pekko/akka-docs-dev/rst/scala/cookbook.rst

.. _stream-cookbook-scala

################
Streams Cookbook
################

Introduction
============

This is a collection of patterns to demonstrate various usage of the Akka Streams API by solving small targeted
problems in the format of "recipes". The purpose of this page is to give inspiration and ideas how to approach
various small tasks involving streams. The recipes in this page can be used directly as-is, but they are most powerful as
starting points: customization of the code snippets is warmly encouraged.

This part also serves as supplementary material for the main body of documentation. It is a good idea to have this page
open while reading the manual and look for examples demonstrating various streaming concepts
as they appear in the main body of documentation.

Working with Flows
==================

In this collection we show simple recipes that involve linear flows. The recipes in this section are rather
general, more targeted recipes are available as separate sections ("Working with rate", "Working with IO").

Logging elements of a stream
----------------------------

**Situation:** During development it is sometimes helpful to see what happens in a particular section of a stream.

The simplest solution is to simply use a ``map`` operation and use ``println`` to print the elements received to the console.
While this recipe is rather simplistic, it is often suitable for a quick debug session.

.. includecode:: code/docs/stream/cookbook/RecipeLoggingElements.scala#println-debug

If a proper logging solution is needed another approach is to create a :class:`PushStage` and override all upstream event
handlers, emitting log information through an Akka :class:`LoggingAdapter`. This small stage does not influence
the elements flowing in the stream, it just emits them unmodified by calling ``ctx.push(elem)`` in its ``onPush``
event handler logic.

.. includecode:: code/docs/stream/cookbook/RecipeLoggingElements.scala#loggingadapter

Flattening a stream of sequences
--------------------------------

**Problem:** A stream is given as a stream of sequence of elements, but a stream of elements needed instead, streaming
all the nested elements inside the sequences separately.

The ``mapConcat`` operation can be used to implement a one-to-many transformation of elements using a mapper function
in the form of ``In ⇒ immutable.Seq[Out]``. In this case we want to map a ``Seq`` of elements to the elements in the
collection itself, so we can just call ``mapConcat(identity)``.

.. includecode:: code/docs/stream/cookbook/RecipeFlattenSeq.scala#flattening-seqs

Draining a stream to a strict collection
----------------------------------------

**Situation:** A finite sequence of elements is given as a stream, but a scala collection is needed instead.

In this recipe we will use the ``grouped`` stream operation that groups incoming elements into a stream of limited
size collections (it can be seen as the almost opposite version of the "Flattening a stream of sequences" recipe
we showed before). By using a ``grouped(MaxAllowedSeqSize).runWith(Sink.head)`` we first create a stream of groups
with maximum size of ``MaxAllowedSeqSize`` and then we take the first element of this stream. What we get is a
:class:`Future` containing a sequence with all the elements of the original up to ``MaxAllowedSeqSize`` size (further
elements are dropped).

.. includecode:: code/docs/stream/cookbook/RecipeToStrict.scala#draining-to-seq

Calculating the digest of a ByteString stream
---------------------------------------------

**Problem:** A stream of bytes is given as a stream of ``ByteStrings`` and we want to calculate the cryptographic digest
of the stream.

This recipe uses a :class:`PushPullStage` to host a mutable :class:`MessageDigest` class (part of the Java Cryptography
API) and update it with the bytes arriving from the stream. When the stream starts, the ``onPull`` handler of the
stage is called, which just bubbles up the ``pull`` event to its upstream. As a response to this pull, a ByteString
chunk will arrive (``onPush``) which we use to update the digest, then it will pull for the next chunk.

Eventually the stream of ``ByteStrings`` depletes and we get a notification about this event via ``onUpstreamFinish``.
At this point we want to emit the digest value, but we cannot do it in this handler directly. Instead we call
``ctx.absorbTermination`` signalling to our context that we do not yet want to finish. When the environment decides that
we can emit further elements ``onPull`` is called again, and we see ``ctx.isFinishing`` returning true (since the upstream
source has been depleted already). Since we only want to emit a final element it is enough to call ``ctx.pushAndFinish``
passing the digest ByteString to be emitted.

