Stream

Stream[+E, +A] is a lazy, pull-based, typed-error stream of elements that may fail with an error of type E. Nothing executes until a terminal operation is called. When you run a stream synchronously, you get Either[E, Z] — typed errors surface as Left(e), and untyped defects propagate as exceptions:

abstract class Stream[+E, +A] {
  def run[E2 >: E, Z](sink: Sink[E2, A, Z]): Either[E2, Z]
  def runCollect: Either[E, Chunk[A]]
}

Stream is purely functional, referentially transparent, and resource-safe:

Lazy: descriptions of pipelines, not eager computations
Synchronous: all terminal operations return Either[E, Z] directly (no async effects)
Pull-based: execution is driven from the sink backward through the pipeline
Typed errors: distinguish recoverable errors (E) from untyped defects (Throwable)
Resource-safe: RAII semantics ensure resources are released in all cases

Motivation

Traditional eager sequences (like Scala List) fall short in three critical dimensions. Here's what Stream[E, A] solves for each:

1. Efficiency — Wasteful Computation

With eager evaluation, the entire dataset is processed upfront, regardless of how many elements you actually need. This example shows how much work is wasted:

// With Scala List (eager evaluation)
val data = (1 to 1_000_000).toList
val result = data
  .map(_ * 2)          // eagerly: 1M multiplications
  .filter(_ > 10)      // eagerly: 1M comparisons
  .take(10)            // finally: keep only 10
// ❌ Wasted work: computed and discarded 999,990 elements!

The problem: List eagerly applies .map and .filter to all 1 million elements, even though only the first 10 passing elements matter. In data processing pipelines (parsing CSV files, filtering logs, transforming sensor streams), this is enormously wasteful.

With Stream[E, A], the architecture is inverted: the sink (consumer) pulls from the stream. If the sink asks for only 10 elements, only ~20 calculations occur (enough to find 10 valid results after filtering):

import zio.blocks.streams.*

// With Stream (lazy, pull-based evaluation)
val result: Either[Nothing, zio.blocks.chunk.Chunk[Int]] =
  Stream.fromRange((1 to 100))
    .map(_ * 2)
    .filter(_ > 10)
    .run(Sink.take(10))
// ✓ Computation stops after 10 valid elements are produced
// Only necessary work: ~20 multiplications, ~20 comparisons

This short-circuiting behavior is automatic and requires no special syntax.

2. Resource Management — Error-Prone Cleanup

When you open resources (file handles, network connections, database cursors), you must release them in all code paths—success, error, and even mid-stream cancellation. With eager sequences, this burden falls on the caller:

// ❌ With traditional Scala (manual resource management, uses var for mutable state)
import java.io.*

var file: BufferedReader = null
try {
  file = new BufferedReader(new FileReader("build.sbt"))
  var count = 0L
  var char = file.read()
  while (char != -1) {
    if (!Character.isWhitespace(char)) {
      count += 1
    }
    char = file.read()
  }
  count
} catch {
  case e: IOException =>
    throw e
} finally {
  if (file != null) file.close()  // ✓ Manual cleanup in finally
}
// ❌ Problem: You must remember the finally block
// ❌ Problem: If an exception occurs in the loop, cleanup must still run (easy to forget!)
// ❌ Problem: Scale to 10 resources? 50 resources? Manually nesting becomes error-prone

With Stream[E, A], resource cleanup is automatic, composable, and guaranteed—even on error or if the sink cancels early:

import zio.blocks.streams.*
import java.io.*

// With Stream (resource-safe RAII)
// Open a file and count non-whitespace characters
val charCount: Either[IOException, Long] =
  Stream
    .fromJavaReader(new FileReader("build.sbt"))  // lazily acquires file handle
    .filter(!_.isWhitespace)  // process only non-whitespace
    .count  // count all matching characters
// ✓ File automatically closes in finally block (success or error)
// ✓ If FileReader throws, or filter throws, or count throws—cleanup still runs
// ✓ No manual try/finally needed; no resource leak risk
// ✓ Multiple resources (files, connections, etc.) compose naturally

The key difference: Stream releases resources via RAII (Resource Acquisition Is Initialization) — the resource's lifetime is bound to the compiled stream's close() method, which the terminal operation (run) always calls in a finally block.

3. Error Handling — Untyped Errors

Traditional error handling conflates two categories: recoverable domain errors (e.g., parsing failed, validation failed) and fatal defects (e.g., OutOfMemoryError, NullPointerException). This makes it hard to write correct error recovery code:

import scala.util.Try

// With Try/catch (untyped errors)
case class ParseError(msg: String)

def parseLines(lines: List[String]): Try[List[Int]] = Try {
  lines.map { line =>
    line.toInt  // throws NumberFormatException (defect, not domain error!)
  }
}

val result = parseLines(List("1", "abc", "3"))
result match {
  case util.Success(nums) => println(s"Parsed: $nums")
  case util.Failure(e) =>
    // ❌ Can't tell if 'e' is a parse error or a JVM defect
    // ❌ Must handle *all* exceptions the same way
    // ❌ Domain logic mixed with system-level exception handling
    println(s"Error: $e")
}

With Stream[E, A], typed errors (E) are distinct from untyped defects (Throwable), enabling proper error recovery:

import zio.blocks.streams.*

case class ParseError(msg: String)

// With Stream (typed errors)
val result: Either[ParseError, zio.blocks.chunk.Chunk[Int]] =
  Stream
    .fromIterable(List("1", "abc", "3"))
    .flatMap { line =>
      try {
        Stream.succeed(line.toInt)  // success path
      } catch {
        case _: NumberFormatException =>
          Stream.fail(ParseError(s"Not a number: $line"))  // typed error
      }
    }
    .runCollect

result match {
  case Left(parseError) =>
    // ✓ This branch is *only* for domain errors we chose to surface
    println(s"Parse error: ${parseError.msg}")
  case Right(nums) =>
    // ✓ Untyped defects (OutOfMemoryError, etc.) propagate as exceptions
    // ✓ Clear separation: Either[E, Z] is for recovery, uncaught exceptions are fatal
    println(s"Parsed: ${nums}")
}

The key distinction: Either[ParseError, Z] means domain errors are recoverable via Left; any uncaught Throwable defect propagates as an exception, which is correct—you cannot recover from running out of memory, only from bad input.

Construction

Streams can be created from constants, collections, resources, and pull-based sources:

Constant Streams

The simplest streams are single-element or empty streams.

`Stream.empty`

An empty stream that emits no elements and succeeds immediately:

object Stream {
  val empty: Stream[Nothing, Nothing]
}

The empty stream is useful as a base case in recursive stream builders or as a neutral element when concatenating:

import zio.blocks.streams.*

val emptyStream = Stream.empty
// emptyStream: Stream[Nothing, Nothing] = Stream.empty
val result = emptyStream.runCollect
// result: Either[Nothing, Chunk[Nothing]] = Right(IndexedSeq())
// emptyStream contains no elements

`Stream.succeed[A]`

Wraps a single value of any type. Specialized overloads avoid boxing for primitives:

object Stream {
  def succeed[A](a: A): Stream[Nothing, A]
  def succeed(a: Int): Stream[Nothing, Int]
  def succeed(a: Long): Stream[Nothing, Long]
  def succeed(a: Double): Stream[Nothing, Double]
  // ... and Byte, Short, Char, Float, Boolean variants
}

When you call Stream.succeed(value), the stream emits exactly one element and completes successfully. This is useful for wrapping a computed value into the stream abstraction:

import zio.blocks.streams.*

val singleElement = Stream.succeed(42)
// singleElement: Stream[Nothing, Int] = Stream.succeed(...)
val result = singleElement.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(42))

`Stream.fail[E]`

Creates a stream that fails immediately with a typed error:

object Stream {
  def fail[E](error: E): Stream[E, Nothing]
}

Use fail when you need to short-circuit a stream with a known error:

import zio.blocks.streams.*

sealed trait ApiError
case class NotFound(id: String) extends ApiError

val failedStream = Stream.fail(NotFound("user-123"))
// failedStream: Stream[NotFound, Nothing] = Stream.fail(...)
val result = failedStream.runDrain
// result: Either[NotFound, Unit] = Left(NotFound("user-123"))
// result is Left(NotFound("user-123"))

`Stream.die`

Throws an untyped defect (exception) immediately:

object Stream {
  def die(t: Throwable): Stream[Nothing, Nothing]
}

Use die for truly exceptional, unrecoverable conditions that should not be caught as typed errors:

import zio.blocks.streams.*

val dieStream = Stream.die(new Exception("System failure"))
// dieStream: Stream[Nothing, Nothing] = Stream.die(...)

