Please visit https://getkyo.io for an indexed version of this documentation.

Introduction

Kyo is a toolkit for Scala development, spanning from browser-based apps in ScalaJS to high-performance backends on the JVM. It introduces a novel approach based on algebraic effects to deliver straightforward APIs in the pure Functional Programming paradigm. Unlike similar solutions, Kyo achieves this without inundating developers with concepts from Category Theory and avoiding the use of symbolic operators, resulting in a development experience that is both intuitive and robust.

Drawing inspiration from ZIO's effect rotation, Kyo takes a more generalized approach. While ZIO restricts effects to two channels, dependency injection and short-circuiting, Kyo allows for an arbitrary number of effectful channels. This enhancement gives developers greater flexibility in effect management, while also simplifying Kyo's internal codebase through more principled design patterns.

Getting Started

Kyo is available on Maven Central in multiple modules:

Module	Scala 2	Scala 3	Scala JS	Standalone	Description
kyo-prelude		✅	✅		Effects without IO
kyo-core		✅	✅		Async and IO-based effects
kyo-direct		✅	✅		Direct syntax support
kyo-combinators		✅	✅		ZIO-like effect composition
kyo-sttp		✅	✅		Sttp HTTP Client
kyo-tapir		✅			Tapir HTTP Server
kyo-zio		✅			ZIO integration
kyo-caliban		✅			Caliban GraphQL Server
kyo-cache		✅			Caffeine caching
kyo-stats-otel	✅	✅			Stats exporter for OpenTelemetry
kyo-tag		✅	✅	✅	Allocation-free type tags
kyo-data		✅	✅	✅	Low-allocation data types
kyo-scheduler	✅	✅		✅	Reusable adaptive scheduler
kyo-scheduler-zio	✅	✅		✅	Adaptive scheduler for ZIO apps

The modules marked as Standalone are designed to be used independently, without requiring the full Kyo effect system. These modules provide specific functionalities that can be integrated into any Scala project, regardless of whether it uses Kyo's effect system or not.

Example sbt configurations:

libraryDependencies += "io.getkyo" %% "kyo-prelude"       % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-core"          % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-direct"        % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-combinators"   % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-sttp"          % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-tapir"         % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-zio"           % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-caliban"       % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-cache"         % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-stats-otel"    % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-tag"           % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-data"          % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-scheduler"     % "<version>"
libraryDependencies += "io.getkyo" %% "kyo-scheduler-zio" % "<version>"

For ScalaJS (applicable only to to specific modules):

libraryDependencies += "io.getkyo" %%  "kyo-prelude"     % "<version>"
libraryDependencies += "io.getkyo" %%% "kyo-core"        % "<version>"
libraryDependencies += "io.getkyo" %%% "kyo-direct"      % "<version>"
libraryDependencies += "io.getkyo" %%  "kyo-combinators" % "<version>"
libraryDependencies += "io.getkyo" %%% "kyo-sttp"        % "<version>"
libraryDependencies += "io.getkyo" %%  "kyo-tag"         % "<version>"
libraryDependencies += "io.getkyo" %%  "kyo-data"        % "<version>"

Replace <version> with the latest version: .

IDE Support

Kyo utilizes features from the latest Scala 3 versions that are not yet properly supported by IntelliJ IDEA. For the best development experience and to ensure all Kyo features are correctly recognized, we recommend using a Metals-based IDE for your Kyo projects.

Recommended Compiler Flags

We strongly recommend enabling these Scala compiler flags when working with Kyo to catch common mistakes and ensure proper effect handling:

1. -Wvalue-discard: Warns when non-Unit expression results are unused.
2. -Wnonunit-statement: Warns when non-Unit expressions are used in statement position.
3. -Wconf:msg=(discarded.*value|pure.*statement):error: Elevates the warnings from the previous flags to compilation errors.

Add these to your build.sbt:

scalacOptions ++= Seq(
    "-Wvalue-discard", 
    "-Wnonunit-statement", 
    "-Wconf:msg=(discarded.*value|pure.*statement):error")

These flags help catch two common issues in Kyo applications:

1. A pure expression does nothing in statement position: Often suggests that a Kyo computation is being discarded and will never execute, though it can also occur with other pure expressions. Common fixes include using map to chain transformations or explicitly handling the result.

2. Discarded non-Unit value: Most commonly occurs when you pass a computation to a method that can only handle some of the effects that your computation requires. For example, passing a computation that needs both IO and Abort[Exception] effects as a method parameter that only accepts IO can trigger this warning. While this warning can appear in other scenarios (like ignoring any non-Unit value), in Kyo applications it typically signals that you're trying to use a computation in a context that doesn't support all of its required effects.

Note: You may want to selectively disable these warnings in test code, where it's common to assert side effects without using their returned values.

The "Pending" type: <

In Kyo, computations are expressed via the infix type <, known as "Pending". It takes two type parameters:

1. The type of the expected output.
2. The pending effects that need to be handled, represented as an unordered type-level set via a type intersection.

import kyo.*

// 'Int' pending 'Abort[Absent]'
// 'Absent' is Kyo's equivalent of 'None' via the 'Maybe' type
Int < Abort[Absent]

// 'String' pending 'Abort[Absent]' and 'IO'
String < (Abort[Absent] & IO)

Note: The naming convention for effect types is the plural form of the functionalities they manage.

Any type T is automatically considered to be of type T < Any, where Any denotes an absence of pending effects. In simpler terms, this means that every value in Kyo is automatically a computation, but one without any effects that you need to handle.

This design choice streamlines your code by removing the necessity to differentiate between pure values and computations that may have effects. So, when you're dealing with a value of type T < Any, you can safely eval the pure value directly, without worrying about handling any effects.

import kyo.*

// An 'Int' is also an 'Int < Any'
val a: Int < Any = 1

// Since there are no pending effects,
// the computation can produce a pure value
val b: Int = a.eval

Note: This README provides explicit type declarations for clarity. However, Scala's type inference is generally able to infer Kyo types properly.

This unique property removes the need to juggle between map and flatMap. All values are automatically promoted to a Kyo computation with zero pending effects, enabling you to focus on your application logic rather than the intricacies of effect handling.

import kyo.*

// Kyo still supports both `map`
// and `flatMap`.
def example1(
    a: Int < IO,
    b: Int < Abort[Exception]
): Int < (IO & Abort[Exception]) =
    a.flatMap(v => b.map(_ + v))

// But using only `map` is recommended
// since it functions like `flatMap` due
// to effect widening.
def example2(
    a: Int < IO,
    b: Int < Abort[Exception]
): Int < (IO & Abort[Exception]) =
    a.map(v => b.map(_ + v))

The map method automatically updates the set of pending effects. When you apply map to computations that have different pending effects, Kyo reconciles these into a new computation type that combines all the unique pending effects from both operands.

When a computation produces a Unit value, Kyo also offers an andThen method for more fluent code:

import kyo.*

// An example computation that
// produces 'Unit'.
val a: Unit < IO =
    IO(println("hello"))

// Use 'andThen'.
val b: String < IO =
    a.andThen("test")

The pipe method allows for chaining effect handlers without nesting parentheses. It's particularly useful when dealing with multiple effects.

import kyo.*

val a: Int < (Abort[String] & Env[Int]) =
    for
        v <- Abort.get(Right(42))
        e <- Env.get[Int]
    yield v + e

// Handle effects using `pipe`
val b: Result[String, Int] =
    a.pipe(Abort.run(_))   // Handle Abort
     .pipe(Env.run(10))    // Handle Env
     .eval                 // Evaluate the computation

// Equivalent without `pipe`
val c: Result[String, Int] =
    Env.run(10)(Abort.run(a)).eval

// `pipe` also supports multiple functions
val d: Result[String, Int] =
    a.pipe(Abort.run(_), Env.run(10)).eval

// Mixing effect handling, 'map' transformation, and 'eval'
val e: Int =
    a.pipe(
        Abort.run(_),
        Env.run(10),
        _.map(_.getOrElse(24)), // Convert Result to Int
        _.eval
    )

Effect widening

Kyo's set of pending effects is a contravariant type parameter. This encoding permits computations to be widened to encompass a larger set of effects.

import kyo.*

// An 'Int' with an empty effect set (`Any`)
val a: Int < Any =
    1

// Widening the effect set from empty (`Any`)
// to include `IO`
val b: Int < IO =
    a

// Further widening the effect set to include
// both `IO` and `Abort[Exception]`
val c: Int < (IO & Abort[Exception]) =
    b

// Directly widening a pure value to have
// `IO` and `Abort[Exception]`
val d: Int < (IO & Abort[Exception]) =
    42

This characteristic enables a fluent API for effectful code. Methods can accept parameters with a specific set of pending effects while also permitting those with fewer or no effects.

import kyo.*

// The function expects a parameter with both
// 'IO' and 'Abort' effects pending
def example1(v: Int < (IO & Abort[Exception])) =
    v.map(_ + 1)

// A value with only the 'Abort' effect can be
// automatically widened to include 'IO'
def example2(v: Int < Abort[Exception]) =
    example1(v)

// A pure value can also be automatically widened
def example3 = example1(42)

Here, example1 is designed to accept an Int < (Options & Abort[Exception]). However, thanks to the contravariant encoding of the type-level set of effects, example2 and example3 demonstrate that you can also pass in computations with a smaller set of effects—or even a pure value—and they will be automatically widened to fit the expected type.

Using effects

Effects follow a naming convention for common operations:

• init*: Initializes an instance of the container type handled by the effect. For instance, Async.run returns a new Fiber.
• get*: Allows the "extraction" of the value of the container type. Async.get returns a T < Async for a Fiber[T].
• run*: Handles the effect.

Though named run, effect handling doesn't necessarily execute the computation immediately, as the effect handling itself can also be suspended if another effect is pending.

import kyo.*

val a: Int < Abort[Exception] = 42

// Handle the 'Options' effect
// 'Result' is similar to 'Either'
val b: Result[Exception, Int] < Any =
    Abort.run(a)

// Retrieve pure value as there are no more pending effects
val c: Result[Exception, Int] =
    b.eval

The order in which you handle effects in Kyo can significantly influence both the type and value of the result. Since effects are unordered at the type level, the runtime behavior depends on the sequence in which effects are processed.

import kyo.*

def abortStringFirst(a: Int < (Abort[String] & Abort[Exception])): Result[Exception, Result[String, Int]] =
    val b: Result[String, Int] < Abort[Exception] =
        Abort.run[String](a)
    val c: Result[Exception, Result[String, Int]] < Any =
        Abort.run[Exception](b)
    c.eval
end abortStringFirst

// Note how 'Abort' supports type unions. This method's parameter is equivalent to 'abortStringFirst'.
def abortExceptionFirst(a: Int < Abort[String | Exception]): Result[String, Result[Exception, Int]] =
    val b: Result[Exception, Int] < Abort[String] =
        Abort.run[Exception](a)
    val c: Result[String, Result[Exception, Int]] < Any =
        Abort.run[String](b)
    c.eval
end abortExceptionFirst

// The sequence in which effects are handled has a significant impact on the outcome.
// This is especially true for effects that can short-circuit the computation.

val ex = new Exception

// If the computation doesn't short-circuit, only the order of nested types in the result changes.
// This code uses a pure value as the computation as an example.
val a: Result[Exception, Result[String, Int]] = abortStringFirst(1)    // Result.Success(Result.Success(1))
val b: Result[String, Result[Exception, Int]] = abortExceptionFirst(1) // Result.Success(Result.Success(1))

// If there's short-circuiting, the resulting value can be different depending on the handling order.
abortStringFirst(Abort.fail("test"))    // Result.Success(Result.Fail("test"))
abortStringFirst(Abort.fail(ex))        // Result.Fail(ex)

abortExceptionFirst(Abort.fail("test")) // Result.Fail("test")
abortExceptionFirst(Abort.fail(ex))     // Result.Success(Result.Fail(ex))

Direct Syntax

Kyo provides direct syntax for a more intuitive and concise way to express computations, especially when dealing with multiple effects. This syntax leverages two primary constructs: defer and await.

Essentially, await is a syntactic sugar for the map function, allowing developers to directly access values from computations without the need for repetitive map chaining. This makes the code more linear and intuitive.

import kyo.*

// Use the direct syntax
val a: String < (Abort[Exception] & IO) =
    defer {
        val b: String =
            await(IO("hello"))
        val c: String =
            await(Abort.get(Right("world")))
        b + " " + c
    }

// Equivalent desugared
val b: String < (Abort[Exception] & IO) =
    IO("hello").map { b =>
        Abort.get(Right("world")).map { c =>
            b + " " + c
        }
    }

The defer macro translates the defer and await constructs by virtualizing control flow. It modifies value definitions, conditional branches, loops, and pattern matching to express compurations in terms of map.

For added safety, the direct syntax enforces effectful hygiene. Within a defer block, values of the < type must be enclosed by an await block. This approach ensures all effectful computations are explicitly processed, reducing the potential for missed effects or operation misalignment.

import kyo.*

// This code fails to compile
val a: Int < IO =
    defer {
        // Incorrect usage of a '<' value
        // without 'await'
        IO(println(42))
        42
    }

Note: In the absence of effectful hygiene, the side effect IO(println(42)) would be overlooked and never executed. With the hygiene in place, such code results in a compilation error.

The syntac sugar supports a variety of constructs to handle effectful computations. These include pure expressions, value definitions, control flow statements like if-else, logical operations (&& and ||), while, and pattern matching.

import kyo.*

defer {
    // Pure expression
    val a: Int = 5

    // Effectful value
    val b: Int = await(IO(10))

    // Control flow
    val c: String =
        if await(IO(true)) then "True branch" else "False branch"

    // Logical operations
    val d: Boolean =
        await(IO(true)) && await(IO(false))

    val e: Boolean =
        await(IO(true)) || await(IO(true))

    // Loop (for demonstration; this loop
    // won't execute its body)
    while await(IO(false)) do "Looping"

    // Pattern matching
    val matchResult: String =
        await(IO(1)) match
            case 1 => "One"
            case _ => "Other"
}

The defer method in Kyo mirrors Scala's for-comprehensions in providing a constrained yet expressive syntax. In defer, features like nested defer blocks, var declarations, return statements, lazy val, lambda and def with await, try/catch blocks, methods and constructors accepting by-name parameters, throw expressions, as well as class, for-comprehension, trait, and objects are disallowed. This design allows clear virtualization of control flow, eliminating potential ambiguities or unexpected results.

