ZIO Streams: An Introduction

21 minute read

This article is brought to you by Mark Rudolph, a new contributor to Rock the JVM. He’s a senior developer, who has been working with Scala for a number of years. He also has been diving into the ZIO ecosystem, and loves sharing his learnings. Mark has a lot to share, and this article is a comprehensive piece.

In this post, we’re going to go over an introduction to the main components of ZIO Streams, how to work with them when things go right, and what to do when things go wrong.

As a toy example, we’re going to take a brief foray into asynchronous operations, by connecting two streams to the same concurrent data structure.

For a more concrete example, we are going to write a program that will parse markdown files, extract words identified as tags, and then regenerate those files with tag-related metadata injected back into them.

Set up

We’re going to base this discussion off of the latest ZIO 2.0 code, which was officially released on June 24th, 2022. We’re also using an RC of zio-json (at the time of writing, mid-June 2022), which is only used to pretty-print a Map as JSON in our of our examples. These are the dependencies to include for our walk through:

libraryDependencies ++= Seq(
      "dev.zio" %% "zio" % "2.0.0",
      "dev.zio" %% "zio-streams" % "2.0.0",
      "dev.zio" %% "zio-json" % "0.3.0-RC8"

What’s a Stream?

Broadly: A stream is a series of data elements that are made available over time.


A specific example of a stream implementation, is Scala’s LazyList. A LazyList is a linked list, where the tail is lazily evaluated. This fits our broad definition above, because the tail isn’t evaluated until accessed - meaning they are made available over the time. If we investigated this in a repl, we’d see :

scala> val ll = LazyList(1,2,3,4,5)
val ll: scala.collection.immutable.LazyList[Int] = LazyList(<not computed>)

scala> ll.head
val res1: Int = 1

scala> ll.tail
val res2: scala.collection.immutable.LazyList[Int] = LazyList(<not computed>)

Once the LazyList has been created, we can access the head element, but the tail is not yet computed. This is in stark comparison to a List, for which the same exercise would yield:

scala> val l = List(1,2,3,4,5)
val l: List[Int] = List(1, 2, 3, 4, 5)

scala> l.head
val res0: Int = 1

scala> l.tail
val res1: List[Int] = List(2, 3, 4, 5)

Scala’s LazyList also has the bonus of the elements being memoized - meaning they are only computed once. Let’s go back to the repl, and look at our LazyList after the first two elements have been accessed.

scala> ll.tail.head
val res3: Int = 2

scala> ll
val res4: scala.collection.immutable.LazyList[Int] = LazyList(1, 2, <not computed>)

We can see that the first two elements now clearly show! If you haven’t felt the excitement of generating the Fibonacci sequence with LazyLists, I encourage you to do so. However, the most important take-away from this feature, is that memoization is not in our broad definition above - this is not a feature of all streams.


Many messaging platforms, such as Kafka, Pulsar, and/or RabbitMQ have what they advertise as Streams. At the heart of this, there is the concept that a message is produced, and written to disk as an append-only log. Some time later a consumer can read back entries from that log at their own leisure, and there is a guarantee that within the same offset (i.e. the first 1000 elements), the data will be constant, even if re-processed later. This fits our broad definition, because the data is ordered and available over time.


In our example later, we are going to process blog posts to parse tag meta-data. These files are not processed terribly different than our append-only log above; any given file is an ordered collection of elements, we read them in one at a time, and do something with that information as we go. A notable difference from above, is that the elements within an offset can change when not being processed. If I go back and edit a blog post, there is no guarantee that when the file is re-processed that the first 1000 elements will be the same as before.

Stream Recap

We can see that the fluid meaning of what a stream is can solidify itself around a context.

If we’re in the mindset of Kafka, a stream might mean the implication of consistent data within an offset at all times.

If we’re in the mindset of parsing files, a stream might imply meaningfully ordered elements that consistently build a larger concept, at the time of processing. This is to say, here it is more important that a stream of Byte can always be processed into a String by following an encoding pattern. If we go back and re-process it, we don’t care if the contents have changed, just that the content can be processed.

As we move on to ZIO Streams, we should keep our broad definition in mind, so we don’t ensnare ourselves on the implementation details.

