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 Overview of built-in stages and their semantics.
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 (Buffers and working with rate, Working with streaming 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.
mySource.map(elem -> {
System.out.println(elem);
return elem;
});
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.
// customise log levels
mySource.log("before-map")
.withAttributes(Attributes.createLogLevels(onElement, onFinish, onFailure))
.map(i -> analyse(i));
// or provide custom logging adapter
final LoggingAdapter adapter = Logging.getLogger(system, "customLogger");
mySource.log("custom", adapter);
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<Out>. 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).
Source<List<Message>, NotUsed> myData = someDataSource;
Source<Message, NotUsed> flattened = myData.mapConcat(i -> i);
Draining a stream to a strict collection
Situation: A possibly unbounded sequence of elements is given as a stream, which needs to be collected into a Scala collection while ensuring boundedness
A common situation when working with streams is one where we need to collect incoming elements into a Scala collection. This operation is supported via Sink.seq which materializes into a CompletionStage<List<T>>.
The function limit or take should always be used in conjunction in order to guarantee stream boundedness, thus preventing the program from running out of memory.
For example, this is best avoided:
// Dangerous: might produce a collection with 2 billion elements!
final CompletionStage<List<String>> strings = mySource.runWith(Sink.seq(), mat);
Rather, use limit or take to ensure that the resulting List will contain only up to MAX_ALLOWED_SIZE elements:
final int MAX_ALLOWED_SIZE = 100;
// OK. Future will fail with a `StreamLimitReachedException`
// if the number of incoming elements is larger than max
final CompletionStage<List<String>> strings =
mySource.limit(MAX_ALLOWED_SIZE).runWith(Sink.seq(), mat);
// OK. Collect up until max-th elements only, then cancel upstream
final CompletionStage<List<String>> strings =
mySource.take(MAX_ALLOWED_SIZE).runWith(Sink.seq(), mat);
Calculating the digest of a ByteString stream
Situation: A stream of bytes is given as a stream of ByteString s and we want to calculate the cryptographic digest of the stream.
This recipe uses a GraphStage to host a mutable 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 ByteString s 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 with push in this handler directly since there may be no downstream demand. Instead we call emit which will temporarily replace the handlers, emit the provided value when demand comes in and then reset the stage state. It will then complete the stage.
class DigestCalculator extends GraphStage<FlowShape<ByteString, ByteString>> {
private final String algorithm;
public Inlet<ByteString> in = Inlet.<ByteString>create("DigestCalculator.in");
public Outlet<ByteString> out = Outlet.<ByteString>create("DigestCalculator.out");
private FlowShape<ByteString, ByteString> shape = FlowShape.of(in, out);
public DigestCalculator(String algorithm) {
this.algorithm = algorithm;
}
@Override
public FlowShape<ByteString, ByteString> shape() {
return shape;
}
@Override
public GraphStageLogic createLogic(Attributes inheritedAttributes) {
return new GraphStageLogic(shape) {
final MessageDigest digest;
{
try {
digest = MessageDigest.getInstance(algorithm);
} catch(NoSuchAlgorithmException ex) {
throw new RuntimeException(ex);
}
setHandler(out, new AbstractOutHandler() {
@Override
public void onPull() throws Exception {
pull(in);
}
});
setHandler(in, new AbstractInHandler() {
@Override
public void onPush() throws Exception {
ByteString chunk = grab(in);
digest.update(chunk.toArray());
pull(in);
}
@Override
public void onUpstreamFinish() throws Exception {
// If the stream is finished, we need to emit the digest
// before completing
emit(out, ByteString.fromArray(digest.digest()));
completeStage();
}
});
}
};
}
}
final Source<ByteString, NotUsed> digest = data
.via(new DigestCalculator("SHA-256"));
Parsing lines from a stream of ByteStrings
Situation: A stream of bytes is given as a stream of ByteString s 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 Framing helper class contains a convenience method to parse messages from a stream of ByteString s:
final Source<String, NotUsed> lines = rawData
.via(Framing.delimiter(ByteString.fromString("\r\n"), 100, FramingTruncation.ALLOW))
.map(b -> b.utf8String());
Dealing with compressed data streams
Situation: A gzipped stream of bytes is given as a stream of ByteString s, for example from a FileIO source.