From Collections

Streams can be created from existing collections and iterables, making it easy to convert List, Array, Chunk, or custom iterables into lazy streams:

`Stream.apply[A]`

Wraps a variable number of arguments into a stream:

object Stream {
  def apply[A](as: A*): Stream[Nothing, A]
}

This is the most natural way to lift a list of values:

import zio.blocks.streams.*

val numbers = Stream(1, 2, 3, 4, 5)
// numbers: Stream[Nothing, Int] = Stream(1, 2, 3, 4, 5)
val result = numbers.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(1, 2, 3, 4, 5))

`Stream.fromChunk[A]`

Converts a Chunk into a stream. Chunks are immutable, indexed sequences optimized for high-performance operations:

object Stream {
  def fromChunk[A](chunk: Chunk[A]): Stream[Nothing, A]
}

Use this when you already have a Chunk:

import zio.blocks.streams.*
import zio.blocks.chunk.Chunk

val chunk = Chunk(10, 20, 30)
// chunk: Chunk[Int] = IndexedSeq(10, 20, 30)
val stream = Stream.fromChunk(chunk)
// stream: Stream[Nothing, Int] = Stream.fromChunk(...)
val result = stream.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(10, 20, 30))

`Stream.fromIterable[A]`

Converts any Iterable[A] (List, Set, Vector, etc.) into a stream:

object Stream {
  def fromIterable[A](it: Iterable[A]): Stream[Nothing, A]
}

This is useful when integrating with legacy Scala collections:

import zio.blocks.streams.*

val list = List("a", "b", "c")
// list: List[String] = List("a", "b", "c")
val stream = Stream.fromIterable(list)
// stream: Stream[Nothing, String] = Stream.fromIterable(...)
val result = stream.runCollect
// result: Either[Nothing, Chunk[String]] = Right(IndexedSeq("a", "b", "c"))

`Stream.fromIterator[A]`

Converts an Iterator[A] into a stream. The iterator is consumed lazily:

object Stream {
  def fromIterator[A](it: => Iterator[A]): Stream[Nothing, A]
}

Create a stream from an iterator and collect all elements:

import zio.blocks.streams.*

val iter = Iterator(10, 20, 30, 40)
// iter: Iterator[Int] = empty iterator
val stream = Stream.fromIterator(iter)
// stream: Stream[Nothing, Int] = Stream.fromIterator(...)
val result = stream.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(10, 20, 30, 40))

From Ranges

Streams can be created from numeric ranges, providing an efficient way to generate sequences of integers without allocating memory upfront:

`Stream.range`

Emits integers from from (inclusive) to until (exclusive):

object Stream {
  def range(from: Int, until: Int): Stream[Nothing, Int]
}

This is memory-efficient (does not allocate intermediate collections):

import zio.blocks.streams.*

val nums = Stream.range(0, 5)
// nums: Stream[Nothing, Int] = Stream.range(0, 5)
val result = nums.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(0, 1, 2, 3, 4))

`Stream.fromRange`

Converts a Scala Range object:

object Stream {
  def fromRange(range: Range): Stream[Nothing, Int]
}

Create a stream from a Range and collect elements:

import zio.blocks.streams.*

val range = 1 to 10 by 2
// range: Range = Range(1, 3, 5, 7, 9)
val stream = Stream.fromRange(range)
// stream: Stream[Nothing, Int] = Stream.fromRange(...)
val result = stream.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(1, 3, 5, 7, 9))

Generators

These constructors create streams from functions and logic, useful for synthesizing infinite or computed sequences:

`Stream.repeat[A]`

Emits the same value infinitely:

object Stream {
  def repeat[A](a: A): Stream[Nothing, A]
}

Infinite streams are safe because streams are lazy; nothing runs until you call a terminal operation with a stopping condition (like take):

import zio.blocks.streams.*

val infinite = Stream.repeat(42)
// infinite: Stream[Nothing, Int] = Stream.repeat(...)
val first5 = infinite.take(5)
// first5: Stream[Nothing, Int] = Stream.repeat(...).take(5)
val result = first5.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(42, 42, 42, 42, 42))

`Stream.unfold[S, A]`

A stateful generator that emits elements based on a fold-like transition function:

object Stream {
  def unfold[S, A](s: S)(f: S => Option[(A, S)]): Stream[Nothing, A]
}

Each iteration, f receives the current state and returns either None (stop) or Some((element, nextState)). This is useful for generating Fibonacci numbers or other sequences defined by a recurrence relation:

import zio.blocks.streams.*

val fibonacci = Stream.unfold((0, 1)) {
  case (a, b) => Some((a, (b, a + b)))
}
// fibonacci: Stream[Nothing, Int] = Stream.unfold(...)
val first10 = fibonacci.take(10)
// first10: Stream[Nothing, Int] = Stream.unfold(...).take(10)
val result = first10.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(
//   IndexedSeq(0, 1, 1, 2, 3, 5, 8, 13, 21, 34)
// )

Side Effects

These constructors embed effects and deferred computation into streams, running actions at stream execution time:

`Stream.eval[A]`

Runs an arbitrary side effect and emits nothing:

object Stream {
  def eval(f: => Any): Stream[Nothing, Nothing]
}

Use eval when you want a side effect in a stream (e.g., logging, metrics) but no element:

import zio.blocks.streams.*

val sideEffect = Stream.eval(println("Executing side effect"))
// sideEffect: Stream[Nothing, Nothing] = Stream.suspend(...)
val result = sideEffect.runDrain
// Executing side effect
// result: Either[Nothing, Unit] = Right(())

`Stream.attempt[A]`

Wraps a potentially throwing computation, converting non-fatal Throwables into a typed error. Fatal errors (like OutOfMemoryError) are not caught and propagate as exceptions:

object Stream {
  def attempt[A](f: => A): Stream[Throwable, A]
}

Use attempt when you have legacy code that throws exceptions:

import zio.blocks.streams.*

def unsafeJsonParse(s: String): Int = s.toInt

val parsed = Stream.attempt(unsafeJsonParse("42"))
// parsed: Stream[Throwable, Int] = Stream.suspend(...)
val result = parsed.runCollect
// result: Either[Throwable, Chunk[Int]] = Right(IndexedSeq(42))

`Stream.attemptEval`

Evaluates a side effect and converts any thrown exception into a typed Throwable error. Unlike eval, this captures exceptions and emits nothing:

object Stream {
  def attemptEval(f: => Any): Stream[Throwable, Nothing]
}

Use attemptEval when you need to safely execute an effect that might throw, but you don't need to emit any elements:

import zio.blocks.streams.*

val effect = Stream.attemptEval {
  val file = new java.io.File("nonexistent.txt")
  if (!file.exists()) throw new java.io.FileNotFoundException("File not found")
}
val result = effect.runDrain

`Stream.defer[A]`

Defers the execution of a side effect until the stream is run:

object Stream {
  def defer(f: => Unit): Stream[Nothing, Nothing]
}

Defer side effects until the stream executes:

import zio.blocks.streams.*

val deferred = Stream.defer(println("Effect runs when stream executes"))
// deferred: Stream[Nothing, Nothing] = Stream.defer(...)
val result = deferred.runDrain
// Effect runs when stream executes
// result: Either[Nothing, Unit] = Right(())

`Stream.suspend[E, A]`

Defers the creation of a stream until run time, useful for recursive stream definitions:

object Stream {
  def suspend[E, A](stream: => Stream[E, A]): Stream[E, A]
}

Define a recursive stream safely:

import zio.blocks.streams.*

def countDown(n: Int): Stream[Nothing, Int] =
  if (n <= 0) Stream.empty
  else Stream.suspend(Stream.succeed(n) ++ countDown(n - 1))

val result = countDown(5).runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(5, 4, 3, 2, 1))

I/O

Streams can read from external I/O sources like files and readers, automatically managing resource cleanup:

`Stream.fromInputStream`

Reads bytes from a Java InputStream, managing the resource:

object Stream {
  def fromInputStream(is: java.io.InputStream): Stream[java.io.IOException, Int]
}

The stream automatically closes the input stream when done:

import zio.blocks.streams.*
import java.io.ByteArrayInputStream

val data = new ByteArrayInputStream("Hello".getBytes)
// data: ByteArrayInputStream = java.io.ByteArrayInputStream@76fa9d8c
val bytes = Stream.fromInputStream(data)
// bytes: Stream[IOException, Int] = Stream.fromAcquireRelease(...)
val result = bytes.runCollect
// result: Either[IOException, Chunk[Int]] = Right(
//   IndexedSeq(72, 101, 108, 108, 111)
// )

`Stream.fromJavaReader`

Reads characters from a Java Reader:

object Stream {
  def fromJavaReader(r: java.io.Reader): Stream[java.io.IOException, Char]
}