ZIO Stream Components

Before we start discussing the core components of ZIO Streams, let’s first revisit the type signature of a zio: ZIO[R, E, A]. This is an effect that will compute a single value of type A, requires dependencies of type R to do so, and could possibly fail with an error of type E. Note, that E are errors you potentially want to recover from - an error which occurs that is not of type E is called a defect. If there are no dependencies required, then R is Any. If there are no errors you expect to recover from, then E is Nothing. The concept of declaring dependencies, errors, and values in our type signature will be central to our understanding of ZStreams going forward!

We should also briefly mention the Chunk[A]. Due to practicality, and performance, our streams work in batches, and the zio.Chunk[A] is an immutable array-backed collection, containing elements of type A, which will often present itself when working with ZStreams.


A ZStream has the signature: ZStream[R, E, O], which means it will produce some number of O elements, requiring dependencies R to do so, and could possibly fail with an error of type E. Since this is a stream we could possibly be producing somewhere between 0 and infinite O elements.

A ZStream represents the source of data in your work flow.


At the opposite end of our stream, we have a ZSink, with the signature: ZSink[R, E, I, L, Z]. R, and E are as described above. It will consume elements of type I, and produce a value of type Z along with any elements of type L that may be left over.

A ZSink represents the terminating endpoint of data in your workflow.

We should mention that ZIO uses pull-based streams, meaning that elements are processed by being “pulled through the stream” by the sink. In push-based systems, elements would be “pushed through the stream” to the sink.

Referring back to the type signature, L deserves a dedicated note: L describes values that have not been processed by the ZSink. For example, if our intent is to sum every element in the stream, then we would not expect any elements to be left over once processed:

  val sum: ZSink[Any, Nothing, Int, Nothing, Int] =

On the other hand, if our intent is to only operate on the first few elements of a stream, via an operation like take, then there exists the possibility that there are remaining, unprocessed values. Let’s also map our output, so we can see the different output type.

  val take5: ZSink[Any, Nothing, Int, Int, Chunk[Int]] =

  val take5Map: ZSink[Any, Nothing, Int, Int, Chunk[String]] =
    take5.map(chunk => chunk.map(_.toString))

If we knew our collection was finite, we could also return the leftovers that were not operated on - or we could outright ignore them.

  val take5Leftovers: ZSink[Any, Nothing, Int, Nothing, (Chunk[String], Chunk[Int])] =

  val take5NoLeftovers: ZSink[Any, Nothing, Int, Nothing, Chunk[String]] =

A Prelude to ZPipelines

If we have some logic to process a stream already, but suddenly our stream is a different type, we can use contramap to convert the input type appropriately. If using both map and contramap, then there is an equivalent dimap you can call as well.

  // take5Map would work on this.
  val intStream: ZStream[Any, Nothing, Int] =
  ZStream(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)

  // take5Map would not work on this.
  val stringStream: ZStream[Any, Nothing, String] =
    ZStream("0", "1", "2", "3", "4", "5", "6", "7", "8", "9")

  // We can use contramap to use our take5 logic, and operate on stringStream with it.
  val take5Strings: ZSink[Any, Nothing, String, Int, Chunk[String]] =

// This is equivalent to take5Strings above
  val take5Dimap: ZSink[Any, Nothing, String, Int, Chunk[String]] =
    take5.dimap[String, Chunk[String]](_.toInt, _.map(_.toString))

Notice that the type of L is still Int. With contramap, we are operating on the input element to get it to the correct type (String), however, if anything is left over, then it wasn’t operated on - meaning it’s still the input type from take5 (Int).

It’s worthwhile to pause here, and briefly discuss contramap. We are converting a value of type A to a value of type B, with a function that is B => A - this operates like map in reverse. Without diving too far into category theory, you can think of a Covariant Functor as something that may “produce” a value of type A (and implements a map), whereas a Contravariant Functor may “consume” a value of type A (and implements a contramap). With JSON as an example, a contravariant Encoder[A] consumes a value of type A to produce JSON, whereas a covariant Decoder[A] produces a value of type A by consuming JSON.


A ZPipeline converts one ZStream to another ZStream. It can conceptually be thought of as type ZPipeline[Env, Err, In, Out] = ZStream[Env, Err, In] => ZStream[Env, Err, Out].

The main use case of a ZPipeline is to separate out reusable transform logic, that can then be composed together with other ZPipelines, and then placed in between any appropriate ZStream and a ZSink.