The kyo-direct module is constructed as a wrapper around dotty-cps-async.

Note: defer is currently the only user-facing macro in Kyo. All other features use regular language constructs.

Defining an App

KyoApp offers a structured approach similar to Scala's App for defining application entry points. However, it comes with added capabilities, handling a suite of default effects. As a result, the run method within KyoApp can accommodate various effects, such as IO, Async, Resource, Clock, Console, Random, Timer, and Aspect.

import kyo.*

object MyApp extends KyoApp:
    // Use 'run' blocks to execute Kyo computations.
    // The execution of the run block is lazy to avoid
    // field initialization issues.
    run {
        for
            _            <- Console.println(s"Main args: $args")
            currentTime  <- Clock.now
            _            <- Console.println(s"Current time is: $currentTime")
            randomNumber <- Random.nextInt(100)
            _            <- Console.println(s"Generated random number: $randomNumber")
        yield
        // The produced value can be of any type and is
        // automatically printed to the console.
        "example"
    }
end MyApp

While the companion object of KyoApp provides a utility method to run isolated effectful computations, it's crucial to approach it with caution. Direct handling the IO effect through this method compromises referential transparency, an essential property for functional programming.

import kyo.*

// An example computation
val a: Int < IO =
    IO(Math.cos(42).toInt)

// Avoid! Run the application with a timeout
val b: Result[Throwable, Int] =
    import AllowUnsafe.embrace.danger
    KyoApp.Unsafe.runAndBlock(2.minutes)(a)

Core Effects

Kyo's core effects act as the essential building blocks that power your application's various functionalities. Unlike other libraries that might require heavy boilerplate or specialized knowledge, Kyo's core effects are designed to be straightforward and flexible. These core effects not only simplify the management of side-effects, dependencies, and several other aspects but also allow for a modular approach to building maintainable systems.

Abort: Short Circuiting

The Abort effect is a generic implementation for short-circuiting effects. It's equivalent to ZIO's failure channel.

import kyo.*

// The 'get' method "extracts" the value
// from an 'Either' (right projection)
val a: Int < Abort[String] =
    Abort.get(Right(1))

// short-circuiting via 'Left'
val b: Int < Abort[String] =
    Abort.get(Left("failed!"))

// short-circuiting via 'Fail'
val c: Int < Abort[String] =
    Abort.fail("failed!")

// 'catching' automatically catches exceptions
val d: Int < Abort[Exception] =
    Abort.catching(throw new Exception)

Note that the Abort effect has a type parameter and its methods can only be accessed if the type parameter is provided.

IO: Side Effects

Kyo is unlike traditional effect systems since its base type < does not assume that the computation can perform side effects. The IO effect is introduced whenever a side effect needs to be performed.

import kyo.*

def aSideEffect = 1 // placeholder

// 'apply' is used to suspend side effects
val a: Int < IO =
    IO(aSideEffect)

Users shouldn't typically handle the IO effect directly since it triggers the execution of side effects, which breaks referential transparency. Prefer KyoApp instead.

In some specific cases where Kyo isn't used as the main effect system of an application, it might be necessary to handle the IO effect directly. However, this requires explicit acknowledgment of the unsafe nature of the operation using AllowUnsafe.embrace.danger. The run method can only be used if IO is the only pending effect.

import kyo.*

val a: Int < IO =
    IO(42)

// ** Avoid 'IO.Unsafe.run', use 'KyoApp' instead. **
val b: Int =
    import AllowUnsafe.embrace.danger // Required for unsafe operations
    IO.Unsafe.run(a).eval
// ** Avoid 'IO.Unsafe.run', use 'KyoApp' instead. **

The runLazy method accepts computations with other effects but it doesn't guarantee that all side effects are performed before the method returns. If other effects still have to be handled, the side effects can be executed later once the other effects are handled. This a low-level API that must be used with caution.

import kyo.*

// Computation with 'Env' and then 'IO' suspensions
val a: Int < (Env[Int] & IO) =
    Env.get[Int].map { v =>
        IO {
            println(v)
            v
        }
    }

// ** Avoid 'IO.Unsafe.runLazy', use 'KyoApp' instead. **
// Handle the 'IO' effect lazily
val b: Int < Env[Int] =
    import AllowUnsafe.embrace.danger // Required for unsafe operations
    IO.Unsafe.runLazy(a)
// ** Avoid 'IO.Unsafe.runLazy', use 'KyoApp' instead. **

// Since the computation is suspended with the
// 'Env' effect first, the lazy 'IO' execution
// will be triggered once 'Env' is handled
val c: Int =
    Env.run(42)(b).eval

IMPORTANT: Avoid handling the IO effect directly since it breaks referential transparency. Use KyoApp instead.

Env: Dependency Injection

Env is similar to ZIO's environment feature but offers more granular control. Unlike ZIO, which has built-in layering for dependencies, Env allows you to inject individual services directly. However, it lacks ZIO's structured dependency management; you manage and initialize your services yourself.

import kyo.*

// Given an interface
trait Database:
    def count: Int < IO

// The 'Env' effect can be used to summon an instance.
// Note how the computation produces a 'Database' but at the
// same time requires a 'Database' from its environment
val a: Database < Env[Database] =
    Env.get[Database]

// Use the 'Database' to obtain the count
val b: Int < (Env[Database] & IO) =
    a.map(_.count)

// A 'Database' mock implementation
val db = new Database:
    def count = 1

// Handle the 'Env' effect with the mock database
val c: Int < IO =
    Env.run(db)(b)

// Additionally, a computation can require multiple values
// from its environment.

// A second interface to be injected
trait Cache:
    def clear: Unit < IO

// A computation that requires two values
val d: Unit < (Env[Database] & Env[Cache] & IO) =
    Env.get[Database].map { db =>
        db.count.map {
            case 0 =>
                Env.get[Cache].map(_.clear)
            case _ =>
                ()
        }
    }

Layer: Dependency Management

The Layer effect builds upon Env to provide a more structured approach to dependency management. It allows you to define, compose, and provide dependencies in a modular and reusable way.

Layer is defined with two type parameters: Layer[Out, S]

1. Out: This represents the output type of the layer, which is the type of the dependency or service that the layer provides. It can be a single type or a combination of types using & as a type intersection.
2. S: This represents the set of effects that the layer requires to build its output. It includes any effects needed to construct the Out type.

For example, Layer[Database, IO] represents a layer that provides a Database service and has the IO effect to construct it.

Now, let's look at how to create and use layers:

import kyo.*

// Define some services
trait Database:
    def query: String < IO

trait Cache:
    def get: Int < IO

trait Logger:
    def log(msg: String): Unit < IO

// Create layers for each service
val dbLayer: Layer[Database, Any] =
    Layer {
        new Database:
            def query = IO("DB result")
    }

val cacheLayer: Layer[Cache, Any] =
    Layer {
        new Cache:
            def get = IO(42)
    }

val loggerLayer: Layer[Logger, Any] =
    Layer {
        new Logger:
            def log(msg: String) = IO(println(msg))
    }

// The `Layer.init` method provides a way to create a layer from multiple sub-layers, automatically 
// resolving dependencies between them. It can be used for more complex compositions as well
val appLayer: Layer[Database & Cache & Logger, Any] =
    Layer.init[Database & Cache & Logger](dbLayer, cacheLayer, loggerLayer)

// Use the composed layer in a computation
val computation: String < (Env[Database] & Env[Cache] & Env[Logger] & IO) =
    for
        db     <- Env.get[Database]
        cache  <- Env.get[Cache]
        logger <- Env.get[Logger]
        _      <- logger.log("Starting query")
        result <- db.query
        _      <- logger.log(s"Query result: $result")
        cached <- cache.get
        _      <- logger.log(s"Cached value: $cached")
    yield result

// Run the computation with the composed layer
val result: String < (IO & Memo) =
    Env.runLayer(appLayer)(computation)

// The 'Memo' effect is used by Layer to ensure components are initialized only once
val result2: String < IO =
    Memo.run(result)

The Layer type provides instance methods for manually composing layers:

1. to: Combines two layers sequentially, where the output of the first layer is used as input for the second layer.
2. and: Combines two layers in parallel, producing a layer that provides both outputs.
3. using: Combines a layer with another layer that depends on its output, similar to to but keeps both outputs.

Here's an example that demonstrates the differences between these methods:

import kyo.*

trait Database:
    def query: String < IO

trait UserService:
    def getUser(id: Int): String < IO

trait EmailService:
    def sendEmail(to: String, content: String): Unit < IO

// Define layers
val dbLayer: Layer[Database, IO] = Layer {
    new Database:
        def query = IO("DB result")
}

val userServiceLayer: Layer[UserService, Env[Database] & IO] =
    Layer.from { (db: Database) =>
        new UserService:
            def getUser(id: Int) = db.query.map(result => s"User $id: $result")
    }

val emailServiceLayer: Layer[EmailService, IO] = Layer {
    new EmailService:
        def sendEmail(to: String, content: String) =
            IO(println(s"Email sent to $to: $content"))
}

// Example of `to`: Output of dbLayer is used as input for userServiceLayer
val dbToUserService: Layer[UserService, IO] =
    dbLayer.to(userServiceLayer)

// Example of `and`: Combines dbLayer and emailServiceLayer in parallel
val dbAndEmail: Layer[Database & EmailService, IO] =
    dbLayer.and(emailServiceLayer)

// Example of `using`: Similar to `to`, but keeps both Database and UserService
val dbUsingUserService: Layer[Database & UserService, IO] =
    dbLayer.using(userServiceLayer)

// Complex composition
val fullAppLayer: Layer[Database & UserService & EmailService, IO] =
    dbLayer.using(userServiceLayer).and(emailServiceLayer)

// Use the full app layer
val computation: Unit < (Env[Database] & Env[UserService] & Env[EmailService] & IO) =
    for
        db           <- Env.get[Database]
        userService  <- Env.get[UserService]
        emailService <- Env.get[EmailService]
        _            <- db.query
        user         <- userService.getUser(1)
        _            <- emailService.sendEmail("[email protected]", s"User data: $user")
    yield ()

val result: Unit < (IO & Memo) =
    Env.runLayer(fullAppLayer)(computation)

Local: Scoped Values

The Local effect operates on top of IO and enables the definition of scoped values. This mechanism is typically used to store contextual information of a computation. For example, in request processing, locals can be used to store information about the user who initiated the request. In a library for database access, locals can be used to propagate transactions.

import kyo.*

// Local need to be initialized with a default value
val myLocal: Local[Int] =
    Local.init(42)

// The 'get' method returns the current value of the local
val a: Int < IO =
    myLocal.get

// The 'let' method assigns a value to a local within the
// scope of a computation. This code produces 43 (42 + 1)
val b: Int < IO =
    myLocal.let(42)(a.map(_ + 1))

Note: Kyo's effects are designed so locals are properly propagated. For example, they're automatically inherited by forked computations in Async.

Resource: Resource Safety

The Resource effect handles the safe use of external resources like network connections, files, and any other resource that needs to be freed once the computation finalizes. It serves as a mechanism similar to ZIO's Scope.

import java.io.Closeable
import kyo.*

class Database extends Closeable:
    def count: Int < IO = 42
    def close()          = {}

// The `acquire` method accepts any object that
// implements Java's `Closeable` interface
val db: Database < (Resource & Async) =
    Resource.acquire(new Database)

// Use `run` to handle the effect, while also
// closing the resources utilized by the
// computationation
val b: Int < Async =
    Resource.run(db.map(_.count))

// The `ensure` method provides a low-level API to handle the finalization of
// resources directly. The `acquire` method is implemented in terms of `ensure`.

