fs2-core/fs2/Stream

Stream

final class Stream[+F[_], +O]

A stream producing output of type O and which may evaluate F effects.

A stream producing output of type O and which may evaluate F effects.

  • '''Purely functional''' a value of type Stream[F, O] describes an effectful computation. A function that returns a Stream[F, O] builds a description of an effectful computation, but does not perform them. The methods of the Stream class derive new descriptions from others. This is similar to how effect types like cats.effect.IO and monix.Task build descriptions of computations.

  • '''Pull''': to evaluate a stream, a consumer pulls its values from it, by repeatedly performing one pull step at a time. Each step is a F-effectful computation that may yield some O values (or none), and a stream from which to continue pulling. The consumer controls the evaluation of the stream, which effectful operations are performed, and when.

  • '''Non-Strict''': stream evaluation only pulls from the stream a prefix large enough to compute its results. Thus, although a stream may yield an unbounded number of values or, after successfully yielding several values, either raise an error or hang up and never yield any value, the consumer need not reach those points of failure. For the same reason, in general, no effect in F is evaluated unless and until the consumer needs it.

  • '''Abstract''': a stream needs not be a plain finite list of fixed effectful computations in F. It can also represent an input or output connection through which data incrementally arrives. It can represent an effectful computation, such as reading the system's time, that can be re-evaluated as often as the consumer of the stream requires.

=== Special properties for streams ===

There are some special properties or cases of streams:

  • A stream is '''finite''' if we can reach the end after a limited number of pull steps, which may yield a finite number of values. It is '''empty''' if it terminates and yields no values.
  • A '''singleton''' stream is a stream that ends after yielding one single value.
  • A '''pure''' stream is one in which the F is Pure, which indicates that it evaluates no effects.
  • A '''never''' stream is a stream that never terminates and never yields any value.

== Pure Streams and operations ==

We can sometimes think of streams, naively, as lists of O elements with F-effects. This is particularly true for '''pure''' streams, which are instances of Stream which use the Pure effect type. We can convert every ''pure and finite'' stream into a List[O] using the .toList method. Also, we can convert pure ''infinite'' streams into instances of the Stream[O] class from the Scala standard library.

A method of the Stream class is '''pure''' if it can be applied to pure streams. Such methods are identified in that their signature includes no type-class constraint (or implicit parameter) on the F method. Pure methods in Stream[F, O] can be projected ''naturally'' to methods in the List class, which means that applying the stream's method and converting the result to a list gets the same result as first converting the stream to a list, and then applying list methods.

Some methods that project directly to list are map, filter, takeWhile, etc. There are other methods, like exists or find, that in the List class they return a value or an Option, but their stream counterparts return an (either empty or singleton) stream. Other methods, like zipWithPrevious, have a more complicated but still pure translation to list methods.

== Type-Class instances and laws of the Stream Operations ==

Laws (using infix syntax):

append forms a monoid in conjunction with empty:

  • empty append s == s and s append empty == s.
  • (s1 append s2) append s3 == s1 append (s2 append s3)

And cons is consistent with using ++ to prepend a single chunk:

  • s.cons(c) == Stream.chunk(c) ++ s

Stream.raiseError propagates until being caught by handleErrorWith:

  • Stream.raiseError(e) handleErrorWith h == h(e)
  • Stream.raiseError(e) ++ s == Stream.raiseError(e)
  • Stream.raiseError(e) flatMap f == Stream.raiseError(e)

Stream forms a monad with emit and flatMap:

  • Stream.emit >=> f == f (left identity)
  • f >=> Stream.emit === f (right identity - note weaker equality notion here)
  • (f >=> g) >=> h == f >=> (g >=> h) (associativity) where Stream.emit(a) is defined as chunk(Chunk.singleton(a)) andf >=> gis defined asa => a flatMap f flatMap g`

The monad is the list-style sequencing monad:

  • (a ++ b) flatMap f == (a flatMap f) ++ (b flatMap f)
  • Stream.empty flatMap f == Stream.empty

== Technical notes==

''Note:'' since the chunk structure of the stream is observable, and s flatMap Stream.emit produces a stream of singleton chunks, the right identity law uses a weaker notion of equality, === which normalizes both sides with respect to chunk structure:

(s1 === s2) = normalize(s1) == normalize(s2) where == is full equality (a == b iff f(a) is identical to f(b) for all f)

normalize(s) can be defined as s.flatMap(Stream.emit), which just produces a singly-chunked stream from any input stream s.

For instance, for a stream s and a function f: A => B,

  • the result of s.map(f) is a Stream with the same chunking as the s; wheras...
  • the result of s.flatMap(x => S.emit(f(x))) is a Stream structured as a sequence of singleton chunks. The latter is using the definition of map that is derived from the Monad instance.

This is not unlike equality for maps or sets, which is defined by which elements they contain, not by how these are spread between a tree's branches or a hashtable buckets. However, a Stream structure can be observed through the chunks method, so two streams "equal" under that notion may give different results through this method.

''Note:'' For efficiency [[Stream.map]] function operates on an entire chunk at a time and preserves chunk structure, which differs from the map derived from the monad (s map f == s flatMap (f andThen Stream.emit)) which would produce singleton chunk. In particular, if f throws errors, the chunked version will fail on the first ''chunk'' with an error, while the unchunked version will fail on the first ''element'' with an error. Exceptions in pure code like this are strongly discouraged.

Companion
object
class Object
trait Matchable
class Any

Value members

Concrete methods

def ++[F2[x], O2 >: O](s2: => Stream[F2, O2]): Stream[F2, O2]

Appends s2 to the end of this stream.

Appends s2 to the end of this stream.

Example
scala> (Stream(1,2,3) ++ Stream(4,5,6)).toList
res0: List[Int] = List(1, 2, 3, 4, 5, 6)

If this stream is infinite, then the result is equivalent to this.

def >>[F2[x], O2](s2: => Stream[F2, O2])(ev: NotGiven[O <:< Nothing]): Stream[F2, O2]

Alias for flatMap(_ => s2).

Alias for flatMap(_ => s2).

def append[F2[x], O2 >: O](s2: => Stream[F2, O2]): Stream[F2, O2]

Appends s2 to the end of this stream. Alias for s1 ++ s2.

Appends s2 to the end of this stream. Alias for s1 ++ s2.

def as[O2](o2: O2): Stream[F, O2]

Equivalent to val o2Memoized = o2; _.map(_ => o2Memoized).

Equivalent to val o2Memoized = o2; _.map(_ => o2Memoized).

Example
scala> Stream(1,2,3).as(0).toList
res0: List[Int] = List(0, 0, 0)
def attempt: Stream[F, Either[Throwable, O]]

Returns a stream of O values wrapped in Right until the first error, which is emitted wrapped in Left.

Returns a stream of O values wrapped in Right until the first error, which is emitted wrapped in Left.

Example
scala> import cats.effect.SyncIO
scala> (Stream(1,2,3) ++ Stream.raiseError[SyncIO](new RuntimeException) ++ Stream(4,5,6)).attempt.compile.toList.unsafeRunSync()
res0: List[Either[Throwable,Int]] = List(Right(1), Right(2), Right(3), Left(java.lang.RuntimeException))

rethrow is the inverse of attempt, with the caveat that anything after the first failure is discarded.

def attempts[F2[x]](delays: Stream[F2, FiniteDuration])(`evidence$1`: Temporal[F2]): Stream[F2, Either[Throwable, O]]

Retries on failure, returning a stream of attempts that can be manipulated with standard stream operations such as take, collectFirst and interruptWhen.

Retries on failure, returning a stream of attempts that can be manipulated with standard stream operations such as take, collectFirst and interruptWhen.

Note: The resulting stream does not automatically halt at the first successful attempt. Also see retry.

def broadcastThrough[F2[x], O2](pipes: (F2, O) => O2*)(`evidence$2`: Concurrent[F2]): Stream[F2, O2]

Broadcasts every value of the stream through the pipes provided as arguments.

Broadcasts every value of the stream through the pipes provided as arguments.

Each pipe can have a different implementation if required, and they are all guaranteed to see every O pulled from the source stream.

The pipes are all run concurrently with each other, but note that elements are pulled from the source as chunks, and the next chunk is pulled only when all pipes are done with processing the current chunk, which prevents faster pipes from getting too far ahead.

In other words, this behaviour slows down processing of incoming chunks by faster pipes until the slower ones have caught up. If this is not desired, consider using the prefetch and prefetchN combinators on the slow pipes.

def buffer(n: Int): Stream[F, O]

Behaves like the identity function, but requests n elements at a time from the input.

Behaves like the identity function, but requests n elements at a time from the input.

Example
scala> import cats.effect.SyncIO
scala> val buf = new scala.collection.mutable.ListBuffer[String]()
scala> Stream.range(0, 100).covary[SyncIO].
    |   evalMap(i => SyncIO { buf += s">$i"; i }).
    |   buffer(4).
    |   evalMap(i => SyncIO { buf += s"<$i"; i }).
    |   take(10).
    |   compile.toVector.unsafeRunSync()
res0: Vector[Int] = Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
scala> buf.toList
res1: List[String] = List(>0, >1, >2, >3, <0, <1, <2, <3, >4, >5, >6, >7, <4, <5, <6, <7, >8, >9, >10, >11, <8, <9)
def bufferAll: Stream[F, O]

Behaves like the identity stream, but emits no output until the source is exhausted.

Behaves like the identity stream, but emits no output until the source is exhausted.

Example
scala> import cats.effect.SyncIO
scala> val buf = new scala.collection.mutable.ListBuffer[String]()
scala> Stream.range(0, 10).covary[SyncIO].
    |   evalMap(i => SyncIO { buf += s">$i"; i }).
    |   bufferAll.
    |   evalMap(i => SyncIO { buf += s"<$i"; i }).
    |   take(4).
    |   compile.toVector.unsafeRunSync()
res0: Vector[Int] = Vector(0, 1, 2, 3)
scala> buf.toList
res1: List[String] = List(>0, >1, >2, >3, >4, >5, >6, >7, >8, >9, <0, <1, <2, <3)
def bufferBy(f: O => Boolean): Stream[F, O]

Behaves like the identity stream, but requests elements from its input in blocks that end whenever the predicate switches from true to false.