From our example above, let’s say we wanted to sum the stream of Strings representing integer values. Instead of adapting our ZSink.sum with contramap, we can write:

  //stringStream.run(sum) <- This doesn't compile

  val businessLogic: ZPipeline[Any, Nothing, String, Int] =
    ZPipeline.map[String, Int](_.toInt)

  val zio: ZIO[Any, Nothing, Int] =

We can also compose ZPipelines together to form a new ZPipeline with >>>, for example:

  val businessLogic: ZPipeline[Any, Nothing, String, Int] =
    ZPipeline.map[String, Int](_.toInt)

  val filterLogic: ZPipeline[Any, Nothing, Int, Int] =
    ZPipeline.filter[Int](_ > 3)

  val appLogic: ZPipeline[Any, Nothing, String, Int] =
    businessLogic >>> filterLogic

  val zio: ZIO[Any, Nothing, Int] =

An interesting thing we’re seeing here for the first time, is that connecting a ZSink to a ZStream results in a ZIO. In oder to process of our stream logic, we need to connect streams to sinks to produce a ZIO we can evaluate.

Along with the typical collection-like operations you’d expect, there are a number of addition ones that are available directly on ZStream, and can be referenced in the official docs .

Handling Failures

Illustrating failures can be a little lack-luster in a less-interactive medium, such as reading a blog post. For example, we might declare a example of stream with a failure as

  val failStream: ZStream[Any, String, Int] = ZStream(1, 2) ++ ZStream.fail("Abstract reason") ++ ZStream(4, 5)

but what does that mean in use? We’re not going to intentionally instantiate a ZStream with a .fail in the middle of of it. Thinking about how you can get into a failure state is as valuable as how to recover from it. So instead of the example above, let’s do something that feels more real, and implement an InputStream that we can control the success/failure cases.

  class RealFakeInputStream[T <: Throwable](failAt: Int, failWith: => T) extends InputStream {
    val data: Array[Byte] = "0123456789".getBytes
    var counter = 0

    override def read(b: Array[Byte]): Int = {
      if (counter == failAt) throw failWith
      if (counter < data.length) {
        b(0) = data(counter)
        counter += 1
      } else {

    // Not used, but needs to be implemented
    override def read(): Int = ???

Our RealFakeInputStream will make the elements of the string "0123456789" available. In the constructor, we can pass in a failAt to indicate the point when we should throw an exception, and failWith is the exception we should throw. By setting failAt higher than the length of our string, we wont fail. By passing in different instances of failsWith, we can control wether or not if the error is in the E channel of our ZStream[R, E, O].

With this in mind, let’s set up some stream components that we can run together, and test different error cases. Note that we set the chunkSize of our ZStream.fromInputStream to 1.

  // 99 is higher than the length of our data, so we won't fail
  val nonFailingStream: ZStream[Any, IOException, String] =
    ZStream.fromInputStream(new RealFakeInputStream(99, new IOException("")), chunkSize = 1)
      .map(b => new String(Array(b)))

  // We will fail, and the error type matches ZStream error channel
  val failingStream: ZStream[Any, IOException, String] =
    ZStream.fromInputStream(new RealFakeInputStream(5, new IOException("")), chunkSize = 1)
      .map(b => new String(Array(b)))

  // We fail, but the error does not match the ZStream error channel
  val defectStream: ZStream[Any, IOException, String] =
    ZStream.fromInputStream(new RealFakeInputStream(5, new IndexOutOfBoundsException("")), chunkSize = 1)
      .map(b => new String(Array(b)))

  // When recovering, we will use this ZStream as the fall-back
  val recoveryStream: ZStream[Any, Throwable, String] =
    ZStream("a", "b", "c")

  // We will pull values one at a time, and turn them into one string separated by "-"
  val sink: ZSink[Any, Nothing, String, Nothing, String] =

Let’s write a success case program to pull values through, one at a time, and turn them into a single String where the values are separated by “-“.

object ZStreamExample extends ZIOAppDefault {

  override def run: ZIO[Any with ZIOAppArgs with Scope, Any, Any] =

The output would look like sink: 0-1-2-3-4-5-6-7-8-9. As a quick aside, a new feature in ZIO 2 is the .debug(prefix) method, which will log a line containing the computed value of the ZIO, prefixed with prefix: . This is where the sink: portion of the output comes from.