// Example method to execute a function on a database
def withDb[T](f: Database => T < Async): T < (Resource & Async) =
    // Initializes the database ('new Database' is a placeholder)
    IO(new Database).map { db =>
        // Registers `db.close` to be finalized
        Resource.ensure(db.close).map { _ =>
            // Invokes the function
            f(db)
        }
    }

// Execute a function
val c: Int < (Resource & Async) =
    withDb(_.count)

// Close resources
val d: Int < Async =
    Resource.run(c)

Batch: Efficient Data Processing

The Batch effect provides a mechanism for efficient processing of data in batches, allowing for optimized handling of datasets. It includes a type parameter S that represents the possible effects that can occur in the data sources.

import kyo.*

// Using 'Batch.sourceSeq' for processing the entire sequence at once, returning a 'Seq'
val source1 = Batch.sourceSeq[Int, String, Any] { seq =>
    seq.map(i => i.toString)
}

// Using 'Batch.sourceMap' for processing the entire sequence at once, returning a 'Map'
val source2 = Batch.sourceMap[Int, String, IO] { seq =>
    // Source functions can perform arbitrary effects like 'IO' before returning the results
    IO {
        seq.map(i => i -> i.toString).toMap
    }
}

// Using 'Batch.source' for individual effect suspensions
// This is a more generic method that allows effects for each of the inputs
val source3 = Batch.source[Int, String, IO] { seq =>
    val map = seq.map { i =>
        i -> IO((i * 2).toString)
    }.toMap
    (i: Int) => map(i)
}

// Example usage
val result =
    for
        a <- Batch.eval(Seq(1, 2, 3))
        b1 <- source1(a)
        b2 <- source2(a)
        b3 <- source3(a)
    yield (a, b1, b2, b3)

// Handle the effect
val finalResult: Seq[(Int, String, String, String)] < IO =
    Batch.run(result)

When creating a source, it's important to note that the returned sequence must have the same number of elements as the input sequence. This restriction ensures consistent behavior and allows for proper batching of operations.

import kyo.*

// This is valid
val validSource = Batch.sourceSeq[Int, String, Any] { seq =>
    seq.map(_.toString)
}

// This would cause a runtime error
val invalidSource = Batch.sourceSeq[Int, Int, Any] { seq =>
    seq.filter(_ % 2 == 0)
}

It's crucial to understand that the batching is done based on the identity of the provided source function. To ensure proper batching, it's necessary to reuse the function returned by Batch.source. Creating a new source for each operation will prevent effective batching. For example:

import kyo.*

// Correct usage: reusing the source
val source = Batch.sourceSeq[Int, Int, IO] { seq => 
    IO(seq.map(_ * 2))
}

val goodBatch = for
    a <- Batch.eval(1 to 1000)
    b <- source(a)  // This will be batched
    c <- source(b)  // This will also be batched
yield c

// Incorrect usage: creating new sources inline
val badBatch = for
    a <- Batch.eval(1 to 1000)
    b <- Batch.sourceSeq[Int, Int, IO](seq => IO(seq.map(_ * 2)))(a)  // This won't be batched
    c <- Batch.sourceSeq[Int, Int, IO](seq => IO(seq.map(_ * 2)))(b)  // This also won't be batched
yield c

Choice: Exploratory Branching

The Choice effect is designed to aid in handling and exploring multiple options, pathways, or outcomes in a computation. This effect is particularly useful in scenario where you're dealing with decision trees, backtracking algorithms, or any situation that involves dynamically exploring multiple options.

import kyo.*

// Evaluate each of the provided `Seq`s.
// Note how 'get' takes a 'Seq[T]'
// and returns a 'T < Choice'
val a: Int < Choice =
    Choice.get(Seq(1, 2, 3, 4))

// 'dropIf' discards the current element if
// a condition is not met. Produces a 'Seq(1, 2)'
// since values greater than 2 are dropped
val b: Int < Choice =
    a.map(v => Choice.dropIf(v > 2).map(_ => v))

// 'drop' unconditionally discards the
// current choice. Produces a 'Seq(42)'
// since only the value 1 is transformed
// to 42 and all other values are dropped
val c: Int < Choice =
    b.map {
        case 1 => 42
        case _ => Choice.drop
    }

// Handle the effect to evaluate all elements
// and return a 'Seq' with the results
val d: Seq[Int] < Any =
    Choice.run(c)

The Choice effect becomes exceptionally powerful when combined with other effects. This allows you not just to make decisions or explore options in isolation but also to do so in contexts that may involve factors such as asynchronicity, resource management, or even user interaction.

Loop: Efficient Recursion

Loop provides a solution for efficient recursion in Kyo. It offers a set of methods to transform input values through repeated applications of a function until a termination condition is met, allowing for safe and efficient recursive computations without the need for explicit effect suspensions.

import kyo.*
import java.io.IOException

// Iteratively increment an 'Int' value
// until it reaches 5
val a: Int < Any =
    Loop(1)(i =>
        if i < 5 then Loop.continue(i + 1)
        else Loop.done(i)
    )

// Transform with multiple input values
val b: Int < Any =
    Loop(1, 1)((i, j) =>
        if i + j < 5 then Loop.continue(i + 1, j + 1)
        else Loop.done(i + j)
    )

// Mixing 'IO' with 'Loop'
val d: Int < IO =
    Loop(1)(i =>
        if i < 5 then
            IO(println(s"Iteration: $i")).map(_ => Loop.continue(i + 1))
        else
            Loop.done(i)
    )

// Mixing 'Console' with 'Loop'
val e: Int < (IO & Abort[IOException]) =
    Loop(1)(i =>
        if i < 5 then
            Console.println(s"Iteration: $i").map(_ => Loop.continue(i + 1))
        else
            Loop.done(i)
    )

The transform method takes an initial input value and a function that accepts this value. The function should return either Loop.continue with the next input value or Loop.done with the final result. The computation continues until Loop.done is returned. Similarly, transform2 and transform3 allow transformations with multiple input values.

Here's an example showing three versions of the same computation:

import kyo.*

// Version 1: Regular while loop
def whileLoop: Int =
    var i   = 0
    var sum = 0
    while i < 10 do
        sum += i
        i += 1
    sum
end whileLoop

// Version 2: Recursive method loop
def recursiveLoop(i: Int = 0, sum: Int = 0): Int =
    if i < 10 then
        recursiveLoop(i + 1, sum + i)
    else
        sum

// Version 3: Using Loop
def loopsVersion: Int < Any =
    Loop(0, 0)((i, sum) =>
        if i < 10 then
            Loop.continue(i + 1, sum + i)
        else
            Loop.done(sum)
    )

In addition to the transform methods, Loop also provides indexed variants that pass the current iteration index to the transformation function. This can be useful when the logic of the loop depends on the iteration count, such as performing an action every nth iteration or terminating the loop after a certain number of iterations. The indexed methods are available with one, two, or three input values.

import kyo.*
import java.io.IOException

// Print a message every 3 iterations
val a: Int < (IO & Abort[IOException]) =
    Loop.indexed(1)((idx, i) =>
        if idx < 10 then
            if idx % 3 == 0 then
                Console.println(s"Iteration $idx").map(_ => Loop.continue(i + 1))
            else
                Loop.continue(i + 1)
        else
            Loop.done(i)
    )

// Terminate the loop after 5 iterations
val b: Int < Any =
    Loop.indexed(1, 1)((idx, i, j) =>
        if idx < 5 then Loop.continue(i + 1, j + 1)
        else Loop.done(i + j)
    )

// Use the index to calculate the next value
val c: Int < Any =
    Loop.indexed(1, 1, 1)((idx, i, j, k) =>
        if idx < 5 then Loop.continue(i + idx, j + idx, k + idx)
        else Loop.done(i + j + k)
    )

Memo: Function Memoization

The Memo effect in Kyo provides a mechanism for memoizing (caching) the results of function calls. It's implemented as a specialized Var effect that manages a cache of function results.

import kyo.*

val fibonacci: Int => Int < Memo = 
    Memo { n =>
        if (n <= 1) n
        else
            for
                a <- fibonacci(n - 1)
                b <- fibonacci(n - 2)
            yield a + b
    }

val result: (Int, Int) < Memo = 
    Memo.run {
        for
            fib10 <- fibonacci(10)
            fib11 <- fibonacci(11)
        yield (fib10, fib11)
    }

val result2: (Int, Int) < Any =
    Memo.run(result)

Key points about Memo:

• Memo memoizes function results based on both the function's input and a unique internal MemoIdentity for each memoized function.
• Memoization is scoped to the Memo.run block. A new cache is created at the start of the block and discarded at the end.
• Memo works seamlessly with other Kyo effects, allowing memoization of effectful computations.
• The memoization cache uses structural equality for keys, making it effective with immutable data structures.
• Each memoized function has its own cache space, even if created with identical code at different call sites.

For optimizing frequently called functions or computations in performance-critical sections of your code, the Cache effect would be more appropriate. Memo is designed for automatic memoization within a specific computation scope, while Cache provides more fine-grained control over caching behavior and better performance.

Chunk: Efficient Sequences

Chunk is an efficient mechanism for processing sequences of data in a purely functional manner. It offers a wide range of operations optimized for different scenarIO, ensuring high performance without compromising functional programming principles.

Chunk is designed as a lightweight wrapper around arrays, allowing for efficient random access and transformation operations. Its internal representation is carefully crafted to minimize memory allocation and ensure stack safety. Many of its operations have an algorithmic complexity of O(1), making them highly performant for a variety of use cases.

import kyo.*

// Construct chunks
val a: Chunk[Int] = Chunk(1, 2, 3)
val b: Chunk[Int] = Chunk.from(Seq(4, 5, 6))

// Perform O(1) operations
val c = a.append(4)
val d = b.take(2)
val e = c.dropLeft(1)

// Perform O(n) operations
val f = d.map(_.toString)
val g = e.filter(_ % 2 == 0)

Chunk provides two main subtypes: Chunk for regular chunks and Chunk.Indexed for indexed chunks. The table below summarizes the time complexity of various operations for each type:

Description	Operations	Regular Chunk	Indexed Chunk
Creation	Chunk, Chunk.from	O(n)	O(n)
Size and emptiness	size, isEmpty	O(1)	O(1)
Take and drop	take, dropLeft, dropRight, slice	O(1)	O(1)
Append and last	append, last	O(1)	O(1)
Element access	apply, head, tail	N/A	O(1)
Concatenation	concat	O(n)	O(n)
Effectful map and filter	map, filter, collect, takeWhile, dropWhile	O(n)	O(n)
Effectful side effects	foreach, collectUnit	O(n)	O(n)
Effectful fold	foldLeft	O(n)	O(n)
Copying to arrays	toArray, copyTo	O(n)	O(n)
Other operations	flatten, changes, toSeq, toIndexed	O(n)	O(n)

When deciding between Chunk and Chunk.Indexed, consider the primary operations you'll be performing on the data. If you mainly need to append elements, take slices, or drop elements from the beginning or end of the sequence, Chunk is a good choice. Its O(1) complexity for these operations makes it efficient for such tasks.

import kyo.*

val a: Chunk[Int] = Chunk(1, 2, 3, 4, 5)

// Efficient O(1) operations with Chunk
val b: Chunk[Int] = a.append(6)
val c: Chunk[Int] = a.take(3)
val d: Chunk[Int] = a.dropLeft(2)

On the other hand, if you frequently need to access elements by index, Chunk.Indexed is the better option. It provides O(1) element access and supports head and tail operations, which are not available in Chunk.

import kyo.*

val a: Chunk.Indexed[Int] =
    Chunk(1, 2, 3, 4, 5).toIndexed

// Efficient O(1) operations with Chunk.Indexed
val b: Int                 = a(2)
val c: Int                 = a.head
val d: Chunk.Indexed[Int] = a.tail

Keep in mind that converting between Chunk and Chunk.Indexed is an O(n) operation, so it's best to choose the appropriate type upfront based on your usage patterns. However, calling toIndexed on a chunk that is already internally indexed is a no-op and does not incur any additional overhead.

Here's an overview of the main APIs available in Chunk:

import kyo.*

// Creation
val a: Chunk[Int] = Chunk(1, 2, 3)
val b: Chunk[Int] = Chunk.from(Seq(4, 5, 6))

// Size and emptiness
val c: Int     = a.size
val d: Boolean = a.isEmpty

// Take and drop
val e: Chunk[Int] = a.take(2)
val f: Chunk[Int] = a.dropLeft(1)

// Append and last
val g: Chunk[Int] = a.append(4)
val h: Int        = a.last

// Concatenation
val i: Chunk[Int] = a.concat(b)

// Copying to arrays
val n: Array[Int] = a.toArray

// Flatten a nested chunk
val o: Chunk[Int] =
    Chunk(a, b).flattenChunk

// Obtain sequentially distict elements.
// Outputs: Chunk(1, 2, 3, 1)
val p: Chunk[Int] =
    Chunk(1, 1, 2, 3, 3, 1, 1).changes

Stream: Composable Data Processing

The Stream effect provides a powerful mechanism for processing sequences of data in a memory-conscious and composable manner. It offers a rich set of operations for transforming, filtering, and combining streams of data, all while maintaining laziness and ensuring stack safety.

import kyo.*
import java.io.IOException

// Create a stream from a sequence
val a: Stream[Int, Any] =
    Stream.init(Seq(1, 2, 3, 4, 5))

// Map over stream elements
val b: Stream[String, Any] =
    a.map(_.toString)

// Filter stream elements
val c: Stream[Int, Any] =
    a.filter(_ % 2 == 0)

// Take a limited number of elements
val d: Stream[Int, Any] =
    a.take(3)

// Drop elements from the beginning
val e: Stream[Int, Any] =
    a.drop(2)

// Concatenate streams
val f: Stream[Int, Any] =
    a.concat(Stream.init(Seq(6, 7, 8)))

// FlatMap over stream elements
val g: Stream[Int, Any] =
    a.flatMap(x => Stream.init(Seq(x, x * 2)))

// Collect stream results into a Chunk
val h: Chunk[Int] < Any =
    a.run

// Process stream elements without collecting results
val i: Unit < Any =
    a.runDiscard

// Fold over stream elements
val j: Int < Any =
    a.runFold(0)(_ + _)

// Process each element with side effects
val k: Unit < (IO & Abort[IOException]) =
    a.runForeach(Console.println(_))

Streams can be combined with other effects, allowing for powerful and flexible data processing pipelines:

import kyo.*

case class Config(someConfig: String)

// Stream with IO effect
val a: Stream[String, IO] =
    Stream.init(Seq("file1.txt", "file2.txt"))
        .map(fileName => IO(scala.io.Source.fromFile(fileName).mkString))

// Stream with Abort effect
val b: Stream[Int, Abort[NumberFormatException]] =
    Stream.init(Seq("1", "2", "abc", "3"))
        .map(s => Abort.catching[NumberFormatException](s.toInt))

def fetchUserData(config: Config, username: String): Seq[String] < Async =
    Seq(s"user data for $username") // mock implementation

// Combining multiple effects
val c: Stream[String, Env[Config] & Async] =
    Stream.init(Seq("user1", "user2", "user3"))
        .flatMap { username =>
            Stream.init {
                for
                    config <- Env.get[Config]
                    result <- fetchUserData(config, username)
                yield result
            }
        }

// Run the stream and handle effects
val result: Chunk[String] < (Env[Config] & Async) =
    c.run

The Stream effect is useful for processing large amounts of data in a memory-efficient manner, as it allows for lazy evaluation and only keeps a small portion of the data in memory at any given time. It's also composable, allowing you to build complex data processing pipelines by chaining stream operations.

