This post is meant to give some background information on the ReaderTaskEither<R, E, A> type from fp-ts.

To understand ReaderTaskEither<R, E, A> (aka RTE), it’s important to understand some of the lower-level fp-ts “effect types” upon which RTE is built. Note that in fp-ts, some of these types might be encoded slightly differently than below, but the concepts should be the same. Also note that nearly all of the types and concepts below have a history that predates fp-ts (and TypeScript for that matter) by decades - many of these ideas come from languages like Haskell, Scala, PureScript, OCaml, and others.

## IO<A>

type IO<A> = () => A

• Sync/async: sync
• Can fail: no
• Can depend on explicitly-declared contextual info: no

IO<A> represents a lazy, synchronous computation that, when run, may perform side effects and then produce a value of type A. The side effects might be involved with the production of the A value (e.g. reading from a DOM element, localStorage, etc.), or might be unrelated, “unobservable” side effect(s), like synchronously writing to a file, sending a message on a network connection, writing to the DOM, etc. IO<A> is intended to be used for synchronous effectful computations that are not expected to fail, as there is no way to represent failure other than throwing an Error from the function, which is undesirable, and would be unexpected by the caller. If you have a synchronous, effectful operation that can fail, see IOEither<E, A>.

### Aside: IO<void>

IO<void> is a type which represents a side-effecting computation that produces no value at all - its sole purpose is to perform side effects. This could be something like writing to localStorage, writing a log message, dispatching a redux action, drawning to the screen, writing to the DOM, etc.

const mySideEffect: IO<void> = () => {
writeToDatabase('my-key', 'my-value')
writeToLocalStorage('key', 'something')
dispatch(myAction())
document.getElementById('my-id').innerHTML = 'hi'
console.log('hi')
}


### Aside: Lazy<A>

type Lazy<A> = () => A


Lazy<A> (aka a “thunk”) represents a synchronous computation that produces a value of type A when the function is called. By convention, the Lazy<A> type does not typically imply the presence of side effects - the conventional semantics of a type like Lazy<A> are more about the deferral of a potentially expensive, but typically pure computation.

Lazy<A> and IO<A> have the same type, so what’s the difference? There’s not really a difference other than semantics and convention - Lazy<A> would typically be expected to be used for a lazy, but pure computation, whereas IO<A> is used for deferring the execution of side effects.

One other reason for the existence of both of these types is that in fp-ts v1, the IO<A> type was encoded slightly differently than it is in v2+, so there actually was a type-level difference between Lazy<A> and IO<A> at that time. In fp-ts v2+, the encoding of IO<A> was changed to simply () => A (or the interface encoding of that function type), so there is no longer a practical difference in the types.

## IOEither<E, A>

type IOEither<E, A> = IO<Either<E, A>>
= () => Either<E, A>;

• Sync/async: sync
• Can fail: yes
• Can depend on contextual info: no

IOEither<E, A> represents a synchronous, lazy computation that can produce a value of type A, or fail with an error of type E. This is intended to be used for synchronous effectful code that has the possibility of failure. The IO<_> part implies the presence of side effects, because if there were no effects, it would probably be better to just use Either<E, A>, or potentially Lazy<Either<E, A>>. The Either<E, A> part of the type allows for the representation of errors in the effectful computation.

## Promise<A>

• Sync/async: async
• Can fail: yes (with non-generic/non-polymorphic any error)
• Can depend on explicit contextual info: no

Promise<A> Represents an eagerly-executed (i.e. non-lazy) async computation that can eventually succeed with a value of type A, or fail with an error of type any. The computation starts to execute as soon as the Promise<A> is constructed, so the type is therefore not lazy, and not referentially transparent, which makes Promise<A> a less appealing choice for pure functional programming. Promise<A> also has an implicit memoization of the success or failure result.

Because the computation is async, there is no way to synchronously extract a value of type A directly out of a Promise<A>. In other words, there is no function of type Promise<A> => A. The value that is eventually produced by a Promise<A> can be accessed by chaining on a continuation callback via .then((a: A) => ...). Or you can use the await approach, which is essentially just syntax sugar for .then.

type Task<A> = () => Promise<A>

• Sync/async: async
• Can fail: no (by convention)
• Can depend on contextual info: no

Task<A> represents a lazily-executed async computation that can eventually produce a value of type A. In fp-ts, Task<A> is currently implemented as a Lazy<Promise<A>> or () => Promise<A>, so technically, a Task<A> can fail “under the hood,” but by convention, Task<A> is intended to be used for async computations that are not expected to fail. In other words, if you use a Task<A> for a computation, and the underlying Promise<A> fails, you’ve made a programming error in your choice of Task<A> as your effect type, and you’ve not likely made any attempt to handle errors from the Task<A>, so your program will likely and rightfully crash. The canonical example of an async operation that is not expected to fail is a basic deferred function call, like a setTimeout. If you are dealing with an async operation that has the possibility of failure, you should use TaskEither<E, A> instead.

