Packages

package root

Definition Classes: root

package org

Definition Classes: root

package apache

Definition Classes: org

package pekko

Definition Classes: apache

package stream

Definition Classes: pekko

package impl

The architecture of Apache Pekko Streams internally consists of several distinct layers:

* The DSLs like org.apache.pekko.stream.scaladsl.Flow, org.apache.pekko.stream.scaladsl.Source etc. are the user facing API for composing streams. These DSLs are a thin wrappers around the internal org.apache.pekko.stream.impl.TraversalBuilder builder classes. There are Java alternatives of these DSLs in javadsl which basically wrap their scala counterpart, delegating method calls. * The org.apache.pekko.stream.stage.GraphStage API is the user facing API for creating new stream operators. These classes are used by the org.apache.pekko.stream.impl.fusing.GraphInterpreter which executes islands (subgraphs) of these operators * The high level DSLs use the org.apache.pekko.stream.impl.TraversalBuilder classes to build instances of org.apache.pekko.stream.impl.Traversal which are the representation of a materializable stream description. These builders are immutable and safely shareable. Unlike the top-level DSLs, these are classic, i.e. elements are treated as Any. * The org.apache.pekko.stream.impl.Traversal is the immutable, efficient representation of a stream processing graph that can be materialized. The builders exist solely for the purpose of producing a traversal in the end. * The org.apache.pekko.stream.impl.PhasedFusingActorMaterializer is the class that is responsible for traversing and interpreting a org.apache.pekko.stream.impl.Traversal. It delegates the actual task of creating executable entities and Publishers/Producers to org.apache.pekko.stream.impl.PhaseIslands which are plugins that understand atomic operators in the graph and able to turn them into executable entities. * The org.apache.pekko.stream.impl.fusing.GraphInterpreter and its actor backed wrapper org.apache.pekko.stream.impl.fusing.ActorGraphInterpreter are used to execute synchronous islands (subgraphs) of org.apache.pekko.stream.stage.GraphStages.

For the execution layer, refer to org.apache.pekko.stream.impl.fusing.GraphInterpreter.

Design goals

The central piece for both the DSLs and materialization is the org.apache.pekko.stream.impl.Traversal. This is the representation of a Pekko Stream, basically a org.apache.pekko.stream.scaladsl.RunnableGraph. The design goals for org.apache.pekko.stream.impl.Traversal are:

* Be able to materialize a graph in one pass over the traversal * Unify materialization and fusing. The materializer should be able to construct all the necessary data structures for the interpreters and for connecting them in one go. * Avoid allocations as much as possible. * Biased implementation for the 90% case. Common cases should be as fast as possible: * wiring linear chains should be very fast. * assume that most graphs are mostly linear, with only a few generalized graph constructs thrown in. * materialization should not pay the price of island tracking if there is only a single island * assume that the number of islands is low in general * avoid "copiedModule" i.e. wrappers that exist solely for the purpose of establishing new port identities for operators that are used multiple times in the same graph. * Avoid hashmaps and prefer direct array lookup wherever possible

Semantically, a traversal is a list of commands that the materializer must execute to turn the description to a running stream. In fact, the traversal is nothing more than an immutable list, that is expressed as a tree. A tree is used to make immutable appends fast (immutable lists only have prepend as O(1) operation, append is O(N)). The materializer "recovers" the original sequence by using a local, mutable stack to properly traverse the tree structure. This is way cheaper than to immutably append to the traversal at each addition.

The "tree-ness" is expressed by explicit org.apache.pekko.stream.impl.Concat nodes that express that two traversals need to be traversed in a certain sequence, stashing away the second on a local stack until the first is fully traversed.