.. includecode:: code/docs/stream/cookbook/RecipeDigest.scala#calculating-digest

Parsing lines from a stream of ByteStrings
------------------------------------------

**Problem:** A stream of bytes is given as a stream of ``ByteStrings`` containing lines terminated by line ending
characters (or, alternatively, containing binary frames delimited by a special delimiter byte sequence) which
needs to be parsed.

We express our solution as a :class:`StatefulStage` because it has support for emitting multiple elements easily
through its ``emit(iterator, ctx)`` helper method. Since an incoming ByteString chunk might contain multiple lines (frames)
this feature comes in handy.

To create the parser we only need to hook into the ``onPush`` handler. We maintain a buffer of bytes (expressed as
a :class:`ByteString`) by simply concatenating incoming chunks with it. Since we don't want to allow unbounded size
lines (records) we always check if the buffer size is larger than the allowed ``maximumLineBytes`` value, and terminate
the stream if this invariant is violated.

After we updated the buffer, we try to find the terminator sequence as a subsequence of the current buffer. To be
efficient, we also maintain a pointer ``nextPossibleMatch`` into the buffer so that we only search that part of the
buffer where new matches are possible.

The search for a match is done in two steps: first we try to search for the first character of the terminator sequence
in the buffer. If we find a match, we do a full subsequence check to see if we had a false positive or not. The parsing
logic is recursive to be able to parse multiple lines (records) contained in the decoding buffer.

.. includecode:: code/docs/stream/cookbook/RecipeParseLines.scala#parse-lines

Implementing reduce-by-key
--------------------------

**Situation:** Given a stream of elements, we want to calculate some aggregated value on different subgroups of the
elements.

The "hello world" of reduce-by-key style operations is *wordcount* which we demonstrate below. Given a stream of words
we first create a new stream ``wordStreams`` that groups the words according to the ``identity`` function, i.e. now
we have a stream of streams, where every substream will serve identical words.

To count the words, we need to process the stream of streams (the actual groups containing identical words). By mapping
over the groups and using ``fold`` (remember that ``fold`` automatically materializes and runs the stream it is used
on) we get a stream with elements of ``Future[String,Int]``. Now all we need is to flatten this stream, which
can be achieved by calling ``mapAsynch(identity)``.

There is one tricky issue to be noted here. The careful reader probably noticed that we put a ``buffer`` between the
``mapAsync()`` operation that flattens the stream of futures and the actual stream of futures. The reason for this is
that the substreams produced by ``groupBy()`` can only complete when the original upstream source completes. This means
that ``mapAsync()`` cannot pull for more substreams because it still waits on folding futures to finish, but these
futures never finish if the additional group streams are not consumed. This typical deadlock situation is resolved by
this buffer which either able to contain all the group streams (which ensures that they are already running and folding)
or fails with an explicit error instead of a silent deadlock.

.. includecode:: code/docs/stream/cookbook/RecipeReduceByKey.scala#word-count

By extracting the parts specific to *wordcount* into

* a ``groupKey`` function that defines the groups
* a ``foldZero`` that defines the zero element used by the fold on the substream given the group key
* a ``fold`` function that does the actual reduction

we get a generalized version below:

.. includecode:: code/docs/stream/cookbook/RecipeReduceByKey.scala#reduce-by-key-general

.. note::
  Please note that the reduce-by-key version we discussed above is sequential, in other words it is **NOT** a
  parallelization pattern like mapReduce and similar frameworks.

Sorting elements to multiple groups with groupBy
------------------------------------------------

**Situation:** The ``groupBy`` operation strictly partitions incoming elements, each element belongs to exactly one group.
Sometimes we want to map elements into multiple groups simultaneously.

