RunnableGraph.Run is called against a live
ActorSystem.
Why
Raw actor messaging requires you to handle buffer overflow, message loss, and backpressure by hand: every actor that consumes faster or slower than its peers becomes a coordination problem you have to solve case-by-case. Streams handle all of this automatically, giving you a safe and composable programming model on top of actors so you can focus on what flows through the pipeline rather than how to keep it from breaking under load.Overview
| Concept | What it means in GoAkt Streams |
|---|---|
| Correctness | Exactly-once delivery semantics within a local pipeline |
| Backpressure | Credit-based demand pull prevents unbounded buffering |
| Composability | Stages are plain Go values; pipelines are assembled declaratively |
| Actor-native | Every stage is an actor; supervision, scheduling, and routing apply naturally |
| Type-safety | Source[T], Flow[In, Out], Sink[T] carry type parameters through the pipeline |
Core abstractions
| Type | Role |
|---|---|
Source[T] | Origin of stream elements; produces values of type T |
Flow[In, Out] | Transformation stage; consumes In, emits Out |
Sink[T] | Terminal consumer; processes elements and drives backpressure |
SubFlow[K, T] | ”Stream of streams” — partition by key, transform per-substream, merge back |
RunnableGraph | Fully assembled pipeline; a value type that can be Run multiple times |
StreamHandle | Live handle returned by Run; controls lifecycle and exposes metrics |
Quick start
Linear pipeline
Use the fluentFrom / Via / To builder for straight-line pipelines:
Type-changing flows
When aFlow changes the element type, use the package-level Via free function or ViaLinear:
Sources
Sources are the origin of stream data. All sources apply backpressure — they produce only as many elements as downstream demands.Finite sources
Channel source
Bridges an external Go channel into the pipeline. Backpressure naturally limits how fast the goroutine sends:Actor source
Pulls elements from a GoAkt actor using thePullRequest / PullResponse[T] protocol. Useful for integrating existing
actor-based producers:
Tick source
Emits the current time on a fixed interval. Runs indefinitely untilStop or Abort is called:
Network source
Reads[]byte frames from a net.Conn. Each Read call produces one element; demand controls how many reads
are batched per upstream request:
Reference
| Constructor | Description |
|---|---|
Of[T](values...) | Finite source from a fixed set of values |
Range(start, end) | Integer range [start, end) |
Unfold[S, T](seed, step) | Generates values from a seed with a stateful step function |
FromChannel[T](ch) | Reads from a Go channel; completes when the channel closes |
FromActor[T](pid) | Pulls from a GoAkt actor via PullRequest / PullResponse[T] |
Tick(interval) | Emits time.Time on a fixed interval; runs until cancelled |
Merge[T](sources...) | Fans N sources into one; completes when all inputs complete |
Concat[T](sources...) | Consumes sub-sources sequentially; completes when the last completes |
Combine[T, U, V](left, right, fn) | Zips two sources pairwise via fn (mixed input types) |
Zip[T](sources...) | Zips N same-typed sources into []T tuples |
ZipWith[T, V](fn, sources...) | N-input zip via user combine fn |
MergePreferred[T](idx, sources...) | Like Merge but drains slot idx first when it has data |
MergePrioritized[T](weights, srcs) | Weighted random slot selection over slots that have data |
MergeLatest[T](sources...) | Emits []T snapshots of the latest value on every input |
MergeSequence[T](extractSeq, srcs) | Emits in ascending seq order across N inputs |
Broadcast[T](src, n) | Fans one source out to N independent branches |
Balance[T](src, n) | Distributes one source across N branches with round-robin backpressure |
Partition[T](src, n, fn) | Routes each element to one of N branches via predicate fn |
Unzip[T, A, B](src, fn) | Splits a source into two sources of different element types |
FromConn(conn, bufSize) | Reads []byte frames from a net.Conn |
Flows
Flows are lazy transformation stages. Each flow operator returns a newFlow value; multiple flows can be chained
without materializing anything.