While traversing the traversal (basically following Concat nodes), the materializer will visit the following command types:

* org.apache.pekko.stream.impl.MaterializeAtomic: An atomic module needs to be materialized. This node also contains wiring information which we discuss later. * Materialized value computation. This is a stack based "sublanguage" to compute the final materialized value on a stack, maintained by the materializer * org.apache.pekko.stream.impl.PushNotUsed push a NotUsed value on the stack * org.apache.pekko.stream.impl.Pop pop the top of the stack and throw away * org.apache.pekko.stream.impl.Transform take the top of the stack, transform it with the provided function and put the result back on the top of the stack * org.apache.pekko.stream.impl.Compose take the top two values of the stack, invoke the provided function with these values as arguments, then put the calculated value on the top of the stack * Materialized values of atomic operators when visiting a org.apache.pekko.stream.impl.MaterializeAtomic must be pushed to the stack automatically. There are no explicit PUSH commands for this * Attributes calculation. These also are a stack language, although much simpler than the materialized value commands. For any materialized operator, the top of the attributes stack should be provided as the current effective attributes. * org.apache.pekko.stream.impl.PushAttributes combines the attributes on the top of the stack with the given ones and puts the result on the attributes stack * org.apache.pekko.stream.impl.PopAttributes removes the top of the attributes stack. * Island tracking. Islands serve two purposes. First, they allow a large graph to be cut into parts that execute concurrently with each other, using asynchronous message passing between themselves. Second, they are an extension point where "plugins" (org.apache.pekko.stream.impl.PhaseIsland) can be used to specially handle subgraphs. Islands can be nested in each other. This makes "holes" in the parent island. Islands also need a stack as exiting a "hole" means returning to the parent, enclosing island and continuing where left. * org.apache.pekko.stream.impl.EnterIsland instructs the materializer that the following commands will belong to the materialization of a new island (a subgraph). The org.apache.pekko.stream.impl.IslandTag signals to the materializer which org.apache.pekko.stream.impl.PhaseIsland should be used to turn operators of this island into executable entities. * org.apache.pekko.stream.impl.ExitIsland instructs the materializer that the current island is done and the parent island is now the active one again.

Please note that the stack based materialized value computation eliminates the issues present in the older materializer which expressed these computations as an AST. We had to use optimizations for this tree so that long Keep.left chains don't explode the stack visiting a large AST. The stack based language sidesteps this issue completely as the number of these commands don't increase the stack space required to execute them, unless the computation itself requires it (which is not the case in any sane stream combination).

Graph model, offsets, slots

As a mental model, the wiring part of the Traversal (i.e. excluding the stack based sub-commands tracking materialized values, attributes, islands, i.e. things that don't contribute to the wiring structure of the graph) translates everything to a single, global, contiguous Array. Every input and output port of each operator is mapped to exactly one slot of this "mental array". Input and output ports that are considered wired together simply map to the same slot. (In practice, these slots might not be mapped to an actual global array, but multiple local arrays using some translation logic, but we will explain this later)

Input ports are mapped simply to contiguous numbers in the order they are visited. Take for example a simple traversal:

Operator1[in1, in2, out] - Operator2[out] - Operator3[in]

This results in the following slot assignments:

* Operator1.in1 -> 0 * Operator1.in2 -> 1 * Operator3.in -> 2

The materializer simply visits Stage1, Stage2, Stage3 in order, visiting the input ports of each operator in order. It then simply assigns numbers from a counter that is incremented after visiting an input port. (Please note that all org.apache.pekko.stream.impl.StreamLayout.AtomicModules maintain a stable order of their ports, so this global ordering is well defined)

Before explaining how output wiring works, it is important to settle some terminology. When we talk about ports we refer to their location in the "mental array" as slots. However, there are other entities that needs to reference various positions in this "mental array", but in these cases we use the term _offset_ to signify that these are only used for bookkeeping, they have no "place" in the "array" themselves. In particular:

* offset of a module: The offset of an org.apache.pekko.stream.impl.StreamLayout.AtomicModule is defined as the value of the input port counter when visiting the org.apache.pekko.stream.impl.MaterializeAtomic node to materialize that module. In other words, the offset of a module is the slot of its first input port (if there is any). Since modules might not have any input ports it can be that different modules share the same offset, simply because the the first one visited does not increase the input port counter. * offset of segments, islands: Defined similarly to module. The offset of an island or a segment is simply the value of the input port counter (or the first unallocated slot).

