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https://github.com/tailcallhq/zio-compose

A Scala DSL to write type-safe programs for distributed computing
https://github.com/tailcallhq/zio-compose

distributed-computing functional-programming scala

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A Scala DSL to write type-safe programs for distributed computing

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README 

        
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ZIO Compose is a library that helps you write programs that can be serialized and sent over the wire.



## Introduction



The basic idea behind having serializable programs is if code and data are on different machines, one of them **must be

moved** to the other before the code can be executed on the data.

Typically, in big-data applications it's much more efficient to move code than the other way around.



There are other use-cases that don't involve big-data where you would want a serializable program. For eg: Building a

rule engine,

where the rules are implemented using a DSL and the DSL is serialized and sent to the server for execution.



ZIO Compose intends to take care of such use cases. It intends to provide a complete DSL to write any kind of

distributed computation using Scala in a type-safe manner. It's built on top of [ZIO Schema].



[zio schema]: https://github.com/zio/zio-schema/pulls



## Installation



Update your resolvers and add `zio-compose` as a dependency in your build.sbt.



```scala

libraryDependencies += "com.tusharmath" %% "zio-compose" % version

```



# Getting started



- [Getting started](#getting-started)

  - [Lambda](#lambda)

  - [Serialization](#serialization)

  - [Conditional](#conditional)

  - [Piping](#piping)

  - [Lenses](#lenses)

  - [Transformations](#transformations)

  - [Looping](#looping)

  - [Scopes](#scopes)

  - [Fibonacci](#fibonacci)



1. Here is a simple program that adds two numbers -



   ```scala

   import compose.Lambda._



   val program = constant(1) + constant(2)

   ```



2. Programs can be executed using the default interpreter:



   ```scala

   import zio._



   object ZIOCompose extends ZIOAppDefault {

     val run = for {

       res <- Interpreter.eval(program)

       _   <- ZIO.succeed(println(s"Result: ${res}"))

     } yield ()

   }

   ```



## Lambda



The core data type in ZIO Compose is `Lambda`. It is also type aliased by `~>` (tilde, greater than). A lambda `A ~> B`

represents a serializable unary function that takes in an input of type `A` and produces and output of type `B`. For eg:



```scala

val c1: Any ~> Int = Lambda.constant(100)

```



The above lambda `c1` is a function that takes in any input and produces an `Int` value.



Another example of lambda is `identity[A]` which like the scala's `identity` function, takes in a type `A` and returns

itself as output. The only difference is that zio-compose's `identity` is serializable.



## Serialization



Any lambda from `A ~> B` can be serialized into JSON by performing a few steps.



```scala

// A program that adds two numbers

val program: Any ~> Int = constant(1) + constant(2)



// Call the `compile` method to create an execution plan

val compilation: ExecutionPlan = program.compile



// call `json` on the execution plan to encode it as JSON

val json: String = compilation.compile.json

```



## Conditional



Conditional operations can be implemented on `Lambda`s that return a `Boolean` using the `diverge` operator.

The following program returns `"Yes"` if the condition is true and `"No"` if the condition is false.



```scala

import Lambda._



val program = (constant(1) > constant(2)).diverge(

  isTrue = constant("Yes"),

  isFalse = constant("No")

)

```



Since `1 < 2` the condition is `false` and the output thus becomes `"no"`.



## Piping



Two lambdas can be composed using the `pipe` or `compose` operator. For eg: if there exists a lambda `l1: A ~> B` and a

lambda `l2: B ~> C` then they can be composed using the pipe operator as —



```scala

val l1: A ~> B = ???

val l2: B ~> C = ???

val l12: A ~> C = A >>> B

```



This is the semantic equivalent of `l2(l1(a))` , where `a` is of type `A`.



## Lenses



ZIO Compose has support for lenses which allows very precise control over getting and setting values over record types.

For eg: Let's say there is a type `User` and we want to get the `age` of that user. We could do something like this —



```scala

import zio.schema._

import compose.macros.DeriveAccessors



case class User(firstName: String, lastName: String, age: Int)



object User {



  // Derive the Schema

  implicit val schema: Schema[User] = DeriveSchema.gen[User]



  // Derive accessors

  val lens = DeriveAccessors.gen[User]

}

```



The `schema` field inside of `User` provides access to the meta-data and structure of the type `User`.

Whereas `lens` internally uses `schema` to navigate through an instance to lookup or update it's fields in a type-safe

manner. Let's see that in action —



```scala

val user: Any ~> User = constant(User("John", "Doe", 23))

val age: User ~> Int = User.lens.age.get

val program: Any ~> Int = user >>> age

```



Here we create a user using `constant` and then using the derived lens we create a Lambda from `User ~> Int`.

We compose the two lambdas together using the `>>>` operator (alias to `pipe`).

The final program is a type-safe, serializable function that can take anything and produce an integer.