Behaves like the identity stream, but requests elements from its input in blocks that end whenever the predicate switches from true to false.

Example
scala> import cats.effect.SyncIO
scala> val buf = new scala.collection.mutable.ListBuffer[String]()
scala> Stream.range(0, 10).covary[SyncIO].
    |   evalMap(i => SyncIO { buf += s">$i"; i }).
    |   bufferBy(_ % 2 == 0).
    |   evalMap(i => SyncIO { buf += s"<$i"; i }).
    |   compile.toVector.unsafeRunSync()
res0: Vector[Int] = Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
scala> buf.toList
res1: List[String] = List(>0, >1, <0, <1, >2, >3, <2, <3, >4, >5, <4, <5, >6, >7, <6, <7, >8, >9, <8, <9)
def changes[O2 >: O](eq: Eq[O2]): Stream[F, O2]

Emits only elements that are distinct from their immediate predecessors, using natural equality for comparison.

Emits only elements that are distinct from their immediate predecessors, using natural equality for comparison.

Example
scala> Stream(1,1,2,2,2,3,3).changes.toList
res0: List[Int] = List(1, 2, 3)
def changesBy[O2](f: O => O2)(eq: Eq[O2]): Stream[F, O]

Emits only elements that are distinct from their immediate predecessors according to f, using natural equality for comparison.

Emits only elements that are distinct from their immediate predecessors according to f, using natural equality for comparison.

Note that f is called for each element in the stream multiple times and hence should be fast (e.g., an accessor). It is not intended to be used for computationally intensive conversions. For such conversions, consider something like: src.map(o => (o, f(o))).changesBy(_._2).map(_._1)

Example
scala> Stream(1,1,2,4,6,9).changesBy(_ % 2).toList
res0: List[Int] = List(1, 2, 9)
def chunkAll: Stream[F, Chunk[O]]

Collects all output chunks in to a single chunk and emits it at the end of the source stream. Note: if more than 2^32-1 elements are collected, this operation will fail.

Collects all output chunks in to a single chunk and emits it at the end of the source stream. Note: if more than 2^32-1 elements are collected, this operation will fail.

Example
scala> (Stream(1) ++ Stream(2, 3) ++ Stream(4, 5, 6)).chunkAll.toList
res0: List[Chunk[Int]] = List(Chunk(1, 2, 3, 4, 5, 6))
def chunkLimit(n: Int): Stream[F, Chunk[O]]

Outputs chunk with a limited maximum size, splitting as necessary.

Outputs chunk with a limited maximum size, splitting as necessary.

Example
scala> (Stream(1) ++ Stream(2, 3) ++ Stream(4, 5, 6)).chunkLimit(2).toList
res0: List[Chunk[Int]] = List(Chunk(1), Chunk(2, 3), Chunk(4, 5), Chunk(6))
def chunkMin(n: Int, allowFewerTotal: Boolean): Stream[F, Chunk[O]]

Outputs chunks of size larger than N

Outputs chunks of size larger than N

Chunks from the source stream are split as necessary.

If allowFewerTotal is true, if the stream is smaller than N, should the elements be included

Example
scala> (Stream(1,2) ++ Stream(3,4) ++ Stream(5,6,7)).chunkMin(3).toList
res0: List[Chunk[Int]] = List(Chunk(1, 2, 3, 4), Chunk(5, 6, 7))
def chunkN(n: Int, allowFewer: Boolean): Stream[F, Chunk[O]]

Outputs chunks of size n.

Outputs chunks of size n.

Chunks from the source stream are split as necessary. If allowFewer is true, the last chunk that is emitted may have less than n elements.

Example
scala> Stream(1,2,3).repeat.chunkN(2).take(5).toList
res0: List[Chunk[Int]] = List(Chunk(1, 2), Chunk(3, 1), Chunk(2, 3), Chunk(1, 2), Chunk(3, 1))
def chunks: Stream[F, Chunk[O]]

Outputs all chunks from the source stream.

Outputs all chunks from the source stream.

Example
scala> (Stream(1) ++ Stream(2, 3) ++ Stream(4, 5, 6)).chunks.toList
res0: List[Chunk[Int]] = List(Chunk(1), Chunk(2, 3), Chunk(4, 5, 6))
def collect[O2](pf: PartialFunction[O, O2]): Stream[F, O2]

Filters and maps simultaneously. Calls collect on each chunk in the stream.

Filters and maps simultaneously. Calls collect on each chunk in the stream.

Example
scala> Stream(Some(1), Some(2), None, Some(3), None, Some(4)).collect { case Some(i) => i }.toList
res0: List[Int] = List(1, 2, 3, 4)
def collectFirst[O2](pf: PartialFunction[O, O2]): Stream[F, O2]

Emits the first element of the stream for which the partial function is defined.

Emits the first element of the stream for which the partial function is defined.

Example
scala> Stream(None, Some(1), Some(2), None, Some(3)).collectFirst { case Some(i) => i }.toList
res0: List[Int] = List(1)
def collectWhile[O2](pf: PartialFunction[O, O2]): Stream[F, O2]

Like collect but terminates as soon as the partial function is undefined.

Like collect but terminates as soon as the partial function is undefined.

Example
scala> Stream(Some(1), Some(2), Some(3), None, Some(4)).collectWhile { case Some(i) => i }.toList
res0: List[Int] = List(1, 2, 3)
def compile[F2[x], G[_], O2 >: O](compiler: Compiler[F2, G]): CompileOps[F2, G, O2]

Gets a projection of this stream that allows converting it to an F[..] in a number of ways.

Gets a projection of this stream that allows converting it to an F[..] in a number of ways.

Example
scala> import cats.effect.SyncIO
scala> val prg: SyncIO[Vector[Int]] = Stream.eval(SyncIO(1)).append(Stream(2,3,4)).compile.toVector
scala> prg.unsafeRunSync()
res2: Vector[Int] = Vector(1, 2, 3, 4)
def concurrently[F2[x], O2](that: Stream[F2, O2])(F: Concurrent[F2]): Stream[F2, O]

Runs the supplied stream in the background as elements from this stream are pulled.

Runs the supplied stream in the background as elements from this stream are pulled.

The resulting stream terminates upon termination of this stream. The background stream will be interrupted at that point. Early termination of that does not terminate the resulting stream.

Any errors that occur in either this or that stream result in the overall stream terminating with an error.

Upon finalization, the resulting stream will interrupt the background stream and wait for it to be finalized.

This method is equivalent to this mergeHaltL that.drain, just more efficient for this and that evaluation.

Example
scala> import cats.effect.IO, cats.effect.unsafe.implicits.global
scala> val data: Stream[IO,Int] = Stream.range(1, 10).covary[IO]
scala> Stream.eval(fs2.concurrent.SignallingRef[IO,Int](0)).flatMap(s => Stream(s).concurrently(data.evalMap(s.set))).flatMap(_.discrete).takeWhile(_ < 9, true).compile.last.unsafeRunSync()
res0: Option[Int] = Some(9)
def cons[O2 >: O](c: Chunk[O2]): Stream[F, O2]

Prepends a chunk onto the front of this stream.

Prepends a chunk onto the front of this stream.

Example
scala> Stream(1,2,3).cons(Chunk(-1, 0)).toList
res0: List[Int] = List(-1, 0, 1, 2, 3)
def cons1[O2 >: O](o: O2): Stream[F, O2]

Prepends a single value onto the front of this stream.

Prepends a single value onto the front of this stream.

Example
scala> Stream(1,2,3).cons1(0).toList
res0: List[Int] = List(0, 1, 2, 3)
def consChunk[O2 >: O](c: Chunk[O2]): Stream[F, O2]

Prepends a chunk onto the front of this stream.

Prepends a chunk onto the front of this stream.

Example
scala> Stream(1,2,3).consChunk(Chunk.vector(Vector(-1, 0))).toList
res0: List[Int] = List(-1, 0, 1, 2, 3)
def covaryAll[F2[x], O2 >: O]: Stream[F2, O2]

Lifts this stream to the specified effect and output types.

Lifts this stream to the specified effect and output types.

Example
scala> import cats.effect.IO
scala> Stream.empty.covaryAll[IO,Int]
res0: Stream[IO,Int] = Stream(..)
def covaryOutput[O2 >: O]: Stream[F, O2]

Lifts this stream to the specified output type.

Lifts this stream to the specified output type.

Example
scala> Stream(Some(1), Some(2), Some(3)).covaryOutput[Option[Int]]
res0: Stream[Pure,Option[Int]] = Stream(..)
def debounce[F2[x]](d: FiniteDuration)(F: Temporal[F2]): Stream[F2, O]

Debounce the stream with a minimum period of d between each element.

Debounce the stream with a minimum period of d between each element.

Use-case: if this is a stream of updates about external state, we may want to refresh (side-effectful) once every 'd' milliseconds, and every time we refresh we only care about the latest update.

Returns

A stream whose values is an in-order, not necessarily strict subsequence of this stream, and whose evaluation will force a delay d between emitting each element. The exact subsequence would depend on the chunk structure of this stream, and the timing they arrive.

Example
scala> import scala.concurrent.duration._, cats.effect.IO, cats.effect.unsafe.implicits.global
scala> val s = Stream(1, 2, 3) ++ Stream.sleep_[IO](500.millis) ++ Stream(4, 5) ++ Stream.sleep_[IO](10.millis) ++ Stream(6)
scala> val s2 = s.debounce(100.milliseconds)
scala> s2.compile.toVector.unsafeRunSync()
res0: Vector[Int] = Vector(3, 6)
def debug[O2 >: O](formatter: O2 => String, logger: String => Unit): Stream[F, O]

Logs the elements of this stream as they are pulled.