Read characters from a string reader:

import zio.blocks.streams.*
import java.io.StringReader

val reader = new StringReader("hello world")
// reader: StringReader = java.io.StringReader@14706a8e
val stream = Stream.fromJavaReader(reader)
// stream: Stream[IOException, Char] = Stream.fromAcquireRelease(...)
val result = stream.runCollect
// result: Either[IOException, Chunk[Char]] = Right(
//   IndexedSeq('h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd')
// )

`Stream.fromInputStreamUnmanaged`

Reads bytes from a Java InputStream without automatic resource management. The caller is responsible for closing the stream:

object Stream {
  def fromInputStreamUnmanaged(is: java.io.InputStream): Stream[java.io.IOException, Int]
}

Use this when you need to manage the stream's lifecycle yourself, for example when the stream is created from a long-lived resource:

import zio.blocks.streams.*
import java.io.ByteArrayInputStream

val data = new ByteArrayInputStream("Data".getBytes)
val bytes = Stream.fromInputStreamUnmanaged(data)
val result = bytes.runCollect
// Caller must close data when done

`Stream.fromJavaReaderUnmanaged`

Reads characters from a Java Reader without automatic resource management. The caller is responsible for closing the reader:

object Stream {
  def fromJavaReaderUnmanaged(r: java.io.Reader): Stream[java.io.IOException, Char]
}

Use this when you need to manage the reader's lifecycle yourself:

import zio.blocks.streams.*
import java.io.StringReader

val reader = new StringReader("managed externally")
val stream = Stream.fromJavaReaderUnmanaged(reader)
val result = stream.runCollect
// Caller must close reader when done

Transformations

Streams provide powerful operations for transforming elements, flattening nested structures, filtering, and managing state:

Element-wise Transformations

These operations apply functions to stream elements one-by-one, applying the transformation lazily as elements are pulled:

`Stream#map[B]`

Applies a function to each element:

trait Stream[+E, +A] {
  def map[B](f: A => B): Stream[E, B]
}

map does not run immediately; it builds up a description of the transformation. Only when you call a terminal operation does the mapping happen:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3)
val doubled = nums.map(_ * 2)
// doubled: Stream[Nothing, Int] = Stream(1, 2, 3).map(...)
val result = doubled.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(2, 4, 6))

Key point: Stream#map is covariant in the output type because it preserves the error type and only transforms elements. The implicit JvmType.Infer[A] and JvmType.Infer[B] enable compile-time dispatch to unboxed fast paths for primitive types (Int, Long, Double, etc.).

`Stream#mapError[E2]`

Transforms typed errors without affecting elements:

trait Stream[+E, +A] {
  inline def mapError[E2](f: E => E2): Stream[E2, A]
}

Use mapError to convert one error type to another:

import zio.blocks.streams.*

sealed trait ApiError
case class ServerError(msg: String) extends ApiError
case class NetworkError() extends ApiError

val mayFail: Stream[NetworkError, String] = Stream.fail(NetworkError())
val mapped = mayFail.mapError(e => ServerError("Connection failed"))

`Stream#filter`

Emits only elements that satisfy a predicate:

trait Stream[+E, +A] {
  def filter(pred: A => Boolean): Stream[E, A]
}

Short-circuits: as soon as the sink says "stop," filtering stops:

import zio.blocks.streams.Stream

val nums = Stream(1, 2, 3, 4, 5)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3, 4, 5)
val evens = nums.filter(_ % 2 == 0)
// evens: Stream[Nothing, Int] = Stream(1, 2, 3, 4, 5).filter(...)
val result = evens.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(2, 4))

`Stream#collect[B]`

Applies a partial function, emitting only defined results:

trait Stream[+E, +A] {
  def collect[B](pf: PartialFunction[A, B]): Stream[E, B]
}

This combines filtering and mapping in one step:

import zio.blocks.streams.*

val mixed = Stream(1, "a", 2, "b", 3)
// mixed: Stream[Nothing, Int | String] = Stream(1, a, 2, b, 3)
val numbers = mixed.collect { case n: Int => n }
// numbers: Stream[Nothing, Int] = Stream(1, a, 2, b, 3).collect(...)
val result = numbers.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(1, 2, 3))

Stateful Transformations

These operations maintain internal state while processing elements, allowing you to fold computations into the transformation:

`Stream#mapAccum[S, B]`

Maintains state while transforming each element:

trait Stream[+E, +A] {
  def mapAccum[S, B](init: S)(f: (S, A) => (S, B)): Stream[E, B]
}

mapAccum threads a state value through the transformation. At each step, you receive the current state and the element, return a new state and output element:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3)
val indexed = nums.mapAccum(0)((idx, x) => (idx + 1, (idx, x)))
// indexed: Stream[Nothing, Tuple2[Int, Int]] = Stream.suspend(...)
val result = indexed.runCollect
// result: Either[Nothing, Chunk[Tuple2[Int, Int]]] = Right(
//   IndexedSeq((0, 1), (1, 2), (2, 3))
// )

`Stream#scan[S]`

Like mapAccum, but also emits the state at each step (not the mapped value):

trait Stream[+E, +A] {
  def scan[S](init: S)(f: (S, A) => S): Stream[E, S]
}

This is useful for computing running totals, moving averages, or other cumulative statistics:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3, 4)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3, 4)
val cumsum = nums.scan(0)(_ + _)
// cumsum: Stream[Nothing, Int] = Stream(1, 2, 3, 4).scan(...)
val result = cumsum.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(0, 1, 3, 6, 10))

Flat-Mapping (Nested Streams)

flatMap[E2, B] — Maps each element to a stream and flattens the results.:

trait Stream[+E, +A] {
  def flatMap[E2, B](f: A => Stream[E2, B]): Stream[E | E2, B]
}

Stream#flatMap is sequential: streams are processed one at a time, in order. This is essential for resource safety: if each inner stream acquires a resource, Stream#flatMap ensures they are released in proper FIFO order:

import zio.blocks.streams.*

val ids = Stream(1, 2, 3)
// ids: Stream[Nothing, Int] = Stream(1, 2, 3)
val expanded = ids.flatMap(id => Stream(s"${id}-a", s"${id}-b"))
// expanded: Stream[Nothing, String] = Stream(1, 2, 3).flatMap(...)
val result = expanded.runCollect
// result: Either[Nothing, Chunk[String]] = Right(
//   IndexedSeq("1-a", "1-b", "2-a", "2-b", "3-a", "3-b")
// )

`Stream.flattenAll[E, A]`

Flattens a stream of streams into a single stream, processing them sequentially:

object Stream {
  def flattenAll[E, A](streams: Stream[E, Stream[E, A]]): Stream[E, A]
}

This is equivalent to flatMap(identity). Use flattenAll when you already have a stream of streams and want to flatten it without applying a transformation:

import zio.blocks.streams.*

val nested = Stream.fromIterable(List(
  Stream(1, 2),
  Stream(3, 4)
))
// nested: Stream[Nothing, Stream[Nothing, Int]] = Stream.fromIterable(...)
val flat = Stream.flattenAll(nested)
// flat: Stream[Nothing, Int] = Stream.fromIterable(...).flatMap(...)
val result = flat.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(1, 2, 3, 4))

Windowing

Streams can be grouped, sliced, and scanned to process data in temporal windows. These operations group elements into chunks and slide windows over the stream for batch processing:

`Stream#grouped[A]`

Collects elements into fixed-size chunks:

trait Stream[+E, +A] {
  def grouped(n: Int): Stream[E, Chunk[A]]
}

The last chunk may contain fewer than n elements:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3, 4, 5)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3, 4, 5)
val groups = nums.grouped(2)
// groups: Stream[Nothing, Chunk[Int]] = Stream(1, 2, 3, 4, 5).grouped(2)
val result = groups.runCollect
// result: Either[Nothing, Chunk[Chunk[Int]]] = Right(
//   IndexedSeq(IndexedSeq(1, 2), IndexedSeq(3, 4), IndexedSeq(5))
// )

`Stream#sliding[A]`

Creates a sliding window of size n, optionally stepping by step elements:

trait Stream[+E, +A] {
  def sliding(n: Int, step: Int = 1): Stream[E, Chunk[A]]
}

This is useful for computing local statistics or detecting patterns in sequences:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3, 4, 5)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3, 4, 5)
val windows = nums.sliding(3, step = 1)
// windows: Stream[Nothing, Chunk[Int]] = Stream(1, 2, 3, 4, 5).sliding(3, 1)
val result = windows.runCollect
// result: Either[Nothing, Chunk[Chunk[Int]]] = Right(
//   IndexedSeq(IndexedSeq(1, 2, 3), IndexedSeq(2, 3, 4), IndexedSeq(3, 4, 5))
// )