To achieve the desired result, we attack the problem in two steps:

* first, using a function ``topicMapper`` that gives a list of topics (groups) a message belongs to, we transform our
  stream of ``Message`` to a stream of ``(Message, Topic)`` where for each topic the message belongs to a separate pair
  will be emitted. This is achieved by using ``mapConcat``
* Then we take this new stream of message topic pairs (containing a separate pair for each topic a given message
  belongs to) and feed it into groupBy, using the topic as the group key.

.. includecode:: code/docs/stream/cookbook/RecipeMultiGroupBy.scala#multi-groupby

Working with Graphs
===================

In this collection we show recipes that use stream graph elements to achieve various goals.

Triggering the flow of elements programmatically
------------------------------------------------

**Situation:** Given a stream of elements we want to control the emission of those elements according to a trigger signal.
In other words, even if the stream would be able to flow (not being backpressured) we want to hold back elements until a
trigger signal arrives.

This recipe solves the problem by simply zipping the stream of ``Message`` elments with the stream of ``Trigger``
signals. Since ``Zip`` produces pairs, we simply map the output stream selecting the first element of the pair.

.. includecode:: code/docs/stream/cookbook/RecipeManualTrigger.scala#manually-triggered-stream

Alternatively, instead of using a ``Zip``, and then using ``map`` to get the first element of the pairs, we can avoid
creating the pairs in the first place by using ``ZipWith`` which takes a two argument function to produce the output
element. If this function would return a pair of the two argument it would be exactly the behavior of ``Zip`` so
``ZipWith`` is a generalization of zipping.

.. includecode:: code/docs/stream/cookbook/RecipeManualTrigger.scala#manually-triggered-stream-zipwith


Balancing jobs to a fixed pool of workers
-----------------------------------------

**Situation:** Given a stream of jobs and a worker process expressed as a :class:`Flow` create a pool of workers
that automatically balances incoming jobs to available workers, then merges the results.

We will express our solution as a function that takes a worker flow and the number of workers to be allocated and gives
a flow that internally contains a pool of these workers. To achieve the desired result we will create a :class:`Flow`
from a graph.

The graph consists of a ``Balance`` node which is a special fan-out operation that tries to route elements to available
downstream consumers. In a ``for`` loop we wire all of our desired workers as outputs of this balancer element, then
we wire the outputs of these workers to a ``Merge`` element that will collect the results from the workers.

To convert the graph to a :class:`Flow` we need to define special graph nodes that will correspond to the input and
output ports of the resulting :class:`Flow`. This is achieved by defining a pair of undefined sink and source which
we return from the builder block.

.. includecode:: code/docs/stream/cookbook/RecipeWorkerPool.scala#worker-pool

Working with rate
=================

This collection of recipes demonstrate various patterns where rate differences between upstream and downstream
needs to be handled by other strategies than simple backpressure.

Dropping elements
-----------------

**Situation:** Given a fast producer and a slow consumer, we want to drop elements if necessary to not slow down
the producer too much.

This can be solved by using the most versatile rate-transforming operation, ``conflate``. Conflate can be thought as
a special ``fold`` operation that collapses multiple upstream elements into one aggregate element if needed to keep
the speed of the upstream unaffected by the downstream.

When the upstream is faster, the fold process of the ``conflate`` starts. This folding needs a zero element, which
is given by a ``seed`` function that takes the current element and produces a zero for the folding process. In our
case this is ``identity`` so our folding state starts form the message itself. The folder function is also
special: given the aggregate value (the last message) and the new element (the freshest element) our aggregate state
becomes simply the freshest element. This choice of functions results in a simple dropping operation.

.. includecode:: code/docs/stream/cookbook/RecipeSimpleDrop.scala#simple-drop

Dropping broadcast
------------------

**Situation:** The default ``Broadcast`` graph element is properly backpressured, but that means that a slow downstream
consumer can hold back the other downstream consumers resulting in lowered throughput. In other words the rate of
``Broadcast`` is the rate of its slowest downstream consumer. In certain cases it is desirable to allow faster consumers
to progress independently of their slower siblings by dropping elements if necessary.