So if Task<A> is just a lazy Promise<A>, why not just use Promise<A>? The reason is that a value of type Task<A> is a pure and referentially-transparent description of an effectful computation, whereas a value of type Promise<A> is an impure, referentially-opaque, already-running (or possibly already-completed) effectful computation. Another intuition is that a Task<A> is a “canned” or “freeze-dried” side effect that you can pass around, compose, substitute, etc., and then open or thaw it out it at the right time; whereas, a Promise<A> is the contents of the can - it’s a little messier to pass around. The simple act of making the evaluation of the Promise<A> lazy makes Task<A> pure. When run, the Task<A> will perform impure side effects, but the Task itself is pure. The same idea applies to IO<A> = () => A compared to an effectful expression that produces an A - the act of deferring the side effects make the IO<A> type pure. This idea may take some time and hands-on practice to sink in. Purity and referential transparency are important concepts in pure functional programming because they allow you to make assumptions about the behavior of your program based on provable mathematical laws, perform substitutions of expressions, variables, and values both in actuality and mentally, and generally reason about the behavior of your program just by looking at the types. Without some of these principles, some of which are enforced by the compiler and some of which are followed by convention and discipline, you can’t really make any assumptions about the behavior of a program, because any piece of code can do just about anything it wants at any time. By constraining ourselves to a set of well-behaved types and principles, we can eliminate whole classes of bugs and unexpected behaviors, and make our code more maintainable and reusable.

type TaskEither<E, A> = Task<Either<E, A>>
= () => Promise<Either<E, A>>;

• Sync/async: async
• Can fail: yes
• Can depend on contextual info: no

TaskEither<E, A> represents a lazy async computation that can succeed with a value of type A, or fail with an error of type E. The underlying Promise can actually also fail “under the hood” with it’s own unknown (any) error type, but by convention, errors in TaskEither<E, A> are meant to be lifted into the Either<E, A> in the Promise’s “success channel.” If you end up with a failed Promise in a TaskEither, you’ve made a programming error by not lifting an error into the Either<E, A> somewhere, and your program will likely crash. This pitfall is an unfortunate consequence of using Promise<A> as the basis of the Task-based effects in fp-ts. However, Promise<A> is so ubiquitous in JavaScript and TypeScript, that by using Promise<A> under the hood, you gain the ability to more easily interop with most existing JavaScript libraries, and you can leverage some existing familiarity with Promise<A> for learning purposes. There are async effect libraries that are not backed by Promise<A> that can be explored for a better understanding of this.

Like Promise<A>, you can’t “get the value out” of a Task<A> nor anything based on Task. You access the value by composing on functions like map, chain, and others.

type Reader<R, A> = (r: R) => A

• Sync/async: sync
• Can fail: no
• Can depend on contextual info: yes

Reader<R, A> represents a synchronous computation that produces a value of type A by reading from some contextual value provided via the function argument of type R. A Reader<R, A> is just as simple as it looks - it’s just a function that takes an argument R and produces a value A. The key aspect of Reader is encoding the ability for a computation to utilize an explicit input value to perform its computation. If you think about the effect types we’ve seen so far (IO<A>, TaskEither<E, A>, etc.), they only deal with outputs - the E error type or the A success type. Reader introduces the concept of an input, and gives you the power to compose effects that depend on different inputs to run. How Reader is used in practice is where it gets more interesting. Note that a Reader<R, A> does not typically imply the presence of side effects by itself - for something like that, you’d probably use type ReaderIO<R, A> = Reader<R, IO<A>>, ReaderIOEither<R, E, A> = Reader<R, IOEither<E, A>>, or the ReaderTask* types.

type ReaderIO<R, A> = Reader<R, IO<A>>
= (r: R) => () => A;

= Reader<R, () => Either<E, A>>
= (r: R) => () => Either<E, A>;


As mentioned above, these types simply combine the capabilities of Reader<R, A> with IO<A> or IOEither<E, A> - i.e. reading from some input value in order to perform a side-effectful computation.

Note that ReaderIOEither may not exist in fp-ts at the time of this writing, but it’s easy to create by just following or copying how something like ReaderTask<R, A> or ReaderTaskEither<R, E, A> are implemented. See also fp-ts-contrib for other variations like StateTaskEither<S, E, A>.

This approach of “stacking” effect types is similar to how monad transformers work, and is sometimes referred to as vertical composition of effects - a “stack of effects” or an “effect stack.” The idea is that you create a “more capable” effect type (i.e. one that is able to handle more flexible or expressive effects) by combining the capabilities of less-capable effects.

type ReaderTask<R, A> = Reader<R, Task<A>>
= (r: R) => () => Promise<A>

• Sync/async: async
• Can fail: no
• Can depend on contextual info: yes

As you might imagine, a ReaderTask<R, A> combines the capabilities of Reader<R, A> and Task<A>. Reader provides the ability to depend on some input to run, and Task provides the ability to perform an async computation that can’t fail. To add the ability to fail, continue on to ReaderTaskEither<R, E, A>.