The Compression helper class contains convenience methods for decompressing data streams compressed with Gzip or Deflate.
final Source<String, NotUsed> uncompressed = compressed
.via(Compression.gunzip(100))
.map(b -> b.utf8String());
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 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 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.
final int MAXIMUM_DISTINCT_WORDS = 1000;
final Source<Pair<String, Integer>, NotUsed> counts = words
// split the words into separate streams first
.groupBy(MAXIMUM_DISTINCT_WORDS, i -> i)
//transform each element to pair with number of words in it
.map(i -> new Pair<>(i, 1))
// add counting logic to the streams
.reduce((left, right) -> new Pair<>(left.first(), left.second() + right.second()))
// get a stream of word counts
.mergeSubstreams();
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:
static public <In, K, Out> Flow<In, Pair<K, Out>, NotUsed> reduceByKey(
int maximumGroupSize,
Function<In, K> groupKey,
Function<In, Out> map,
Function2<Out, Out, Out> reduce) {
return Flow.<In> create()
.groupBy(maximumGroupSize, groupKey)
.map(i -> new Pair<>(groupKey.apply(i), map.apply(i)))
.reduce((left, right) -> new Pair<>(left.first(), reduce.apply(left.second(), right.second())))
.mergeSubstreams();
}
final int MAXIMUM_DISTINCT_WORDS = 1000;
Source<Pair<String, Integer>, NotUsed> counts = words.via(reduceByKey(
MAXIMUM_DISTINCT_WORDS,
word -> word,
word -> 1,
(left, right) -> left + right));
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 Pair<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.
final Function<Message, List<Topic>> topicMapper = m -> extractTopics(m);
final Source<Pair<Message, Topic>, NotUsed> messageAndTopic = elems
.mapConcat((Message msg) -> {
List<Topic> topicsForMessage = topicMapper.apply(msg);
// Create a (Msg, Topic) pair for each of the topics
// the message belongs to
return topicsForMessage
.stream()
.map(topic -> new Pair<Message, Topic>(msg, topic))
.collect(toList());
});
SubSource<Pair<Message, Topic>, NotUsed> multiGroups = messageAndTopic
.groupBy(2, pair -> pair.second())
.map(pair -> {
Message message = pair.first();
Topic topic = pair.second();
// do what needs to be done
});
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 elements with the stream of Trigger signals. Since Zip produces pairs, we simply map the output stream selecting the first element of the pair.
final RunnableGraph<Pair<TestPublisher.Probe<Trigger>, TestSubscriber.Probe<Message>>> g =
RunnableGraph.<Pair<TestPublisher.Probe<Trigger>, TestSubscriber.Probe<Message>>>fromGraph(
GraphDSL.create(
triggerSource,
messageSink,
(p, s) -> new Pair<>(p, s),
(builder, source, sink) -> {
SourceShape<Message> elements =
builder.add(Source.from(Arrays.asList("1", "2", "3", "4")).map(t -> new Message(t)));
FlowShape<Pair<Message, Trigger>, Message> takeMessage =
builder.add(Flow.<Pair<Message, Trigger>>create().map(p -> p.first()));
final FanInShape2<Message, Trigger, Pair<Message, Trigger>> zip =
builder.add(Zip.create());
builder.from(elements).toInlet(zip.in0());
builder.from(source).toInlet(zip.in1());
builder.from(zip.out()).via(takeMessage).to(sink);
return ClosedShape.getInstance();
}
)
);
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.
final RunnableGraph<Pair<TestPublisher.Probe<Trigger>, TestSubscriber.Probe<Message>>> g =
RunnableGraph.<Pair<TestPublisher.Probe<Trigger>, TestSubscriber.Probe<Message>>>fromGraph(
GraphDSL.create(
triggerSource,
messageSink,
(p, s) -> new Pair<>(p, s),
(builder, source, sink) -> {
final SourceShape<Message> elements =
builder.add(Source.from(Arrays.asList("1", "2", "3", "4")).map(t -> new Message(t)));
final FanInShape2<Message, Trigger, Message> zipWith =
builder.add(ZipWith.create((msg, trigger) -> msg));
builder.from(elements).toInlet(zipWith.in0());
builder.from(source).toInlet(zipWith.in1());
builder.from(zipWith.out()).to(sink);
return ClosedShape.getInstance();
}
)
);
Balancing jobs to a fixed pool of workers
Situation: Given a stream of jobs and a worker process expressed as a 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 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 make the worker stages run in parallel we mark them as asynchronous with async().