Combining Streams

Streams can be sequentially concatenated, zipped together, or merged:

Sequential Concatenation

++[E2, A2] or concat[E2, A2] — Emits all elements of the first stream, then all elements of the second stream.:

trait Stream[+E, +A] {
  def ++[E2, A2](that: Stream[E2, A2]): Stream[E | E2, A | A2] = concat(that)
}

The error type is the union of both streams' error types. Evaluation is sequential: the second stream only starts when the first completes:

import zio.blocks.streams.*

val first = Stream(1, 2)
// first: Stream[Nothing, Int] = Stream(1, 2)
val second = Stream(3, 4)
// second: Stream[Nothing, Int] = Stream(3, 4)
val combined = first ++ second
// combined: Stream[Nothing, Int] = Stream(1, 2) ++ Stream(3, 4)
val result = combined.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(1, 2, 3, 4))

Zipping

Zips two streams together as tuples (an extension method, not an instance method):

extension [E, A](stream: Stream[E, A])
  def &&[E2, B, C](that: Stream[E2, B])(
    using Tuples[A, B] { Out = C }
  ): Stream[E | E2, C]

The result streams have the same length as the shorter input:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3)
val chars = Stream('a', 'b')
// chars: Stream[Nothing, Char] = Stream(a, b)
val zipped = nums && chars
// zipped: Stream[Nothing, Tuple2[Int, Char]] = Stream.fromReader(...)
val result = zipped.runCollect
// result: Either[Nothing, Chunk[Tuple2[Int, Char]]] = Right(
//   IndexedSeq((1, 'a'), (2, 'b'))
// )

Other Operations

Common utilities for deduplication, draining, and error recovery:

Filtering Duplicates

These operations remove duplicate elements, useful for deduplicating streams before processing:

`Stream#distinct[A]`

Emits only unique elements (using a mutable HashSet internally):

trait Stream[+E, +A] {
  def distinct(implicit jtA: JvmType.Infer[A]): Stream[E, A]
}

This consumes memory proportional to the number of unique elements:

import zio.blocks.streams.*

val nums = Stream(1, 2, 2, 3, 3, 3)
// nums: Stream[Nothing, Int] = Stream(1, 2, 2, 3, 3, ...)
val unique = nums.distinct
// unique: Stream[Nothing, Int] = Stream.suspend(...)
val result = unique.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(1, 2, 3))

`Stream#distinctBy[K]`

Emits only elements whose key (computed by f) has not been seen before:

trait Stream[+E, +A] {
  def distinctBy[K](f: A => K)(implicit jtA: JvmType.Infer[A]): Stream[E, A]
}

This deduplicates elements by a computed key, keeping only the first occurrence of each key:

import zio.blocks.streams.*

case class Person(id: Int, name: String)

val people = Stream(
  Person(1, "Alice"),
  Person(2, "Bob"),
  Person(1, "Alice2"),  // same id as first, dropped
  Person(3, "Charlie")
)

val unique = people.distinctBy(_.id)
val result = unique.runCollect

Skipping and Taking

These operations skip or limit elements, allowing you to keep or drop unwanted portions of the stream:

`Stream#drop`

Skips the first n elements:

trait Stream[+E, +A] {
  def drop(n: Long): Stream[E, A]
}

Dropping the first 3 elements and collecting the remainder:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
val remaining = nums.drop(3)
val result = remaining.runCollect

`Stream#take`

Emits at most the first n elements, then stops:

trait Stream[+E, +A] {
  def take(n: Long): Stream[E, A]
}

This naturally short-circuits: the stream stops pulling from upstream:

import zio.blocks.streams.*

val nums = Stream.range(0, 1000)
// nums: Stream[Nothing, Int] = Stream.range(0, 1000)
val first10 = nums.take(10)
// first10: Stream[Nothing, Int] = Stream.range(0, 1000).take(10)
val result = first10.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(
//   IndexedSeq(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
// )

`Stream#takeWhile`

Emits elements while a predicate is true, then stops:

trait Stream[+E, +A] {
  def takeWhile(pred: A => Boolean): Stream[E, A]
}

Taking elements while they are less than 6 stops early without processing the rest:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3, 4, 5, ...)
val firstFive = nums.takeWhile(_ < 6)
// firstFive: Stream[Nothing, Int] = Stream(1, 2, 3, 4, 5, ...).takeWhile(...)
val result = firstFive.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(1, 2, 3, 4, 5))

Interspersing

intersperse[A1 >: A] — Inserts a separator value between every two elements.:

trait Stream[+E, +A] {
  def intersperse[A1 >: A](sep: A1): Stream[E, A1]
}

This is useful for rendering comma-separated lists or row delimiters:

import zio.blocks.streams.*

val items = Stream("a", "b", "c")
// items: Stream[Nothing, String] = Stream(a, b, c)
val separated = items.intersperse(", ")
// separated: Stream[Nothing, String] = Stream(a, b, c).intersperse(...)
val result = separated.runCollect
// result: Either[Nothing, Chunk[String]] = Right(
//   IndexedSeq("a", ", ", "b", ", ", "c")
// )

Repeating

repeated — Repeats each element once, then emits the entire stream again, repeatedly.:

trait Stream[+E, +A] {
  def repeated: Stream[E, A]
}

This creates an infinite repetition of the stream:

import zio.blocks.streams.*

val original = Stream(1, 2)
// original: Stream[Nothing, Int] = Stream(1, 2)
val repeated = original.repeated.take(6)
// repeated: Stream[Nothing, Int] = Stream(1, 2).repeated.take(6)
val result = repeated.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(1, 2, 1, 2, 1, 2))

Side Effects

tapEach — Applies a function to each element for side effects, passing the element through unchanged.:

trait Stream[+E, +A] {
  def tapEach(f: A => Unit)(implicit jtA: JvmType.Infer[A]): Stream[E, A]
}

Use tapEach for logging or metrics:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3)
val logged = nums.tapEach(x => println(s"Element: $x"))
// logged: Stream[Nothing, Int] = Stream(1, 2, 3).map(...)
val result = logged.runCollect
// Element: 1
// Element: 2
// Element: 3
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(1, 2, 3))

Error Handling

Streams distinguish between recoverable business errors and unexpected exceptions, providing separate recovery mechanisms for each:

Typed Error vs Untyped Defect

ZIO Blocks distinguishes two error channels:

Typed errors (E): Recoverable business logic errors. Returned as Left(e) from terminal operations.
Untyped defects (Throwable): Unexpected exceptions (bugs, system failures). Propagate as thrown exceptions.

Internally, typed errors are wrapped in StreamError (a non-fatal exception) and caught by the terminal operation to surface as Left(e). Untyped Throwables are not caught and propagate upward.

This separation allows you to:

Use catchAll and orElse for business logic errors
Use catchDefect or try-catch for unexpected exceptions
Avoid accidentally silencing real bugs by catching all errors

Streams distinguish between recoverable domain errors and fatal defects, with flexible recovery patterns:

Recovering from Typed Errors

These operations handle typed errors gracefully by recovering with alternative streams:

`Stream#catchAll[E2, A1]`

Recovers from any typed error by switching to a recovery stream:

trait Stream[+E, +A] {
  def catchAll[E2, A1](f: E => Stream[E2, A1]): Stream[E2, A | A1]
}

The recovery function receives the error and can return a new stream:

import zio.blocks.streams.*

sealed trait Error
case object NotFound extends Error

val mayFail: Stream[Error, String] = Stream.fail(NotFound)
// mayFail: Stream[Error, String] = Stream.fail(...)
val recovered = mayFail.catchAll(_ => Stream.succeed("default"))
// recovered: Stream[Nothing, String] = Stream.fail(...).catchAll(...)
val result = recovered.runCollect
// result: Either[Nothing, Chunk[String]] = Right(IndexedSeq("default"))

`Stream#orElse[E2, A1]`

If this stream fails, tries the fallback stream. The fallback is evaluated lazily, only on error:

trait Stream[+E, +A] {
  def orElse[E2, A1](that: => Stream[E2, A1]): Stream[E2, A | A1]
}

|| is an alias for orElse:

import zio.blocks.streams._

val primary = Stream.fail("error")
// primary: Stream[String, Nothing] = Stream.fail(...)
val fallback = Stream.succeed(42)
// fallback: Stream[Nothing, Int] = Stream.succeed(...)
val result = (primary || fallback).runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(42))

Recovering from Defects

catchDefect[E1, A1] — Catches untyped defects (exceptions not wrapped as typed errors) using a partial function.:

trait Stream[+E, +A] {
  def catchDefect[E1, A1](
    f: PartialFunction[Throwable, Stream[E1, A1]]
  ): Stream[E | E1, A | A1]
}

Use catchDefect when you need to handle unexpected exceptions that were not wrapped by attempt:

import zio.blocks.streams.*

val risky = Stream.die(new IllegalArgumentException("Not allowed"))
val safe = risky.catchDefect {
  case e: IllegalArgumentException => Stream.succeed(-1)
}
val result = safe.runCollect

Resource Management

The Problem: Resources like files, database connections, and network sockets must be explicitly closed after use. If you just process them in a stream and forget to close, you leak resources. If an error occurs during processing, manual cleanup code might be skipped.