Below is a step-by-step expansion of ReaderTaskEither<R, E, A> into it’s underlying type:

type ReaderTaskEither<R, E, A> = Reader<R, TaskEither<E, A>>
= (r: R) => TaskEither<E, A>
= (r: R) => Task<Either<E, A>>
= (r: R) => () => Promise<Either<E, A>>

• Sync/async: async
• Can fail: yes
• Can depend on contextual info: yes

A ReaderTaskEither<R, E, A> is a type that combines the powers of a few of the less-capable effect types, into a type that provides the most commonly-needed capabilities for day-to-day application programming:

• Reader - grants the ability to depend on contextual information to perform a computation, without having to have the information ahead of time.
• Separates the description of the computation based on potentially abstract dependencies from the act of providing its concrete dependencies
• Reader is the FP version of dependency-injection - you write your code using abstract dependencies that you expect to be given to you by some external caller or layer of your program. The dependencies often flow to the top-level of the program, where they are provided to all Reader-based effects right before running the whole effect stack to perform the computation.
• Task - grants the ability to perform async, side-effectful work
• Separates the description of the async computation from the execution of it
• Either - grants the ability to perform a computation that can fail with a known error type

One high-level thing to note is that you can “provide” the Reader environment by passing in the R value, and you are left with a still-pure TaskEither<E, A> value, which you can continue to use in a pure way. The computation only runs when the TaskEither<E, A> is “run” (i.e. called), which finally constructs the Promise<Either<E, A>> and starts the effectful computation. You typically “run” an effect at the last possible moment, so that you can build your code using completely pure functions, and only drop down into impure execution at the very “end,” like the end of a main program or the end of some context within your application, like when you pass control back to a framework (i.e. the end of a DOM event handler function), or you are leaving some context and the effect needs to happen at that time. The definition of this elusive “end” concept takes some time and hands-on practice to fully grasp.

## “Effect rotation”

One interesting property of RTE is the ability for it to represent some of it’s less-capable underlying effect types, through creative application of its type params.

E.g. to denote an effect that has no Reader environment (e.g. an effect that doesn’t depend on any contextual info), you can use the unknown type (or some other “empty” type) as the R, like:

type NoEnvRTE<E, A> = ReaderTaskEither<unknown, E, A>


The unknown means that you can “provide” the environment required by this function by passing literally any value - it doesn’t matter what it is, and nothing uses it. The unknown argument is simply there as a placeholder to satisfy the type. You might notice this looks like just a TaskEither<E, A>, and it essentially is - it is isomorphic with TaskEither<E, A>, which means you can convert this type to and from TaskEither<E, A> without losing anything.

To denote an async computation that can’t fail, you can use never for the E type:

type NoErrorRTE<R, A> = ReaderTaskEither<R, never, A>


For the error type, we use never, because the never type has no inhabitants, so it’s impossible to ever get this type into the failed state, because there’s no way to create a value of type never. This type is isomorphic with Reader<R, Task<A>> or ReaderTask<R, A>. This type is interesting because it can be used as a signal that you’ve “handled” any errors, probably by converting any possible errors in a value of type A in the success channel, and eliminating the need for any successive functions to deal with any errors at all.

For a computation that has no Reader env and that additionally can’t fail, you can use ReaderTaskEither<unknown, never, A>, which is isomorphic with Task<A>. To reiterate, the reason we use unknown for the reader type and never for the E type is that we want to be able to provide this reader with any value to satisfy the reader argument, but we want to ensure that the effect can’t fail by disallowing any value from appearing in the Left of the Either<E, A>. Ensuring an effect can’t fail means that you have to handle any possible errors by expressing them as a value of type A in the success channel.

This ability for this one type RTE<R, E, A> to represent these different variations of effects was dubbed “effect rotation” by John De Goes, the creator of ZIO, in one of his earlier articles about the ideas behind ZIO. This is considered “rotation” because with monad transformers, you might create these effect combinations by (vertically) “stacking” different effect types, but with RTE, you achieve these effect capability variations by applying different types at a single, “flat” level (i.e. “horizontally”). (RTE itself is a stack of effect effect types, so the analogy isn’t perfect, but the way the RTE type is expressed is is similar to ZIO in spirit).

## Composing effects

For all of the above effect types (IO, Task, RTE, etc.), it’s possible to create Functor, Applicative, Monad, and a variety of other typeclass instances. These typeclass instances allow us to compose or combine our effectful computations into more complex computations and to eventually build whole programs based on RTE. If you’ve never done it before, it’s useful to go through the exercise of implementing the following functions for all of the above types, e.g.:

// Functor
const ioMap = <A, B>(f: (a: A) => B) => (io: IO<A>): IO<B> => { ... }

// Applicative
const ioOf = <A>(a: A): IO<A> => { ... }
const ioApply = <A, B>(ff: IO<(a: A) => B>) => (io: IO<A>): IO<B> => { ... }


Try implementing these for IO<A>, then try again with all the other effect types. This is an interesting exercise to see how function-based types work with operations like map and chain, compared to how the simpler data structures like Option, Either, and RemoteData work. The main guidance is to “follow the types” - think about what types you are given and what type of value you are trying to produce in the end.
ReaderTaskEither<R, E, A> combines the capabilities of Reader<R, A>, Task<A> and Either<E, A> to create a type that can handle most effectful computations needed for application development. In another document, we’ll explore more real-world usage examples and patterns of RTE.