Mapping and filtering
Streaming flat-map
When the per-element expansion is itself a stream (e.g. paginated API calls, per-row database queries), use the streaming flat-map operators instead ofFlatMap. Each input element produces a Source[Out] whose elements are
flattened into the main pipeline:
breadth elements from upstream, and
the next batch is requested only as sub-sources complete — so a slow inner source naturally throttles the outer
pipeline. Sub-pipeline handles are tracked and aborted on cancellation or failure to avoid leaking goroutines.
Windowing and rate control
Stateful flows
Parallel processing
ParallelMap applies a function concurrently with up to n goroutines. Use OrderedParallelMap when output order
must match input order:
Reference
| Constructor | Description |
|---|---|
Map[In, Out](fn) | Type-changing transformation; no error path |
TryMap[In, Out](fn) | Transformation with error; ErrorStrategy controls failure handling |
Filter[T](predicate) | Keeps only elements where predicate returns true |
FlatMap[In, Out](fn) | Expands each element into a slice of outputs |
FlatMapConcat[In, Out](fn) | Per-element streaming flat-map; sub-sources drained sequentially |
FlatMapMerge[In, Out](breadth, fn) | Per-element streaming flat-map; up to breadth sub-sources concurrent |
Flatten[T]() | Unwraps []T elements into individual elements |
Batch[T](n, maxWait) | Groups into []T slices of at most n; flushes early after maxWait |
Buffer[T](size, strategy) | Asynchronous buffer with configurable overflow strategy |
Throttle[T](n, per) | Limits throughput to at most n elements per per duration |
Deduplicate[T]() | Suppresses consecutive duplicate elements (T must be comparable) |
Scan[In, State](zero, fn) | Running accumulation; emits each intermediate state |
WithContext[T](key, value) | Marks a named tracing boundary; passes elements through unchanged |
ParallelMap[In, Out](n, fn) | Applies fn concurrently with up to n goroutines; unordered output |
OrderedParallelMap[In, Out](n, fn) | Like ParallelMap but preserves input order via min-heap resequencing |
Sinks
Sinks are the terminal consumers of a pipeline. They drive backpressure by signalling demand upstream; the pipeline does not produce faster than the sink can consume.Common sinks
Actor and channel sinks
Reference
| Constructor | Description |
|---|---|
ForEach[T](fn) | Calls fn for each element |
Collect[T]() | Accumulates all elements; retrieve via Collector[T].Items() |
Fold[T, U](zero, fn) | Reduces to a single value; retrieve via FoldResult[U].Value() |
First[T]() | Captures the first element then cancels upstream |
Ignore[T]() | Discards all elements |
Chan[T](ch) | Writes each element to a Go channel; applies backpressure when full |
ToActor[T](pid) | Forwards each element to a GoAkt actor via Tell |
ToActorNamed[T](system, name) | Resolves actor by name and forwards via Tell |
Fan-out and fan-in
Broadcast
Broadcast fans a single source out to N independent branches. Every branch receives every element. Backpressure is
enforced by the slowest branch: the hub pulls from upstream only when all active branches have outstanding demand.
Balance
Balance distributes elements across N branches using round-robin routing with backpressure. Each element goes to
exactly one branch — the next one with available demand. Use this to parallelise work across independent consumers:
Partition
Partition routes each element to exactly one of N branches based on a user predicate. Out-of-range or
already-cancelled slot results are dropped silently. Backpressure is conservative: the hub pulls from upstream only
when every active branch has outstanding demand, so the destination slot for any incoming element is guaranteed
capacity.
Unzip
Unzip splits a single source into two sources of potentially different element types. It is composed of
Broadcast(src, 2) plus a Map per branch and follows the same backpressure rules as Broadcast.
