.. _stream-cookbook-java: ################ 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. If you need a quick reference of the available processing stages used in the recipes see :ref:`stages-overview`. 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 (:ref:`stream-rate-java`, :ref:`stream-io-java`). 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/javadsl/cookbook/RecipeLoggingElements.java#println-debug Another approach to logging is to use ``log()`` operation which allows configuring logging for elements flowing through the stream as well as completion and erroring. .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeLoggingElements.java#log-custom Flattening a stream of sequences -------------------------------- **Situation:** 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 -> List``. In this case we want to map a ``List`` of elements to the elements in the collection itself, so we can just call ``mapConcat(l -> l)``. .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeFlattenList.java#flattening-lists 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(MAX_ALLOWED_SIZE)`` we create a stream of groups with maximum size of ``MaxAllowedSeqSize`` and then we take the first element of this stream by attaching a ``Sink.head()``. What we get is a :class:`CompletionStage` containing a sequence with all the elements of the original up to ``MAX_ALLOWED_SIZE`` size (further elements are dropped). .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeToStrict.java#draining-to-list Calculating the digest of a ByteString stream --------------------------------------------- **Situation:** 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/javadsl/cookbook/RecipeDigest.java#calculating-digest .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeDigest.java#calculating-digest2 .. _cookbook-parse-lines-java: Parsing lines from a stream of ByteStrings ------------------------------------------ **Situation:** 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. The :class:`Framing` helper class contains a convenience method to parse messages from a stream of ``ByteStrings``: .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeParseLines.java#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 that groups the words according to the ``i -> i`` 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). ``groupBy`` returns a :class:`SubSource`, which means that we transform the resulting substreams directly. In this case we use the ``reduce`` combinator to aggregate the word itself and the number of its occurrences within a :class:`Pair`. Each substream will then emit one final value—precisely such a pair—when the overall input completes. As a last step we merge back these values from the substreams into one single output stream. One noteworthy detail pertains to the ``MAXIMUM_DISTINCT_WORDS`` parameter: this defines the breadth of the merge operation. Akka Streams is focused on bounded resource consumption and the number of concurrently open inputs to the merge operator describes the amount of resources needed by the merge itself. Therefore only a finite number of substreams can be active at any given time. If the ``groupBy`` operator encounters more keys than this number then the stream cannot continue without violating its resource bound, in this case ``groupBy`` will terminate with a failure. .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeReduceByKeyTest.java#word-count By extracting the parts specific to *wordcount* into * a ``groupKey`` function that defines the groups * a ``map`` map each element to value that is used by the reduce on the substream * a ``reduce`` function that does the actual reduction we get a generalized version below: .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeReduceByKeyTest.java#reduce-by-key-general .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeReduceByKeyTest.java#reduce-by-key-general2 .. note:: Please note that the reduce-by-key version we discussed above is sequential in reading the overall input stream, 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 :class:`Pair`` 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/javadsl/cookbook/RecipeMultiGroupByTest.java#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/javadsl/cookbook/RecipeManualTrigger.java#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/javadsl/cookbook/RecipeManualTrigger.java#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. .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeWorkerPool.java#worker-pool .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeWorkerPool.java#worker-pool2 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 a versatile rate-transforming operation, ``conflate``. Conflate can be thought as a special ``reduce`` 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 reducing process of the ``conflate`` starts. Our reducer function simply takes the freshest element. This cin a simple dropping operation. .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeSimpleDrop.java#simple-drop There is a version of ``conflate`` named ``conflateWithSeed`` that allows to express more complex aggregations, more similar to a ``fold``. 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/javadsl/cookbook/RecipeDroppyBroadcast.java#droppy-bcast .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeDroppyBroadcast.java#droppy-bcast2 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 ``conflateWithSeed`` 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 flow 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/javadsl/cookbook/RecipeMissedTicks.java#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/javadsl/cookbook/RecipeHold.java#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:`Optional` can be used for ``currentValue`` or if the element type is a subclass of Object a null can be used with the same purpose). In the downstream ``onPull()`` handler the difference from the previous version is that we call ``holdDownstream()`` 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 ``holdDownstream()`` has been called it either relieves the upstream producer, or both the upstream producer and downstream consumer by calling ``pushAndPull()`` .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeHold.java#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/javadsl/cookbook/RecipeGlobalRateLimit.java#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/javadsl/cookbook/RecipeGlobalRateLimit.java#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/javadsl/cookbook/RecipeByteStrings.java#bytestring-chunker .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeByteStrings.java#bytestring-chunker2 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/javadsl/cookbook/RecipeByteStrings.java#bytes-limiter .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeByteStrings.java#bytes-limiter2 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/javadsl/cookbook/RecipeByteStrings.java#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. There is a built-in operation that allows to do this directly: .. includecode:: ../code/docs/stream/javadsl/cookbook/RecipeKeepAlive.java#inject-keepalive