Logs the elements of this stream as they are pulled.

By default, toString is called on each element and the result is printed to standard out. To change formatting, supply a value for the formatter param. To change the destination, supply a value for the logger param.

This method does not change the chunk structure of the stream. To debug the chunk structure, see debugChunks.

Logging is not done in F because this operation is intended for debugging, including pure streams.

Example
scala> Stream(1, 2).append(Stream(3, 4)).debug(o => s"a: $o").toList
a: 1
a: 2
a: 3
a: 4
res0: List[Int] = List(1, 2, 3, 4)
def debugChunks[O2 >: O](formatter: Chunk[O2] => String, logger: String => Unit): Stream[F, O]

Like debug but logs chunks as they are pulled instead of individual elements.

Like debug but logs chunks as they are pulled instead of individual elements.

Example
scala> Stream(1, 2, 3).append(Stream(4, 5, 6)).debugChunks(c => s"a: $c").buffer(2).debugChunks(c => s"b: $c").toList
a: Chunk(1, 2, 3)
b: Chunk(1, 2)
a: Chunk(4, 5, 6)
b: Chunk(3, 4)
b: Chunk(5, 6)
res0: List[Int] = List(1, 2, 3, 4, 5, 6)
def delayBy[F2[x]](d: FiniteDuration)(`evidence$5`: Temporal[F2]): Stream[F2, O]

Returns a stream that when run, sleeps for duration d and then pulls from this stream.

Returns a stream that when run, sleeps for duration d and then pulls from this stream.

Alias for sleep_[F](d) ++ this.

def delete(p: O => Boolean): Stream[F, O]

Skips the first element that matches the predicate.

Skips the first element that matches the predicate.

Example
scala> Stream.range(1, 10).delete(_ % 2 == 0).toList
res0: List[Int] = List(1, 3, 4, 5, 6, 7, 8, 9)
def drain: Stream[F, INothing]

Removes all output values from this stream.

Removes all output values from this stream.

Often used with merge to run one side of the merge for its effect while getting outputs from the opposite side of the merge.

Example
scala> import cats.effect.SyncIO
scala> Stream.eval(SyncIO(println("x"))).drain.compile.toVector.unsafeRunSync()
res0: Vector[INothing] = Vector()
def drop(n: Long): Stream[F, O]

Drops n elements of the input, then echoes the rest.

Drops n elements of the input, then echoes the rest.

Example
scala> Stream.range(0,10).drop(5).toList
res0: List[Int] = List(5, 6, 7, 8, 9)
def dropLast: Stream[F, O]

Drops the last element.

Drops the last element.

Example
scala> Stream.range(0,10).dropLast.toList
res0: List[Int] = List(0, 1, 2, 3, 4, 5, 6, 7, 8)
def dropLastIf(p: O => Boolean): Stream[F, O]

Drops the last element if the predicate evaluates to true.

Drops the last element if the predicate evaluates to true.

Example
scala> Stream.range(0,10).dropLastIf(_ > 5).toList
res0: List[Int] = List(0, 1, 2, 3, 4, 5, 6, 7, 8)
def dropRight(n: Int): Stream[F, O]

Outputs all but the last n elements of the input.

Outputs all but the last n elements of the input.

This is a '''pure''' stream operation: if s is a finite pure stream, then s.dropRight(n).toList is equal to this.toList.reverse.drop(n).reverse.

Example
scala> Stream.range(0,10).dropRight(5).toList
res0: List[Int] = List(0, 1, 2, 3, 4)
def dropThrough(p: O => Boolean): Stream[F, O]

Like dropWhile, but drops the first value which tests false.

Like dropWhile, but drops the first value which tests false.

Example
scala> Stream.range(0,10).dropThrough(_ != 4).toList
res0: List[Int] = List(5, 6, 7, 8, 9)

'''Pure:''' if this is a finite pure stream, then this.dropThrough(p).toList is equal to this.toList.dropWhile(p).drop(1)

def dropWhile(p: O => Boolean): Stream[F, O]

Drops elements from the head of this stream until the supplied predicate returns false.

Drops elements from the head of this stream until the supplied predicate returns false.

Example
scala> Stream.range(0,10).dropWhile(_ != 4).toList
res0: List[Int] = List(4, 5, 6, 7, 8, 9)

'''Pure''' this operation maps directly to List.dropWhile

def either[F2[x], O2](that: Stream[F2, O2])(`evidence$6`: Concurrent[F2]): Stream[F2, Either[O, O2]]

Like [[merge]], but tags each output with the branch it came from.

Like [[merge]], but tags each output with the branch it came from.

Example
scala> import scala.concurrent.duration._, cats.effect.IO, cats.effect.unsafe.implicits.global
scala> val s1 = Stream.awakeEvery[IO](1000.millis).scan(0)((acc, _) => acc + 1)
scala> val s = s1.either(Stream.sleep_[IO](500.millis) ++ s1).take(10)
scala> s.take(10).compile.toVector.unsafeRunSync()
res0: Vector[Either[Int,Int]] = Vector(Left(0), Right(0), Left(1), Right(1), Left(2), Right(2), Left(3), Right(3), Left(4), Right(4))
def enqueueNoneTerminated[F2[x], O2 >: O](queue: Queue[F2, Option[O2]]): Stream[F2, Nothing]

Enqueues the elements of this stream to the supplied queue and enqueues None when this stream terminates.

Enqueues the elements of this stream to the supplied queue and enqueues None when this stream terminates.

def enqueueNoneTerminatedChunks[F2[x], O2 >: O](queue: Queue[F2, Option[Chunk[O2]]]): Stream[F2, Nothing]

Enqueues the chunks of this stream to the supplied queue and enqueues None when this stream terminates.

Enqueues the chunks of this stream to the supplied queue and enqueues None when this stream terminates.

def enqueueUnterminated[F2[x], O2 >: O](queue: Queue[F2, O2]): Stream[F2, Nothing]

Enqueues the elements of this stream to the supplied queue.

Enqueues the elements of this stream to the supplied queue.

def enqueueUnterminatedChunks[F2[x], O2 >: O](queue: Queue[F2, Chunk[O2]]): Stream[F2, Nothing]

Enqueues the chunks of this stream to the supplied queue.

Enqueues the chunks of this stream to the supplied queue.

def evalFilter[F2[x]](f: O => F2[Boolean]): Stream[F2, O]

Like filter, but allows filtering based on an effect.

Like filter, but allows filtering based on an effect.

Note: The result Stream will consist of chunks that are empty or 1-element-long. If you want to operate on chunks after using it, consider buffering, e.g. by using buffer.

def evalFilterAsync[F2[x]](maxConcurrent: Int)(f: O => F2[Boolean])(`evidence$10`: Concurrent[F2]): Stream[F2, O]

Like filter, but allows filtering based on an effect, with up to maxConcurrent concurrently running effects. The ordering of emitted elements is unchanged.

Like filter, but allows filtering based on an effect, with up to maxConcurrent concurrently running effects. The ordering of emitted elements is unchanged.

def evalFilterNot[F2[x]](f: O => F2[Boolean]): Stream[F2, O]

Like filterNot, but allows filtering based on an effect.

Like filterNot, but allows filtering based on an effect.

Note: The result Stream will consist of chunks that are empty or 1-element-long. If you want to operate on chunks after using it, consider buffering, e.g. by using buffer.

def evalFilterNotAsync[F2[x]](maxConcurrent: Int)(f: O => F2[Boolean])(`evidence$11`: Concurrent[F2]): Stream[F2, O]

Like filterNot, but allows filtering based on an effect, with up to maxConcurrent concurrently running effects. The ordering of emitted elements is unchanged.

Like filterNot, but allows filtering based on an effect, with up to maxConcurrent concurrently running effects. The ordering of emitted elements is unchanged.

def evalMap[F2[x], O2](f: O => F2[O2]): Stream[F2, O2]

Alias for flatMap(o => Stream.eval(f(o))).

Alias for flatMap(o => Stream.eval(f(o))).

Example
scala> import cats.effect.SyncIO
scala> Stream(1,2,3,4).evalMap(i => SyncIO(println(i))).compile.drain.unsafeRunSync()
res0: Unit = ()

Note this operator will de-chunk the stream back into chunks of size 1, which has performance implications. For maximum performance, evalMapChunk is available, however, with caveats.

def evalMapAccumulate[F2[x], S, O2](s: S)(f: (S, O) => F2[(S, O2)]): Stream[F2, (S, O2)]

Like [[Stream#mapAccumulate]], but accepts a function returning an F[_].

Like [[Stream#mapAccumulate]], but accepts a function returning an F[_].

Example
scala> import cats.effect.SyncIO
scala> Stream(1,2,3,4).covary[SyncIO].evalMapAccumulate(0)((acc,i) => SyncIO((i, acc + i))).compile.toVector.unsafeRunSync()
res0: Vector[(Int, Int)] = Vector((1,1), (2,3), (3,5), (4,7))
def evalMapChunk[F2[x], O2](f: O => F2[O2])(`evidence$7`: Applicative[F2]): Stream[F2, O2]

Like evalMap, but operates on chunks for performance. This means this operator is not lazy on every single element, rather on the chunks.

Like evalMap, but operates on chunks for performance. This means this operator is not lazy on every single element, rather on the chunks.

For instance, evalMap would only print twice in the follow example (note the take(2)):

Example
scala> import cats.effect.SyncIO
scala> Stream(1,2,3,4).evalMap(i => SyncIO(println(i))).take(2).compile.drain.unsafeRunSync()
res0: Unit = ()

But with evalMapChunk, it will print 4 times:

scala> Stream(1,2,3,4).evalMapChunk(i => SyncIO(println(i))).take(2).compile.drain.unsafeRunSync()
res0: Unit = ()
def evalMapFilter[F2[x], O2](f: O => F2[Option[O2]]): Stream[F2, O2]

Effectfully maps and filters the elements of the stream depending on the optionality of the result of the application of the effectful function f.