For example:

Module1[in1 = 0, in2 = 1] - Module2[out] - Module3[in = 2]

The offset of Module1 is 0, while Module2 and Module3 share the same offset of 2. Note that only input ports (slots) contribute to the offset of a module in a traversal.

Output ports are wired relative to the offset of the module they are contained in. When the materializer visits a org.apache.pekko.stream.impl.MaterializeAtomic node, it contains an Array that maps ports to a relative offset. To calculate the slot that is assigned to an output port the following formula is used:

slot = offsetOfModule + outToSlots(out.id)

Where outToSlots is the array contained in the org.apache.pekko.stream.impl.MaterializeAtomic node.

Relative addressing

The power of this structure comes from the fact that slots are assigned in a relative manner:

* input ports are assigned in sequence so the slots assigned to the input ports of a subgraph depend on the subgraph's position in the traversal * output ports are assigned relative to the offset of their owner module, which is in turn relative to its first (potential) input port (which is relative, too, because of the previous point)

This setup allows combining subgraphs without touching their internal wirings as all their internal wirings will properly resolve due to everything being relative:

+---------------+ +----+ | | | | |---------Graph1---------|--- .... ---|----Graph2----|

It is important to note that due to reusability, a Pekko Stream graph may contain the same atomic or composite multiple times in the same graph. Since these must be distinguished from each other somehow, they need port mapping (i.e. a new set of ports) to ensure that the ports of one graph are distinguishable from another. Because how the traversal relative addressing works, these are _temporary_ though, once all internal wirings are ready, these mappings can be effectively dropped as the global slot assignments uniquely identify what is wired to what. For example since Graph1 is visited before Graph2 all of the slots or offsets it uses are different from Graph2 leaving no room for misinterpretation.

Port mapping

Port mapping is the way how the DSL can distinguish between multiple instances of the same graph included multiple times. For example in the Graph DSL:

val merge1 = builder.add(Merge) val merge2 = builder.add(Merge)

the port merge1.out must be different from merge2.out.

For efficiency reasons, the linear and graph DSLs use different org.apache.pekko.stream.impl.TraversalBuilder types to build the org.apache.pekko.stream.impl.Traversal (we will discuss these next). One of the differences between the two builders are their approach to port mapping.

The simpler case is the org.apache.pekko.stream.impl.LinearTraversalBuilder. This builder only allows building linear chains of operators, hence, it can only have at most one OutPort and InPort unwired. Since there is no possible ambiguity between these two port types, there is no need for port mapping for these. Conversely, for those internal ports that are already wired, there is no need for port mapping as their relative wiring is not ambiguous (see previous section). As a result, the org.apache.pekko.stream.impl.LinearTraversalBuilder does not use any port mapping.

The generic graph builder class org.apache.pekko.stream.impl.CompositeTraversalBuilder needs port mapping as it allows adding any kind of builders in any order. When adding a module (encoded as another org.apache.pekko.stream.impl.TraversalBuilder) there are two entities in play:

* The module (builder) to be added. This builder has a few ports unwired which are usually packaged in a Shape which is stored alongside with the builder in the Graph of the DSL. When invoking methods on this builder these set of ports must be used. * The module that we are growing. This module needs a new set of ports to be used as it might add this module multiple times and needs to disambiguate these ports.

Adding to the org.apache.pekko.stream.impl.CompositeTraversalBuilder involves the following steps (pseudocode):

val newShape = shape.deepCopy() // Copy the shape of the module we want to add val newBuilder = builder.add(submodule, newShape) // Add the module, and register it with the new shape newBuilder.wire(newShape.in, ...) // Use the new ports to wire

What happens in the background is that Shape.deepCopy creates copies of the ports, and fills their mappedTo field to point to their original port counterpart. Whenever we call wire in the outer module, it delegates calls to the submodule, but using the original port (as the submodule builder has no knowledge of the external mapping):

submodule.assign(port.mappedTo, ...) // enclosing module delegating to submodule, translating ports back

Visualizing this relationship:

It is worth to note that the submodule might also continue this map-and-delegate chain to further submodules until a builder is reached that can directly perform the operation. In other words, the depth of nesting is equal to the length of mappedTo chaining.