Now let’s turn our attention to the failingStream.

  override def run: ZIO[Any with ZIOAppArgs with Scope, Any, Any] =

The above code will fail less than gracefully, with something like

<FAIL> sink: Fail(java.io.IOException: ,StackTrace(Runtime(6,1655962329,),Chunk(com.alterationx10.zse.ZStreamExample.run(ZStreamExample.scala:53),com.alterationx10.zse.ZStreamExample.run(ZStreamExample.scala:54))))
timestamp=2022-06-23T05:32:09.924145Z level=ERROR thread=#zio-fiber-0 message="" cause="Exception in thread "zio-fiber-6" java.io.IOException: java.io.IOException:
	at com.alterationx10.zse.ZStreamExample.run(ZStreamExample.scala:53)
	at com.alterationx10.zse.ZStreamExample.run(ZStreamExample.scala:54)"

Let’s see how we can recover with orElse/orElseEither:

  override def run: ZIO[Any with ZIOAppArgs with Scope, Any, Any] =

outputs sink: 0-1-2-3-4-a-b-c. From the result, we can see that the elements of the original stream were processed up to the point of failure, and then elements of the recovery stream started to be processed afterwards.

  override def run: ZIO[Any with ZIOAppArgs with Scope, Any, Any] =
      .run(ZSink.collectAll[Either[String, String]])

outputs sink: Chunk(Left(0),Left(1),Left(2),Left(3),Left(4),Right(a),Right(b),Right(c)). The interesting thing of orElseEither is that we can distinguish the transition when failingStream failed, and recoveryStream was used, as the former values would be a Left and latter values would be Right. Note that we adjusted our ZSink here, sone the output changed from String to Either[String, String].

If we want to recover from specific failures via PartialFunction[E, ZStream[R1, E1, A1]]s we can use catchSome ( or catchSomeCause for causes - a.k.a defects).

  override def run: ZIO[Any with ZIOAppArgs with Scope, Any, Any] =
    failingStream.catchSome {
      case _: IOException => recoveryStream

  override def run: ZIO[Any with ZIOAppArgs with Scope, Any, Any] =
    defectStream.catchSomeCause {
      case Fail(e: IOException, _) => recoveryStream
      case Die(e: IndexOutOfBoundsException, _) => recoveryStream

Both of the examples above result in sink: 0-1-2-3-4-a-b-c. We can see that with catchSomeCause we can also drill into the cause, and be able to recover from errors that are not included in the error channel of our ZStream.

If we want to exhaustively recover from errors via E => ZStream[R1, E2, A1]) (and causes), we can use catchAll (or catchAllCause).

  override def run: ZIO[Any with ZIOAppArgs with Scope, Any, Any] =
    failingStream.catchAll {
      case _: IOException => recoveryStream
      case _ => ZStream("x", "y", "z")

  override def run: ZIO[Any with ZIOAppArgs with Scope, Any, Any] =
    defectStream.catchAllCause(_ => recoveryStream)

Both of the examples above result in sink: 0-1-2-3-4-a-b-c.

If we want to push our error handling downwards, we can also transform a ZStream[R, E, A] to ZStream[R, Nothing, Either[E, A]] via either.

  override def run: ZIO[Any with ZIOAppArgs with Scope, Any, Any] =
      .run(ZSink.collectAll[Either[IOException, String]])

will succeed, and output sink: Chunk(Right(0),Right(1),Right(2),Right(3),Right(4),Left(java.io.IOException: )). We can see that we have our processed values up until the failures as Rights, ending with a Left of the failure, and we did not have to provide a recovery stream. If we felt this level of processing was good enough, we could use failingStream.either.collectRight.run(sink) to get sink: 0-1-2-3-4.

Async ZStreams

So far, we’ve discussed fairly bounded streams, from defined/known collections of values. It won’t be long before we want to “feed” data into a stream we’ve previously created. Luckily, we can create a ZStream from a Queue/Hub directly, which are asynchronous data structures in ZIO. In our example, we’ll use a Queue, and with a reference to that queue we can offer values to it elsewhere. Additional, we can also create a ZSink from a Queue/Hub!

If we can set both the endpoint and the source as a queue, what if we joined two ZStreams together with the same queue? Let’s look at a toy example, where we “process” one stream to sanitize values, that we then feed that into another stream that now doesn’t have to address that concern.