Effectfully maps and filters the elements of the stream depending on the optionality of the result of the application of the effectful function f.

Example
scala> import cats.effect.SyncIO, cats.syntax.all._
scala> Stream(1, 2, 3, 4, 5).evalMapFilter(n => SyncIO((n * 2).some.filter(_ % 4 == 0))).compile.toList.unsafeRunSync()
res0: List[Int] = List(4, 8)
def evalScan[F2[x], O2](z: O2)(f: (O2, O) => F2[O2]): Stream[F2, O2]

Like [[Stream#scan]], but accepts a function returning an F[_].

Like [[Stream#scan]], but accepts a function returning an F[_].

Example
scala> import cats.effect.SyncIO
scala> Stream(1,2,3,4).covary[SyncIO].evalScan(0)((acc,i) => SyncIO(acc + i)).compile.toVector.unsafeRunSync()
res0: Vector[Int] = Vector(0, 1, 3, 6, 10)
def evalTap[F2[x], O2](f: O => F2[O2])(`evidence$8`: Functor[F2]): Stream[F2, O]

Like observe but observes with a function O => F[O2] instead of a pipe. Not as powerful as observe since not all pipes can be represented by O => F[O2], but much faster. Alias for evalMap(o => f(o).as(o)).

Like observe but observes with a function O => F[O2] instead of a pipe. Not as powerful as observe since not all pipes can be represented by O => F[O2], but much faster. Alias for evalMap(o => f(o).as(o)).

def evalTapChunk[F2[x], O2](f: O => F2[O2])(`evidence$9`: Applicative[F2]): Stream[F2, O]

Alias for evalMapChunk(o => f(o).as(o)).

Alias for evalMapChunk(o => f(o).as(o)).

def exists(p: O => Boolean): Stream[F, Boolean]

Emits true as soon as a matching element is received, else false if no input matches. '''Pure''': this operation maps to List.exists

Emits true as soon as a matching element is received, else false if no input matches. '''Pure''': this operation maps to List.exists

Returns

Either a singleton stream, or a never stream.

  • If this is a finite stream, the result is a singleton stream, with after yielding one single value. If this is empty, that value is the mempty of the instance of Monoid.
  • If this is a non-terminating stream, and no matter if it yields any value, then the result is equivalent to the Stream.never: it never terminates nor yields any value.
Example
scala> Stream.range(0,10).exists(_ == 4).toList
res0: List[Boolean] = List(true)
scala> Stream.range(0,10).exists(_ == 10).toList
res1: List[Boolean] = List(false)
def filter(p: O => Boolean): Stream[F, O]

Emits only inputs which match the supplied predicate.

Emits only inputs which match the supplied predicate.

This is a '''pure''' operation, that projects directly into List.filter

Example
scala> Stream.range(0,10).filter(_ % 2 == 0).toList
res0: List[Int] = List(0, 2, 4, 6, 8)
def filterWithPrevious(f: (O, O) => Boolean): Stream[F, O]

Like filter, but the predicate f depends on the previously emitted and current elements.

Like filter, but the predicate f depends on the previously emitted and current elements.

Example
scala> Stream(1, -1, 2, -2, 3, -3, 4, -4).filterWithPrevious((previous, current) => previous < current).toList
res0: List[Int] = List(1, 2, 3, 4)
def find(f: O => Boolean): Stream[F, O]

Emits the first input (if any) which matches the supplied predicate.

Emits the first input (if any) which matches the supplied predicate.

Example
scala> Stream.range(1,10).find(_ % 2 == 0).toList
res0: List[Int] = List(2)

'''Pure''' if s is a finite pure stream, s.find(p).toList is equal to s.toList.find(p).toList, where the second toList is to turn Option into List.

@nowarn("cat=unused-params")
def flatMap[F2[x], O2](f: O => Stream[F2, O2])(ev: NotGiven[O <:< Nothing]): Stream[F2, O2]

Creates a stream whose elements are generated by applying f to each output of the source stream and concatenated all of the results.

Creates a stream whose elements are generated by applying f to each output of the source stream and concatenated all of the results.

Example
scala> Stream(1, 2, 3).flatMap { i => Stream.chunk(Chunk.seq(List.fill(i)(i))) }.toList
res0: List[Int] = List(1, 2, 2, 3, 3, 3)
def flatten[F2[x], O2](ev: O <:< Stream[F2, O2]): Stream[F2, O2]

Flattens a stream of streams in to a single stream by concatenating each stream. See parJoin and parJoinUnbounded for concurrent flattening of 'n' streams.

Flattens a stream of streams in to a single stream by concatenating each stream. See parJoin and parJoinUnbounded for concurrent flattening of 'n' streams.

def fold[O2](z: O2)(f: (O2, O) => O2): Stream[F, O2]

Folds all inputs using an initial value z and supplied binary operator, and emits a single element stream.

Folds all inputs using an initial value z and supplied binary operator, and emits a single element stream.

Example
scala> Stream(1, 2, 3, 4, 5).fold(0)(_ + _).toList
res0: List[Int] = List(15)
def fold1[O2 >: O](f: (O2, O2) => O2): Stream[F, O2]

Folds all inputs using the supplied binary operator, and emits a single-element stream, or the empty stream if the input is empty, or the never stream if the input is non-terminating.

Folds all inputs using the supplied binary operator, and emits a single-element stream, or the empty stream if the input is empty, or the never stream if the input is non-terminating.

Example
scala> Stream(1, 2, 3, 4, 5).fold1(_ + _).toList
res0: List[Int] = List(15)
def foldMap[O2](f: O => O2)(O2: Monoid[O2]): Stream[F, O2]

Alias for map(f).foldMonoid.

Alias for map(f).foldMonoid.

Example
scala> Stream(1, 2, 3, 4, 5).foldMap(_ => 1).toList
res0: List[Int] = List(5)
def foldMonoid[O2 >: O](O: Monoid[O2]): Stream[F, O2]

Folds this stream with the monoid for O.

Folds this stream with the monoid for O.

Returns

Either a singleton stream or a never stream:

  • If this is a finite stream, the result is a singleton stream. If this is empty, that value is the mempty of the instance of Monoid.
  • If this is a non-terminating stream, and no matter if it yields any value, then the result is equivalent to the Stream.never: it never terminates nor yields any value.
Example
scala> Stream(1, 2, 3, 4, 5).foldMonoid.toList
res0: List[Int] = List(15)
def forall(p: O => Boolean): Stream[F, Boolean]

Emits false and halts as soon as a non-matching element is received; or emits a single true value if it reaches the stream end and every input before that matches the predicate; or hangs without emitting values if the input is infinite and all inputs match the predicate.

Emits false and halts as soon as a non-matching element is received; or emits a single true value if it reaches the stream end and every input before that matches the predicate; or hangs without emitting values if the input is infinite and all inputs match the predicate.

Returns

Either a singleton or a never stream:

  • '''If''' this yields an element x for which ¬ p(x), '''then''' a singleton stream with the value false. Pulling from the resultg performs all the effects needed until reaching the counterexample x.
  • If this is a finite stream with no counterexamples of p, '''then''' a singleton stream with the true value. Pulling from the it will perform all effects of this.
  • If this is an infinite stream and all its the elements satisfy p, then the result is a never stream. Pulling from that stream will pull all effects from this.
Example
scala> Stream(1, 2, 3, 4, 5).forall(_ < 10).toList
res0: List[Boolean] = List(true)
def foreach[F2[x]](f: O => F2[Unit]): Stream[F2, INothing]

Like evalMap but discards the result of evaluation, resulting in a stream with no elements.

Like evalMap but discards the result of evaluation, resulting in a stream with no elements.

Example
scala> import cats.effect.SyncIO
scala> Stream(1,2,3,4).foreach(i => SyncIO(println(i))).compile.drain.unsafeRunSync()
res0: Unit = ()
def groupAdjacentBy[O2](f: O => O2)(eq: Eq[O2]): Stream[F, (O2, Chunk[O])]

Partitions the input into a stream of chunks according to a discriminator function.

Partitions the input into a stream of chunks according to a discriminator function.

Each chunk in the source stream is grouped using the supplied discriminator function and the results of the grouping are emitted each time the discriminator function changes values.

Note: there is no limit to how large a group can become. To limit the group size, use groupAdjacentByLimit.

Example
scala> Stream("Hello", "Hi", "Greetings", "Hey").groupAdjacentBy(_.head).toList.map { case (k,vs) => k -> vs.toList }
res0: List[(Char,List[String])] = List((H,List(Hello, Hi)), (G,List(Greetings)), (H,List(Hey)))
def groupAdjacentByLimit[O2](limit: Int)(f: O => O2)(eq: Eq[O2]): Stream[F, (O2, Chunk[O])]

Like groupAdjacentBy but limits the size of emitted chunks.

Like groupAdjacentBy but limits the size of emitted chunks.

Example
scala> Stream.range(0, 12).groupAdjacentByLimit(3)(_ / 4).toList
res0: List[(Int,Chunk[Int])] = List((0,Chunk(0, 1, 2)), (0,Chunk(3)), (1,Chunk(4, 5, 6)), (1,Chunk(7)), (2,Chunk(8, 9, 10)), (2,Chunk(11)))
def groupWithin[F2[x]](n: Int, timeout: FiniteDuration)(F: Temporal[F2]): Stream[F2, Chunk[O]]

Divides this stream into chunks of elements of size n. Each time a group of size n is emitted, timeout is reset.

Divides this stream into chunks of elements of size n. Each time a group of size n is emitted, timeout is reset.

If the current chunk does not reach size n by the time the timeout period elapses, it emits a chunk containing however many elements have been accumulated so far, and resets timeout.