IMPORTANT: When wiring in the enclosing module the new ports/shape MUST be used, using the original ports/shape will lead to incorrect state.

TraversalBuilders

In order to understand why builders are needed, consider wiring two ports together. Actually, we don't need to wire input ports anywhere. Their slot is implicitly assigned by their position in the traversal, there is no additional state we need to track. On the other hand, we cannot build a org.apache.pekko.stream.impl.MaterializeAtomic node until the mapping array outToSlots is fully calculated. In other words, in reality, we don't wire input ports anywhere, we only assign output ports to slots. The builders exist mainly to keep track all the necessary information to be able to assign output ports, build the outToSlots array and finally the org.apache.pekko.stream.impl.MaterializeAtomic node. The consequence of this that a org.apache.pekko.stream.impl.Traversal can be constructed as soon as all output ports are wired ("unwired" inputs don't matter).

There is a specific builder that is used for the cases where all outputs have been wired: org.apache.pekko.stream.impl.CompletedTraversalBuilder. This builder type simply contains the completed traversal plus some additional data. The reason why this builder type exists is to keep auxiliary data structures required for output port mapping only while they are needed, and shed them as soon as they are not needed anymore. Since builders may recursively contain other builders, as soon as internals are completed those contained builders transition to completed state and drop all additional data structures. This is very GC friendly as many intermediate graphs exist only in a method call, and hence most of the additional data structures are dropped before method return and can be efficiently collected by the GC.

The most generic builder is org.apache.pekko.stream.impl.CompositeTraversalBuilder. There are two main considerations this builder needs to consider:

* Enclosed modules (builders) must have a stable position in the final traversal for relative addressing to work. Since module offsets are calculated by traversal position, and outputs are wired relative to module offset, this is critical. * Enclosed builders might not be complete yet (i.e. have unwired outputs) and hence they cannot immediately give a Traversal.

The composite builder keeps a temporary list of traversal steps (in reverse order because of immutable lists) it needs to create once it is completed (all outputs wired). These steps refer to the traversal of submodules as a org.apache.pekko.stream.impl.BuilderKey which is just a placeholder where the traversal of the submodule will be stitched in. This org.apache.pekko.stream.impl.BuilderKey is also a key to a map which contains the evolving builder. The importance of this "preimage" traversal is that it keeps position of submodules stable, making relative addressing possible.

Once the composite is completed, it takes these steps (now reversing it back to normal), and builds the traversal using the submodule traversals referred to by org.apache.pekko.stream.impl.BuilderKey. Note that at this point all the submodules are org.apache.pekko.stream.impl.CompletedTraversalBuilders because there are no unwired outputs and hence the Traversal can be assembled. As the builder evolves over time, more and more of its org.apache.pekko.stream.impl.BuilderKeys will refer to org.apache.pekko.stream.impl.CompletedTraversalBuilders, shedding much of the temporary data structures.

Refer to org.apache.pekko.stream.impl.CompositeTraversalBuilder for more details.

The org.apache.pekko.stream.impl.LinearTraversalBuilder is a much simpler beast. For efficiency, it tries to work as much as possible directly on the org.apache.pekko.stream.impl.Traversal avoiding auxiliary structures. The two main considerations for this builder are:

* org.apache.pekko.stream.scaladsl.Source and org.apache.pekko.stream.scaladsl.Flow contain an unwired output port. Yet, we would like to build the traversal directly as much as possible, even though the builder is not yet completed * org.apache.pekko.stream.impl.CompositeTraversalBuilders might be included in a linear chain. These cannot provide a traversal before they are fully completed.

The linear builder, although it is one class, comes in basically two flavors:

* Purely linear builder: this contains only other linear builders, or all the composites that it includes have been fully wired before and hence their traversal is now fully incorporated. Basically this kind of builder only contains the org.apache.pekko.stream.impl.Traversal and only a couple of extra fields. * Linear builder with an incomplete composite at the end (output): In this case, we have an incomplete composite. It can only be at the end, since this is the only position where an output port can be unwired. We need to carry this builder with us until the output port is finally wired, in which case we incorporate its traversal into the already complete one, and hopefully transition to a purely linear builder.