Merge
Merge fans N independent sources into a single downstream. Elements arrive in non-deterministic order; completion
happens when all inputs have completed:
Concat
Concat consumes sub-sources sequentially: the next sub-pipeline only spawns after the current one completes,
bounding in-flight cost at one sub-pipeline regardless of source count. Per-source ordering is preserved across the
boundary.
Combine (zip, two-input mixed-type)
Combine pairs elements from two sources with a combine function. It completes when either source is exhausted.
Use it when the two inputs have different element types:
Zip / ZipWith (N-input same-type)
Zip combines N same-typed sources into a stream of []T tuples; ZipWith does the same with a user combine
function. Both complete as soon as any input is exhausted.
MergePreferred
LikeMerge, but always drains the priority slot first when it has data, falling back to the lowest-indexed
non-empty slot otherwise. Useful when one input represents a higher-priority feed (e.g. a control channel) that
should be drained ahead of background traffic. Panics on out-of-range index.
MergePrioritized
Picks the next slot by weighted random over slots that currently have data. Slots with weight 0 are only selected when they are the only ones with data. Panics onweights / sources length mismatch.
MergeLatest
Emits[]T snapshots of the latest value seen on every input. Each upstream emission queues one snapshot, but the
first only fires once every input has emitted at least once. Completes when every input completes.
MergeSequence
Emits in strict ascending sequence-number order across N inputs by buffering out-of-order elements in a min-heap. Inputs must collectively produce a contiguous range of sequence numbers starting from 0. If all inputs complete with the next-expected sequence still missing, the stream fails with a missing-sequence error.Substreams (SubFlow)
Three constructors produce aSubFlow, each with different routing semantics: GroupBy partitions by a key function (one substream per distinct key, materialised lazily), SplitWhen starts a new substream when a predicate is true (the triggering element becomes the FIRST of the new substream), and SplitAfter ends the current substream after a predicate-true element (the triggering element becomes the LAST of the closing substream). Per-substream state — Scan accumulators, Deduplicate’s last-seen value, anything stateful in the per-substream chain — is genuinely independent because each substream is a real GoAkt actor pipeline materialised lazily on its first element. MergeSubstreams collapses the partitioned stream back into a flat Source[T] whose elements interleave non-deterministically across substreams while preserving order within a single substream.
GroupBy time and applied to upstream elements regardless of how many SubFlowVia calls follow — the per-substream output type can change freely without affecting routing.
Splitting on a delimiter
SplitWhen and SplitAfter take a predicate instead of a key function and produce a SubFlow[int, T] whose synthetic substream IDs increment on each rotation. They differ in where the triggering element lands:
maxSubstreams cap is generally unnecessary and 0 (unbounded) is fine.
Cardinality cap
ThemaxSubstreams parameter caps the number of concurrently-open substreams; pass 0 for unbounded. Exceeding the cap terminates the stream with ErrTooManySubstreams, so unbounded key cardinality is a visible failure rather than a silent memory leak:
Per-substream backpressure
Each substream has an independent in-flight cap (default256 elements). When a substream is at capacity, the configured OverflowStrategy decides what happens to new elements with that key. Substream feed sources acknowledge consumption to the splitter in batches (matching the flowActor watermark refill at perKeyBuffer/4), so per-key memory is bounded without per-element protocol overhead.
| Strategy | Behaviour |
|---|---|
DropTail (default) | Discard the newest element when the substream is at capacity |
DropHead / BackpressureSource | Currently collapse to drop-newest — DropHead would break per-key ordering and BackpressureSource would deadlock the splitter on inbound |
FailSource | Terminate the stream with ErrSubstreamOverflow |
OnDrop hook and the droppedElements metric. For tighter or more elaborate bounds (e.g. time-based windowing per key), compose a Buffer or Throttle flow inside the per-substream chain.
