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Version: 1.0.18

Basic Concurrency

ZIO has low-level support for concurrency using fibers. While fibers are very powerful, they are low-level. To improve productivity, ZIO provides high-level operations built on fibers.

When you can, you should always use high-level operations, rather than working with fibers directly. For the sake of completeness, this section introduces both fibers and some of the high-level operations built on them.


ZIO's concurrency is built on fibers, which are lightweight "green threads" implemented by the ZIO runtime system.

Unlike operating system threads, fibers consume almost no memory, have growable and shrinkable stacks, don't waste resources blocking, and will be garbage collected automatically if they are suspended and unreachable.

Fibers are scheduled by the ZIO runtime and will cooperatively yield to each other, which enables multitasking, even when operating in a single-threaded environment (like JavaScript, or even the JVM when configured with one thread).

All effects in ZIO are executed by some fiber. If you did not create the fiber, then the fiber was created by some operation you are using (if the operation is concurrent or parallel), or by the ZIO runtime system.

Even if you only write "single-threaded" code, with no parallel or concurrent operations, then there will be at least one fiber: the "main" fiber that executes your effect.

The Fiber Data Type

Every ZIO fiber is responsible for executing some effect, and the Fiber data type in ZIO represents a "handle" on that running computation. The Fiber data type is most similar to Scala's Future data type.

The Fiber[E, A] data type in ZIO has two type parameters:

  • E Failure Type. The fiber may fail with a value of this type.
  • A Success Type. The fiber may succeed with a value of this type.

Fibers do not have an R type parameter, because they model effects that are already running, and which already had their required environment provided to them.

Forking Effects

The most fundamental way of creating a fiber is to take an existing effect and fork it. Conceptually, forking an effect begins executing the effect on a new fiber, giving you a reference to the newly-created Fiber.

The following code creates a single fiber, which executes fib(100):

def fib(n: Long): UIO[Long] = UIO {
if (n <= 1) UIO.succeed(n)
else fib(n - 1).zipWith(fib(n - 2))(_ + _)

val fib100Fiber: UIO[Fiber[Nothing, Long]] =
for {
fiber <- fib(100).fork
} yield fiber

Joining Fibers

One of the methods on Fiber is Fiber#join, which returns an effect. The effect returned by Fiber#join will succeed or fail as per the fiber:

for {
fiber <- IO.succeed("Hi!").fork
message <- fiber.join
} yield message

Awaiting Fibers

Another method on Fiber is Fiber#await, which returns an effect containing an Exit value, which provides full information on how the fiber completed.

for {
fiber <- IO.succeed("Hi!").fork
exit <- fiber.await
} yield exit

Interrupting Fibers

A fiber whose result is no longer needed may be interrupted, which immediately terminates the fiber, safely releasing all resources and running all finalizers.

Like await, Fiber#interrupt returns an Exit describing how the fiber completed.

for {
fiber <- IO.succeed("Hi!").forever.fork
exit <- fiber.interrupt
} yield exit

By design, the effect returned by Fiber#interrupt does not resume until the fiber has completed. If this behavior is not desired, you can fork the interruption itself:

for {
fiber <- IO.succeed("Hi!").forever.fork
_ <- fiber.interrupt.fork // I don't care!
} yield ()

Composing Fibers

ZIO lets you compose fibers with Fiber#zip or Fiber#zipWith.

These methods combine two fibers into a single fiber that produces the results of both. If either fiber fails, then the composed fiber will fail.

for {
fiber1 <- IO.succeed("Hi!").fork
fiber2 <- IO.succeed("Bye!").fork
fiber =
tuple <- fiber.join
} yield tuple

Another way fibers compose is with Fiber#orElse. If the first fiber succeeds, the composed fiber will succeed with its result; otherwise, the composed fiber will complete with the exit value of the second fiber (whether success or failure).

for {
fiber1 <-"Uh oh!").fork
fiber2 <- IO.succeed("Hurray!").fork
fiber = fiber1.orElse(fiber2)
message <- fiber.join
} yield message


ZIO provides many operations for performing effects in parallel. These methods are all named with a Par suffix that helps you identify opportunities to parallelize your code.

For example, the ordinary ZIO#zip method zips two effects together, sequentially. But there is also a ZIO#zipPar method, which zips two effects together in parallel.

The following table summarizes some of the sequential operations and their corresponding parallel versions:

Zips two effects into oneZIO#zipZIO#zipPar
Zips two effects into oneZIO#zipWithZIO#zipWithPar
Zips multiple effects into oneZIO#tupledZIO#tupledPar
Collects from many effectsZIO.collectAllZIO.collectAllPar
Effectfully loop over valuesZIO.foreachZIO.foreachPar
Reduces many valuesZIO.reduceAllZIO.reduceAllPar
Merges many valuesZIO.mergeAllZIO.mergeAllPar

For all the parallel operations, if one effect fails, then others will be interrupted, to minimize unnecessary computation.

If the fail-fast behavior is not desired, potentially failing effects can be first converted into infallible effects using the ZIO#either or ZIO#option methods.


ZIO lets you race multiple effects in parallel, returning the first successful result:

for {
winner <- IO.succeed("Hello").race(IO.succeed("Goodbye"))
} yield winner

If you want the first success or failure, rather than the first success, then you can use left.either race right.either, for any effects left and right.


ZIO lets you timeout any effect using the ZIO#timeout method, which returns a new effect that succeeds with an Option. A value of None indicates the timeout elapsed before the effect completed.

import zio.duration._


If an effect times out, then instead of continuing to execute in the background, it will be interrupted so no resources will be wasted.

Next Steps

If you are comfortable with basic concurrency, then the next step is to learn about testing effects.