public static <In, Out> Flow<In, Out, NotUsed> balancer(
Flow<In, Out, NotUsed> worker, int workerCount) {
return Flow.fromGraph(GraphDSL.create(b -> {
boolean waitForAllDownstreams = true;
final UniformFanOutShape<In, In> balance =
b.add(Balance.<In>create(workerCount, waitForAllDownstreams));
final UniformFanInShape<Out, Out> merge =
b.add(Merge.<Out>create(workerCount));
for (int i = 0; i < workerCount; i++) {
b.from(balance.out(i)).via(b.add(worker.async())).toInlet(merge.in(i));
}
return FlowShape.of(balance.in(), merge.out());
}));
}
Flow<Message, Message, NotUsed> balancer = balancer(worker, 3);
Source<Message, NotUsed> processedJobs = data.via(balancer);
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 in a simple dropping operation.
final Flow<Message, Message, NotUsed> droppyStream =
Flow.of(Message.class).conflate((lastMessage, newMessage) -> newMessage);
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.
// Makes a sink drop elements if too slow
public <T> Sink<T, CompletionStage<Done>> droppySink(Sink<T, CompletionStage<Done>> sink, int size) {
return Flow.<T> create()
.buffer(size, OverflowStrategy.dropHead())
.toMat(sink, Keep.right());
}
RunnableGraph.fromGraph(GraphDSL.create(builder -> {
final int outputCount = 3;
final UniformFanOutShape<Integer, Integer> bcast =
builder.add(Broadcast.create(outputCount));
builder.from(builder.add(myData)).toFanOut(bcast);
builder.from(bcast).to(builder.add(droppySink(mySink1, 10)));
builder.from(bcast).to(builder.add(droppySink(mySink2, 10)));
builder.from(bcast).to(builder.add(droppySink(mySink3, 10)));
return ClosedShape.getInstance();
}));
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)
final Flow<Tick, Integer, NotUsed> missedTicks =
Flow.of(Tick.class).conflateWithSeed(tick -> 0, (missed, tick) -> missed + 1);
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 GraphStage to build our custom element. 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(). The downstream onPull handler is very similar, we immediately relieve the downstream by emitting currentValue.
class HoldWithInitial<T> extends GraphStage<FlowShape<T, T>> {
public Inlet<T> in = Inlet.<T>create("HoldWithInitial.in");
public Outlet<T> out = Outlet.<T>create("HoldWithInitial.out");
private FlowShape<T, T> shape = FlowShape.of(in, out);
private final T initial;
public HoldWithInitial(T initial) {
this.initial = initial;
}
@Override
public FlowShape<T, T> shape() {
return shape;
}
@Override
public GraphStageLogic createLogic(Attributes inheritedAttributes) {
return new GraphStageLogic(shape) {
private T currentValue = initial;
{
setHandler(in, new AbstractInHandler() {
@Override
public void onPush() throws Exception {
currentValue = grab(in);
pull(in);
}
});
setHandler(out, new AbstractOutHandler() {
@Override
public void onPull() throws Exception {
push(out, currentValue);
}
});
}
@Override
public void preStart() {
pull(in);
}
};
}
}
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 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 check if we have received the first value and only emit if we have. This leads to that when the first element comes in we must check if there possibly already was demand from downstream so that we in that case can push the element directly.
class HoldWithWait<T> extends GraphStage<FlowShape<T, T>> {
public Inlet<T> in = Inlet.<T>create("HoldWithInitial.in");
public Outlet<T> out = Outlet.<T>create("HoldWithInitial.out");
private FlowShape<T, T> shape = FlowShape.of(in, out);
@Override
public FlowShape<T, T> shape() {
return shape;
}
@Override
public GraphStageLogic createLogic(Attributes inheritedAttributes) {
return new GraphStageLogic(shape) {
private T currentValue = null;
private boolean waitingFirstValue = true;
{
setHandler(in, new AbstractInHandler() {
@Override
public void onPush() throws Exception {
currentValue = grab(in);
if (waitingFirstValue) {
waitingFirstValue = false;
if (isAvailable(out)) push(out, currentValue);
}
pull(in);
}
});
setHandler(out, new AbstractOutHandler() {
@Override
public void onPull() throws Exception {
if (!waitingFirstValue) push(out, currentValue);
}
});
}
@Override
public void preStart() {
pull(in);
}
};
}
}
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 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.