Per-substream error strategy
When a per-key sub-pipeline fails,SubstreamErrorStrategy controls how the splitter reacts. The default mirrors the FailFast contract of linear flows; the alternatives let one substream’s failure stay isolated.
| Strategy | Behaviour |
|---|---|
SubstreamFailAll (default) | Surface the error verbatim via StreamHandle.Err() and terminate the entire stream |
SubstreamDrop | Blocklist the failing key and complete that substream silently; sibling substreams finish normally; further elements with the blocklisted key are dropped |
SubstreamRestart | Discard the failed pipeline; the next element with that key spawns a fresh substream from scratch |
Reference
| Constructor / method | Description |
|---|---|
GroupBy[T, K](src, maxSubstreams, keyFn) | Partition src by keyFn; returns SubFlow[K, T] |
SplitWhen[T](src, predicate) | New substream begins when predicate is true; element is FIRST of new substream |
SplitAfter[T](src, predicate) | Current substream ends after predicate is true; element is LAST of that substream |
SubFlowVia[K, In, Out](sf, flow) | Append a transformation to each substream’s pipeline |
MergeSubstreams[K, T](sf) | Collapse substreams back into a flat Source[T] (non-deterministic interleave) |
SubFlow.WithSubstreamBuffer(perKeyBuffer, strategy) | Per-substream in-flight cap and overflow strategy |
SubFlow.WithErrorStrategy(strategy) | Per-substream failure handling policy |
Pipeline DSL (Graph builder)
For non-linear topologies (fan-out branches that later merge, or multiple independent pipelines sharing a source) use theGraph DSL. Nodes are identified by string names and connected by referencing upstream names:
AddFlow and AddSink:
MergeInto(name, from...)interleaves elements from multiple upstream nodes (non-deterministic order).ConcatInto(name, from...)consumes upstreams sequentially, preserving per-source ordering across the boundary.
Cross-node stream refs
SourceRef[T] and SinkRef[T] are wire-portable handles to a stream endpoint actor. Pass one inside any registered remote message and a different node materialises it back into an ordinary Source[T] or Sink[T] — the same fluent builder works whether the producer and consumer share an actor system or live on opposite ends of a cluster. The model mirrors Akka StreamRefs.
Refs encode the producer node’s address (host, port, actor name), so cross-node resolution is a direct RemoteLookup against the producer’s remote server. There is no reliance on cluster-registry replication — the consumer can resolve and subscribe the moment the producer node is reachable, regardless of how loaded the cluster’s actor registry is.
Setup
Cross-node refs only work when both sides of the connection have the wire control protocol and the element type registered with their remoting layer. Without this registration the consumer’s bridge can’t subscribe (control messages won’t deserialize) or the producer’s elements can’t be serialised — symptoms include hangingRun calls, stream-level "no serializer found for message type ..." errors, and elements silently routed to dead-letter.
Required on every node that participates:
stream.RemoteOptions()registers the small subscribe / request / complete / error / cancel control-plane wire types. Append it to your existingremote.NewConfigoptions on every node — producer side, consumer side, and any node that might hold or forward a ref. Forgetting it on either side is the most common cause of hangs: the bridge sendsstreamSubscribeWireand the endpoint never sees a recognisable message because its remoting layer can’t decode the type.- Element types must be registered with
remote.WithSerializables(new(MyEvent))— same as for any otherrctx.Tellto a remote PID. Refs do not wrap elements in an envelope, so missing element-type registration on the producer side fails the send, and missing it on the consumer side fails the receive.