Var: Stateful Computations

The Var effect allows for stateful computations, similar to the State monad. It enables the management of state within a computation in a purely functional manner.

import kyo.*

// Get the current value
val a: Int < Var[Int] =
    Var.get[Int]

// Set a new value and return the previous one
val b: Int < Var[Int] =
    Var.set(10)

// Update the state and return the new value
val c: Int < Var[Int] =
    Var.update[Int](v => v + 1)

// Use in a computation
val d: String < Var[Int] =
    Var.use[Int](v => v.toString)

// Handle the effect and discard state
val e: String < Any =
    Var.run(10)(d)

Var is particularly useful when you need to maintain and manipulate state across multiple steps of a computation.

import kyo.*

// A computation that uses `Var` to maintain a counter
def counter[S](n: Int): Int < (Var[Int] & S) =
    if n <= 0 then
        Var.get[Int]
    else
        for
            _      <- Var.update[Int](_ + 1)
            result <- counter(n - 1)
        yield result

// Initialize the counter with an initial state
val a: Int < Any =
    Var.run(0)(counter(10))

By combining Var with other effects like Async, you can create stateful computations that can be safely executed concurrently.

Emit: Accumulating Values

The Emit effect is designed to accumulate values throughout a computation, similar to the Writer monad. It collects a Chunk of values alongside the main result of a computation.

import kyo.*

// Add a value
val a: Emit.Ack < Emit[Int] =
    Emit(42)

// Add multiple values
val b: String < Emit[Int] =
    for
        _ <- Emit(1)
        _ <- Emit(2)
        _ <- Emit(3)
    yield "r"

// Handle the effect to obtain the
// accumulated log and the result.
// Evaluates to `(Chunk(1, 2, 3), "r")`
val c: (Chunk[Int], String) < Any =
    Emit.run(b)

When running Emit, the accumulated values are returned in a Chunk. The collected values and the result are returned as a tuple by Emit.run, with the Chunk as the first element. A computation can also use multiple Emit of different types.

import kyo.*

val a: String < (Emit[Int] & Emit[String]) =
    for
        _ <- Emit(1)
        _ <- Emit("log")
        _ <- Emit(2)
    yield "result"

// Note how `run` requires an explicit type
// parameter when a computation has multiple
// pending `Sum`s.
val b: (Chunk[Int], (Chunk[String], String)) < Any =
    Emit.run[Int](Emit.run[String](a))

The Emit effect is useful for collecting diagnostic information, accumulating intermediate results, or building up data structures during a computation.

Aspect: Aspect-Oriented Programming

The Aspect effect provides a way to modify or intercept behavior across multiple points in a program without directly changing the affected code. It works by allowing users to provide implementations for abstract operations at runtime, similar to dependency injection but with more powerful composition capabilities.

Aspects are created using Aspect.init and are typically stored as vals at module level. Once initialized, an aspect can be used to wrap computations that need to be modified, and its behavior can be customized using the let method to provide specific implementations within a given scope. This pattern allows for clean separation between the definition of interceptable operations and their actual implementations.

An aspect is parameterized by two type constructors, Input[_] and Output[_], along with an effect type S. These type constructors define the shape of values that can be processed and produced by the aspect. The underscore in Input[_] and Output[_] indicates that these are higher-kinded types - they each take a type parameter. This allows aspects to work with generic data structures while preserving type information throughout the transformation chain.

The simplest way to work with aspects is to use Const[A], which represents a plain value of type A. This is useful when you want to transform values directly without additional context or metadata. As you'll see in the more advanced example later, you can also create custom type constructors when you need to carry additional information through the transformation pipeline.

Here's a basic example using Const:

import kyo.*

case class Invalid(reason: String) extends Exception

// Simple aspect that transforms integers
val numberAspect = Aspect.init[Const[Int], Const[Int], Abort[Throwable] & IO]

// Basic processing function
def process(n: Int): Int < (Abort[Throwable] & IO) =
    numberAspect(n)(x => x * 2)

// Add validation via a Cut
val validationCut =
    Aspect.Cut[Const[Int], Const[Int], Abort[Throwable] & IO](
        [C] =>
            (input, cont) =>
                if input > 0 then cont(input)
                else Abort.fail(Invalid("negative number"))
    )

// Add logging via another Cut
val loggingCut =
    Aspect.Cut[Const[Int], Const[Int], Abort[Throwable] & IO](
        [C] =>
            (input, cont) =>
                for
                    _      <- Console.println(s"Processing: $input")
                    result <- cont(input)
                    _      <- Console.println(s"Result: $result")
                yield result
    )

// Compose both cuts into one
val composedCut =
    Aspect.Cut.andThen(validationCut, loggingCut)

// Success case
val successExample: Unit < (Abort[Throwable] & IO) =
    for
        result <-
            numberAspect.let(composedCut) {
                process(5) // Will succeed: 5 * 2 -> 10
            }
        _ <- Console.println(s"Success result: $result")
    yield ()

// Failure case
val failureExample: Unit < (Abort[Throwable] & IO) =
    for
        result <-
            numberAspect.let(composedCut) {
                process(-3) // Will fail with Invalid("negative number")
            }
        _ <- Console.println("This won't be reached due to Abort")
    yield ()

Aspects support multi-shot continuations, meaning that cut implementations can invoke the continuation function multiple times or not at all. This enables control flow modifications like retry logic, fallback behavior, or conditional execution. Internally, aspects function as a form of reified ArrowEffect that can be stored, passed around, and modified at runtime. They maintain state through a Local map of active implementations, allowing them to be dynamically activated and deactivated through operations like let and sandbox.

The following example demonstrates these capabilities with generic type constructors:

import kyo.*

// Define wrapper types that preserve the generic parameter
case class Request[+A](value: A, metadata: Map[String, String])
case class Response[+A](value: A, status: Int)

// Initialize aspect that can transform any Request to Response
val serviceAspect = Aspect.init[Request, Response, IO & Abort[Throwable]]

// Example service using the aspect
def processRequest[A](request: Request[A]): Response[A] < (IO & Abort[Throwable]) =
    serviceAspect(request) { req =>
        Response(req.value, status = 200)
    }

// Add authentication via a Cut
val authCut =
    Aspect.Cut[Request, Response, IO & Abort[Throwable]](
        [C] =>
            (input, cont) =>
                input.metadata.get("auth-token") match
                    case Some("valid-token") => cont(input)
                    case _                   => IO(Response(input.value, status = 401))
    )

// Add logging via another Cut
val loggingCut =
    Aspect.Cut[Request, Response, IO & Abort[Throwable]](
        [C] =>
            (input, cont) =>
                for
                    _      <- Console.println(s"Processing request: ${input}")
                    result <- cont(input)
                    _      <- Console.println(s"Response: ${result}")
                yield result
    )

// Compose both cuts into one
val composedCut =
    Aspect.Cut.andThen(authCut, loggingCut)

// Example requests
val req1 = Request("hello", Map("auth-token" -> "valid-token"))
val req2 = Request(42, Map("auth-token" -> "invalid"))

// Use the service with both aspects
val example: Unit < (IO & Abort[Throwable]) =
    for
        r1 <-
            serviceAspect.let(composedCut) {
                processRequest(req1)
            }
        r2 <-
            serviceAspect.let(composedCut) {
                processRequest(req2)
            }
        _ <- Console.println(s"Results: $r1, $r2")
    yield ()

Check: Runtime Assertions

The Check effect provides a mechanism for runtime assertions and validations. It allows you to add checks throughout your code that can be handled in different ways, such collecting failures or discarding them.

import kyo.*

// Create a simple check
val a: Unit < Check =
    Check(1 + 1 == 2, "Basic math works")

// Checks can be composed with other effects
val b: Int < (Check & IO) =
    for
        value <- IO(42)
        _     <- Check(value > 0, "Value is positive")
    yield value

// Handle checks by converting the first failed check to Abort
val c: Int < (Abort[CheckFailed] & IO) =
    Check.runAbort(b)

// Discard check failures and continue execution
val e: Int < IO =
    Check.runDiscard(b)

The CheckFailed exception class, which is used to represent failed checks, includes both the failure message and the source code location (via Frame) where the check failed, making it easier to locate and debug issues.

Console: Console Interaction

import kyo.*
import java.io.IOException

// Read a line from the console
val a: String < (IO & Abort[IOException]) =
    Console.readln

// Print to stdout
val b: Unit < (IO & Abort[IOException]) =
    Console.print("ok")

// Print to stdout with a new line
val c: Unit < (IO & Abort[IOException]) =
    Console.println("ok")

// Print to stderr
val d: Unit < (IO & Abort[IOException]) =
    Console.printErr("fail")

// Print to stderr with a new line
val e: Unit < (IO & Abort[IOException]) =
    Console.printlnErr("fail")

// Explicitly specifying the 'Console' implementation
val f: Unit < (IO & Abort[IOException]) =
    Console.let(Console.live)(e)

Clock: Time Management and Scheduled Tasks

The Clock effect provides utilities for time-related operations, including getting the current time, creating stopwatches, and managing deadlines.

import kyo.*

// Obtain the current time
val a: Instant < IO =
    Clock.now

// Create a stopwatch
val b: Clock.Stopwatch < IO =
    Clock.stopwatch

// Measure elapsed time with a stopwatch
val c: Duration < IO =
    for
        sw      <- Clock.stopwatch
        elapsed <- sw.elapsed
    yield elapsed

// Create a deadline
val d: Clock.Deadline < IO =
    Clock.deadline(5.seconds)

// Check time left until deadline
val e: Duration < IO =
    for
        deadline <- Clock.deadline(5.seconds)
        timeLeft <- deadline.timeLeft
    yield timeLeft

// Check if a deadline is overdue
val f: Boolean < IO =
    for
        deadline <- Clock.deadline(5.seconds)
        isOverdue <- deadline.isOverdue
    yield isOverdue

// Run with an explicit `Clock` implementation
val g: Instant < IO =
    Clock.let(Clock.live)(Clock.now)

Clock both safe (effectful) and unsafe (non-effectful) versions of its operations. The safe versions are suspended in IO and should be used in most cases. The unsafe versions are available through the unsafe property and should be used with caution, typically only in performance-critical sections or when integrating with non-effectful code.

Clock also offers methods to schedule background tasks:

import kyo.*

// An example computation to
// be scheduled
val a: Unit < IO =
    IO(())

// Recurring task with a delay between
// executions
val b: Fiber[Nothing, Unit] < IO =
    Clock.repeatWithDelay(
        startAfter = 1.minute,
        delay = 1.minute
    )(a)

// Without an initial delay
val c: Fiber[Nothing, Unit] < IO =
    Clock.repeatWithDelay(1.minute)(a)

// Schedule at a specific interval, regarless
// of the duration of each execution
val d: Fiber[Nothing, Unit] < IO =
    Clock.repeatAtInterval(
        startAfter = 1.minute,
        interval = 1.minute
    )(a)

// Without an initial delay
val e: Fiber[Nothing, Unit] < IO =
    Clock.repeatAtInterval(1.minute)(a)

Use the returned Fiber to control scheduled tasks.

import kyo.*

// Example task
val a: Fiber[Nothing, Unit] < IO =
    Clock.repeatAtInterval(1.second)(())

// Try to cancel a task
def b(task: Fiber[Nothing, Unit]): Boolean < IO =
    task.interrupt

// Check if a task is done
def c(task: Fiber[Nothing, Unit]): Boolean < IO =
    task.done

System: Environment Variables and System Properties

The System effect provides a safe and convenient way to access environment variables and system properties. It offers methods to retrieve values with proper type conversion and fallback options.

import kyo.*

// Get an environment variable as a String
val a: Maybe[String] < IO =
    System.env[String]("PATH")

// Get an environment variable with a default value
val b: String < IO =
    System.env[String]("CUSTOM_VAR", "default")

// Get a system property as an Int.
val c: Maybe[Int] < (Abort[NumberFormatException] & IO) =
    System.property[Int]("java.version")

// Get a system property with a default value
val d: Int < (Abort[NumberFormatException] & IO) =
    System.property[Int]("custom.property", 42)

// Get the line separator for the current platform
val e: String < IO =
    System.lineSeparator

// Get the current user's name
val f: String < IO =
    System.userName

// Use a custom System implementation
val g: String < IO =
    System.let(System.live)(System.userName)

The System effect provides built-in parsers for common types like String, Int, Boolean, Double, Long, Char, Duration, and UUID. Custom parsers can be implemented by providing an implicit System.Parser[E, A] instance.

Random: Random Values

import kyo.*

// Generate a random 'Int'
val a: Int < IO = Random.nextInt

// Generate a random 'Int' within a bound
val b: Int < IO = Random.nextInt(42)

// A few method variants
val c: Long < IO    = Random.nextLong
val d: Double < IO  = Random.nextDouble
val e: Boolean < IO = Random.nextBoolean
val f: Float < IO   = Random.nextFloat
val g: Double < IO  = Random.nextGaussian

// Obtain a random value from a sequence
val h: Int < IO =
    Random.nextValue(List(1, 2, 3))

// Explicitly specify the `Random` implementation
val k: Int < IO =
    Random.let(Random.live)(h)

Log: Logging

Log is designed to streamline the logging process without requiring the instantiation of a Logger. Log messages automatically include source code position information (File, Line, Column), enhancing the clarity and usefulness of the logs.

import kyo.*

// Log provide trace, debug, info,
// warn, and error method variants.
val a: Unit < IO =
    Log.error("example")

// Each variant also has a method overload
// that takes a 'Throwable' as a second param
val d: Unit < IO =
    Log.error("example", new Exception)

Stat: Observability

Stat is a pluggable implementation that provides counters, histograms, gauges, and tracing. It uses Java's service loading to locate exporters.