public class Limiter extends AbstractActor {
public static class WantToPass {}
public static final WantToPass WANT_TO_PASS = new WantToPass();
public static class MayPass {}
public static final MayPass MAY_PASS = new MayPass();
public static class ReplenishTokens {}
public static final ReplenishTokens REPLENISH_TOKENS = new ReplenishTokens();
private final int maxAvailableTokens;
private final FiniteDuration tokenRefreshPeriod;
private final int tokenRefreshAmount;
private final List<ActorRef> waitQueue = new ArrayList<>();
private final Cancellable replenishTimer;
private int permitTokens;
public static Props props(int maxAvailableTokens, FiniteDuration tokenRefreshPeriod,
int tokenRefreshAmount) {
return Props.create(Limiter.class, maxAvailableTokens, tokenRefreshPeriod,
tokenRefreshAmount);
}
private Limiter(int maxAvailableTokens, FiniteDuration tokenRefreshPeriod,
int tokenRefreshAmount) {
this.maxAvailableTokens = maxAvailableTokens;
this.tokenRefreshPeriod = tokenRefreshPeriod;
this.tokenRefreshAmount = tokenRefreshAmount;
this.permitTokens = maxAvailableTokens;
this.replenishTimer = system.scheduler().schedule(
this.tokenRefreshPeriod,
this.tokenRefreshPeriod,
self(),
REPLENISH_TOKENS,
context().system().dispatcher(),
self());
receive(open());
}
PartialFunction<Object, BoxedUnit> open() {
return ReceiveBuilder
.match(ReplenishTokens.class, rt -> {
permitTokens = Math.min(permitTokens + tokenRefreshAmount, maxAvailableTokens);
})
.match(WantToPass.class, wtp -> {
permitTokens -= 1;
sender().tell(MAY_PASS, self());
if (permitTokens == 0) {
context().become(closed());
}
}).build();
}
PartialFunction<Object, BoxedUnit> closed() {
return ReceiveBuilder
.match(ReplenishTokens.class, rt -> {
permitTokens = Math.min(permitTokens + tokenRefreshAmount, maxAvailableTokens);
releaseWaiting();
})
.match(WantToPass.class, wtp -> {
waitQueue.add(sender());
})
.build();
}
private void releaseWaiting() {
final List<ActorRef> toBeReleased = new ArrayList<>(permitTokens);
for (int i = 0; i < permitTokens && i < waitQueue.size(); i++) {
toBeReleased.add(waitQueue.remove(i));
}
permitTokens -= toBeReleased.size();
toBeReleased.stream().forEach(ref -> ref.tell(MAY_PASS, self()));
if (permitTokens > 0) {
context().become(open());
}
}
@Override
public void postStop() {
replenishTimer.cancel();
waitQueue.stream().forEach(ref -> {
ref.tell(new Status.Failure(new IllegalStateException("limiter stopped")), self());
});
}
}
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.
public <T> Flow<T, T, NotUsed> limitGlobal(ActorRef limiter, FiniteDuration maxAllowedWait) {
final int parallelism = 4;
final Flow<T, T, NotUsed> f = Flow.create();
return f.mapAsync(parallelism, element -> {
final Timeout triggerTimeout = new Timeout(maxAllowedWait);
final CompletionStage<Object> limiterTriggerFuture =
PatternsCS.ask(limiter, Limiter.WANT_TO_PASS, triggerTimeout);
return limiterTriggerFuture.thenApplyAsync(response -> element, system.dispatcher());
});
}
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 ByteString s we want to produce a stream of ByteString s containing the same bytes in the same sequence, but capping the size of ByteString s. In other words we want to slice up ByteString s into smaller chunks if they exceed a size threshold.