- Asymmetric registration. The producer registers
MyEventbut the consumer does not (or vice versa). The end with the missing registration logs a"no serializer found"warning when the message arrives; for the consumer that means elements never reach the local sink. Always register the same element types on both ends. - Registering the value type as a non-pointer.
remote.WithSerializableskeys on the exact type passed in — typically*MyEventfromnew(MyEvent). Sending a value-type element over the wire still works because the bridge’swireFormhelper auto-wraps non-pointer values into a*Tregistered key, but if you pass a struct value (instead ofnew(...)or an interface pointer) intoWithSerializablesitself, the registry stores the wrong type and lookups miss. Stick to thenew(MyEvent)form. - Skipping
stream.RemoteOptions()on a forwarding-only node. Any node that may receive aSourceRef[T]/SinkRef[T]inside another message — even just to inspect or relay it — needsstream.RemoteOptions()registered, because the ref struct itself is serialised through the same registry. A node missing it will fail to deserialize incoming refs. - Disabling remoting entirely. Cross-node refs require
actor.WithRemote(...)on both ends. Without itref.Source(sys)/ref.Sink(sys)materialise a bridge that can’t reach the remote endpoint and surfaces an"actor not found"resolution error after the retry budget expires. - Producer node unreachable at resolve time. Refs carry the producer node’s host:port, so resolution is a direct
RemoteLookupagainst the producer’s remote server — there is no dependency on async cluster registry propagation. If the producer node is briefly unreachable when the consumer resolves (network blip, just-restarting node), the bridge retries with jittered exponential backoff for up to 10s. After that it surfaces a"resolve source ref"/"resolve sink ref"error rather than hanging — handle it in the receiving side just like any remote-Tell failure.
remote.WithSerializables round-trips automatically, just as it would for any other rctx.Tell to a remote PID.
SourceRef — publish a producer
A SourceRef[T] exposes a local Source[T] to a remote consumer. The ref is a plain serialisable value — wrap it in any user-defined message type you like (registered with the same remoting layer) and ship it however your application already moves data between nodes.
ref.Source(...).Run(...). Constructing the ref is cheap.
SinkRef — publish a consumer
The mirror image: a SinkRef[T] exposes a local Sink[T] so a remote node can pipe data into it.
Semantics
- Single subscription per ref. A second
ref.Source(...)/ref.Sink(...)materialisation observes a stream-level error ("already subscribed"while the first stream is active,"already consumed"after it finishes). Refs are one-shot, matching Akka StreamRefs. - Wire-level credit. The consumer’s downstream demand drives
streamRequestWireto the producer; the producer ships exactly that many elements before pausing. A bounded pending queue (1024 elements) on the producer side converts a slow-consumer scenario into a visible"backpressure overflow"stream error rather than unbounded buffering on the producer node. - Bounded lifecycle. After the stream completes (success or error), the endpoint actor stays alive for a 30-second grace window so a racing late subscriber gets a clean rejection rather than telling a dead actor and hanging. After the grace window the endpoint reaps itself via
system.ScheduleOnce. Refs that are never consumed are reaped at actor-system shutdown. - Death detection. Bridges
Watchtheir remote endpoint as defense in depth — an unexpected endpoint death (panic, system shutdown mid-stream, expired grace on a stale ref) surfaces as a stream error on the bridge’sStreamHandle.Err(), never a hang. Combined with retry-with-jitter resolution that absorbs cluster propagation latency, this keeps cross-node graphs reliable under load.
Reference
| Constructor / method | Description |
|---|---|
Source[T].SourceRef(ctx, sys) | Publish a Source[T] as a wire-portable SourceRef[T] |
(SourceRef[T]).Source(sys) Source[T] | Adapt a ref back into a Source[T] for use in a local graph |
Sink[T].SinkRef(ctx, sys) | Publish a Sink[T] as a wire-portable SinkRef[T] |
(SinkRef[T]).Sink(sys) Sink[T] | Adapt a ref back into a Sink[T] for use in a local graph |
RemoteOptions() remote.Option | Registers the control-plane wire protocol with remote.NewConfig |
Backpressure
GoAkt Streams uses credit-based demand propagation. The sink signals how many elements it can accept; each stage propagates that demand upstream. Sources produce only what is requested.StageConfig):
| Parameter | Default | Purpose |
|---|---|---|
InitialDemand | 224 | First batch of demand sent by a sink to its upstream |
RefillThreshold | 64 | Credit level at which a refill is requested |
BufferSize | 256 | Mailbox buffer (bounded mailbox capacity) |
- When
credit > RefillThreshold, no refill is sent — the pipeline is healthy. - When
credit ≤ RefillThreshold, the stage requestsInitialDemand - creditmore elements from upstream. - This keeps in-flight data bounded while keeping the pipeline saturated.