The module kyo-stats-otel provides exporters for OpenTelemetry.

import kyo.*
import kyo.stats.*

// Initialize a Stat instance
// for a scope path
val stats: Stat =
    Stat.initScope("my_application", "my_module")

// Initialize a counter
val a: Counter =
    stats.initCounter("my_counter")

// It's also possible to provide
// metadata when initializing
val b: Histogram =
    stats.initHistogram(
        name = "my_histogram",
        description = "some description"
    )

// Gauges take a by-name function to
// be observed periodically
val c: Gauge =
    stats.initGauge("free_memory") {
        Runtime.getRuntime().freeMemory().toDouble
    }

Metrics are automatically garbage collected once no strong references to them are present anymore.

Note: Although stats initialization perform side effects, Kyo chooses to consider the operation pure since stats are meant to be initialized in a static scope for optimal performance.

Tracing can be performed via the traceSpan method. It automatically initializes the span and closes it at the end of the traced computation even in the presence of failures or asynchronous operations. Nested traces are bound to their parent span via Local.

import kyo.*

val stats2: Stat =
    Stat.initScope("my_application", "my_module")

// Some example computation
val a: Int < IO =
    IO(42)

// Trace the execution of the
// `a` example computation
val b: Int < IO =
    stats2.traceSpan("my_span")(a)

Path: File System Utilities

Path provides utilities for interacting with the file system. It offers methods for reading, writing, and manipulating files and directories in a purely functional manner.

import kyo.*

// Create a Path instance representing a path
val path: Path = Path("tmp", "file.txt")

// Read the entire contents of a file as a String
val content: String < IO =
    path.read

// Write a String to a file
val writeResult: Unit < IO =
    path.write("Hello, world!")

// Check if a path exists
val exists: Boolean < IO =
    path.exists

// Create a directory
val createDir: Unit < IO =
    Path("tmp", "test").mkDir

Path instances are created by providing a list of path segments, which can be either Strings or other Path instances. This allows for easy composition of paths. Path also provides methods for other common file operations:

• Reading: read, readBytes, readLines, readStream, readLinesStream, readBytesStream
• Writing: write, writeBytes, writeLines, append, appendBytes, appendLines
• Directory operations: list, walk
• File metadata: exists, isDir, isFile, isLink
• File manipulation: mkDir, mkFile, move, copy, remove, removeAll

All methods that perform side effects are suspended using the IO effect, ensuring referential transparency. Methods that work with streams of data, such as readStream and walk, return a Stream of the appropriate type, suspended using the Resource effect to ensure proper resource handling.

import kyo.*
import java.io.IOException

val path: Path = Path("tmp", "file.txt")

// Read a file as a stream of lines
val lines: Stream[String, Resource & IO] =
    path.readLinesStream()

// Process the stream
val result: Unit < (Resource & Console & Async & Abort[IOException]) =
    lines.map(line => Console.println(line)).runDiscard

// Walk a directory tree
val tree: Stream[Path, IO] =
    Path("tmp").walk

// Process each file in the tree
val processedTree: Unit < (Console & Async & Abort[IOException]) =
    tree.map(file => file.read.map(content => Console.println(s"File: ${file}, Content: $content"))).runDiscard

Path integrates with Kyo's Stream API, allowing for efficient processing of file contents using streams. The sink and sinkLines extension methods on Stream enable writing streams of data back to files.

import kyo.*

// Create a stream of bytes
val bytes: Stream[Byte, IO] = Stream.init(Seq[Byte](1, 2, 3))

// Write the stream to a file
val sinkResult: Unit < (Resource & IO) =
    bytes.sink(Path("path", "to", "file.bin"))

Process: Process Execution

Process provides a way to spawn and interact with external processes from within Kyo. It offers a purely functional interface for process creation, execution, and management.

import kyo.*

// Create a simple command
val command: Process.Command = Process.Command("echo", "Hello, World!")

// Spawn the process and obtain the result
val result: String < IO = command.text

The core of Process is the Process.Command type, which represents a command to be executed. It can be created using the Process.Command.apply method, which takes a variable number of arguments representing the command and its arguments.

The Process object also provides a jvm sub-object for spawning JVM processes directly.

import kyo.*

class MyClass extends KyoApp:
    run {
        Console.println(s"Executed with args: $args")
    }
end MyClass

// Spawn a new JVM process
val jvmProcess: Process < IO =
    Process.jvm.spawn(classOf[MyClass], List("arg1", "arg2"))

Once a Process.Command is created, it can be executed using various methods:

• spawn: Spawns the process and returns a Process instance.
• text: Spawns the process, waits for it to complete, and returns the standard output as a string.
• stream: Spawns the process and returns an InputStream of the standard output.
• exitValue: Spawns the process, waits for it to complete, and returns the exit code.
• waitFor: Spawns the process, waits for it to complete, and returns the exit code.

Process.Command instances can be transformed and combined using methods like pipe, andThen, +, map, and cwd, env, stdin, stdout, stderr for modifying the process's properties.

import java.io.File
import java.nio.file.Path
import kyo.*

// Create a piped command
val pipedCommand = Process.Command("echo", "Hello, World!").pipe(Process.Command("wc", "-w"))

// Modify the command's environment and working directory
val modifiedCommand = pipedCommand.env(Map("VAR" -> "value")).cwd(Path.of("/path/to/dir"))

// Spawn the modified command
val modifiedResult: String < IO = modifiedCommand.text

Process also provides Input and Output types for fine-grained control over the process's standard input, output, and error streams.

import java.io.File
import kyo.*

// Create a command with custom input and output
val command = Process.Command("my-command")
    .stdin(Process.Input.fromString("input data"))
    .stdout(Process.Output.FileRedirect(new File("output.txt")))
    .stderr(Process.Output.Inherit)

The Process type returned by spawn provides methods for interacting with the spawned process, such as waitFor, exitValue, destroy, and isAlive.

Concurrent Effects

The kyo.concurrent package provides utilities for dealing with concurrency in Scala applications. It's a powerful set of effects designed for easier asynchronous programming, built on top of other core functionalities provided by the kyo package.

Async: Green Threads

The Async effect allows for the asynchronous execution of computations via a managed thread pool. The core function, run, spawns a new "green thread," also known as a fiber, to handle the given computation. This provides a powerful mechanism for parallel execution and efficient use of system resources. Moreover, fibers maintain proper propagation of Local, ensuring that context information is carried along during the forking process.

import kyo.*

// Fork a computation. The parameter is
// taken by reference and automatically
// suspended with 'IO'
val a: Fiber[Nothing, Int] < IO =
    Async.run(Math.cos(42).toInt)

// It's possible to "extract" the value of a
// 'Fiber' via the 'get' method. This is also
// referred as "joining the fiber"
val b: Int < Async =
    a.map(_.get)

The parallel methods fork multiple computations in parallel, join the fibers, and return their results.

import kyo.*

// An example computation
val a: Int < IO =
    IO(Math.cos(42).toInt)

// There are method overloadings for up to four
// parallel computations. Paramters taken by
// reference
val b: (Int, String) < Async =
    Async.parallel(a, "example")

// Run with unlimited concurrency - starts all
// computations immediately
val c: Seq[Int] < Async =
    Async.parallelUnbounded(Seq(a, a.map(_ + 1)))

// Run with controlled concurrency (max 2 tasks)
val d: Seq[Int] < Async =
    Async.parallel(2)(Seq(a, a.map(_ + 1)))

// The 'Fiber.parallel' method is similar but
// it doesn't automatically join the fibers and
// produces a 'Fiber[Seq[T]]'
val e: Fiber[Nothing, Seq[Int]] < IO =
    Fiber.parallel(2)(Seq(a, a.map(_ + 1)))

For better resource management, prefer Async.parallel(n)(seq) to control the maximum number of concurrent computations. If any computation fails or is interrupted, all other computations are automatically interrupted.

The race methods are similar to parallel but they return the first computation to complete with either a successful result or a failure. Once the first result is produced, the other computations are automatically interrupted.

import kyo.*

// An example computation
val a: Int < IO =
    IO(Math.cos(42).toInt)

// There are method overloadings for up to four
// computations. Pameters taken by reference
val b: Int < Async =
    Async.race(a, a.map(_ + 1))

// It's also possible to to provide a 'Seq'
// of computations
val c: Int < Async =
    Async.race(Seq(a, a.map(_ + 1)))

// 'Fiber.race' produces a 'Fiber' without
// joining it
val d: Fiber[Nothing, Int] < IO =
    Fiber.race(Seq(a, a.map(_ + 1)))

The sleep and timeout methods pause a computation or time it out after a duration.

import kyo.*

// A computation that sleeps for 1s
val a: Unit < Async =
    Async.sleep(1.second)

// Times out and interrupts the provided
// computation in case it doesn't produce
// a result within 1s
val b: Int < (Abort[Timeout] & Async) =
    Async.timeout(1.second)(Math.cos(42).toInt)

The fromFuture method sprovide interoperability with Scala's Future.

import kyo.*
import scala.concurrent.Future

// An example 'Future' instance
val a: Future[Int] = Future.successful(42)

// Transform a 'Future' into a 'Fiber'
val b: Fiber[Throwable, Int] < IO =
    Fiber.fromFuture(a)

Important: Keep in mind that Scala's Future lacks built-in support for interruption. As a result, any computations executed through Future will run to completion, even if they're involved in a race operation where another computation finishes first.

A Fiber instance also provides a few relevant methods.

import kyo.*
import scala.concurrent.*

// An example fiber
val a: Fiber[Nothing, Int] = Fiber.success(42)

// Check if the fiber is done
val b: Boolean < IO =
    a.done

// Instance-level version of 'Async.get'
val c: Int < Async =
    a.get

// Avoid this low-level API to attach a
// a callback to a fiber
val d: Unit < IO =
    a.onComplete(println(_))

// A variant of `get` that returns a `Result`
// with the failed or successful result
val e: Result[Nothing, Int] < Async =
    a.getResult

// Try to interrupt/cancel a fiber
val f: Boolean < IO =
    a.interrupt

// Transforms a fiber into a Scala 'Future'
val h: Future[Int] < IO =
    a.toFuture

// 'Fiber' provides a monadic API with both
// 'map' and 'flatMap'
val i: Fiber[Nothing, Int] < IO =
    a.flatMap(v => Fiber.success(v + 1))

Similarly to IO, users should avoid handling the Async effect directly and rely on KyoApp instead. If strictly necessary, there are two methods to handle the Async effect:

1. run takes a computation that has only the Async effect pending and returns a Fiber instance without blocking threads.
2. runAndBlock accepts computations with arbitrary pending effects but it handles asynchronous operations by blocking the current thread.

import kyo.*

// An example computation with fibers
val a: Int < Async =
    Async.run(Math.cos(42).toInt).map(_.get)

// Avoid handling 'Async' directly
val b: Fiber[Nothing, Int] < IO =
    Async.run(a)

// The 'runAndBlock' method accepts
// arbitrary pending effects but relies
// on thread blocking and requires a timeout
val c: Int < (Abort[Timeout] & IO) =
    Async.runAndBlock(5.seconds)(a)

Note: Handling the Async effect doesn't break referential transparency as with IO but its usage is not trivial due to the limitations of the pending effects. Prefer KyoApp instead.

The Async effect also offers a low-level API to create Promises as way to integrate external async operations with fibers. These APIs should be used only in low-level integration code.

import kyo.*

// Initialize a promise
val a: Promise[Nothing, Int] < IO =
    Promise.init[Nothing, Int]

// Try to fulfill a promise
val b: Boolean < IO =
    a.map(_.complete(Result.success(42)))

// Fullfil the promise with
// another fiber
val c: Boolean < IO =
    a.map(fiber => Async.run(1).map(fiber.become(_)))

A Promise is basically a Fiber with all the regular functionality plus the complete and become methods to manually fulfill the promise.

Retry: Automatic Retries

Retry provides a mechanism for retrying computations that may fail, with configurable policies for backoff and retry limits. This is particularly useful for operations that might fail due to transient issues, such as network requests or database operations.

import kyo.*
import scala.concurrent.duration.*

// Define a computation that might fail
val unreliableComputation: Int < Abort[Exception] =
    Abort.catching[Exception](throw new Exception("Temporary failure"))

// Customize retry schedule
val shedule = 
    Schedule.exponentialBackoff(initial = 100.millis, factor = 2, maxBackoff = 5.seconds)
        .take(5)

val a: Int < (Abort[Exception] & Async) =
    Retry[Exception](shedule)(unreliableComputation)

The Retry effect automatically adds the Async effect to handle the provided Schedule. Retry will continue attempting the computation until it succeeds, the retry schedule is done, or an unhandled exception is thrown. If all retries fail, the last failure is propagated.

Queue: Concurrent Queuing

The Queue effect operates atop of IO and provides thread-safe queue data structures based on the high-performance JCTools library on the JVM. For ScalaJS, a simple ArrayQueue is used.

Warning: The actual capacity of a Queue is rounded up to the next power of two for performance reasons. For example, if you specify a capacity of 10, the actual capacity will be 16.