This can be achieved with a single GraphStage. The main logic of our stage is in emitChunk() which implements the following logic:
- if the buffer is empty, and upstream is not closed we pull for more bytes, if it is closed we complete
- 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 emitChunk() the only difference is that the push handler also stores the incoming chunk by appending to the end of the buffer.
class Chunker extends GraphStage<FlowShape<ByteString, ByteString>> {
private final int chunkSize;
public Inlet<ByteString> in = Inlet.<ByteString>create("Chunker.in");
public Outlet<ByteString> out = Outlet.<ByteString>create("Chunker.out");
private FlowShape<ByteString, ByteString> shape = FlowShape.of(in, out);
public Chunker(int chunkSize) {
this.chunkSize = chunkSize;
}
@Override
public FlowShape<ByteString, ByteString> shape() {
return shape;
}
@Override
public GraphStageLogic createLogic(Attributes inheritedAttributes) {
return new GraphStageLogic(shape) {
private ByteString buffer = ByteString.empty();
{
setHandler(out, new AbstractOutHandler(){
@Override
public void onPull() throws Exception {
if (isClosed(in)) emitChunk();
else pull(in);
}
});
setHandler(in, new AbstractInHandler() {
@Override
public void onPush() throws Exception {
ByteString elem = grab(in);
buffer = buffer.concat(elem);
emitChunk();
}
@Override
public void onUpstreamFinish() throws Exception {
if (buffer.isEmpty()) completeStage();
else {
// There are elements left in buffer, so
// we keep accepting downstream pulls and push from buffer until emptied.
//
// It might be though, that the upstream finished while it was pulled, in which
// case we will not get an onPull from the downstream, because we already had one.
// In that case we need to emit from the buffer.
if (isAvailable(out)) emitChunk();
}
}
});
}
private void emitChunk() {
if (buffer.isEmpty()) {
if (isClosed(in)) completeStage();
else pull(in);
} else {
Tuple2<ByteString, ByteString> split = buffer.splitAt(chunkSize);
ByteString chunk = split._1();
buffer = split._2();
push(out, chunk);
}
}
};
}
}
Source<ByteString, NotUsed> chunksStream =
rawBytes.via(new Chunker(CHUNK_LIMIT));
Limit the number of bytes passing through a stream of ByteStrings
Situation: Given a stream of ByteString s we want to fail the stream if more than a given maximum of bytes has been consumed.
This recipe uses a GraphStage 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.
class ByteLimiter extends GraphStage<FlowShape<ByteString, ByteString>> {
final long maximumBytes;
public Inlet<ByteString> in = Inlet.<ByteString>create("ByteLimiter.in");
public Outlet<ByteString> out = Outlet.<ByteString>create("ByteLimiter.out");
private FlowShape<ByteString, ByteString> shape = FlowShape.of(in, out);
public ByteLimiter(long maximumBytes) {
this.maximumBytes = maximumBytes;
}
@Override
public FlowShape<ByteString, ByteString> shape() {
return shape;
}
@Override
public GraphStageLogic createLogic(Attributes inheritedAttributes) {
return new GraphStageLogic(shape) {
private int count = 0;
{
setHandler(out, new AbstractOutHandler() {
@Override
public void onPull() throws Exception {
pull(in);
}
});
setHandler(in, new AbstractInHandler() {
@Override
public void onPush() throws Exception {
ByteString chunk = grab(in);
count += chunk.size();
if (count > maximumBytes) {
failStage(new IllegalStateException("Too much bytes"));
} else {
push(out, chunk);
}
}
});
}
};
}
}
Flow<ByteString, ByteString, NotUsed> limiter =
Flow.of(ByteString.class).via(new ByteLimiter(SIZE_LIMIT));
Compact ByteStrings in a stream of ByteStrings
Situation: After a long stream of transformations, due to their immutable, structural sharing nature ByteString s 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 ByteString s.
The recipe is a simple use of map, calling the compact() method of the ByteString elements. This does copying of the underlying arrays, so this should be the last element of a long chain if used.
Source<ByteString, NotUsed> compacted = rawBytes.map(ByteString::compact);
Injecting keep-alive messages into a stream of ByteStrings
Situation: Given a communication channel expressed as a stream of ByteString s 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:
Flow<ByteString, ByteString, NotUsed> keepAliveInject =
Flow.of(ByteString.class).keepAlive(
scala.concurrent.duration.Duration.create(1, TimeUnit.SECONDS),
() -> keepAliveMessage);
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