The Solution: ZIO Blocks streams provide three patterns for safe, automatic resource cleanup:

`Stream.fromAcquireRelease[R, E, A]`

Acquires a resource, uses it in a stream, and guarantees cleanup regardless of success or failure:

object Stream {
  def fromAcquireRelease[R, E, A](
    acquire: => R,                                    // How to open the resource
    release: R => Unit = (r: R) =>                    // How to close it (defaults to .close())
      r match { 
        case ac: AutoCloseable => ac.close()
        case _ => ()
      }
  )(use: R => Stream[E, A]): Stream[E, A]
}

This is the fundamental pattern for safe resource handling:

Acquire — opens the resource (runs once, before streaming)
Use — streams elements from the resource
Release — closes the resource in a finally block (always runs, even on error)

Here's an example with automatic cleanup:

import zio.blocks.streams.*

case class DatabaseConnection(id: String) {
  def close(): Unit = println(s"Closing connection $id")
  def query(q: String): List[String] = List("result1", "result2")
}

val managed = Stream.fromAcquireRelease(
  acquire = {
    println("Opening database connection")
    DatabaseConnection("db-1")
  },
  release = _.close()  // Guaranteed to run even if streaming fails
)(conn => Stream.fromIterable(conn.query("SELECT *")))

val result = managed.runCollect
// Output:
// Opening database connection
// Closing database connection  <-- always happens

Even if the stream fails, cleanup runs:

import zio.blocks.streams.*

val managed = Stream.fromAcquireRelease(
  acquire = { println("Opening"); "resource" },
  release = { r => println(s"Closing $r") }
)(_ => Stream.fail("error occurred"))

val result = managed.runCollect
// Output:
// Opening
// Closing resource  <-- cleanup still runs even with error
// result: Either[String, Chunk[Nothing]] = Left("error occurred")

`Stream.fromResource[R, E, A]`

Uses a ZIO Blocks Resource[R] (more abstract, composable resource type) within a stream:

object Stream {
  def fromResource[R, E, A](resource: Resource[R])(use: R => Stream[E, A]): Stream[E, A]
}

Use fromResource when you already have a Resource value, or when you need resource composition. The resource is acquired at stream start and released when the stream terminates:

import zio.blocks.streams.*
import zio.blocks.scope.Resource

val resource = Resource.acquireRelease(acquire = {
  println("Acquiring resource")
  42
})(release = { value =>
  println(s"Releasing resource with value: $value")
})

val stream = Stream.fromResource(resource) { value =>
  Stream(value, value * 2, value * 3)
}

val result = stream.runCollect

`Stream#ensuring`

Adds a cleanup action to any stream, regardless of how it is created. The finalizer runs in a finally block:

trait Stream[+E, +A] {
  def ensuring(finalizer: => Unit): Stream[E, A]
}

Use ensuring for simple cleanup tasks that don't fit the acquire-release pattern:

import zio.blocks.streams.*

val stream = Stream(1, 2, 3)
  .ensuring {
    println("Stream finished (success or error)")
  }

val result = stream.runCollect

The finalizer always runs, in a finally block:

import zio.blocks.streams.*

val managed = Stream(1, 2, 3)
  .ensuring { println("Cleaned up") }

val result = managed.runCollect

Running Streams

All terminal operations are synchronous and return Either[E, Z]. The error type is the union of the stream's error type and any sink-specific error type.

Collecting Results

These operations accumulate or examine stream results, running the entire stream to completion:

`Stream#runCollect`

Collects all elements into a Chunk[A]:

trait Stream[+E, +A] {
  def runCollect: Either[E, Chunk[A]]
}

This is the most common terminal operation for extracting results:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3, 4, 5)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3, 4, 5)
val result = nums.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(1, 2, 3, 4, 5))
// result is Right(Chunk(1, 2, 3, 4, 5))

`Stream#run[E2, Z]`

Runs the stream with a custom sink, producing result Z:

trait Stream[+E, +A] {
  def run[E2 >: E, Z](sink: Sink[E2, A, Z]): Either[E2, Z]
}

Use run when you need a specialized sink operation:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3, 4, 5)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3, 4, 5)
val sum = nums.run(Sink.foldLeft(0)((acc, x) => acc + x))
// sum: Either[Nothing, Int] = Right(15)
// sum is Right(15)

Discarding Results

These operations consume streams without collecting their elements, useful when you only care about side effects:

`Stream#runDrain`

Consumes all elements and discards them, returning Unit:

trait Stream[+E, +A] {
  def runDrain: Either[E, Unit]
}

Use runDrain when you only care about side effects:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3)
val sideEffect = nums.tapEach(x => println(s"Processing $x"))
// sideEffect: Stream[Nothing, Int] = Stream(1, 2, 3).map(...)
val result = sideEffect.runDrain
// Processing 1
// Processing 2
// Processing 3
// result: Either[Nothing, Unit] = Right(())

`Stream#runForeach`

Applies a function to each element for side effects:

trait Stream[+E, +A] {
  def runForeach(f: A => Unit): Either[E, Unit]
}

Alias foreach also exists:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3)
val result = nums.foreach(x => println(s"Got: $x"))

Aggregations

These operations reduce streams to single values, aggregating elements into results:

`Stream#runFold[Z]`

Folds all elements using an accumulator, returning the final result:

trait Stream[+E, +A] {
  def runFold[Z](z: Z)(f: (Z, A) => Z): Either[E, Z]
}

This is the most general aggregation, equivalent to reduce or fold on eager sequences:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3, 4)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3, 4)
val sum = nums.runFold(0)(_ + _)
// sum: Either[Nothing, Int] = Right(10)

Specialized overloads for primitives avoid boxing:

def runFold(z: Int)(f: (Int, A) => Int): Either[E, Int]
def runFold(z: Long)(f: (Long, A) => Long): Either[E, Long]
def runFold(z: Double)(f: (Double, A) => Double): Either[E, Double]

`Stream#count`

Returns the number of elements:

trait Stream[+E, +A] {
  def count: Either[E, Long]
}

Counting elements in a stream:

import zio.blocks.streams.*

val nums = Stream(10, 20, 30, 40, 50)
// nums: Stream[Nothing, Int] = Stream(10, 20, 30, 40, 50)
val total = nums.count
// total: Either[Nothing, Long] = Right(5L)

`Stream#head`

Returns the first element (or None if empty):

trait Stream[+E, +A] {
  def head: Either[E, Option[A]]
}

Getting the first element without collecting the entire stream:

import zio.blocks.streams.*

val nums = Stream(10, 20, 30, 40, 50)
// nums: Stream[Nothing, Int] = Stream(10, 20, 30, 40, 50)
val first = nums.head
// first: Either[Nothing, Option[Int]] = Right(Some(10))

`Stream#last`

Returns the last element (or None if empty):

trait Stream[+E, +A] {
  def last: Either[E, Option[A]]
}

Getting the final element of the stream:

import zio.blocks.streams.*

val nums = Stream(10, 20, 30, 40, 50)
// nums: Stream[Nothing, Int] = Stream(10, 20, 30, 40, 50)
val last = nums.last
// last: Either[Nothing, Option[Int]] = Right(Some(50))

`Stream#find[A]`

Returns the first element satisfying a predicate:

trait Stream[+E, +A] {
  def find(pred: A => Boolean): Either[E, Option[A]]
}

Finding the first element matching a condition:

import zio.blocks.streams.*

val nums = Stream(10, 20, 30, 40, 50)
// nums: Stream[Nothing, Int] = Stream(10, 20, 30, 40, 50)
val firstEven = nums.find(_ % 2 == 0)
// firstEven: Either[Nothing, Option[Int]] = Right(Some(10))

`Stream#exists[A]`

Returns true if any element satisfies a predicate, short-circuiting:

trait Stream[+E, +A] {
  def exists(pred: A => Boolean): Either[E, Boolean]
}

Checking if any element is greater than 35:

import zio.blocks.streams.*

val nums = Stream(10, 20, 30, 40, 50)
// nums: Stream[Nothing, Int] = Stream(10, 20, 30, 40, 50)
val hasLargeValue = nums.exists(_ > 35)
// hasLargeValue: Either[Nothing, Boolean] = Right(true)