Now let's look at an example where we are updating a field using lenses in the User type -



```scala

val user: Any ~> User = constant(User("John", "Doe", 23))

val program: Any ~> User = (user <*> constant(12)) >>> User.lens.age.set

```



The `set` methods on lens is a binary function, so it needs two arguments - 1. The whole object which needs to be

updated and 2. the value it needs to set. In our case `age.set` would have a type like this - `(User, Int) ~> User`.

That's why we use the `<*>` operator (alias to `zip`) to combine the two inputs and send it to the `set` function.



## Transformations



Transformations from one type to another are easily possible using the lens API, however it can become a bit verbose and

boilerplate sometimes.

ZIO Compose provides a DSL to simplify transformations. Here is an example of converting `User` to `Customer`, we start

by defining the types, schema and it's lens.



```scala

case class Customer(name: String, age: Int, allowed: Boolean)



object Customer {

  implicit val schema = DeriveSchema.gen[Customer]

  val lens = DeriveAccessors.gen[Customer]

}

```



We then take each field of the user, perform some transformations on the field themselves and then set it in a customer.

A transformation can be defined using the `->>` operator.



```scala

val t1: User ~> Customer = (User.lens.age.get + constant(10)) ->> Customer.lens.age.set

,

```



A `Transformation`, is nothing but a pair of a getter and setter. We can combine multiple transformations using

the `transform` operator -



```scala

import Lambda._



val user2Customer: User ~> Customer = transform(

  (User.lens.age.get + constant(10)) ->> Customer.lens.age.set,

  (User.lens.firstName.get ++ constant(" ") ++ Person.lens.lastName.get) ->> Customer.lens.name.set,

  (User.lens.age.get > constant(18)) ->> Customer.lens.isAllowed.set,

)

```



The final output of the transformation is a function from `User ~> Customer`. We can then pipe in an actual user

instance to produce a customer as follows —



```scala

val program: Any ~> Customer = constant(User("John", "Doe", 20)) >>> user2Customer

```



## Looping



With ZIO Compose one can loop over a lambda in multiple ways. For eg:

Let's say I want to add all numbers between 0 to 10. We can do this by creating a type `Sum` which maintains

intermediary state of our program like this —



```scala

import compose.macros.DeriveSchema



case class Sum(count: Int, result: Int)



object Sum {

  implicit val schema = DeriveSchema.gen[Sum]

  val lens = DeriveAccessor.gen[Sum]

}

```



Then we make a lambda of type `Sum ~> Sum` to represent one iteration of our loop. In the iteration we perform two

operations -



1. Increase the value of `count` by one.

2. Add the value of `count` to `result`.



```scala

import Lambda._



val iteration: Sum ~> Sum = transform(

  Sum.lens.count.get.inc ->> Sum.lens.count.set,

  Sum.lens.result.get + Sum.lens.count.get ->> Sum.lens.result.set

)

```



We then use the `repeatWhile` operator to keep iterating while the condition is true.



```scala

val sum: Any ~> Sum = iteration.repeatWhile(Sum.lens.count.get < constant(10))

```



To get the exact value of the sum we can again use the lens API as follows —



```scala

val program: Any ~> Int = sum >>> Sum.lens.result.get

```



## Scopes



Scopes allows us to define and update variables within a given context.

They turn out to be pretty handy when we want to share some data across different part of our program without having to

pass it using `pipe`.

Below we take an arbitrary example where have two numbers and we want to check if their sum is greater than their

product.



```scala

import Lambda._



val program = scope { implicit ctx =>

  val a = Ref.make(key = "a", value = 10)

  val b = Ref.make(key = "b", value = 5)

  val result = Ref.make(key = "result", value = false)



  (a.get + b.get) > (a.get * b.get) >>> result.set

}

```



A `Ref` is like a `ZRef` with `get` and `set` methods on it.

It needs a unique key within the scope of it's usage and a default value at the time of initialization.

However, it can only be initialized inside a `scope { }` block. The `{implicit ctx =>` provides context in which the

scoped variable is available.



## Fibonacci



Here is an advanced example of a program that calculates fibonacci numbers and is completely serializable.



```scala

import compose._

import zio.schema._



case class Fib(a: Int, b: Int, i: Int)



object Fib {

  implicit val schema: Schema[Fib] = DeriveSchema.gen[Fib]

  val lens = DeriveAccessor.gen[Fib]

}



def fib = constant(Fib(0, 1, 0)) >>>

  transform(

    Fib.lens.b.get ->> Fib.lens.a.set,

    Fib.lens.a.get + Fib.lens.b.get ->> Fib.lens.b.set,

    Fib.lens.i.get.inc ->> Fib.lens.i.set,

  ).repeatWhile(Fib.lens.i.get =!= constant(20)) >>> Fib.lens.b.get

```



**PS:** If you like what you see, give the repo a ⭐️ 🙏