Bounded queues

import kyo.*

// A bounded queue that rejects new
// elements once full
val a: Queue[Int] < IO =
    Queue.init(capacity = 42)

// Obtain the number of items in the queue
// via the method 'size' in 'Queue'
val b: Int < (IO & Abort[Closed]) =
    a.map(_.size)

// Get the queue capacity
val c: Int < IO =
    a.map(_.capacity)

// Try to offer a new item
val d: Boolean < (IO & Abort[Closed]) =
    a.map(_.offer(42))

// Try to poll an item
val e: Maybe[Int] < (IO & Abort[Closed]) =
    a.map(_.poll)

// Try to 'peek' an item without removing it
val f: Maybe[Int] < (IO & Abort[Closed]) =
    a.map(_.peek)

// Check if the queue is empty
val g: Boolean < (IO & Abort[Closed]) =
    a.map(_.empty)

// Check if the queue is full
val h: Boolean < (IO & Abort[Closed]) =
    a.map(_.full)

// Drain the queue items
val i: Seq[Int] < (IO & Abort[Closed]) =
    a.map(_.drain)

// Close the queue. If successful,
// returns a Some with the drained
// elements
val j: Maybe[Seq[Int]] < IO =
    a.map(_.close)

Unbounded queues

import kyo.*

// Avoid `Queue.unbounded` since if queues can
// grow without limits, the GC overhead can make
// the system fail
val a: Queue.Unbounded[Int] < IO =
    Queue.Unbounded.init()

// A 'dropping' queue discards new entries
// when full
val b: Queue.Unbounded[Int] < IO =
    Queue.Unbounded.initDropping(capacity = 42)

// A 'sliding' queue discards the oldest
// entries if necessary to make space for new
// entries
val c: Queue.Unbounded[Int] < IO =
    Queue.Unbounded.initSliding(capacity = 42)

// Note how 'dropping' and 'sliding' queues
// return 'Queue.Unbounded`. It provides
// an additional method to 'add' new items
// unconditionally
val d: Unit < IO =
    c.map(_.add(42))

Concurrent access policies

It's also possible to specify a concurrent Access policy as the second parameter of the Queue.init methods. This configuration has an effect only on the JVM and is ignored in ScalaJS.

Policy	Full Form	Description
Mpmc	Multiple Producers, Multiple Consumers	Supports multiple threads/fibers simultaneously enqueuing and dequeuing elements. This is the most flexible but may incur the most overhead due to the need to synchronize between multiple producers and consumers.
Mpsc	Multiple Producers, Single Consumer	Allows multiple threads/fibers to enqueue elements but restricts dequeuing to a single consumer. This can be more efficient than Mpmc when only one consumer is needed.
Spmc	Single Producer, Multiple Consumers	Allows only a single thread/fiber to enqueue elements, but multiple threads/fibers can dequeue elements. Useful when only one source is generating elements to be processed by multiple consumers.
Spsc	Single Producer, Single Consumer	The most restrictive but potentially fastest policy. Only one thread/fiber can enqueue elements, and only one thread/fiber can dequeue elements.

Each policy is suitable for different scenarIO and comes with its own trade-offs. For example, Mpmc is highly flexible but can be slower due to the need for more complex synchronization. Spsc, being the most restrictive, allows for optimizations that could make it faster for specific single-producer, single-consumer scenarIO.

You can specify the access policy when initializing a queue, and it is important to choose the one that aligns with your application's needs for optimal performance.

import kyo.*

// Initialize a bounded queue with a
// Multiple Producers, Multiple
// Consumers policy
val a: Queue[Int] < IO =
    Queue.init(
        capacity = 42,
        access = Access.MultiProducerMultiConsumer
    )

Channel: Backpressured Communication

The Channel effect serves as an advanced concurrency primitive, designed to facilitate seamless and backpressured data transfer between various parts of your application. Built upon the Async effect, Channel not only ensures thread-safe communication but also incorporates a backpressure mechanism. This mechanism temporarily suspends fibers under specific conditions—either when waiting for new items to arrive or when awaiting space to add new items.

Warning: The actual capacity of a Channel is rounded up to the next power of two for performance reasons. For example, if you specify a capacity of 10, the actual capacity will be 16.

import kyo.*

// A 'Channel' is initialized
// with a fixed capacity
val a: Channel[Int] < IO =
    Channel.init(capacity = 42)

// It's also possible to specify
// an 'Access' policy
val b: Channel[Int] < IO =
    Channel.init(
        capacity = 42,
        access = Access.MultiProducerMultiConsumer
    )

While Channel share similarities with Queue—such as methods for querying size (size), adding an item (offer), or retrieving an item (poll)—they go a step further by offering backpressure-sensitive methods, namely put and take.

import kyo.*

// An example channel
val a: Channel[Int] < IO =
    Channel.init(capacity = 42)

// Adds a new item to the channel.
// If there's no capacity, the fiber
// is automatically suspended until
// space is made available
val b: Unit < (Async & Abort[Closed]) =
    a.map(_.put(42))

// Takes an item from the channel.
// If the channel is empty, the fiber
// is suspended until a new item is
// made available
val c: Int < (Async & Abort[Closed]) =
    a.map(_.take)

// 'putFiber' returns a `Fiber` that
// will complete once the put completes
val d: Fiber[Closed, Unit] < IO =
    a.map(_.putFiber(42))

// 'takeFiber' also returns a fiber
val e: Fiber[Closed, Int] < IO =
    a.map(_.takeFiber)

// Closes the channel. If successful,
// returns a Some with the drained
// elements. All pending puts and takes
// are automatically interrupted
val f: Maybe[Seq[Int]] < IO =
    a.map(_.close)

The ability to suspend fibers during put and take operations allows Channel to provide a more controlled form of concurrency. This is particularly beneficial for rate-sensitive or resource-intensive tasks where maintaining system balance is crucial.

Important: While a Channel comes with a predefined item capacity, it's crucial to understand that there is no upper limit on the number of fibers that can be suspended by it. In scenarIO where your application spawns an unrestricted number of fibers—such as an HTTP service where each incoming request initiates a new fiber—this can lead to significant memory consumption. The channel's internal queue for suspended fibers could grow indefinitely, making it a potential source of unbounded queuing and memory issues. Exercise caution in such use-cases to prevent resource exhaustion.

Hub: Broadcasting with Backpressure

Hub provide a broadcasting mechanism where messages are sent to multiple listeners simultaneously. They are similar to Channel, but they are uniquely designed for scenarIO involving multiple consumers. The key feature of Hub is their ability to apply backpressure automatically. This means if the Hub and any of its listeners' buffers are full, the Hub will pause both the producers and consumers to prevent overwhelming the system. Unlike Channel, Hub don't offer customization in concurrent access policy as they are inherently meant for multi-producer, multi-consumer environments.

import kyo.*
import kyo.Hub.Listener

// Initialize a Hub with a buffer
val a: Hub[Int] < IO =
    Hub.init[Int](3)

// Hub provide APIs similar to
// channels: size, offer, isEmpty,
// isFull, putFiber, put
val b: Boolean < (IO & Abort[Closed]) =
    a.map(_.offer(1))

// But reading from hubs can only
// happen via listener. Listeners
// only receive messages sent after
// their cration. To create call
// `listen`:
val c: Listener[Int] < (IO & Abort[Closed]) =
    a.map(_.listen)

// Each listener can have an
// additional message buffer
val d: Listener[Int] < (IO & Abort[Closed]) =
    a.map(_.listen(bufferSize = 3))

// Listeners provide methods for
// receiving messages similar to
// channels: size, isEmpty, isFull,
// poll, takeFiber, take
val e: Int < (Async & Abort[Closed]) =
    d.map(_.take)

// A listener can be closed
// individually. If successful,
// a Some with the backlog of
// pending messages is returned
val f: Maybe[Seq[Int]] < (IO & Abort[Closed]) =
    d.map(_.close)

// If the Hub is closed, all
// listeners are automatically
// closed. The returned backlog
// only include items pending in
// the hub's buffer. The listener
// buffers are discarded
val g: Maybe[Seq[Int]] < IO =
    a.map(_.close)

Hub are implemented with an internal structure that efficiently manages message distribution. At their core, Hub utilize a single channel for incoming messages. This central channel acts as the primary point for all incoming data. For each listener attached to a Hub, a separate channel is created. These individual channels are dedicated to each listener, ensuring that messages are distributed appropriately.

The functioning of Hub is orchestrated by a dedicated fiber. This fiber continuously monitors the main incoming channel. Whenever a new message arrives, it takes this message and concurrently distributes it to all the listener channels. This process involves submitting the message to each listener's channel in parallel, ensuring simultaneous delivery of messages.

After distributing a message, the fiber waits until all the listener channels have successfully received it. This waiting mechanism is crucial for maintaining the integrity of message distribution, ensuring that each listener gets the message before the fiber proceeds to the next one and backpressure is properly applied.

Meter: Computational Limits

The Meter effect offers utilities to regulate computational execution, be it limiting concurrency or managing rate. It is equipped with a range of pre-set limitations, including mutexes, semaphores, and rate limiters, allowing you to apply fine-grained control over task execution.

import kyo.*

// 'mutex': One computation at a time
val a: Meter < IO =
    Meter.initMutex

// 'semaphore': Limit concurrent tasks
val b: Meter < IO =
    Meter.initSemaphore(concurrency = 42)

// 'rateLimiter': Tasks per time window
val c: Meter < IO =
    Meter.initRateLimiter(
        rate = 10,
        period = 1.second
    )

// 'pipeline': Combine multiple 'Meter's
val d: Meter < IO =
    Meter.pipeline(a, b, c)

The Meter class comes with a handful of methods designed to provide insights into and control over computational execution.

import kyo.*

// An example 'Meter'
val a: Meter < IO =
    Meter.initMutex

// Get the number available permits
val b: Int < (Async & Abort[Closed]) =
    a.map(_.availablePermits)

// Get the number of waiting fibers
val c: Int < (Async & Abort[Closed]) =
    a.map(_.pendingWaiters)

// Use 'run' to execute tasks
// respecting meter limits
val d: Int < (Async & Abort[Closed]) =
    a.map(_.run(Math.cos(42).toInt))

// 'tryRun' executes if a permit is
// available; returns 'None' otherwise
val e: Maybe[Int] < (Async & Abort[Closed]) =
    a.map(_.tryRun(Math.cos(42).toInt))

Latch: Countdown Synchronization

The Latch effect serves as a coordination mechanism for fibers in a concurrent environment, primarily used for task synchronization. It provides a low-level API for controlling the flow of execution and ensuring certain tasks are completed before others, all while maintaining thread safety.

import kyo.*

// Initialize a latch with 'n' permits
val a: Latch < IO =
    Latch.init(3)

// Await until the latch releases
val b: Unit < Async =
    a.map(_.await)

// Release a permit from the latch
val c: Unit < IO =
    a.map(_.release)

// Get the number of pending permits
val d: Int < IO =
    a.map(_.pending)

Barrier: Multi-party Rendezvous

The Barrier effect provides a synchronization primitive that allows a fixed number of parties to wait for each other to reach a common point of execution. It's particularly useful in scenarios where multiple fibers need to synchronize their progress.

import kyo.*

// Initialize a barrier for 3 parties
val a: Barrier < IO =
    Barrier.init(3)

// Wait for the barrier to be released
val b: Unit < Async =
    a.map(_.await)

// Get the number of parties still waiting
val c: Int < IO =
    a.map(_.pending)

// Example usage with multiple fibers
val d: Unit < Async =
    for
        barrier <- Barrier.init(3)
        _       <- Async.parallel(
                     barrier.await,
                     barrier.await,
                     barrier.await
                   )
    yield ()

// Fibers can join the barrier at different points of the computation
val e: Unit < Async =
    for
        barrier <- Barrier.init(3)
        fiber1  <- Async.run(Async.sleep(1.second))
        fiber2  <- Async.run(Async.sleep(2.seconds))
        _       <- Async.parallel(
                     fiber1.get.map(_ => barrier.await),
                     fiber2.get.map(_ => barrier.await),
                     Async.run(barrier.await).map(_.get)
                   )
    yield ()

The Barrier is initialized with a specific number of parties. Each party calls await when it reaches the barrier point. The barrier releases all waiting parties when the last party arrives. After all parties have been released, the barrier cannot be reset or reused.

Atomic: Concurrent State

The Atomic effect provides a set of thread-safe atomic variables to manage mutable state in a concurrent setting. Available atomic types include Int, Long, Boolean, and generic references.

import kyo.*

// Initialize atomic variables
val aInt: AtomicInt < IO =
    AtomicInt.init(0)
val aLong: AtomicLong < IO =
    AtomicLong.init(0L)
val aBool: AtomicBoolean < IO =
    AtomicBoolean.init(false)
val aRef: AtomicRef[String] < IO =
    AtomicRef.init("initial")

// Fetch values
val b: Int < IO =
    aInt.map(_.get)
val c: Long < IO =
    aLong.map(_.get)
val d: Boolean < IO =
    aBool.map(_.get)
val e: String < IO =
    aRef.map(_.get)

// Update values
val f: Unit < IO =
    aInt.map(_.set(1))
val g: Unit < IO =
    aLong.map(_.lazySet(1L))
val h: Boolean < IO =
    aBool.map(_.cas(false, true))
val i: String < IO =
    aRef.map(_.getAndSet("new"))

Adder: Concurrent Accumulation

The Adder effect offers thread-safe variables for efficiently accumulating numeric values. The two primary classes, LongAdder and DoubleAdder, are optimized for high-throughput scenarIO where multiple threads update the same counter.

import kyo.*

// Initialize Adder
val longAdder: LongAdder < IO =
    LongAdder.init
val doubleAdder: DoubleAdder < IO =
    DoubleAdder.init

// Adding values
val a: Unit < IO =
    longAdder.map(_.add(10L))
val b: Unit < IO =
    doubleAdder.map(_.add(10.5))

// Increment and Decrement LongAdder
val c: Unit < IO =
    longAdder.map(_.increment)
val d: Unit < IO =
    longAdder.map(_.decrement)

// Fetch summed values
val e: Long < IO =
    longAdder.map(_.get)
val f: Double < IO =
    doubleAdder.map(_.get)

// Resetting the adders
val g: Unit < IO =
    longAdder.map(_.reset)
val h: Unit < IO =
    doubleAdder.map(_.reset)

Debug: Interactive Development

The Debug effect is a powerful tool for developers during the development process. Unlike other effects in Kyo, Debug intentionally performs side effects (printing to the console) without effect suspensions to provide immediate, visible feedback to developers. This makes it a valuable tool for debugging and understanding code behavior, but it's crucial to use it only in development environments and remove it before moving to production.

import kyo.*

// Note that 'Debug' requires a separate import
import kyo.debug.*

// Wraps a computation, printing the source code location,
// and the result (or exception) of the computation
val a: Int < IO =
    Debug {
        IO(42)
    }