`Stream#forall[A]`

Returns true if all elements satisfy a predicate, short-circuiting:

trait Stream[+E, +A] {
  def forall(pred: A => Boolean): Either[E, Boolean]
}

Checking if all elements are positive:

import zio.blocks.streams.*

val nums = Stream(10, 20, 30, 40, 50)
// nums: Stream[Nothing, Int] = Stream(10, 20, 30, 40, 50)
val allPositive = nums.forall(_ > 0)
// allPositive: Either[Nothing, Boolean] = Right(true)

Integration with Pipeline and Sink

Streams compose with pipelines and sinks to form complete data processing flows:

Using Pipelines

via[B] — Applies a Pipeline[A, B] transformation to the stream.:

trait Stream[+E, +A] {
  final def via[B](pipe: Pipeline[A, B]): Stream[E, B]
}

Pipelines are composable transformations that can be reused across streams and sinks. Common pipelines include Pipeline.map, Pipeline.filter, Pipeline.take, and Pipeline.drop:

import zio.blocks.streams.*

val nums = Stream(1, 2, 3, 4, 5)
// nums: Stream[Nothing, Int] = Stream(1, 2, 3, 4, 5)
val pipe = Pipeline.filter((x: Int) => x > 2).andThen(Pipeline.map(_ * 10))
// pipe: Pipeline[Int, Int] = zio.blocks.streams.Pipeline$Composed@158c4689
val result = nums.via(pipe).runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(30, 40, 50))

Pipelines are useful when you want to build reusable transformation logic:

import zio.blocks.streams.*

def positiveIntsPipe: Pipeline[Int, Int] =
  Pipeline.filter((x: Int) => x > 0)

val mixed = Stream(-2, -1, 0, 1, 2)
// mixed: Stream[Nothing, Int] = Stream(-2, -1, 0, 1, 2)
val positives = mixed.via(positiveIntsPipe)
// positives: Stream[Nothing, Int] = Stream(-2, -1, 0, 1, 2).filter(...)
val result = positives.runCollect
// result: Either[Nothing, Chunk[Int]] = Right(IndexedSeq(1, 2))

Understanding Sinks

A Sink[+E, -A, +Z] is a consumer of elements of type A that produces a result Z or fails with E. Sinks are contravariant in A (they can accept a supertype of what they expect). Common sinks include:

Sink.collectAll: Sink[Nothing, A, Chunk[A]] — collects all elements
Sink.drain: Sink[Nothing, A, Unit] — discards all elements
Sink.count: Sink[Nothing, Any, Long] — counts elements
Sink.foldLeft: Sink[Nothing, A, Z] — folds elements with an accumulator
Sink.head: Sink[Nothing, A, Option[A]] — takes the first element
Sink.foreach: Sink[Nothing, A, Unit] — applies a function to each element

When you call stream.run(sink), the stream is compiled to a Reader and the sink drains it, consuming all elements and producing the result.

Low-Level Pull with Reader

Reader[+Elem] is the low-level, pull-based source that backs every stream at execution time. Most users never interact with Reader directly — it is the compilation target when a stream runs. However, you can open a stream for manual element-by-element pulling using start with a Scope.

Manual Pull via `start`

start — Opens a stream for manual pulling within a Scope. The reader is closed automatically when the scope exits.:

trait Stream[+E, +A] {
  def start(using scope: Scope): scope.$[Reader[A]]
}

Use start to manually pull elements within a resource scope:

streams-examples/src/main/scala/stream/ManualPullUsingStart.scala
/*
 * Copyright 2024-2026 John A. De Goes and the ZIO Contributors
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package stream

import zio.blocks.streams.*
import zio.blocks.streams.io.Reader
import zio.blocks.scope.*

object ManualPullUsingStart extends App {
  Scope.global.scoped { scope =>
    import scope.*

    // Open a stream for manual pulling
    val reader: $[Reader[Int]] = Stream.range(1, 6).start(using scope)

    $(reader) { r =>
      // Iterate through reader values using the protocol directly
      // (cannot use scoped value in nested function, so use it directly)
      var current = r.read(-1)
      while (current != -1) {
        println(current) // prints 1, 2, 3, 4, 5
        current = r.read(-1)
      }
    }
    // reader is closed automatically when scope exits
  }
}

Use Stream#start when you need element-by-element control rather than running through a Sink. The returned Reader is closed automatically when the scope closes.

The Reader Protocol

The pull protocol uses a sentinel value to signal end-of-stream:

read(sentinel) — returns the next element, or sentinel when exhausted
close() — signals the consumer is done
isClosed — checks whether the reader is closed

For primitive types, specialized methods avoid boxing:

readInt(sentinel: Long): Long
readLong(sentinel: Long): Long
readFloat(sentinel: Double): Double
readDouble(sentinel: Double): Double

note

Avoid holding references to a Reader obtained via start outside its Scope. The scope guarantees cleanup; escaping the reader defeats that guarantee.

Implementation Notes

ZIO Blocks Streams achieves zero-boxing via compile-time type detection and dual compilation strategies:

JVM Primitive Specialization

By default, Scala's type system boxes primitive values (Int, Long, Double, etc.) into objects, which wastes memory and is slower. ZIO Blocks' Stream uses JvmType.Infer[A] (a compile-time implicit) to detect primitive types at compile time and dispatch to unboxed, specialized implementations.

For example, Stream#map, Stream#filter, and Stream#scan all have specialized branches for JvmType.Int that use readInt(Long.MinValue) instead of boxing:

if (jvmType eq JvmType.Int) {
  val i = source.readInt(Long.MinValue)(using unsafeEvidence)
  // ... unboxed, fast path
} else {
  val o = reader.read(EndOfStream)  // generic boxed path
  // ...
}

This optimization is transparent: you write normal, high-level code, and the compiler and runtime automatically use the fast path for primitives.

Dual Compilation: Recursive vs Interpreter

Each stream node compiles in two ways:

Recursive (compile): Builds a tree of Reader objects, where each operation wraps the previous one. This is fast for shallow pipelines (< 100 operations).
Flat-Array Interpreter (compileInterpreter): For deep pipelines (> 100 operations), the recursive approach hits Scala's default stack-depth limit (~100) and risks StackOverflowError. Instead, the interpreter compiles the entire pipeline into a flat array of operations, executed iteratively.

The switch happens at DepthCutoff = 100. You should never see this in normal use, but it ensures that pipelines of any depth are safe.

Running the Examples

All code from this guide is available as runnable examples in the schema-examples module.

Clone the repository and navigate to the project:

git clone https://github.com/zio/zio-blocks.git
cd zio-blocks

2. Run individual examples with sbt. Here are the available examples:

Basic Usage

This example demonstrates constructing streams from collections, transforming elements with Stream#map and Stream#filter, and collecting results:

streams-examples/src/main/scala/stream/StreamBasicUsageExample.scala
/*
 * Copyright 2024-2026 John A. De Goes and the ZIO Contributors
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package stream

import zio.blocks.streams.Stream
import util.ShowExpr.show

object StreamBasicUsageExample extends App {
  println("=== Stream Basic Usage ===\n")

  // Construction from values
  println("1. Creating a stream from values:")
  val nums = Stream(1, 2, 3, 4, 5)
  show(nums.runCollect)

  // Map transformation
  println("\n2. Transforming with map:")
  val doubled = Stream(1, 2, 3).map(_ * 2)
  show(doubled.runCollect)

  // Filter operation
  println("\n3. Filtering elements:")
  val evens = Stream(1, 2, 3, 4, 5, 6).filter(_ % 2 == 0)
  show(evens.runCollect)

  // Chaining operations
  println("\n4. Chaining multiple operations:")
  val result = Stream(1, 2, 3, 4, 5)
    .map(_ * 2)
    .filter(_ > 4)
    .runCollect
  show(result)

  // Count operation
  println("\n5. Counting elements:")
  val count = Stream(1, 2, 3, 4, 5).count
  show(count)

  // Take operation (short-circuiting)
  println("\n6. Taking first n elements (short-circuits):")
  val first3 = Stream.range(0, 1000).take(3).runCollect
  show(first3)

  // Drop operation
  println("\n7. Dropping first n elements:")
  val afterDrop = Stream(1, 2, 3, 4, 5).drop(2).runCollect
  show(afterDrop)

  // Empty stream
  println("\n8. Working with empty streams:")
  val empty = Stream.empty.runCollect
  show(empty)

  // Concatenation
  println("\n9. Concatenating streams:")
  val combined = (Stream(1, 2) ++ Stream(3, 4)).runCollect
  show(combined)
}