If we consider the purely linear case, we still need to figure out how can we provide a traversal even though the last output port is unwired. The trick that is used is to wire this output port optimistically to the relative address -1 which is almost always correct (why -1? explained a bit later). If it turns out to be incorrect later, we fix it by the helper method org.apache.pekko.stream.impl.Traversal.rewireFirstTo which tears down the traversal until the wrong module is found, then fixes the port assignment. This is only possible on purely linear layouts though. Again, this is an example of the 90% rule. Most appends will not need this rewiring and hence be as fast as possible while the rarer cases suffering a minor penalty.

In the case where the last module is a composite, the above trick would not work as nothing guarantees that the module that exposed its output port is at an expected position in the traversal. Instead, we simply keep around this composite and delay construction of its part of the traversal. For details see org.apache.pekko.stream.impl.LinearTraversalBuilder as these cases are heavily commented and explained in the code.

There is another peculiarity of the linear builder we need to explain. Namely, it builds the traversal in reverse order, i.e. from Sinks towards Sources. THIS CAN BE SUPER CONFUSING AT TIMES SO PAY ATTENTION! There are two important reasons why this is needed:

* Prepending to immutable lists is more efficient. Even though we encode our traversal list as a tree, we would need stack space at materialization time as much as the length of the list if we would append to it instead of prepending. * Prepending means that most output ports refer to slots visited before, i.e. output relative offsets are negative. This means that during materialization, output ports will be wired to slots that the materializer visited before which enables an efficient one-pass materialization design. The importance of this is discussed later below.

To visualize this, imagine a simple stream:

[Source.out] -> [Map.in, Map.out] -> [Sink.in]

The traversal:

offs = 0 offs = 1 offs = 1 [Sink.in = 0] <- [Map.in = 1, Map.out = -1] <- [Source.out = -1]

Since the traversal steps are reversed compared to the DSL order, it is important to reverse materialized value computation, too.

Islands and local slots

All what we have discussed so far referred to the "mental array", the global address space in which slots are assigned to ports. This model describes the wiring of the graph perfectly, but it does not map to the local data structures needed by materialization when there are islands present. One of the important goals of this layout data structure is to be able to produce the data structures used by the org.apache.pekko.stream.impl.fusing.GraphInterpreter directly, without much translation. Unfortunately if there is an island inside a traversal, it might leave gaps in the address space:

|----Island1-----|----Island2(enclosed)----|-----Island1-----|

Since we visit Island2 before returning to Island1, the naive approach would leave a large gap between the last input port visited before entering Island2 and the first input port visited when returning to Island1. What we would like to have instead is a contiguous slot assignment from the viewpoint of Island1. This is where org.apache.pekko.stream.impl.PhasedFusingActorMaterializer and its org.apache.pekko.stream.impl.IslandTracking helper comes into the picture. These classes do the heavy-lifting of traversing the traversal and then mapping global slots to slots local to the island, delegating then the local wiring to org.apache.pekko.stream.impl.PhaseIsland implementations. For example the org.apache.pekko.stream.impl.GraphStageIsland sees only a contigous slot-space and hence it can directly construct the array for the interpreter. It is not aware of the presence of other islands or how it is represented in the global slot-space.

Materialization

Materialzation is orchestrated by the org.apache.pekko.stream.impl.PhasedFusingActorMaterializer. It basically decodes the traversal and handles islands. This top-level materializer does not really handle the wiring _inside_ an island, it only handles wiring of Publishers and Subscribers that connect islands. Instead it delegates in-island wiring to org.apache.pekko.stream.impl.PhaseIslands. For example a default fused island will be actually wired by org.apache.pekko.stream.impl.GraphStageIsland.

First, look at a traversal that has two islands:

|----Island1-----|----Island2(enclosed)----|-----Island1-----|

In this traversal, we have two islands, and three, so called _segments_. Segments are simply contiguous range of slots between org.apache.pekko.stream.impl.EnterIsland or org.apache.pekko.stream.impl.ExitIsland tags (in any combination). When the materializer encounters either an enter or exit command, it saves various information about the segment it just completed (what is its offset, how long it is measured in input slots, etc.). This information is later used to figure out if a wiring crosses island boundaries or is it local to the island.