One solution to this problem is to append a ``buffer`` element in front of all of the downstream consumers
defining a dropping strategy instead of the default ``Backpressure``. This allows small temporary rate differences
between the different consumers (the buffer smooths out small rate variances), but also allows faster consumers to
progress by dropping from the buffer of the slow consumers if necessary.

.. includecode:: code/docs/stream/cookbook/RecipeDroppyBroadcast.scala#droppy-bcast

Collecting missed ticks
-----------------------

**Situation:** Given a regular (stream) source of ticks, instead of trying to backpressure the producer of the ticks
we want to keep a counter of the missed ticks instead and pass it down when possible.

We will use ``conflate`` to solve the problem. Conflate takes two functions:

* A seed function that produces the zero element for the folding process that happens when the upstream is faster than
  the downstream. In our case the seed function is a constant function that returns 0 since there were no missed ticks
  at that point.
* A fold function that is invoked when multiple upstream messages needs to be collapsed to an aggregate value due
  to the insufficient processing rate of the downstream. Our folding function simply increments the currently stored
  count of the missed ticks so far.

As a result, we have a stream of ``Int`` where the number represents the missed ticks. A number 0 means that we were
able to consume the tick fast enough (i.e. zero means: 1 non-missed tick + 0 missed ticks)

.. includecode:: code/docs/stream/cookbook/RecipeMissedTicks.scala#missed-ticks

Create a stream processor that repeats the last element seen
------------------------------------------------------------

**Situation:** Given a producer and consumer, where the rate of neither is known in advance, we want to ensure that none
of them is slowing down the other by dropping earlier unconsumed elements from the upstream if necessary, and repeating
the last value for the downstream if necessary.

We have two options to implement this feature. In both cases we will use :class:`DetachedStage` to build our custom
element (:class:`DetachedStage` is specifically designed for rate translating elements just like ``conflate``,
``expand`` or ``buffer``). In the first version we will use a provided initial value ``initial`` that will be used
to feed the downstream if no upstream element is ready yet. In the ``onPush()`` handler we just overwrite the
``currentValue`` variable and immediately relieve the upstream by calling ``pull()`` (remember, implementations of
:class:`DetachedStage` are not allowed to call ``push()`` as a response to ``onPush()`` or call ``pull()`` as a response
of ``onPull()``). The downstream ``onPull`` handler is very similar, we immediately relieve the downstream by
emitting ``currentValue``.

.. includecode:: code/docs/stream/cookbook/RecipeHold.scala#hold-version-1

While it is relatively simple, the drawback of the first version is that it needs an arbitrary initial element which is not
always possible to provide. Hence, we create a second version where the downstream might need to wait in one single
case: if the very first element is not yet available.

We introduce a boolean variable ``waitingFirstValue`` to denote whether the first element has been provided or not
(alternatively an :class:`Option` can be used for ``currentValue`` of if the element type is a subclass of AnyRef
a null can be used with the same purpose). In the downstream ``onPull()`` handler the difference from the previous
version is that we call ``hold()`` if the first element is not yet available and thus blocking our downstream. The
upstream ``onPush()`` handler sets ``waitingFirstValue`` to false, and after checking if ``hold()`` has been called it
either releaves the upstream producer, or both the upstream producer and downstream consumer by calling ``pushAndPull()``

.. includecode:: code/docs/stream/cookbook/RecipeHold.scala#hold-version-2

Globally limiting the rate of a set of streams
----------------------------------------------

**Situation:** Given a set of independent streams that we cannot merge, we want to globally limit the aggregate
throughput of the set of streams.

One possible solution uses a shared actor as the global limiter combined with mapAsync to create a reusable
:class:`Flow` that can be plugged into a stream to limit its rate.