However, if no elements at all have been accumulated when timeout expires, empty chunks are not emitted, and timeout is not reset. Instead, the next chunk to arrive is emitted immediately (since the stream is still in a timed out state), and only then is timeout reset. If the chunk received in a timed out state is bigger than n, the first n elements of it are emitted immediately in a chunk, timeout is reset, and the remaining elements are used for the next chunk.

When the stream terminates, any accumulated elements are emitted immediately in a chunk, even if timeout has not expired.

def handleErrorWith[F2[x], O2 >: O](h: Throwable => Stream[F2, O2]): Stream[F2, O2]

If this terminates with Stream.raiseError(e), invoke h(e).

If this terminates with Stream.raiseError(e), invoke h(e).

Example
scala> import cats.effect.SyncIO
scala> Stream(1, 2, 3).append(Stream.raiseError[SyncIO](new RuntimeException)).handleErrorWith(_ => Stream(0)).compile.toList.unsafeRunSync()
res0: List[Int] = List(1, 2, 3, 0)
def head: Stream[F, O]

Emits the first element of this stream (if non-empty) and then halts.

Emits the first element of this stream (if non-empty) and then halts.

Example
scala> Stream(1, 2, 3).head.toList
res0: List[Int] = List(1)
def hold[F2[x], O2 >: O](initial: O2)(`evidence$12`: Concurrent[F2]): Stream[F2, Signal[F2, O2]]

Converts a discrete stream to a signal. Returns a single-element stream.

Converts a discrete stream to a signal. Returns a single-element stream.

Resulting signal is initially initial, and is updated with latest value produced by source. If the source stream is empty, the resulting signal will always be initial.

def holdOption[F2[x], O2 >: O](`evidence$13`: Concurrent[F2]): Stream[F2, Signal[F2, Option[O2]]]

Like hold but does not require an initial value, and hence all output elements are wrapped in Some.

Like hold but does not require an initial value, and hence all output elements are wrapped in Some.

def holdOptionResource[F2[x], O2 >: O](`evidence$15`: Concurrent[F2]): Resource[F2, Signal[F2, Option[O2]]]

Like holdResource but does not require an initial value, and hence all output elements are wrapped in Some.

Like holdResource but does not require an initial value, and hence all output elements are wrapped in Some.

def holdResource[F2[x], O2 >: O](initial: O2)(`evidence$14`: Concurrent[F2]): Resource[F2, Signal[F2, O2]]

Like hold but returns a Resource rather than a single element stream.

Like hold but returns a Resource rather than a single element stream.

def ifEmpty[F2[x], O2 >: O](fallback: => Stream[F2, O2]): Stream[F2, O2]

Falls back to the supplied stream if this stream finishes without emitting any elements. Note: fallback occurs any time stream evaluation finishes without emitting, even when effects have been evaluated.

Falls back to the supplied stream if this stream finishes without emitting any elements. Note: fallback occurs any time stream evaluation finishes without emitting, even when effects have been evaluated.

Example
scala> Stream.empty.ifEmpty(Stream(1, 2, 3)).toList
res0: List[Int] = List(1, 2, 3)
scala> Stream.exec(cats.effect.SyncIO(println("Hello"))).ifEmpty(Stream(1, 2, 3)).compile.toList.unsafeRunSync()
res1: List[Int] = List(1, 2, 3)
def ifEmptyEmit[O2 >: O](o: => O2): Stream[F, O2]

Emits the supplied value if this stream finishes without emitting any elements. Note: fallback occurs any time stream evaluation finishes without emitting, even when effects have been evaluated.

Emits the supplied value if this stream finishes without emitting any elements. Note: fallback occurs any time stream evaluation finishes without emitting, even when effects have been evaluated.

Example
scala> Stream.empty.ifEmptyEmit(0).toList
res0: List[Int] = List(0)
def interleave[F2[x], O2 >: O](that: Stream[F2, O2]): Stream[F2, O2]

Deterministically interleaves elements, starting on the left, terminating when the end of either branch is reached naturally.

Deterministically interleaves elements, starting on the left, terminating when the end of either branch is reached naturally.

Example
scala> Stream(1, 2, 3).interleave(Stream(4, 5, 6, 7)).toList
res0: List[Int] = List(1, 4, 2, 5, 3, 6)
def interleaveAll[F2[x], O2 >: O](that: Stream[F2, O2]): Stream[F2, O2]

Deterministically interleaves elements, starting on the left, terminating when the ends of both branches are reached naturally.

Deterministically interleaves elements, starting on the left, terminating when the ends of both branches are reached naturally.

Example
scala> Stream(1, 2, 3).interleaveAll(Stream(4, 5, 6, 7)).toList
res0: List[Int] = List(1, 4, 2, 5, 3, 6, 7)
def interruptAfter[F2[x]](duration: FiniteDuration)(`evidence$16`: Temporal[F2]): Stream[F2, O]

Interrupts this stream after the specified duration has passed.

Interrupts this stream after the specified duration has passed.

def interruptScope: Stream[F, O]

Creates a scope that may be interrupted by calling scope#interrupt.

Creates a scope that may be interrupted by calling scope#interrupt.

def interruptWhen[F2[x]](haltWhenTrue: Stream[F2, Boolean])(F: Concurrent[F2]): Stream[F2, O]

Ties this stream to the given haltWhenTrue stream. The resulting stream performs all the effects and emits all the outputs from this stream (the fore), until the moment that the haltWhenTrue stream ends, be it by emitting true, error, or cancellation.

Ties this stream to the given haltWhenTrue stream. The resulting stream performs all the effects and emits all the outputs from this stream (the fore), until the moment that the haltWhenTrue stream ends, be it by emitting true, error, or cancellation.

The haltWhenTrue stream is compiled and drained, asynchronously in the background, until the moment it emits a value true or raises an error. This halts as soon as either branch halts.

If the haltWhenTrue stream ends by raising an error, the resulting stream rethrows that same error. If the haltWhenTrue stream is cancelled, then the resulting stream is interrupted (without cancellation).

Consider using the overload that takes a Signal, Deferred or F[Either[Throwable, Unit]].

def interruptWhen[F2[x]](haltWhenTrue: Deferred[F2, Either[Throwable, Unit]]): Stream[F2, O]

Alias for interruptWhen(haltWhenTrue.get).

Alias for interruptWhen(haltWhenTrue.get).

def interruptWhen[F2[x]](haltWhenTrue: Signal[F2, Boolean])(`evidence$17`: Concurrent[F2]): Stream[F2, O]

Alias for interruptWhen(haltWhenTrue.discrete).

Alias for interruptWhen(haltWhenTrue.discrete).

def interruptWhen[F2[x]](haltOnSignal: F2[Either[Throwable, Unit]]): Stream[F2, O]

Interrupts the stream, when haltOnSignal finishes its evaluation.

Interrupts the stream, when haltOnSignal finishes its evaluation.

def intersperse[O2 >: O](separator: O2): Stream[F, O2]

Emits the specified separator between every pair of elements in the source stream.

Emits the specified separator between every pair of elements in the source stream.

Example
scala> Stream(1, 2, 3, 4, 5).intersperse(0).toList
res0: List[Int] = List(1, 0, 2, 0, 3, 0, 4, 0, 5)

This method preserves the Chunking structure of this stream.

def last: Stream[F, Option[O]]

Returns the last element of this stream, if non-empty.

Returns the last element of this stream, if non-empty.

Example
scala> Stream(1, 2, 3).last.toList
res0: List[Option[Int]] = List(Some(3))
def lastOr[O2 >: O](fallback: => O2): Stream[F, O2]

Returns the last element of this stream, if non-empty, otherwise the supplied fallback value.

Returns the last element of this stream, if non-empty, otherwise the supplied fallback value.

Example
scala> Stream(1, 2, 3).lastOr(0).toList
res0: List[Int] = List(3)
scala> Stream.empty.lastOr(0).toList
res1: List[Int] = List(0)
def map[O2](f: O => O2): Stream[F, O2]

Applies the specified pure function to each input and emits the result.

Applies the specified pure function to each input and emits the result.

Example
scala> Stream("Hello", "World!").map(_.size).toList
res0: List[Int] = List(5, 6)
def mapAccumulate[S, O2](init: S)(f: (S, O) => (S, O2)): Stream[F, (S, O2)]

Maps a running total according to S and the input with the function f.

Maps a running total according to S and the input with the function f.

Example
scala> Stream("Hello", "World").mapAccumulate(0)((l, s) => (l + s.length, s.head)).toVector
res0: Vector[(Int, Char)] = Vector((5,H), (10,W))
def mapAsync[F2[x], O2](maxConcurrent: Int)(f: O => F2[O2])(`evidence$18`: Concurrent[F2]): Stream[F2, O2]

Alias for parEvalMap.

Alias for parEvalMap.

def mapAsyncUnordered[F2[x], O2](maxConcurrent: Int)(f: O => F2[O2])(`evidence$19`: Concurrent[F2]): Stream[F2, O2]
def mapChunks[O2](f: Chunk[O] => Chunk[O2]): Stream[F, O2]

Applies the specified pure function to each chunk in this stream.

Applies the specified pure function to each chunk in this stream.

Example
scala> Stream(1, 2, 3).append(Stream(4, 5, 6)).mapChunks { c => val ints = c.toArraySlice; for (i <- 0 until ints.values.size) ints.values(i) = 0; ints }.toList
res0: List[Int] = List(0, 0, 0, 0, 0, 0)
def mask: Stream[F, O]

Behaves like the identity function but halts the stream on an error and does not return the error.

Behaves like the identity function but halts the stream on an error and does not return the error.