It is important to note that the data structure for this is only allocated when there are islands. This is again the 90% rule in action. In addition, these data structures are java.util.ArrayList instances, where lookups according to some value are implemented as simple linear scans. Since in 90% of the cases these structures are very short, this is the most efficient approach. Cases where this can be a performance problem are very-very special and likely not happen in practice (no graph should contain more than a dozen of islands for example).

When it comes to deciding whether a wiring is cross-island or local, there are two cases possible:

* we encountered an output port that is wired backwards (relative address is negative). In this case we already have all the data necessary to resolve the question. * we encountered an output port that is wired forward (relative address is positive). In this case we have not yet visited that part of the traversal where the assignment points.

If we want to keep the one-pass design of the materializer, we need to delay forward wirings until we have all the information needed, i.e. we visit the target in port. The org.apache.pekko.stream.impl.PhasedFusingActorMaterializer has a data structure for tracking forward wires which it consults whenever it visits an input port. Again, this is only allocated if needed, and it is again an array with linear scan lookup. Once the target input port have been found, the rules of the wiring are the same as for backwards wiring.

backward wire (to the visited part) <------+ +------> forward wire (into the unknown) | | |----Island1-----|----Island2(enclosed)-------- ... (this is where we are now)

Remember, the org.apache.pekko.stream.impl.LinearTraversalBuilder builds its org.apache.pekko.stream.impl.Traversal in backwards order, so since most of the graphs are constructed by the linear DSLs almost all wirings will be backwards (90% rule in action again).

Backward wirings

When it comes to resolving wirings and calculating the local slots for all the islands involved there are three distinct cases.

A wiring can be in-segment:

+--------------+ | | |----Island1-----|----Island2(enclosed)----|-----Island1-----|

This means that the slot assigned to the output port still belongs to the current segment. This is easy to detect as the org.apache.pekko.stream.impl.IslandTracking class tracks the offset of the current segment. If the target input slot is larger or equal than this offset, and the wiring is backwards, then the wiring is strictly local to the island. The materializer will simply delegate to the org.apache.pekko.stream.impl.PhaseIsland to do the internal wiring. Since we know the offset of the segment in the local space of this island, calculating the local slot for the org.apache.pekko.stream.impl.PhaseIsland is simple. (This is fully documented with diagrams in org.apache.pekko.stream.impl.IslandTracking)

A wiring can be cross-segment, in-island:

+---------------------------------+ | | |----Island1-----|----Island2(enclosed)----|-----Island1-----|

In this case, the target slot is in another, but already visited segment. The org.apache.pekko.stream.impl.IslandTracking class needs to first find the segment in which the target slot is. Since each segment keeps a reference to its org.apache.pekko.stream.impl.PhaseIsland instance that handles the internal wiring a simple reference equality check will tell us if the target segment is in the same island or not. In this case it is, so all we need is to compensate for any possible holes (punched by enclosed islands) to calculate the local slot for the island and call the appropriate callback on the org.apache.pekko.stream.impl.PhaseIsland. (This is fully documented with diagrams in org.apache.pekko.stream.impl.IslandTracking)

Finally a wiring can be cross-segment, cross-island:

+------------------------+ | | |----Island1-----|----Island2(enclosed)----|-----Island1-----|

This means, that the steps were similar as in the previous case until that point where we check the reference equality of the current org.apache.pekko.stream.impl.PhaseIsland with that of the target segment (we have already found the target segment). In this case, we need to calculate the local slot in the target island (similar to the previous case) and try to wire the two islands together. Now, instead of delegating the wiring to the phases, we ask the output org.apache.pekko.stream.impl.PhaseIsland to provide a Publisher and then we ask the target island to take this Publisher.

Refer to org.apache.pekko.stream.impl.IslandTracking for all the nasty details of local slot resolution. It is also recommended to try out a few examples with org.apache.pekko.stream.impl.PhasedFusingActorMaterializer.Debug turned on, it will detail every step of the island tracking and slot resolution steps.