As the first step we define an actor that will do the accounting for the global rate limit. The actor maintains
a timer, a counter for pending permit tokens and a queue for possibly waiting participants. The actor has
an ``open`` and ``closed`` state. The actor is in the ``open`` state while it has still pending permits. Whenever a
request for permit arrives as a ``WantToPass`` message to the actor the number of available permits is decremented
and we notify the sender that it can pass by answering with a ``MayPass`` message. If the amount of permits reaches
zero, the actor transitions to the ``closed`` state. In this state requests are not immediately answered, instead the reference
of the sender is added to a queue. Once the timer for replenishing the pending permits fires by sending a ``ReplenishTokens``
message, we increment the pending permits counter and send a reply to each of the waiting senders. If there are more
waiting senders than permits available we will stay in the ``closed`` state.

.. includecode:: code/docs/stream/cookbook/RecipeGlobalRateLimit.scala#global-limiter-actor

To create a Flow that uses this global limiter actor we use the ``mapAsync`` function with the combination of the ``ask``
pattern. We also define a timeout, so if a reply is not received during the configured maximum wait period the returned
future from ``ask`` will fail, which will fail the corresponding stream as well.

.. includecode:: code/docs/stream/cookbook/RecipeGlobalRateLimit.scala#global-limiter-flow

.. note::
  The global actor used for limiting introduces a global bottleneck. You might want to assign a dedicated dispatcher
  for this actor.

Working with IO
===============

Chunking up a stream of ByteStrings into limited size ByteStrings
-----------------------------------------------------------------

**Situation:** Given a stream of ByteStrings we want to produce a stream of ByteStrings containing the same bytes in
the same sequence, but capping the size of ByteStrings. In other words we want to slice up ByteStrings into smaller
chunks if they exceed a size threshold.

This can be achieved with a single :class:`PushPullStage`. The main logic of our stage is in ``emitChunkOrPull()``
which implements the following logic:

* if the buffer is empty, we pull for more bytes
* if the buffer is nonEmpty, we split it according to the ``chunkSize``. This will give a next chunk that we will emit,
  and an empty or nonempty remaining buffer.

Both ``onPush()`` and ``onPull()`` calls ``emitChunkOrPull()`` the only difference is that the push handler also stores
the incoming chunk by appending to the end of the buffer.

.. includecode:: code/docs/stream/cookbook/RecipeByteStrings.scala#bytestring-chunker

Limit the number of bytes passing through a stream of ByteStrings
-----------------------------------------------------------------

**Situation:** Given a stream of ByteStrings we want to fail the stream if more than a given maximum of bytes has been
consumed.

This recipe uses a :class:`PushStage` to implement the desired feature. In the only handler we override,
``onPush()`` we just update a counter and see if it gets larger than ``maximumBytes``. If a violation happens
we signal failure, otherwise we forward the chunk we have received.

.. includecode:: code/docs/stream/cookbook/RecipeByteStrings.scala#bytes-limiter

Compact ByteStrings in a stream of ByteStrings
----------------------------------------------

**Situation:** After a long stream of transformations, due to their immutable, structural sharing nature ByteStrings may
refer to multiple original ByteString instances unnecessarily retaining memory. As the final step of a transformation
chain we want to have clean copies that are no longer referencing the original ByteStrings.

The recipe is a simple use of map, calling the ``compact()`` method of the :class:`ByteString` elements. This does
copying of the underlying arrays, so this should be the last element of a long chain if used.

.. includecode:: code/docs/stream/cookbook/RecipeByteStrings.scala#compacting-bytestrings

Injecting keep-alive messages into a stream of ByteStrings
----------------------------------------------------------

**Situation:** Given a communication channel expressed as a stream of ByteStrings we want to inject keep-alive messages
but only if this does not interfere with normal traffic.

All this recipe needs is the ``MergePreferred`` element which is a version of a merge that is not fair. In other words,
whenever the merge can choose because multiple upstream producers have elements to produce it will always choose the
preferred upstream effectively giving it an absolute priority.

.. includecode:: code/docs/stream/cookbook/RecipeKeepAlive.scala#inject-keepalive