Example
scala> import cats.effect.SyncIO
scala> (Stream(1,2,3) ++ Stream.raiseError[SyncIO](new RuntimeException) ++ Stream(4, 5, 6)).mask.compile.toList.unsafeRunSync()
res0: List[Int] = List(1, 2, 3)
def merge[F2[x], O2 >: O](that: Stream[F2, O2])(F: Concurrent[F2]): Stream[F2, O2]

Interleaves the two inputs nondeterministically. The output stream halts after BOTH s1 and s2 terminate normally, or in the event of an uncaught failure on either s1 or s2. Has the property that merge(Stream.empty, s) == s and merge(raiseError(e), s) will eventually terminate with raiseError(e), possibly after emitting some elements of s first.

Interleaves the two inputs nondeterministically. The output stream halts after BOTH s1 and s2 terminate normally, or in the event of an uncaught failure on either s1 or s2. Has the property that merge(Stream.empty, s) == s and merge(raiseError(e), s) will eventually terminate with raiseError(e), possibly after emitting some elements of s first.

The implementation always tries to pull one chunk from each side before waiting for it to be consumed by resulting stream. As such, there may be up to two chunks (one from each stream) waiting to be processed while the resulting stream is processing elements.

Also note that if either side produces empty chunk, the processing on that side continues, w/o downstream requiring to consume result.

If either side does not emit anything (i.e. as result of drain) that side will continue to run even when the resulting stream did not ask for more data.

Note that even when this is equivalent to Stream(this, that).parJoinUnbounded, this implementation is little more efficient

Example
scala> import scala.concurrent.duration._, cats.effect.IO, cats.effect.unsafe.implicits.global
scala> val s1 = Stream.awakeEvery[IO](500.millis).scan(0)((acc, _) => acc + 1)
scala> val s = s1.merge(Stream.sleep_[IO](250.millis) ++ s1)
scala> s.take(6).compile.toVector.unsafeRunSync()
res0: Vector[Int] = Vector(0, 0, 1, 1, 2, 2)
def mergeHaltBoth[F2[x], O2 >: O](that: Stream[F2, O2])(`evidence$20`: Concurrent[F2]): Stream[F2, O2]

Like merge, but halts as soon as either branch halts.

Like merge, but halts as soon as either branch halts.

def mergeHaltL[F2[x], O2 >: O](that: Stream[F2, O2])(`evidence$21`: Concurrent[F2]): Stream[F2, O2]

Like merge, but halts as soon as the s1 branch halts.

Like merge, but halts as soon as the s1 branch halts.

Note: it is not guaranteed that the last element of the stream will come from s1.

def mergeHaltR[F2[x], O2 >: O](that: Stream[F2, O2])(`evidence$22`: Concurrent[F2]): Stream[F2, O2]

Like merge, but halts as soon as the s2 branch halts.

Like merge, but halts as soon as the s2 branch halts.

Note: it is not guaranteed that the last element of the stream will come from s2.

def metered[F2[x]](rate: FiniteDuration)(`evidence$3`: Temporal[F2]): Stream[F2, O]

Throttles the stream to the specified rate. Unlike debounce, metered doesn't drop elements.

Throttles the stream to the specified rate. Unlike debounce, metered doesn't drop elements.

Provided rate should be viewed as maximum rate: resulting rate can't exceed the output rate of this stream.

def meteredStartImmediately[F2[x]](rate: FiniteDuration)(`evidence$4`: Temporal[F2]): Stream[F2, O]

Provides the same functionality as metered but begins immediately instead of waiting for rate

Provides the same functionality as metered but begins immediately instead of waiting for rate

def noneTerminate: Stream[F, Option[O]]

Emits each output wrapped in a Some and emits a None at the end of the stream.

Emits each output wrapped in a Some and emits a None at the end of the stream.

s.noneTerminate.unNoneTerminate == s

Example
scala> Stream(1,2,3).noneTerminate.toList
res0: List[Option[Int]] = List(Some(1), Some(2), Some(3), None)
def onComplete[F2[x], O2 >: O](s2: => Stream[F2, O2]): Stream[F2, O2]

Run s2 after this, regardless of errors during this, then reraise any errors encountered during this.

Run s2 after this, regardless of errors during this, then reraise any errors encountered during this.

Note: this should not be used for resource cleanup! Use bracket or onFinalize instead.

Example
scala> Stream(1, 2, 3).onComplete(Stream(4, 5)).toList
res0: List[Int] = List(1, 2, 3, 4, 5)
def onFinalize[F2[x]](f: F2[Unit])(F2: Applicative[F2]): Stream[F2, O]

Runs the supplied effectful action at the end of this stream, regardless of how the stream terminates.

Runs the supplied effectful action at the end of this stream, regardless of how the stream terminates.

def onFinalizeCase[F2[x]](f: ExitCase => F2[Unit])(F2: Applicative[F2]): Stream[F2, O]

Like onFinalize but provides the reason for finalization as an ExitCase[Throwable].

Like onFinalize but provides the reason for finalization as an ExitCase[Throwable].

def onFinalizeCaseWeak[F2[x]](f: ExitCase => F2[Unit])(F2: Applicative[F2]): Stream[F2, O]

Like onFinalizeCase but does not introduce a scope, allowing finalization to occur after subsequent appends or other scope-preserving transformations.

Like onFinalizeCase but does not introduce a scope, allowing finalization to occur after subsequent appends or other scope-preserving transformations.

Scopes can be manually introduced via scope if desired.

See onFinalizeWeak for more details on semantics.

def onFinalizeWeak[F2[x]](f: F2[Unit])(F2: Applicative[F2]): Stream[F2, O]

Like onFinalize but does not introduce a scope, allowing finalization to occur after subsequent appends or other scope-preserving transformations.

Like onFinalize but does not introduce a scope, allowing finalization to occur after subsequent appends or other scope-preserving transformations.

Scopes can be manually introduced via scope if desired.

Example use case: a.concurrently(b).onFinalizeWeak(f).compile.resource.use(g) In this example, use of onFinalize would result in b shutting down before g is run, because onFinalize creates a scope, whose lifetime is extended over the compiled resource. By using onFinalizeWeak instead, f is attached to the scope governing concurrently.

def parEvalMap[F2[x], O2](maxConcurrent: Int)(f: O => F2[O2])(F: Concurrent[F2]): Stream[F2, O2]

Like Stream#evalMap, but will evaluate effects in parallel, emitting the results downstream in the same order as the input stream. The number of concurrent effects is limited by the maxConcurrent parameter.

Like Stream#evalMap, but will evaluate effects in parallel, emitting the results downstream in the same order as the input stream. The number of concurrent effects is limited by the maxConcurrent parameter.

See Stream#parEvalMapUnordered if there is no requirement to retain the order of the original stream.

Example
scala> import cats.effect.IO, cats.effect.unsafe.implicits.global
scala> Stream(1,2,3,4).covary[IO].parEvalMap(2)(i => IO(println(i))).compile.drain.unsafeRunSync()
res0: Unit = ()
def parEvalMapUnordered[F2[x], O2](maxConcurrent: Int)(f: O => F2[O2])(`evidence$23`: Concurrent[F2]): Stream[F2, O2]

Like Stream#evalMap, but will evaluate effects in parallel, emitting the results downstream. The number of concurrent effects is limited by the maxConcurrent parameter.

Like Stream#evalMap, but will evaluate effects in parallel, emitting the results downstream. The number of concurrent effects is limited by the maxConcurrent parameter.

See Stream#parEvalMap if retaining the original order of the stream is required.

Example
scala> import cats.effect.IO, cats.effect.unsafe.implicits.global
scala> Stream(1,2,3,4).covary[IO].parEvalMapUnordered(2)(i => IO(println(i))).compile.drain.unsafeRunSync()
res0: Unit = ()
def parZip[F2[x], O2](that: Stream[F2, O2])(`evidence$24`: Concurrent[F2]): Stream[F2, (O, O2)]

Concurrent zip.

Concurrent zip.

It combines elements pairwise and in order like zip, but instead of pulling from the left stream and then from the right stream, it evaluates both pulls concurrently. The resulting stream terminates when either stream terminates.

The concurrency is bounded following a model of successive races: both sides start evaluation of a single element concurrently, and whichever finishes first waits for the other to catch up and the resulting pair to be emitted, at which point the process repeats. This means that no branch is allowed to get ahead by more than one element.

Notes:

  • Effects within each stream are executed in order, they are only concurrent with respect to each other.
  • The output of parZip is guaranteed to be the same as zip, although the order in which effects are executed differs.
def parZipWith[F2[x], O2 >: O, O3, O4](that: Stream[F2, O3])(f: (O2, O3) => O4)(`evidence$25`: Concurrent[F2]): Stream[F2, O4]

Like parZip, but combines elements pairwise with a function instead of tupling them.

Like parZip, but combines elements pairwise with a function instead of tupling them.

def pauseWhen[F2[x]](pauseWhenTrue: Stream[F2, Boolean])(`evidence$26`: Concurrent[F2]): Stream[F2, O]

Pause this stream when pauseWhenTrue emits true, resuming when false is emitted.

Pause this stream when pauseWhenTrue emits true, resuming when false is emitted.

def pauseWhen[F2[x]](pauseWhenTrue: Signal[F2, Boolean])(`evidence$27`: Concurrent[F2]): Stream[F2, O]

Pause this stream when pauseWhenTrue is true, resume when it's false.

Pause this stream when pauseWhenTrue is true, resume when it's false.

def prefetch[F2[x]](`evidence$28`: Concurrent[F2]): Stream[F2, O]

Alias for prefetchN(1).

Alias for prefetchN(1).

def prefetchN[F2[x]](n: Int)(`evidence$29`: Concurrent[F2]): Stream[F2, O]

Behaves like identity, but starts fetches up to n chunks in parallel with downstream consumption, enabling processing on either side of the prefetchN to run in parallel.

Behaves like identity, but starts fetches up to n chunks in parallel with downstream consumption, enabling processing on either side of the prefetchN to run in parallel.

def printlns[F2[x], O2 >: O](F: Console[F2], showO: Show[O2]): Stream[F2, INothing]

Prints each element of this stream to standard out, converting each element to a String via Show.