Utilities

Useful utilities are:

* org.apache.pekko.stream.impl.PhasedFusingActorMaterializer.Debug: if this flag is turned on, the materializer will log the steps it takes * org.apache.pekko.stream.impl.TraversalBuilder.printTraversal: Prints the Traversal in a readable format * org.apache.pekko.stream.impl.TraversalBuilder.printWiring: Prints the calculated port assignments. Useful for debugging if everything is wired to the right thing.

Definition Classes: stream

package javadsl

Definition Classes: stream

package scaladsl

Scala API: The flow DSL allows the formulation of stream transformations based on some input.

Scala API: The flow DSL allows the formulation of stream transformations based on some input. The starting point is called Source and can be a collection, an iterator, a block of code which is evaluated repeatedly or a org.reactivestreams.Publisher. A flow with an attached input and open output is also a Source.

A flow may also be defined without an attached input or output and that is then a Flow. The Flow can be connected to the Source later by using Source#via with the flow as argument, and it remains a Source.

Transformations can be appended to Source and Flow with the operations defined in FlowOps. Each DSL element produces a new flow that can be further transformed, building up a description of the complete transformation pipeline.

The termination point of a flow is called Sink and can for example be a Future or org.reactivestreams.Subscriber. A flow with an attached output and open input is also a Sink.

If a flow has both an attached input and an attached output it becomes a RunnableGraph. In order to execute this pipeline the flow must be materialized by calling RunnableGraph#run on it.

You can create your Source, Flow and Sink in any order and then wire them together before they are materialized by connecting them using Flow#via and Flow#to, or connecting them into a GraphDSL with fan-in and fan-out elements.

See Reactive Streams for details on org.reactivestreams.Publisher and org.reactivestreams.Subscriber.

It should be noted that the streams modeled by this library are “hot”, meaning that they asynchronously flow through a series of processors without detailed control by the user. In particular it is not predictable how many elements a given transformation step might buffer before handing elements downstream, which means that transformation functions may be invoked more often than for corresponding transformations on strict collections like scala.collection.immutable.List. *An important consequence* is that elements that were produced into a stream may be discarded by later processors, e.g. when using the #take operator.

By default every operation is executed within its own org.apache.pekko.actor.Actor to enable full pipelining of the chained set of computations. This behavior is determined by the org.apache.pekko.stream.Materializer which is required by those methods that materialize the Flow into a series of org.reactivestreams.Processor instances. The returned reactive stream is fully started and active.