// Similar to `apply`, but also prints intermediate steps
// of the computation, providing a trace of execution
val b: Int < IO =
    Debug.trace {
        IO(41).map(_ + 1)
    }

// Allows printing of specific values along with their 
// variable names, useful for inspecting particular states.
// The return type of 'values' is 'Unit', not an effectful
// computation.
val c: Unit < IO =
    IO {
        val x = 42
        val y = "Hello"
        Debug.values(x, y)
    }

Data Types

Maybe: Allocation-free Optional Values

Maybe provides an allocation-free alternative to Scala's standard Option type. It is designed to be a drop-in replacement for Option, offering similar functionality while minimizing memory allocation.

import kyo._

// Create a 'Maybe' value
val a: Maybe[Int] = Maybe(42)

// 'Absent' represents the absence of a value
val b: Maybe[Int] = Absent

// 'Maybe.when' conditionally creates a 'Maybe' value
val c: Maybe[Int] = Maybe.when(true)(42)

// 'Maybe.fromOption' converts an 'Option' to a 'Maybe'
val d: Maybe[Int] = Maybe.fromOption(Some(42))

// 'isEmpty' checks if the 'Maybe' is empty
val e: Boolean = a.isEmpty

// 'isDefined' checks if the 'Maybe' has a value
val f: Boolean = a.isDefined

// 'get' retrieves the value, throwing if empty
val g: Int = a.get

// 'getOrElse' provides a default value if empty
val h: Int = b.getOrElse(0)

// 'fold' applies a function based on emptiness
val i: String = a.fold("Empty")(_.toString)

// 'map' transforms the value if present
val j: Maybe[String] = a.map(_.toString)

// 'flatMap' allows chaining 'Maybe' operations
val k: Maybe[Int] = a.flatMap(v => Maybe(v + 1))

// 'filter' conditionally keeps or discards the value
val l: Maybe[Int] = a.filter(_ > 0)

// 'contains' checks if the 'Maybe' contains a value
val m: Boolean = a.contains(42)

// 'exists' checks if a predicate holds for the value
val n: Boolean = a.exists(_ > 0)

// 'foreach' applies a side-effecting function if non-empty
a.foreach(println)

// 'collect' applies a partial function if defined
val o: Maybe[String] = a.collect { case 42 => "forty-two" }

// 'orElse' returns an alternative if empty
val p: Maybe[Int] = b.orElse(Maybe(0))

// 'zip' combines two 'Maybe' values into a tuple
val q: Maybe[(Int, String)] = a.zip(Maybe("hello"))

// 'toOption' converts a 'Maybe' to an 'Option'
val r: Option[Int] = a.toOption

// Using 'Maybe' in a for-comprehension
val s: Maybe[Int] = for {
  x <- Maybe(1)
  y <- Maybe(2)
  if x < y
} yield x + y

// Nesting 'Maybe' values
val nested: Maybe[Maybe[Int]] = Maybe(Maybe(42))
val flattened: Maybe[Int] = nested.flatten

// Pattern matching with 'Present' and 'Absent'
val result: String = 
    flattened match
        case Present(value) => s"Value: $value"
        case Absent        => "No value"

Duration: Time Representation

Duration provides a convenient and efficient way to represent and manipulate time durations. It offers a wide range of operations and conversions, making it easy to work with time intervals in various units.

import kyo.*
import kyo.Duration.Units.*

// Create durations using convenient extension methods
val a: Duration = 5.seconds
val b: Duration = 100.millis
val c: Duration = 1.hour

// Perform arithmetic operations
val d: Duration = a + b
val e: Duration = c * 2

// Compare durations
val f: Boolean = a > b
val g: Boolean = c <= 60.minutes

// Convert to different time units
val h: Long = c.toMinutes // 60
val i: Long = a.toMillis  // 5000

// Create durations from various units
val j: Duration = Duration.fromNanos(1000000)
val k: Duration = Duration.fromUnits(2, Weeks)

// Convert to and from Java and Scala durations
import java.time.Duration as JavaDuration
import scala.concurrent.duration.Duration as ScalaDuration

val l: Duration = Duration.fromJava(JavaDuration.ofSeconds(30))
val m: Duration = Duration.fromScala(ScalaDuration(1, "day"))

val n: JavaDuration = c.toJava
val o: ScalaDuration = b.toScala

// Special durations
val p: Duration = Duration.Zero
val q: Duration = Duration.Infinity

// Render duration as a string
val r: String = a.show // "Duration(5000000000 ns)"

Duration is implemented as an opaque type alias for Long, representing nanoseconds internally. This design ensures type safety while maintaining high performance.

Result: Typed Failure Handling

Result is a type that combines features of Scala's Try and Either types, designed to represent the result of a computation that may either succeed with a value or fail with an exception. It provides a flexible way to handle both successful outcomes and typed failures.

import kyo._
import scala.util.Try

// Create a 'Result' from a value
val a: Result[Nothing, Int] = Result.success(42)

// Create a 'Result' from an failure
val b: Result[Exception, Int] = Result.fail(new Exception("Oops"))

// Use 'apply' to create a 'Result' from a block of code
val c: Result[Nothing, Int] = Result(42 / 0)

// 'isSuccess' checks if the 'Result' is a success
val d: Boolean = a.isSuccess

// 'isFail' checks if the 'Result' is a failure
val e: Boolean = b.isFail

// 'get' retrieves the value if successful, otherwise throws
val f: Int = a.get

// 'getOrElse' provides a default value for failures
val g: Int = b.getOrElse(0)

// 'fold' applies a function based on success or failure
val h: String = a.fold(e => "failure " + e)(_.toString)

// 'map' transforms the value if successful
val i: Result[Nothing, String] = a.map(_.toString)

// 'flatMap' allows chaining 'Result' operations
val j: Result[Nothing, Int] = a.flatMap(v => Result.success(v + 1))

// 'flatten' removes one level of nesting from a 'Result[Result[T]]'
val k: Result[Nothing, Result[Nothing, Int]] = Result.success(a)
val l: Result[Nothing, Int] = k.flatten

// 'filter' conditionally keeps or discards the value
val m: Result[NoSuchElementException, Int] = a.filter(_ > 0)

// 'recover' allows handling failures with a partial function
val n: Result[Exception, Int] = b.recover { case Result.Fail(_: ArithmeticException) => 0 }

// 'recoverWith' allows handling failures with a partial function returning a 'Result'
val o: Result[Exception, Int] = b.recoverWith { case Result.Fail(_: ArithmeticException) => Result.success(0) }

// 'toEither' converts a 'Result' to an 'Either'
val p: Either[Throwable, Int] = a.toEither

// 'toTry' converts a 'Result' to a 'Try'
val q: Try[Int] = a.toTry

Under the hood, Result is defined as an opaque type that is a supertype of Success[T] and Failure[T]. Success[T] represents a successful result and is encoded as either the value itself (T) or a special SuccessFailure[T] case class. The SuccessFailure[T] case class is used to handle the rare case where a Failure[T] needs to be wrapped in a Success[T]. On the other hand, a failed Result is always represented by a Failure[T] case class, which contains the exception that caused the failure. This means that creating a Failure[T] does incur an allocation cost. Additionally, some methods on Result, such as fold, map, and flatMap, may allocate in certain cases due to the need to catch and handle exceptions.

TypeMap: Type-Safe Heterogeneous Maps

TypeMap provides a type-safe heterogeneous map implementation, allowing you to store and retrieve values of different types using their types as keys. This is particularly useful for managing multiple types of data in a single structure with type safety.

import kyo.*

// Create an empty TypeMap
val empty: TypeMap[Any] = TypeMap.empty

// Constructors for up to 4 elements
val map1: TypeMap[String] = TypeMap("Hello")
val map2: TypeMap[String & Int] = TypeMap("Hello", 42)
val map3: TypeMap[String & Int & Boolean] = TypeMap("Hello", 42, true)
val map4: TypeMap[String & Int & Boolean & Double] = TypeMap("Hello", 42, true, 3.14)

// Add a value to an existing TypeMap
val mapWithNewValue: TypeMap[String & Int] = map1.add(42)

// Retrieve a value from the TypeMap
val str: String = map2.get[String]
val num: Int = map2.get[Int]

// Combine two TypeMaps
val combined: TypeMap[String & Int & Boolean] = map2.union(TypeMap(true))

// Filter the TypeMap to only include subtypes of a given type
val pruned: TypeMap[String] = map2.prune[String]

// Check if the TypeMap is empty and get its size
val isEmpty: Boolean = map2.isEmpty
val size: Int = map2.size

// Get a string representation of the TypeMap
val representation: String = map2.show

The type parameter A in TypeMap[A] represents the intersection type of all stored values, ensuring type safety when retrieving values.

Ansi: Text Color and Formatting

The Ansi object provides utilities for adding ANSI color and formatting to strings, as well as a code highlighting feature. This can be useful for creating colorful console output or formatting text for better readability.

import kyo.*

// The 'String' extension methods require a separate import
import kyo.Ansi.*

// Add colors to strings
val redText: String = "Error".red
val blueText: String = "Info".blue

// Add text formatting
val boldText: String = "Important".bold
val underlinedText: String = "Underlined".underline

// Combine colors and formatting
val importantError: String = "Critical Error".red.bold

// Strip ANSI codes from a string
val plainText: String = "\u001b[31mColored\u001b[0m".stripAnsi

// Highlight code snippets
val code = """
def hello(name: String): Unit =
println(s"Hello, $name!")
"""
lazy val highlightedCode: String = Ansi.highlight(code)

// Highlight code with custom header and trailer
lazy val customHighlight: String = 
    Ansi.highlight(
        header = "// File: example.scala",
        code = code,
        trailer = "// End of file",
        startLine = 1
    )

The Ansi object provides the following color extensions for strings:

• black, red, green, yellow, blue, magenta, cyan, white, grey

And the following formatting extensions:

• bold, dim, italic, underline

The code highlighting feature supports basic syntax highlighting for Scala keywords, string literals, and comments.

Integrations

Cache: Memoized Functions via Caffeine

Kyo provides caching through memoization. A single Cache instance can be reused by multiple memoized functions. This allows for flexible scoping of caches, enabling users to use the same cache for various operations.

import kyo.*

val a: Int < Async =
    for

        // The initialization takes a
        // builder function that mirrors
        // Caffeine's builder
        cache <- Cache.init(_.maxSize(100))

        // Create a memoized function
        fun = cache.memo { (v: String) =>
            // Note how the implementation
            // can use other effects
            IO(v.toInt)
        }

        // Use the function
        v <- fun("10")
    yield v

Although multiple memoized functions can reuse the same Cache, each function operates as an isolated cache and doesn't share any values with others. Internally, cache entries include the instance of the function as part of the key to ensure this separation. Only the cache space is shared, allowing for efficient use of resources without compromising the independence of each function's cache.

Requests: HTTP Client via Sttp

Requests provides a simplified API for Sttp 3 implemented on top of Kyo's concurrent package.

To perform a request, use the apply method. It takes a builder function based on Sttp's request building API.

import kyo.*
import kyo.Requests.Backend
import sttp.client3.*

// Perform a request using a builder function
val a: String < (Async & Abort[FailedRequest]) =
    Requests(_.get(uri"https://httpbin.org/get"))

// Alternatively, requests can be
// defined separately
val b: String < (Async & Abort[FailedRequest]) =
    Requests.request(Requests.basicRequest.get(uri"https://httpbin.org/get"))

// It's possible to use the default implementation or provide
// a custom `Backend` via `let`

// An example request
val c: String < (Async & Abort[FailedRequest]) =
    Requests(_.get(uri"https://httpbin.org/get"))

// Implementing a custom mock backend
val backend: Backend =
    new Backend:
        def send[T: Flat](r: Request[T, Any]) =
            Response.ok(Right("mocked")).asInstanceOf[Response[T]]

// Use the custom backend
val d: String < (Async & Abort[FailedRequest]) =
    Requests.let(backend)(a)

Please refer to Sttp's documentation for details on how to build requests. Streaming is currently unsupported.

Users are free to use any JSON libraries supported by Sttp; however, zio-json is recommended, as it is used in Kyo's tests and modules requiring HTTP communication, such as AIs.

Routes: HTTP Server via Tapir

Routes integrates with the Tapir library to help set up HTTP servers. The method Routes.add is used for adding routes. This method requires the definition of a route, which can be an Tapir Endpoint instance or a builder function. Additionally, the method requires the implementation of the endpoint, which is provided as the second parameter group. To start the server, the Routes effect is handled, which initializes the HTTP server with the specified routes.

import kyo.*
import sttp.tapir.*
import sttp.tapir.server.netty.*

// A simple health route using an endpoint builder
val a: Unit < Routes =
    Routes.add(
        _.get.in("health")
            .out(stringBody)
    ) { _ =>
        "ok"
    }

// The endpoint can also be defined separately
val health2 = endpoint.get.in("health2").out(stringBody)

val b: Unit < Routes =
    Routes.add(health2)(_ => "ok")

// Starting the server by handling the effect
val c: NettyKyoServerBinding < Async =
    Routes.run(a.andThen(b))

// Alternatively, a customized server configuration can be used
val d: NettyKyoServerBinding < Async =
    Routes.run(NettyKyoServer().port(9999))(a.andThen(b))

The parameters for Tapir's endpoint type are aligned with Kyo effects as follows:

Endpoint[SECURITY_INPUT, INPUT, ERROR_OUTPUT, OUTPUT, CAPABILITIES]

This translates to the endpoint function format:

INPUT => OUTPUT < (Env[SECURITY_INPUT] & Abort[ERROR_OUTPUT])

Currently, the CAPABILITIES parameter is not supported in Kyo since streaming functionality is not available. An example of using these parameters is shown below:

import kyo.*
import sttp.model.*
import sttp.tapir.*

// An endpoint with an 'Int' path input and 'StatusCode' error output
val a: Unit < Routes =
    Routes.add(
        _.get.in("test" / path[Int]("id"))
            .errorOut(statusCode)
            .out(stringBody)
    ) { (id: Int) =>
        if id == 42 then "ok"
        else Abort.fail(StatusCode.NotFound)
        // returns a 'String < Abort[StatusCode]'
    }

For further examples, Kyo's example ledger service provides practical applications of these concepts.