After our ZStream practice above, and a reminder that businessLogic converts Strings to Ints, we might first write something like this:

  // Bad code!
  val program: ZIO[Any, Throwable, ExitCode] = for {
    queue <- Queue.unbounded[Int]
    producer <- nonFailingStream.via(businessLogic)
    result <- ZStream.fromQueue(queue)
  } yield ExitCode.success

and it will almost do what we think! We expected it to funnel all of the elements of the first ZStream through the second, and them sum them in the ZSink.sum to produce a resulting value. However, it will process the values, and feed them to the second ZStream, but our program will hang. Why? Because we’re working with asynchronous things now. Our result has processed all the elements from our producer - but it continues to wait for any new values that could be passed into the our queue. It can’t complete! We need to signal to result that we should finalize, by closing the queue. We could make a Promise, and use it to await before closing the queue, but luckily this functionality is already included with ZSink.fromQueueWithShutdown

Now, we might be tempted to write:

  // Still bad code!
  val program: ZIO[Any, Throwable, ExitCode] = for {
    queue <- Queue.unbounded[Int]
    producer <- nonFailingStream.via(businessLogic)
    result <- ZStream.fromQueue(queue)
  } yield ExitCode.success

and it will work even less than expected! We will be greeted by an exception, because our queue will be closed before we initialize the ZStream of our result with it! We need to think asynchronously top-to-bottom, and realize that we need to fork both our producer and result, and then join them, so our queue is appropriately closed after we’ve feed all of out data into it, and our second stream has opened from it to process the values in it.

  val program: ZIO[Any, Throwable, ExitCode] = for {
    queue <- Queue.unbounded[Int]
    producer <- nonFailingStream.via(businessLogic)
    result <- ZStream.fromQueue(queue)
    _ <- producer.join
    _ <- result.join
  } yield ExitCode.success

Asynchronous code can be tricky to debug, but becomes a lot easier if you recognize the scope of context you are working in - i.e Are we processing the content of a file, which is finite, or are we streaming generated events to a websocket, which could be infinite.

Example: Processing Files

Let’s walk through a simple, but working example application that parses markdown blog entries, automatically looks for tagged words, and re-generates a file with tags automatically added, and linked to their respective page. We’ll also generate a “search” file, that is a JSON object that could be used by a JavaScript front end to find all pages that use a particular tag.

The Content

We’d normally read these from files, but for the purpose of this post, we’ll have the example markdown as a String containing the sample content.

  val post1: String = "hello-word.md"
  val post1_content: Array[Byte] =
      |title: "Hello World"
      |tags: []
      |## Generic Heading
      |Even pretend blog posts need a #generic intro.

  val post2: String = "scala-3-extensions.md"
  val post2_content: Array[Byte] =
      |title: "Scala 3 for You and Me"
      |tags: []
      |## Cool Heading
      |This is a post about #Scala and their re-work of #implicits via thing like #extensions.

  val post3: String = "zio-streams.md"
  val post3_content: Array[Byte] =
      |title: "ZIO Streams: An Introduction"
      |tags: []
      |## Some Heading
      |This is a post about #Scala and #ZIO #ZStreams!

  val fileMap: Map[String, Array[Byte]] = Map(
    post1 -> post1_content,
    post2 -> post2_content,
    post3 -> post3_content

We’ll take note that we have tags: [] in the post front-matter, and our content has words prefixed with a # that we want to index on. We’ll also make a helper Map to represent how we’d call the file name to get the content later, as if we were reading from actual files.


Let’s break up our concerns, and cover a first step of collecting all the tags we’ll need, and then use those results for the blog post regeneration. Finally, we’ll put everything together into an application we can run start-to-end.

Collecting tags

Let’s outline the process of collecting our tags. The general steps we want to take are:

  1. Filter words that match our tagging pattern
  2. Remove any punctuation, in case the last work in the sentence is tag - as well as to remove the #.
  3. Convert our parsed tags to lowercase
  4. Put them all in a Set[String] to avoid duplicates.

Let’s also set up some helpers to looks for our #tag, as well as a reusable regex to remove punctuation when we need to.