Definition Classes: stream

package snapshot

Definition Classes: stream

package stage

Definition Classes: stream

AbstractGraphStageWithMaterializedValue

GraphStageLogicWithLogging

GraphStageWithMaterializedValue

TimerGraphStageLogicWithLogging

package testkit

Definition Classes: stream

package typed

Definition Classes: stream

org.apache.pekko.stream

stage

package stage

Content Hierarchy

Ordering

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Visibility

Public
Protected

Type Members

abstract class AbstractGraphStageWithMaterializedValue[+S <: Shape, M] extends GraphStageWithMaterializedValue[S, M]
Java API: A GraphStage represents a reusable graph stream processing operator.
Java API: A GraphStage represents a reusable graph stream processing operator.
Extend this AbstractGraphStageWithMaterializedValue if you want to provide a materialized value, represented by the type parameter M. If your GraphStage does not need to provide a materialized value you can instead extend GraphStage which materializes a NotUsed value.
A GraphStage consists of a Shape which describes its input and output ports and a factory function that creates a GraphStageLogic which implements the processing logic that ties the ports together.
See also GraphStageWithMaterializedValue for Scala DSL for this operator.
abstract class AbstractInHandler extends InHandler
Java API: callbacks for an input port where termination logic is predefined (completing when upstream completes, failing when upstream fails).
abstract class AbstractInOutHandler extends InHandler with OutHandler
Java API: callback combination for output and input ports where termination logic is predefined (completing when upstream completes, failing when upstream fails, completing when downstream cancels).
abstract class AbstractOutHandler extends OutHandler
Java API: callbacks for an output port where termination logic is predefined (completing when downstream cancels).
trait AsyncCallback[T] extends AnyRef
An asynchronous callback holder that is attached to a GraphStageLogic.
An asynchronous callback holder that is attached to a GraphStageLogic. Initializing AsyncCallback#invoke will eventually lead to the registered handler being called.
This holder has the same lifecycle as a stream and cannot be used before materialization is done.
Typical use cases are exchanging messages between stream and substreams or invoking from external world sending event to a stream
abstract class GraphStage[S <: Shape] extends GraphStageWithMaterializedValue[S, NotUsed]
A GraphStage represents a reusable graph stream processing operator.
A GraphStage represents a reusable graph stream processing operator.
A GraphStage consists of a Shape which describes its input and output ports and a factory function that creates a GraphStageLogic which implements the processing logic that ties the ports together.
abstract class GraphStageLogic extends AnyRef
Represents the processing logic behind a GraphStage.
Represents the processing logic behind a GraphStage. Roughly speaking, a subclass of GraphStageLogic is a collection of the following parts: * A set of InHandler and OutHandler instances and their assignments to the Inlets and Outlets of the enclosing GraphStage * Possible mutable state, accessible from the InHandler and OutHandler callbacks, but not from anywhere else (as such access would not be thread-safe) * The lifecycle hooks preStart and postStop * Methods for performing stream processing actions, like pulling or pushing elements
The operator logic is completed once all its input and output ports have been closed. This can be changed by setting setKeepGoing to true.
The postStop lifecycle hook on the logic itself is called once all ports are closed. This is the only tear down callback that is guaranteed to happen, if the actor system or the materializer is terminated the handlers may never see any callbacks to onUpstreamFailure, onUpstreamFinish or onDownstreamFinish. Therefore operator resource cleanup should always be done in postStop.
abstract class GraphStageLogicWithLogging extends GraphStageLogic with StageLogging
Java API: GraphStageLogic with StageLogging.
abstract class GraphStageWithMaterializedValue[+S <: Shape, +M] extends Graph[S, M]
Scala API: A GraphStage represents a reusable graph stream processing operator.
Scala API: A GraphStage represents a reusable graph stream processing operator.
Extend this GraphStageWithMaterializedValue if you want to provide a materialized value, represented by the type parameter M. If your GraphStage does not need to provide a materialized value you can instead extende GraphStage which materializes a NotUsed value.
A GraphStage consists of a Shape which describes its input and output ports and a factory function that creates a GraphStageLogic which implements the processing logic that ties the ports together.
See also AbstractGraphStageWithMaterializedValue for Java DSL for this operator.
trait InHandler extends AnyRef
Collection of callbacks for an input port of a GraphStage
trait OutHandler extends AnyRef
Collection of callbacks for an output port of a GraphStage
trait StageLogging extends AnyRef
Simple way to obtain a pekko.event.LoggingAdapter when used together with an pekko.stream.Materializer.
Simple way to obtain a pekko.event.LoggingAdapter when used together with an pekko.stream.Materializer. If used with a different materializer pekko.event.NoLogging will be returned.
Make sure to only access log from GraphStage callbacks (such as pull, push or the async-callback).
Note, abiding to pekko.stream.ActorAttributes.logLevels has to be done manually, the logger itself is configured based on the logSource provided to it. Also, the log itself would not know if you're calling it from a "on element" context or not, which is why these decisions have to be handled by the operator itself.
abstract class TimerGraphStageLogic extends GraphStageLogic
Provides timer related facilities to a GraphStageLogic.
Provides timer related facilities to a GraphStageLogic.
To be thread safe the methods of this class must only be called from either the constructor of the graph operator during materialization or one of the methods invoked by the graph operator machinery, such as onPush and onPull.
abstract class TimerGraphStageLogicWithLogging extends TimerGraphStageLogic with StageLogging
Java API: TimerGraphStageLogic with StageLogging.

Value Members

object GraphStageLogic

Packages

Design goals

Graph model, offsets, slots

Relative addressing

Port mapping

TraversalBuilders

Islands and local slots

Materialization

Backward wirings

Utilities

stage

package stage

Type Members

Value Members

Ungrouped

stage