ZIOs: Integration with ZIO

The ZIOs effect provides seamless integration between Kyo and the ZIO library. The effect is designed to enable gradual adoption of Kyo within a ZIO codebase. The integration properly suspends side effects and propagates fiber cancellations/interrupts between both libraries.

import kyo.*
import zio.*

// Use the 'get' method to extract a 'ZIO' effect
val a: Int < (Abort[Nothing] & Async) =
    ZIOs.get(ZIO.succeed(42))

// 'get' also supports error handling with 'Abort'
val b: Int < (Abort[String] & Async) =
    ZIOs.get(ZIO.fail("error"))

// Handle the 'ZIO' effect to obtain a 'ZIO' effect
val c: Task[Int] =
    ZIOs.run(a)

Kyo and ZIOs effects can be seamlessly mixed and matched within computations, allowing developers to leverage the power of both libraries. Here are a few examples showcasing this integration:

import kyo.*
import zio.*

// Note how ZIO includes the
// IO and Async effects
val a: Int < (Abort[Nothing] & Async) =
    for
        v1 <- ZIOs.get(ZIO.succeed(21))
        v2 <- IO(21)
        v3 <- Async.run(-42).map(_.get)
    yield v1 + v2 + v3

// Using fibers from both libraries
val b: Int < (Abort[Nothing] & Async) =
    for
        f1 <- ZIOs.get(ZIO.succeed(21).fork)
        f2 <- Async.run(IO(21))
        v1 <- ZIOs.get(f1.join)
        v2 <- f2.get
    yield v1 + v2

// Transforming ZIO effects within Kyo computations
val c: Int < (Abort[Nothing] & Async) =
    ZIOs.get(ZIO.succeed(21)).map(_ * 2)

// Transforming Kyo effects within ZIO effects
val d: Task[Int] =
    ZIOs.run(IO(21).map(_ * 2))

Note: Support for ZIO environments (R in ZIO[R, E, A]) is currently in development. Once implemented, it will be possible to use ZIO effects with environments directly within Kyo computations.

Cats: Integration with Cats Effect

The Cats effect provides seamless integration between Kyo and the Cats Effect library. This integration is designed to enable gradual adoption of Kyo within a Cats Effect codebase. The integration properly suspends side effects and propagates fiber cancellations/interrupts between both libraries.

import kyo.*
import cats.effect.IO as CatsIO

// Use the 'get' method to extract a 'IO' effect from Cats Effect:
val a: Int < (Abort[Throwable] & Async) =
    Cats.get(CatsIO.pure(42))

// Handle the 'Cats' effect to obtain a 'CatsIO' effect:
val b: CatsIO[Int] =
    Cats.run(a)

Kyo and Cats effects can be seamlessly mixed and matched within computations, allowing developers to leverage the power of both libraries. Here are a few examples showcasing this integration:

import kyo.*
import cats.effect.IO as CatsIO
import cats.effect.kernel.Outcome.Succeeded

// Note how Cats includes the IO, Async, and Abort[Nothing] effects:
val a: Int < (Abort[Nothing] & Async) =
    for
        v1 <- Cats.get(CatsIO.pure(21))
        v2 <- IO(21)
        v3 <- Async.run(-42).map(_.get)
    yield v1 + v2 + v3

// Using fibers from both libraries:
val b: Int < (Abort[Nothing] & Async) =
    for
        f1 <- Cats.get(CatsIO.pure(21).start)
        f2 <- Async.run(IO(21))
        v1 <- Cats.get(f1.joinWith(CatsIO(99)))
        v2 <- f2.get
    yield v1 + v2

// Transforming Cats Effect IO within Kyo computations:
val c: Int < (Abort[Nothing] & Async) =
    Cats.get(CatsIO.pure(21)).map(_ * 2)

// Transforming Kyo effects within Cats Effect IO:
val d: CatsIO[Int] =
    Cats.run(IO(21).map(_ * 2))

Resolvers: GraphQL Server via Caliban

Resolvers integrates with the Caliban library to help setup GraphQL servers.

The first integration is that you can use Kyo effects inside your Caliban schemas by importing kyo.given.

• If your Kyo effects is (Abort[Throwable] & ZIO) or a subtype of it (ZIO includes Async & IO), a Caliban Schema can be derived automatically.
• If your Kyo effect is something else, a Caliban schema can be derived if it has a Runner for that effect as part of ZIO environment.

import caliban.schema.*
import kyo.*
import kyo.given

// this works by just importing kyo.*
case class Query(k: Int < Abort[Throwable]) derives Schema.SemiAuto

// for other effects, you need to extend `SchemaDerivation[Runner[YourCustomEffects]]`
type CustomEffects = Var[Int] & Env[String]
object schema extends SchemaDerivation[Runner[CustomEffects]]

case class Query2(k: Int < CustomEffects) derives schema.SemiAuto

Then, the Resolvers effect allows easily turning these schemas into a GraphQL server. The method Resolvers.get is used for importing a GraphQL object from Caliban into Kyo. You can then run this effect using Resolvers.run to get an HTTP server. This effect requires ZIO because Caliban uses ZIO internally to run.

import caliban.*
import caliban.schema.*
import kyo.*
import kyo.given
import sttp.tapir.server.netty.*
import zio.Task

case class Query(k: Int < Abort[Throwable]) derives Schema.SemiAuto
val api = graphQL(RootResolver(Query(42)))

val a: NettyKyoServerBinding < (Async & Abort[CalibanError]) =
    Resolvers.run { Resolvers.get(api) }

// similarly to the tapir integration, you can also pass a `NettyKyoServer` explicitly
val b: NettyKyoServerBinding < (Async & Abort[CalibanError]) =
    Resolvers.run(NettyKyoServer().port(9999)) { Resolvers.get(api) }

// you can turn this into a ZIO as seen in the ZIO integration
val c: Task[NettyKyoServerBinding] = ZIOs.run(b)

When using arbitrary Kyo effects, you need to provide the Runner for that effect when calling the run function.

import caliban.*
import caliban.schema.*
import kyo.*
import kyo.given
import zio.Task

type CustomEffects = Var[Int] & Env[String]
object schema extends SchemaDerivation[Runner[CustomEffects]]
case class Query(k: Int < CustomEffects) derives schema.SemiAuto

val api = graphQL(RootResolver(Query(42)))

// runner for our CustomEffects
val runner = new Runner[CustomEffects]:
    def apply[T: Flat](v: T < CustomEffects): Task[T] = ZIOs.run(Env.run("kyo")(Var.run(0)(v)))

val d = Resolvers.run(runner) { Resolvers.get(api) }

AIs: LLM Abstractions via OpenAI

Coming soon..

Restrictions

Nested Effects

In addition recursion, Kyo's unboxed representation of computations in certain scenarIO introduces a restriction where it's not possible to handle effects of computations with nested effects like Int < IO < IO.

import kyo.*

// An example computation with
// nested effects
val a: Int < IO < Abort[Absent] =
    Abort.get(Some(IO(1)))

// Can't handle a effects of a
// computation with nested effects

// Abort.run(a)
// Compilation failure:
//   Method doesn't accept nested Kyo computations.
//   Cannot prove 'scala.Int < kyo.IO' isn't nested. This error can be reported an unsupported pending effect is passed to a method. If that's not the case, provide an implicit evidence 'kyo.Flat[scala.Int < kyo.IO]'.

// Use `flatten` before handling
Abort.run(a.flatten)

Kyo performs checks at compilation time to ensure that nested effects are not used. This includes generic methods where the type system cannot confirm whether the computation is nested:

import kyo.*

// def test[T](v: T < Abort[Absent]) =
//   Abort.run(v)
// Compilation failure:
//   Method doesn't accept nested Kyo computations.
//   Cannot prove 'T' isn't nested. This error can be reported an unsupported pending effect is passed to a method. If that's not the case, provide an implicit evidence 'kyo.Flat[T]'.

// It's possible to provide an implicit
// evidence of `Flat` to resolve
def test[T](v: T < Abort[Absent])(using Flat[T]) =
    Abort.run(v)

All APIs that trigger effect handling have this restriction, which includes not only methods that handle effects directly but also methods that use effect handling internally.

ZIO-like Combinators

For ZIO users, Kyo's core API can be frustrating for three reasons:

1. It is minimal by design.

While its uncluttered namespaces make it more approachable for beginners, users addicted to ZIO's powerful and intuitive combinators may find it unwieldy and possibly not worth the effort.

2. Effects are handled by functions that take effects as arguments, rather than by methods on effects.

ZIO users are used to having a large menu of combinators on ZIO values that can be chained together to manipulate effects fluently. kyo-core, by contrast, requires nesting effects within method calls, inverting the order in which users handle effects and requiring them either to create deeply nested expressions or to break expressions up into many intermediate expressions.

3. Factory methods are distributed among different objects

Being more modular that ZIO, Kyo segregates its effect types more cleanly, placing its effect constructors in the companion objects to their corresponding types. This is not a problem given the minimal API that Kyo offers, but ZIO users will miss typing ZIO. and seeing a rich menu of factory methods pop up on their IDE.

kyo-combinators alleviates these frustrations by providing:

1. Factory methods on the Kyo object, styled after those found on ZIO, for many of the core Kyo effect types.
2. Extension methods on Kyo effects modeled on ZIO combinators.

Generally speaking, the names of kyo-combinators methods are the same as the corresponding methods in ZIO. When this is not possible or doesn't make sense, kyo-combinators tries to keep close to ZIO conventions.

Simple example

import kyo.*
import scala.concurrent.duration.*
import java.io.IOException

trait HelloService:
    def sayHelloTo(saluee: String): Unit < (IO & Abort[Throwable])

object HelloService:
    object Live extends HelloService:
        override def sayHelloTo(saluee: String): Unit < (IO & Abort[Throwable]) =
            Kyo.suspendAttempt { // Adds IO & Abort[Throwable] effect
                println(s"Hello $saluee!")
            }
    end Live
end HelloService

val keepTicking: Nothing < (Console & Async & Abort[IOException]) =
    (Console.print(".") *> Kyo.sleep(1.second)).forever

val effect: Unit < (Console & Async & Resource & Abort[Throwable] & Env[NameService]) =
    for
        nameService <- Kyo.service[NameService]       // Adds Env[NameService] effect
        _           <- keepTicking.forkScoped         // Adds Console, Async, and Resource effects
        saluee      <- Console.readln                 // Uses Console effect
        _           <- Kyo.sleep(2.seconds)           // Uses Async (semantic blocking)
        _           <- nameService.sayHelloTo(saluee) // Adds Abort[Throwable] effect
    yield ()

// There are no combinators for handling IO or blocking Async, since this should
// be done at the edge of the program
IO.Unsafe.run {                              // Handles IO
    Async.runAndBlock(Duration.Inf) { // Handles Async
        Kyo.scoped {                   // Handles Resource
            effect
                .provideAs[HelloService](HelloService.Live) // Handles Env[HelloService]
                .catchAbort((thr: Throwable) =>             // Handles Abort[Throwable]
                    Kyo.debug(s"Failed printing to console: ${throwable}")
                )
                .provideDefaultConsole // Handles Console
        }
    }
}

Failure conversions

One notable departure from the ZIO API worth calling out is a set of combinators for converting between failure effects. Whereas ZIO has a single channel for describing errors, Kyo has different effect types that can describe failure in the basic sense of "short-circuiting": Abort and Choice (an empty Seq being equivalent to a short-circuit). Abort[Absent] can also be used like Choice to model short-circuiting an empty result. It's useful to be able to move between these effects easily, so kyo-combinators provides a number of extension methods, usually in the form of def effect1ToEffect2.

Some examples:

val abortEffect: Int < Abort[String] = ???

// Converts failures to empty failure
val maybeEffect: Int < Abort[Absent] = abortEffect.abortToEmpty

// Converts empty failure to a single "choice" (or Seq)
val choiceEffect: Int < Choice = maybeEffect.emptyAbortToChoice

// Fails with Nil#head exception if empty and succeeds with Seq.head if non-empty
val newAbortEffect: Int < Abort[Throwable] = choiceEffect.choiceToThrowable

// Throws a throwable Abort failure (will actually throw unless suspended)
val unsafeEffect: Int < Any = newAbortEffect.implicitAborts

// Catch any suspended throws
val safeEffect: Int < Abort[Throwable] = unsafeEffect.explicitAborts

Acknowledgements

Kyo's development was originally inspired by the paper "Do Be Do Be Do" and its implementation in the Unison programming language. Kyo's design evolved from using interface-based effects to suspending concrete values associated with specific effects, making it more efficient when executed on the JVM.

Additionally, Kyo draws inspiration from ZIO in various aspects. The core mechanism for algebraic effects can be seen as a generalization of ZIO's effect rotation, and many of Kyo's effects are directly influenced by ZIO's mature set of primitives. For instance, Env and Abort correspond to ZIO's effect channels, Resource function similarly to Scope, and Hub was introduced based on ZIO.

Kyo's asynchronous primitives take several aspects from Twitter's util and Finagle, including features like async root compression, to provide stack safety, and support for cancellations (interruptions in Kyo).

Lastly, the name "Kyo" is derived from the last character of Nam-myoho-renge-kyo, the mantra practiced in SGI Buddhism. It literally translates to "Sutra," referring to a compiled teaching of Shakyamuni Buddha, and is also interpreted as the "threads" that weave the fundamental fabric of life's reality.

License

See the LICENSE file for details.