Along with our ZSink, we can make compose our ZPipeline components together with the >>> operator, and bundle them together as:

  val hashFilter: String => Boolean =
    str =>
      str.startsWith("#") &&
        str.count(_ == '#') == 1 &&
        str.length > 2

  val punctRegex: Regex = """\p{Punct}""".r

  val parseHash: ZPipeline[Any, Nothing, String, String] =

  val removePunctuation: ZPipeline[Any, Nothing, String, String] =
    ZPipeline.map[String, String](str => punctRegex.replaceAllIn(str, ""))

  val lowerCase: ZPipeline[Any, Nothing, String, String] =
    ZPipeline.map[String, String](_.toLowerCase)

  val collectTagPipeline: ZPipeline[Any, CharacterCodingException, Byte, String] =
    ZPipeline.utf8Decode >>>
      ZPipeline.splitLines >>> // This removes return characters, in case our tag is at the end of the line
      ZPipeline.splitOn(" ") >>> // We want to parse word-by-word
      parseHash >>>
      removePunctuation >>>

  val collectTags: ZSink[Any, Nothing, String, Nothing, Set[String]] =

Regenerating Files

Now that we have the tags for any given file, we can regenerate our blog post. We want to

  1. Automatically inject the tags into the front-matter of the content
  2. Add a link to every #tag, which takes us to a special page on our blog that lists all posts with that tag (e.g. [#TagWord](/tag/tagword)).
  val addTags: Set[String] => ZPipeline[Any, Nothing, String, String] =
    tags =>
      ZPipeline.map[String, String](_.replace("tags: []", s"tags: [${tags.mkString(", ")}]"))

  val addLink: ZPipeline[Any, Nothing, String, String] =
    ZPipeline.map[String, String] { line =>
      line.split(" ").map { word =>
        if (hashFilter(word)) {
          s"[$word](/tag/${punctRegex.replaceAllIn(word.toLowerCase, "")})"
        } else {
      }.mkString(" ")

  val addNewLine: ZPipeline[Any, Nothing, String, String] =
    ZPipeline.map[String, String](_.appended('\n'))

  val regeneratePostPipeline: Set[String] => ZPipeline[Any, CharacterCodingException, Byte, Byte] =
    ZPipeline.utf8Decode >>>
      ZPipeline.splitLines >>> // we want to operate own whole lines this time
      addTags(_) >>>
      addLink >>>
      addNewLine >>> // since we split on out new line characters, we should add one back in
      ZPipeline.utf8Encode // Back to a format to write to a file

    val writeFile: String => ZSink[Any, Throwable, Byte, Byte, Long] =

Building or Program

With all of our processes defined, we can now build our program!

  val parseProgram: ZIO[Console, Throwable, ExitCode] = for {
    tagMap <- ZIO.foreach(fileMap) { (k, v) =>
        .map(tags => k -> tags)
    _ <- ZIO.foreachDiscard(fileMap) { kv =>
      Console.printLine(s"// Generating file ${kv._1}") *>
    _ <- Console.printLine("// Generating file search.json")
    searchMap = tagMap.values.toSet.flatten.map(t => t -> tagMap.filter(_._2.contains(t)).keys.toSet).toMap
    _ <- ZStream.fromIterable(searchMap.toJsonPretty.getBytes)
  } yield ExitCode.success

We’re just running these three samples serially, but if we wanted to speed up processing, we could do a ZIO.foreachPar to process files in parallel. Below is the content of the files we generated, and note that it’s additionally formatted from the rules being applied to this post:


title: "Hello World"
tags: [generic]


## Generic Heading

Even pretend blog posts need a [#generic](/tag/generic) intro.



title: "ZIO Streams: An Introduction"
tags: [scala, zio, zstreams]


## Some Heading

This is a post about [#Scala](/tag/scala) and [#ZIO](/tag/zio)


title: "Scala 3 for You and Me"
tags: [scala, implicits, extensions]


## Cool Heading

This is a post about [#Scala](/tag/scala) and their re-work of
[#implicits](/tag/implicits) via thing like [#extensions.](/tag/extensions)


  "zstreams": ["zio-streams.md"],
  "implicits": ["scala-3-extensions.md"],
  "generic": ["hello-word.md"],
  "extensions": ["scala-3-extensions.md"],
  "zio": ["zio-streams.md"],
  "scala": ["scala-3-extensions.md", "zio-streams.md"]

Wrapping up

Hopefully, after reading through this introduction, you are now more familiar and comfortable with ZStreams, how we can process data via ZPipelines, and finalizing the processing of data to a ZSinks - as well as methods for recovering from errors, should something go wrong during processing.

Additionally, I hope that the simple examples introducing the the prospect of working with ZStreams asynchronously, as well as the start-to-finish processing of files provides enough insight to get you started tackling your own real-world use cases.