To run this example:

sbt "streams-examples/runMain stream.StreamBasicUsageExample"

Flat-Mapping Nested Streams

This example shows how Stream#flatMap sequences multiple streams and flattens the results:

streams-examples/src/main/scala/stream/StreamFlatMapExample.scala
/*
 * Copyright 2024-2026 John A. De Goes and the ZIO Contributors
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package stream

import zio.blocks.streams.Stream
import util.ShowExpr.show

object StreamFlatMapExample extends App {
  println("=== Stream FlatMap and Nested Streams ===\n")

  // Basic flatMap
  println("1. Basic flatMap - expand each element into a stream:")
  val expanded = Stream(1, 2, 3).flatMap(x => Stream(x, x * 10))
  show(expanded.runCollect)

  // FlatMap with different stream sizes
  println("\n2. FlatMap with varying sizes:")
  val varySizes = Stream(1, 2, 3).flatMap(x => Stream.range(0, x))
  show(varySizes.runCollect)

  // FlatMap with string expansion
  println("\n3. Expanding into string streams:")
  val ids          = Stream("a", "b")
  val expanded_ids = ids.flatMap(id => Stream(s"${id}_1", s"${id}_2", s"${id}_3"))
  show(expanded_ids.runCollect)

  // FlattenAll for deeply nested streams
  println("\n4. Flattening nested streams with flattenAll:")
  val nested = Stream(
    Stream(1, 2),
    Stream(3, 4),
    Stream(5, 6)
  )
  val flat = Stream.flattenAll(nested)
  show(flat.runCollect)

  // Sequential processing guarantees
  println("\n5. Sequential processing (important for side effects and resources):")
  var order   = scala.collection.mutable.Buffer[String]()
  val tracked = Stream(1, 2, 3).flatMap { x =>
    order += s"expand($x)"
    Stream(x, x + 100).tapEach(y => order += s"emit($y)")
  }
  val _ = tracked.runCollect
  show(order.toList)

  // FlatMap with error recovery
  println("\n6. FlatMap can propagate errors:")
  sealed trait Error
  case object InvalidId extends Error

  val mayFail = Stream(1, 2, -1, 3).flatMap { x =>
    if (x < 0) Stream.fail(InvalidId)
    else Stream(x, x * 2)
  }
  show(mayFail.runCollect)
}

Run this example:

sbt "streams-examples/runMain stream.StreamFlatMapExample"

Error Handling

This example demonstrates typed error recovery with fail, catchAll, and orElse:

streams-examples/src/main/scala/stream/StreamErrorHandlingExample.scala
/*
 * Copyright 2024-2026 John A. De Goes and the ZIO Contributors
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package stream

import zio.blocks.streams.Stream
import util.ShowExpr.show

object StreamErrorHandlingExample extends App {
  println("=== Stream Error Handling ===\n")

  sealed trait ApiError
  case object NotFound                    extends ApiError
  case class ValidationError(msg: String) extends ApiError
  case class ServerError(code: Int)       extends ApiError

  // Basic fail
  println("1. Creating a failing stream:")
  val failed: Stream[ApiError, String] = Stream.fail(NotFound)
  show(failed.runCollect)

  // catchAll for recovery
  println("\n2. Recovering from errors with catchAll:")
  val recovered = Stream.fail(NotFound).catchAll(_ => Stream.succeed("default-value"))
  show(recovered.runCollect)

  // orElse for recovery
  println("\n3. Using orElse (lazy fallback evaluation):")
  val fallback = Stream.fail(NotFound) || Stream(1, 2, 3)
  show(fallback.runCollect)

  // Error transformation with error-producing flatMap
  println("\n4. Producing typed errors in flatMap:")
  val errorExample = Stream(1, 2, 3, 4).flatMap { x =>
    if (x == 3) Stream.fail[ApiError](ValidationError("cannot process"))
    else Stream(x)
  }
  show(errorExample.runCollect)

  // Handling errors in flatMap chains
  println("\n5. Error handling in flatMap chains:")
  val chain = Stream(1, 2, 3, 4).flatMap { x =>
    if (x == 3) Stream.fail(ValidationError(s"Cannot process $x"))
    else Stream(x * 10)
  }
  show(chain.runCollect)

  // Recovering from errors in flatMap
  println("\n6. Recovering from errors with catchAll in chains:")
  val recovered_chain = Stream(1, 2, 3, 4).flatMap { x =>
    if (x == 3) Stream.fail(ValidationError(s"Cannot process $x"))
    else Stream(x * 10)
  }
    .catchAll(_ => Stream.succeed(-1))

  show(recovered_chain.runCollect)

  // Handling typed errors from attempt
  println("\n7. Recovering typed errors from Stream.attempt with catchAll:")
  val risky = Stream.attempt("not-a-number".toInt)
  val safe  = risky.catchAll { case _: NumberFormatException =>
    Stream.succeed(-1)
  }
  show(safe.runCollect)

  // Multiple error branches
  println("\n8. Distinguishing error types in recovery:")
  val multi_errors = Stream(1, 2, 3, 4).flatMap { x =>
    x match {
      case 2 => Stream.fail(NotFound)
      case 3 => Stream.fail(ValidationError("Invalid data"))
      case _ => Stream(x * 10)
    }
  }.catchAll {
    case NotFound             => Stream("missing")
    case ValidationError(msg) => Stream(s"invalid: $msg")
    case _                    => Stream("unknown error")
  }

  show(multi_errors.runCollect)
}

Run this example:

sbt "streams-examples/runMain stream.StreamErrorHandlingExample"

Resource Management

This example shows how fromAcquireRelease and ensuring manage resources safely:

streams-examples/src/main/scala/stream/StreamResourceExample.scala
/*
 * Copyright 2024-2026 John A. De Goes and the ZIO Contributors
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package stream

import zio.blocks.streams.Stream
import util.ShowExpr.show
import scala.collection.mutable.Buffer

object StreamResourceExample extends App {
  println("=== Stream Resource Management ===\n")

  // Simulated resource type
  case class Database(name: String) {
    private var closed = false

    def query(q: String): List[String] = {
      if (closed) throw new Exception("Database is closed")
      q match {
        case "users" => List("Alice", "Bob", "Charlie")
        case "ids"   => List("1", "2", "3")
        case _       => List()
      }
    }

    def close(): Unit = {
      println(s"  → Closing database: $name")
      closed = true
    }

    def isClosed: Boolean = closed
  }

  // Basic resource management
  println("1. Basic fromAcquireRelease - automatic cleanup:")
  val log = Buffer[String]()

  val managed = Stream.fromAcquireRelease(
    acquire = {
      log += "opened"
      Database("main")
    },
    release = { db =>
      db.close()
      log += "closed"
    }
  )(db => Stream.fromIterable(db.query("users")))

  val result1 = managed.runCollect
  show(result1)
  show(log.toList)

  // Ensuring cleanup
  println("\n2. Using ensuring for guaranteed cleanup:")
  log.clear()
  var finalizing = false

  val withEnsure = Stream(1, 2, 3)
    .tapEach(x => log += s"processing $x")
    .ensuring {
      finalizing = true
      log += "finalizing"
    }

  val result2 = withEnsure.runCollect
  show(result2)
  show(log.toList)
  show(finalizing)

  // Error safety
  println("\n3. Cleanup happens even on error:")
  log.clear()

  sealed trait Error
  case object ProcessingFailed extends Error

  val errorStream = Stream.fromAcquireRelease(
    acquire = {
      log += "opened"
      Database("error-test")
    },
    release = { db =>
      db.close()
      log += "closed"
    }
  )(db =>
    Stream(1, 2, 3).flatMap { x =>
      if (x == 2) Stream.fail(ProcessingFailed)
      else Stream(x)
    }
  )

  val result3 = errorStream.runCollect
  show(result3)
  show(log.toList)

  // Multiple nested resources
  println("\n4. Multiple nested resources with proper cleanup order:")
  log.clear()

  val nested = Stream.fromAcquireRelease(
    acquire = {
      log += "open db1"
      Database("db1")
    },
    release = db => {
      db.close()
      log += "close db1"
    }
  )(db1 =>
    Stream.fromAcquireRelease(
      acquire = {
        log += "open db2"
        Database("db2")
      },
      release = db2 => {
        db2.close()
        log += "close db2"
      }
    ) { db2 =>
      val data = db1.query("users") ++ db2.query("ids")
      Stream.fromIterable(data).tapEach(x => log += s"emit $x")
    }
  )

  val result4 = nested.runCollect
  show(result4)
  show(log.toList)

