the endpoint DSL we just have a sequence of status code - error case pairs. There is no guarantee that one enum case is only used once in that mapping, or that every case is used at least once. For this reason the mapping from ProjectError to ProjectEndpointError is generated as a transformation function:

 impl ProjectError {
  pub fn to_project_endpoint_error(&self) -> Option {
    match self {
      ProjectError::Status400 { errors } => Some(crate::model::ProjectEndpointError::ArgValidation { errors: errors.clone() }), 
      // ...

For each client trait we also generate a live implementation, represented by a struct containing configuration for the client:

#[derive(Clone, Debug)]
pub struct ProjectLive {
    pub base_url: reqwest::Url,
    pub allow_insecure: bool,

And the implementation of the client trait for these live structs are just using reqwest (a HTTP client library for Rust) to construct the request from the input parameters exactly the way the endpoint definition described:

async fn get_project(&self, project_id: &str, authorization: &str) -> Result {
  let mut url = self.base_url.clone();

  let mut headers = reqwest::header::HeaderMap::new();
  // ...

  let mut builder = reqwest::Client::builder();
  // ...
  let client =;
  let result = client
  match result.status().as_u16() {
    200 => {
      let body = result.json::().await?;
    404 => {
      let body = result.json::().await?;
      Err(ProjectError::Status404 { message: body.message })
    // ...

Putting it all together

At this point we have seen how ZIO Http describes endpoints, how ZIO Schema encodes Scala types, how we can use ZIO Parser to have composable printers and how ZIO Prelude can help with working with state in a purely functional code. The only thing remaining is to wire everything together and define an easy to use function that, when executed, creates all the required Rust files ready to be compiled.

We can create a class for this:

final case class ClientCrateGenerator(name: String, version: String, description: String, homepage: String, endpoints: Chunk[RustEndpoints]):

Here endpoints is a collection of a group of endpoints, as it was shown earlier. So first you can use RustEndpoint.fromEither and ++ to create a RustEndpoints value for each API you have, and then generate a client for all of those in one run with this class.

The first thing to do is collect all the referenced Schema from all the endpoints:

private val allSchemas = union _)

Then we define a ZIO function (it is an effectful function, manipulating the filesystem!) to generate the files:

def generate(targetDirectory: Path): ZIO[Any, Throwable, Unit] =
    clientModel <- ZIO.fromEither(RustModel.fromSchemas(allSchemas.toSeq))
                      .mapError(err => new RuntimeException(s"Failed to generate client model: $err"))
    cargoFile = targetDirectory / "Cargo.toml"
    srcDir = targetDirectory / "src"
    libFile = srcDir / ""
    modelFile = srcDir / ""

    requiredCrates = clientModel.requiredCrates union union _)

    _ <- Files.createDirectories(targetDirectory)
    _ <- Files.createDirectories(srcDir)
    _ <- writeCargo(cargoFile, requiredCrates)
    _ <- writeLib(libFile)
    _ <- writeModel(modelFile, clientModel.definitions)
    _ <- ZIO.foreachDiscard(endpoints): endpoints =>
           val clientFile = srcDir / s"${}.rs"
           writeClient(clientFile, endpoints)
  yield ()

The steps are straightforward:

Create a RustModel using all the collected Schema[?] values

Create all the required directories

Write a cargo file - having all the dependencies and other metadata required to compile the Rust project

Write a lib file - this is just a series of pub mod xyz; lines, defining the generated modules which are put in different fields

Write all the generated Rust types into a

For each endpoint group create a module containing the client trait and implementation

For working with the file system - creating directories, writing data into files, we can use the [ZIO NIO] library providing ZIO wrappers for all these functionalities.


Finally, some links:

The code generator is open source and available at - the code and the repository itself is not documented at the moment, except by this blog post. The generated Golem client for Rust is published as a crate to

The new Golem CLI, using the generated client, is also open sourced and can be found at

Finally you can learn more about Golem itself at

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