Prints each element of this stream to standard out, converting each element to a String via Show.

def rechunkRandomly[F2[x]](minFactor: Double, maxFactor: Double): Stream[F2, O]

Rechunks the stream such that output chunks are within [inputChunk.size * minFactor, inputChunk.size * maxFactor].

Rechunks the stream such that output chunks are within [inputChunk.size * minFactor, inputChunk.size * maxFactor].

def rechunkRandomlyWithSeed[F2[x]](minFactor: Double, maxFactor: Double)(seed: Long): Stream[F2, O]

Rechunks the stream such that output chunks are within [inputChunk.size * minFactor, inputChunk.size * maxFactor]. The pseudo random generator is deterministic based on the supplied seed.

Rechunks the stream such that output chunks are within [inputChunk.size * minFactor, inputChunk.size * maxFactor]. The pseudo random generator is deterministic based on the supplied seed.

def reduce[O2 >: O](f: (O2, O2) => O2): Stream[F, O2]

Alias for fold1.

Alias for fold1.

def reduceSemigroup[O2 >: O](S: Semigroup[O2]): Stream[F, O2]

Reduces this stream with the Semigroup for O.

Reduces this stream with the Semigroup for O.

Example
scala> Stream("The", "quick", "brown", "fox").intersperse(" ").reduceSemigroup.toList
res0: List[String] = List(The quick brown fox)
def repartition[O2 >: O](f: O2 => Chunk[O2])(S: Semigroup[O2]): Stream[F, O2]

Repartitions the input with the function f. On each step f is applied to the input and all elements but the last of the resulting sequence are emitted. The last element is then appended to the next input using the Semigroup S.

Repartitions the input with the function f. On each step f is applied to the input and all elements but the last of the resulting sequence are emitted. The last element is then appended to the next input using the Semigroup S.

Example
scala> Stream("Hel", "l", "o Wor", "ld").repartition(s => Chunk.array(s.split(" "))).toList
res0: List[String] = List(Hello, World)
def repeat: Stream[F, O]

Repeat this stream an infinite number of times.

Repeat this stream an infinite number of times.

s.repeat == s ++ s ++ s ++ ...

Example
scala> Stream(1,2,3).repeat.take(8).toList
res0: List[Int] = List(1, 2, 3, 1, 2, 3, 1, 2)
def repeatN(n: Long): Stream[F, O]

Repeat this stream a given number of times.

Repeat this stream a given number of times.

s.repeatN(n) == s ++ s ++ s ++ ... (n times)

Example
scala> Stream(1,2,3).repeatN(3).take(100).toList
res0: List[Int] = List(1, 2, 3, 1, 2, 3, 1, 2, 3)
def rethrow[F2[x], O2](ev: O <:< Either[Throwable, O2], rt: RaiseThrowable[F2]): Stream[F2, O2]

Converts a Stream[F,Either[Throwable,O]] to a Stream[F,O], which emits right values and fails upon the first Left(t). Preserves chunkiness.

Converts a Stream[F,Either[Throwable,O]] to a Stream[F,O], which emits right values and fails upon the first Left(t). Preserves chunkiness.

Example
scala> import cats.effect.SyncIO
scala> Stream(Right(1), Right(2), Left(new RuntimeException), Right(3)).rethrow[SyncIO, Int].handleErrorWith(_ => Stream(-1)).compile.toList.unsafeRunSync()
res0: List[Int] = List(-1)
def scan[O2](z: O2)(f: (O2, O) => O2): Stream[F, O2]

Left fold which outputs all intermediate results.

Left fold which outputs all intermediate results.

Example
scala> Stream(1,2,3,4).scan(0)(_ + _).toList
res0: List[Int] = List(0, 1, 3, 6, 10)

More generally: Stream().scan(z)(f) == Stream(z) Stream(x1).scan(z)(f) == Stream(z, f(z,x1)) Stream(x1,x2).scan(z)(f) == Stream(z, f(z,x1), f(f(z,x1),x2)) etc

def scan1[O2 >: O](f: (O2, O2) => O2): Stream[F, O2]

Like [[scan]], but uses the first element of the stream as the seed.

Like [[scan]], but uses the first element of the stream as the seed.

Example
scala> Stream(1,2,3,4).scan1(_ + _).toList
res0: List[Int] = List(1, 3, 6, 10)
def scanChunks[S, O2 >: O, O3](init: S)(f: (S, Chunk[O2]) => (S, Chunk[O3])): Stream[F, O3]

Like scan but f is applied to each chunk of the source stream. The resulting chunk is emitted while the resulting state is used in the next invocation of f.

Like scan but f is applied to each chunk of the source stream. The resulting chunk is emitted while the resulting state is used in the next invocation of f.

Many stateful pipes can be implemented efficiently (i.e., supporting fusion) with this method.

def scanChunksOpt[S, O2 >: O, O3](init: S)(f: S => Option[Chunk[O2] => (S, Chunk[O3])]): Stream[F, O3]

More general version of scanChunks where the current state (i.e., S) can be inspected to determine if another chunk should be pulled or if the stream should terminate. Termination is signaled by returning None from f. Otherwise, a function which consumes the next chunk is returned wrapped in Some.

More general version of scanChunks where the current state (i.e., S) can be inspected to determine if another chunk should be pulled or if the stream should terminate. Termination is signaled by returning None from f. Otherwise, a function which consumes the next chunk is returned wrapped in Some.

Example
scala> def take[F[_],O](s: Stream[F,O], n: Int): Stream[F,O] =
    |   s.scanChunksOpt(n) { n => if (n <= 0) None else Some((c: Chunk[O]) => if (c.size < n) (n - c.size, c) else (0, c.take(n))) }
scala> take(Stream.range(0,100), 5).toList
res0: List[Int] = List(0, 1, 2, 3, 4)
def scanMap[O2](f: O => O2)(O2: Monoid[O2]): Stream[F, O2]

Alias for map(f).scanMonoid.

Alias for map(f).scanMonoid.

Example
scala> Stream("a", "aa", "aaa", "aaaa").scanMap(_.length).toList
res0: List[Int] = List(0, 1, 3, 6, 10)
def scanMonoid[O2 >: O](O: Monoid[O2]): Stream[F, O2]

Folds this stream with the monoid for O while emitting all intermediate results.

Folds this stream with the monoid for O while emitting all intermediate results.

Example
scala> Stream(1, 2, 3, 4).scanMonoid.toList
res0: List[Int] = List(0, 1, 3, 6, 10)
def scope: Stream[F, O]

Introduces an explicit scope.

Introduces an explicit scope.

Scopes are normally introduced automatically, when using bracket or similar operations that acquire resources and run finalizers. Manual scope introduction is useful when using onFinalizeWeak/onFinalizeCaseWeak, where no scope is introduced.

def sliding(n: Int): Stream[F, Chunk[O]]

Groups inputs in fixed size chunks by passing a "sliding window" of size n over them. If the input contains less than or equal to n elements, only one chunk of this size will be emitted.

Groups inputs in fixed size chunks by passing a "sliding window" of size n over them. If the input contains less than or equal to n elements, only one chunk of this size will be emitted.

Throws
scala.IllegalArgumentException

if n <= 0

Example
scala> Stream(1, 2, 3, 4).sliding(2).toList
res0: List[fs2.Chunk[Int]] = List(Chunk(1, 2), Chunk(2, 3), Chunk(3, 4))
def sliding(size: Int, step: Int): Stream[F, Chunk[O]]

Groups inputs in fixed size chunks by passing a "sliding window" of size with step over them. If the input contains less than or equal to size elements, only one chunk of this size will be emitted.

Groups inputs in fixed size chunks by passing a "sliding window" of size with step over them. If the input contains less than or equal to size elements, only one chunk of this size will be emitted.

Throws
scala.IllegalArgumentException

if size <= 0 | step <= 0

Example
scala> Stream(1, 2, 3, 4, 5).sliding(2, 3).toList
res0: List[fs2.Chunk[Int]] = List(Chunk(1, 2), Chunk(4, 5))
scala> Stream(1, 2, 3, 4, 5).sliding(3, 2).toList
res1: List[fs2.Chunk[Int]] = List(Chunk(1, 2, 3), Chunk(3, 4, 5))
def spawn[F2[x]](`evidence$30`: Concurrent[F2]): Stream[F2, Fiber[F2, Throwable, Unit]]

Starts this stream and cancels it as finalization of the returned stream.

Starts this stream and cancels it as finalization of the returned stream.

def split(f: O => Boolean): Stream[F, Chunk[O]]

Breaks the input into chunks where the delimiter matches the predicate. The delimiter does not appear in the output. Two adjacent delimiters in the input result in an empty chunk in the output.

Breaks the input into chunks where the delimiter matches the predicate. The delimiter does not appear in the output. Two adjacent delimiters in the input result in an empty chunk in the output.

Example
scala> Stream.range(0, 10).split(_ % 4 == 0).toList
res0: List[Chunk[Int]] = List(empty, Chunk(1, 2, 3), Chunk(5, 6, 7), Chunk(9))
def switchMap[F2[x], O2](f: O => Stream[F2, O2])(F: Concurrent[F2]): Stream[F2, O2]

Like Stream.flatMap but interrupts the inner stream when new elements arrive in the outer stream.

Like Stream.flatMap but interrupts the inner stream when new elements arrive in the outer stream.

The implementation will try to preserve chunks like Stream.merge.

Finializers of each inner stream are guaranteed to run before the next inner stream starts.

When the outer stream stops gracefully, the currently running inner stream will continue to run.

When an inner stream terminates/interrupts, nothing happens until the next element arrives in the outer stream(i.e the outer stream holds the stream open during this time or else the stream terminates)

When either the inner or outer stream fails, the entire stream fails and the finalizer of the inner stream runs before the outer one.

def tail: Stream[F, O]

Emits all elements of the input except the first one.