  // AutoCloseable integration
  println("\n5. Using AutoCloseable for simpler cleanup:")
  log.clear()

  class AutoCloseableDb extends AutoCloseable {
    def close(): Unit =
      log += "auto-closed"
  }

  val autoCloseable = Stream.fromAcquireRelease(
    acquire = {
      log += "acquired"
      new AutoCloseableDb
    }
    // release defaults to calling .close() on AutoCloseable
  )(db => Stream.succeed(42))

  val result5 = autoCloseable.runCollect
  show(result5)
  show(log.toList)
}

object X extends App {
  import zio.blocks.streams.*
  import java.io.*

  val charCount: Either[IOException, Long] =
    Stream
      .fromJavaReader(new StringReader("Hello\nWorld")) // lazily acquires reader
      .filter(!_.isWhitespace)                          // process only non-whitespace
      .count                                            // count all matching characters

  println(charCount) // prints Right(10) — count of non-whitespace characters
}

Run this example:

sbt "streams-examples/runMain stream.StreamResourceExample"

Windowing and Scanning

This example demonstrates grouped, sliding, and scan for windowing and stateful transformations:

streams-examples/src/main/scala/stream/StreamWindowingExample.scala
/*
 * Copyright 2024-2026 John A. De Goes and the ZIO Contributors
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package stream

import zio.blocks.streams.Stream
import util.ShowExpr.show

object StreamWindowingExample extends App {
  println("=== Stream Windowing and Stateful Transformations ===\n")

  // Grouped - fixed-size windows
  println("1. Grouping into fixed-size chunks:")
  val nums    = Stream(1, 2, 3, 4, 5, 6, 7)
  val grouped = nums.grouped(3)
  show(grouped.runCollect)

  // Grouped with incomplete last chunk
  println("\n2. Last chunk may be smaller:")
  val ungrouped = Stream(1, 2, 3, 4, 5).grouped(2)
  show(ungrouped.runCollect)

  // Sliding window
  println("\n3. Sliding window (default step = 1):")
  val sliding1 = Stream(1, 2, 3, 4, 5).sliding(3)
  show(sliding1.runCollect)

  // Sliding with custom step
  println("\n4. Sliding window with step > 1:")
  val sliding2 = Stream(1, 2, 3, 4, 5, 6, 7, 8).sliding(3, step = 2)
  show(sliding2.runCollect)

  // Scan - running aggregate
  println("\n5. Scan for running sum (accumulator pattern):")
  val cumsum = Stream(1, 2, 3, 4, 5).scan(0)(_ + _)
  show(cumsum.runCollect)

  // Scan for running product
  println("\n6. Scan for running product:")
  val cumprod = Stream(1, 2, 3, 4).scan(1)(_ * _)
  show(cumprod.runCollect)

  // MapAccum - accumulator + transform
  println("\n7. MapAccum for indexed transformation:")
  val indexed = Stream("a", "b", "c").mapAccum(0)((idx, x) => (idx + 1, (idx, x)))
  show(indexed.runCollect)

  // MapAccum with state structure
  println("\n8. MapAccum with complex state:")
  case class Stats(count: Int, sum: Int, max: Int)

  val stats = Stream(5, 3, 8, 2, 9).mapAccum(Stats(0, 0, Int.MinValue)) { case (s, x) =>
    (
      Stats(s.count + 1, s.sum + x, math.max(s.max, x)),
      (x, Stats(s.count + 1, s.sum + x, math.max(s.max, x)))
    )
  }
  show(stats.runCollect)

  // Combining windowing with filtering
  println("\n9. Windowing + filtering (only windows with sum > 5):")
  val filtered = Stream(1, 2, 3, 4, 5, 6)
    .sliding(3, step = 1)
    .filter(chunk => chunk.foldLeft(0)(_ + _) > 5)
  show(filtered.runCollect)

  // Chaining scan with other operations
  println("\n10. Scan + filter for conditional processing:")
  val conditional = Stream(1, 1, 2, 1, 1, 3)
    .scan(0)(_ + _)
    .filter(_ >= 3) // emit when cumsum >= 3
  show(conditional.runCollect)

  // Intersperse - useful with grouping
  println("\n11. Intersperse (insert separator between elements):")
  val separated = Stream(1, 2, 3).intersperse(0)
  show(separated.runCollect)

  // Grouped + intersperse for row formatting
  println("\n12. Grouped + intersperse for CSV-like output:")
  val rows = Stream(1, 2, 3, 4, 5, 6)
    .grouped(2)
    .map(chunk => chunk.toList.mkString(","))
    .intersperse("\n")
  show(rows.runCollect)
}

Run this example:

sbt "streams-examples/runMain stream.StreamWindowingExample"

Motivation​

Construction​

Constant Streams​

Stream.empty​

Stream.succeed[A]​

Stream.fail[E]​

Stream.die​

From Collections​

Stream.apply[A]​

Stream.fromChunk[A]​

Stream.fromIterable[A]​

Stream.fromIterator[A]​

From Ranges​

Stream.range​

Stream.fromRange​

Generators​

Stream.repeat[A]​

Stream.unfold[S, A]​

Side Effects​

Stream.eval[A]​

Stream.attempt[A]​

Stream.attemptEval​

Stream.defer[A]​

Stream.suspend[E, A]​

I/O​

Stream.fromInputStream​

Stream.fromJavaReader​

Stream.fromInputStreamUnmanaged​

Stream.fromJavaReaderUnmanaged​

Transformations​

Element-wise Transformations​

Stream#map[B]​

Stream#mapError[E2]​

Stream#filter​

Stream#collect[B]​

Stateful Transformations​

Stream#mapAccum[S, B]​

Stream#scan[S]​

Flat-Mapping (Nested Streams)​

Stream.flattenAll[E, A]​

Windowing​

Stream#grouped[A]​

Stream#sliding[A]​

Combining Streams​

Sequential Concatenation​

Zipping​

Other Operations​

Filtering Duplicates​

Stream#distinct[A]​

Stream#distinctBy[K]​

Skipping and Taking​

Stream#drop​

Stream#take​

Stream#takeWhile​

Interspersing​

Repeating​

Side Effects​

Error Handling​

Typed Error vs Untyped Defect​

Recovering from Typed Errors​

Stream#catchAll[E2, A1]​

Stream#orElse[E2, A1]​

Recovering from Defects​

Resource Management​

Stream.fromAcquireRelease[R, E, A]​

Stream.fromResource[R, E, A]​

Stream#ensuring​

Running Streams​

Collecting Results​

Stream#runCollect​

Stream#run[E2, Z]​

Discarding Results​

Stream#runDrain​

Stream#runForeach​

Aggregations​

Stream#runFold[Z]​

Stream#count​

Stream#head​

Stream#last​

Stream#find[A]​

Motivation

Construction

Constant Streams

`Stream.empty`

`Stream.succeed[A]`

`Stream.fail[E]`

`Stream.die`

From Collections

`Stream.apply[A]`

`Stream.fromChunk[A]`

`Stream.fromIterable[A]`

`Stream.fromIterator[A]`

From Ranges

`Stream.range`

`Stream.fromRange`

Generators

`Stream.repeat[A]`

`Stream.unfold[S, A]`

Side Effects

`Stream.eval[A]`

`Stream.attempt[A]`

`Stream.attemptEval`

`Stream.defer[A]`

`Stream.suspend[E, A]`

I/O

`Stream.fromInputStream`

`Stream.fromJavaReader`

`Stream.fromInputStreamUnmanaged`

`Stream.fromJavaReaderUnmanaged`

Transformations

Element-wise Transformations

`Stream#map[B]`

`Stream#mapError[E2]`

`Stream#filter`

`Stream#collect[B]`

Stateful Transformations

`Stream#mapAccum[S, B]`

`Stream#scan[S]`

Flat-Mapping (Nested Streams)

`Stream.flattenAll[E, A]`

Windowing

`Stream#grouped[A]`

`Stream#sliding[A]`

Combining Streams

Sequential Concatenation

Zipping

Other Operations

Filtering Duplicates

`Stream#distinct[A]`

`Stream#distinctBy[K]`

Skipping and Taking

`Stream#drop`

`Stream#take`

`Stream#takeWhile`

Interspersing

Repeating

Side Effects

Error Handling

Typed Error vs Untyped Defect

Recovering from Typed Errors

`Stream#catchAll[E2, A1]`

`Stream#orElse[E2, A1]`

Recovering from Defects

Resource Management

`Stream.fromAcquireRelease[R, E, A]`

`Stream.fromResource[R, E, A]`

`Stream#ensuring`

Running Streams

Collecting Results

`Stream#runCollect`

`Stream#run[E2, Z]`

Discarding Results

`Stream#runDrain`

`Stream#runForeach`

Aggregations

`Stream#runFold[Z]`

`Stream#count`

`Stream#head`

`Stream#last`

`Stream#find[A]`