Emits all elements of the input except the first one.

Example
scala> Stream(1,2,3).tail.toList
res0: List[Int] = List(2, 3)
def take(n: Long): Stream[F, O]

Emits the first n elements of this stream.

Emits the first n elements of this stream.

Example
scala> Stream.range(0,1000).take(5).toList
res0: List[Int] = List(0, 1, 2, 3, 4)
def takeRight(n: Int): Stream[F, O]

Emits the last n elements of the input.

Emits the last n elements of the input.

Example
scala> Stream.range(0,1000).takeRight(5).toList
res0: List[Int] = List(995, 996, 997, 998, 999)
def takeThrough(p: O => Boolean): Stream[F, O]

Like takeWhile, but emits the first value which tests false.

Like takeWhile, but emits the first value which tests false.

Example
scala> Stream.range(0,1000).takeThrough(_ != 5).toList
res0: List[Int] = List(0, 1, 2, 3, 4, 5)
def takeWhile(p: O => Boolean, takeFailure: Boolean): Stream[F, O]

Emits the longest prefix of the input for which all elements test true according to f.

Emits the longest prefix of the input for which all elements test true according to f.

Example
scala> Stream.range(0,1000).takeWhile(_ != 5).toList
res0: List[Int] = List(0, 1, 2, 3, 4)
def through[F2[x], O2](f: Stream[F, O] => Stream[F2, O2]): Stream[F2, O2]

Transforms this stream using the given Pipe.

Transforms this stream using the given Pipe.

Example
scala> Stream("Hello", "world").through(text.utf8Encode).toVector.toArray
res0: Array[Byte] = Array(72, 101, 108, 108, 111, 119, 111, 114, 108, 100)
def through2[F2[x], O2, O3](s2: Stream[F2, O2])(f: (Stream[F, O], Stream[F2, O2]) => Stream[F2, O3]): Stream[F2, O3]

Transforms this stream and s2 using the given Pipe2.

Transforms this stream and s2 using the given Pipe2.

def timeout[F2[x]](timeout: FiniteDuration)(`evidence$31`: Temporal[F2]): Stream[F2, O]

Fails this stream with a TimeoutException if it does not complete within given timeout.

Fails this stream with a TimeoutException if it does not complete within given timeout.

override def toString: String
Definition Classes
Any
def translate[F2[x], G[_]](u: FunctionK[F2, G]): Stream[G, O]

Translates effect type from F to G using the supplied FunctionK.

Translates effect type from F to G using the supplied FunctionK.

def unchunk: Stream[F, O]

Converts the input to a stream of 1-element chunks.

Converts the input to a stream of 1-element chunks.

Example
scala> (Stream(1,2,3) ++ Stream(4,5,6)).unchunk.chunks.toList
res0: List[Chunk[Int]] = List(Chunk(1), Chunk(2), Chunk(3), Chunk(4), Chunk(5), Chunk(6))
def withFilter(f: O => Boolean): Stream[F, O]

Alias for filter Implemented to enable filtering in for comprehensions

Alias for filter Implemented to enable filtering in for comprehensions

def zip[F2[x], O2](that: Stream[F2, O2]): Stream[F2, (O, O2)]

Determinsitically zips elements, terminating when the end of either branch is reached naturally.

Determinsitically zips elements, terminating when the end of either branch is reached naturally.

Example
scala> Stream(1, 2, 3).zip(Stream(4, 5, 6, 7)).toList
res0: List[(Int,Int)] = List((1,4), (2,5), (3,6))
def zipAll[F2[x], O2 >: O, O3](that: Stream[F2, O3])(pad1: O2, pad2: O3): Stream[F2, (O2, O3)]

Determinsitically zips elements, terminating when the ends of both branches are reached naturally, padding the left branch with pad1 and padding the right branch with pad2 as necessary.

Determinsitically zips elements, terminating when the ends of both branches are reached naturally, padding the left branch with pad1 and padding the right branch with pad2 as necessary.

Example
scala> Stream(1,2,3).zipAll(Stream(4,5,6,7))(0,0).toList
res0: List[(Int,Int)] = List((1,4), (2,5), (3,6), (0,7))
def zipAllWith[F2[x], O2 >: O, O3, O4](that: Stream[F2, O3])(pad1: O2, pad2: O3)(f: (O2, O3) => O4): Stream[F2, O4]

Determinsitically zips elements with the specified function, terminating when the ends of both branches are reached naturally, padding the left branch with pad1 and padding the right branch with pad2 as necessary.

Determinsitically zips elements with the specified function, terminating when the ends of both branches are reached naturally, padding the left branch with pad1 and padding the right branch with pad2 as necessary.

Example
scala> Stream(1,2,3).zipAllWith(Stream(4,5,6,7))(0, 0)(_ + _).toList
res0: List[Int] = List(5, 7, 9, 7)
def zipLeft[F2[x], O2](that: Stream[F2, O2]): Stream[F2, O]

Like zip, but selects the left values only. Useful with timed streams, the example below will emit a number every 100 milliseconds.

Like zip, but selects the left values only. Useful with timed streams, the example below will emit a number every 100 milliseconds.

Example
scala> import scala.concurrent.duration._, cats.effect.IO, cats.effect.unsafe.implicits.global
scala> val s = Stream.range(0, 5) zipLeft Stream.fixedDelay[IO](100.millis)
scala> s.compile.toVector.unsafeRunSync()
res0: Vector[Int] = Vector(0, 1, 2, 3, 4)
def zipRight[F2[x], O2](that: Stream[F2, O2]): Stream[F2, O2]

Like zip, but selects the right values only. Useful with timed streams, the example below will emit a number every 100 milliseconds.

Like zip, but selects the right values only. Useful with timed streams, the example below will emit a number every 100 milliseconds.

Example
scala> import scala.concurrent.duration._, cats.effect.IO, cats.effect.unsafe.implicits.global
scala> val s = Stream.fixedDelay[IO](100.millis) zipRight Stream.range(0, 5)
scala> s.compile.toVector.unsafeRunSync()
res0: Vector[Int] = Vector(0, 1, 2, 3, 4)
def zipWith[F2[x], O2 >: O, O3, O4](that: Stream[F2, O3])(f: (O2, O3) => O4): Stream[F2, O4]

Determinsitically zips elements using the specified function, terminating when the end of either branch is reached naturally.

Determinsitically zips elements using the specified function, terminating when the end of either branch is reached naturally.

Example
scala> Stream(1, 2, 3).zipWith(Stream(4, 5, 6, 7))(_ + _).toList
res0: List[Int] = List(5, 7, 9)
def zipWithIndex: Stream[F, (O, Long)]

Zips the elements of the input stream with its indices, and returns the new stream.

Zips the elements of the input stream with its indices, and returns the new stream.

Example
scala> Stream("The", "quick", "brown", "fox").zipWithIndex.toList
res0: List[(String,Long)] = List((The,0), (quick,1), (brown,2), (fox,3))
def zipWithNext: Stream[F, (O, Option[O])]

Zips each element of this stream with the next element wrapped into Some. The last element is zipped with None.

Zips each element of this stream with the next element wrapped into Some. The last element is zipped with None.

Example
scala> Stream("The", "quick", "brown", "fox").zipWithNext.toList
res0: List[(String,Option[String])] = List((The,Some(quick)), (quick,Some(brown)), (brown,Some(fox)), (fox,None))
def zipWithPrevious: Stream[F, (Option[O], O)]

Zips each element of this stream with the previous element wrapped into Some. The first element is zipped with None.

Zips each element of this stream with the previous element wrapped into Some. The first element is zipped with None.

Example
scala> Stream("The", "quick", "brown", "fox").zipWithPrevious.toList
res0: List[(Option[String],String)] = List((None,The), (Some(The),quick), (Some(quick),brown), (Some(brown),fox))
def zipWithPreviousAndNext: Stream[F, (Option[O], O, Option[O])]

Zips each element of this stream with its previous and next element wrapped into Some. The first element is zipped with None as the previous element, the last element is zipped with None as the next element.

Zips each element of this stream with its previous and next element wrapped into Some. The first element is zipped with None as the previous element, the last element is zipped with None as the next element.

Example
scala> Stream("The", "quick", "brown", "fox").zipWithPreviousAndNext.toList
res0: List[(Option[String],String,Option[String])] = List((None,The,Some(quick)), (Some(The),quick,Some(brown)), (Some(quick),brown,Some(fox)), (Some(brown),fox,None))
def zipWithScan[O2](z: O2)(f: (O2, O) => O2): Stream[F, (O, O2)]

Zips the input with a running total according to S, up to but not including the current element. Thus the initial z value is the first emitted to the output:

Zips the input with a running total according to S, up to but not including the current element. Thus the initial z value is the first emitted to the output:

See also
Example
scala> Stream("uno", "dos", "tres", "cuatro").zipWithScan(0)(_ + _.length).toList
res0: List[(String,Int)] = List((uno,0), (dos,3), (tres,6), (cuatro,10))
def zipWithScan1[O2](z: O2)(f: (O2, O) => O2): Stream[F, (O, O2)]

Zips the input with a running total according to S, including the current element. Thus the initial z value is the first emitted to the output:

Zips the input with a running total according to S, including the current element. Thus the initial z value is the first emitted to the output:

See also
Example
scala> Stream("uno", "dos", "tres", "cuatro").zipWithScan1(0)(_ + _.length).toList
res0: List[(String, Int)] = List((uno,3), (dos,6), (tres,10), (cuatro,16))

Deprecated methods

@deprecated("Use translate instead", "3.0")
def translateInterruptible[F2[x], G[_]](u: FunctionK[F2, G]): Stream[G, O]

Translates effect type from F to G using the supplied FunctionK.

Translates effect type from F to G using the supplied FunctionK.

Deprecated