Error handling
Each stage can be configured with an independentErrorStrategy:
| Strategy | Behaviour |
|---|---|
FailFast (default) | Propagate error downstream, shut down the pipeline |
Resume | Skip the failed element; request a replacement from upstream |
Retry | Re-process the element up to RetryConfig.MaxAttempts times before failing |
Supervise | Delegate to actor supervision (currently equivalent to FailFast) |
Overflow strategies
Overflow strategies apply when a stage’s internal buffer is full:| Strategy | Behaviour |
|---|---|
DropTail (default) | Discard the newest incoming element |
DropHead | Discard the oldest buffered element |
DropBuffer | Discard the entire buffer |
Backpressure | Block the upstream (apply natural backpressure) |
Fail | Treat overflow as a fatal error |
Stage fusion
Adjacent stateless stages (Map, Filter) are automatically fused into a single actor, eliminating intermediate
mailbox enqueues and reducing allocations.
| Mode | Behaviour |
|---|---|
FuseStateless (default) | Fuses adjacent Map and Filter stages into one actor |
FuseNone | No fusion; each stage is a separate actor (useful for debugging) |
FuseAggressive | Fuses all fusable adjacent stages |
FuseNone when profiling
individual stage throughput.
StreamHandle lifecycle
RunnableGraph.Run returns a StreamHandle for controlling the pipeline at runtime:
| Method | Purpose |
|---|---|
ID() | Unique stream identifier |
Done() | Channel closed when the stream terminates (naturally or by error) |
Err() | Terminal error; valid only after Done() is closed |
Stop(ctx) | Orderly shutdown; drains in-flight elements, then stops all stages |
Abort() | Immediate termination; discards buffered elements |
Metrics() | Returns a StreamMetrics snapshot |
Observability
Metrics
StreamHandle.Metrics() returns a live snapshot of element counts:
Tracer
Attach aTracer to any Flow or Sink for per-element distributed tracing:
Configuration reference
StageConfig carries per-stage configuration. Apply it via the builder methods on Flow, Sink, or Source:
| Field | Type | Default | Purpose |
|---|---|---|---|
InitialDemand | int64 | 224 | First demand batch from sink to upstream |
RefillThreshold | int64 | 64 | Credit level that triggers a refill request |
ErrorStrategy | ErrorStrategy | FailFast | Element-level failure handling |
RetryConfig.MaxAttempts | int | 1 | Retry attempts when ErrorStrategy is Retry |
OverflowStrategy | OverflowStrategy | DropTail | Behaviour when internal buffer is full |
PullTimeout | time.Duration | 5s | Timeout for FromActor pull requests |
BufferSize | int | 256 | Bounded mailbox capacity (BufferSize × 2 slots) |
Mailbox | actor.Mailbox | nil | Override the default mailbox implementation |
Name | string | auto | Custom actor name for this stage |
Tags | map[string]string | nil | Key/value labels propagated to metrics and traces |
Tracer | Tracer | nil | Optional distributed tracing hook |
Fusion | bool | true | Whether this stage may participate in stage fusion |
MicroBatch | int | 1 | Accumulate this many elements before forwarding (flow stages only) |
OnDrop | func(any, string) | nil | Called when an element is dropped due to overflow or exhausted retries |
Comparison with other approaches
Go channels and goroutines
Go channels are the native concurrency primitive. They are low-level, composable, and require no dependencies — but they demand that the developer hand-craft every concern that GoAkt Streams handles automatically.| Concern | Channels + goroutines | GoAkt Streams |
|---|---|---|
| Backpressure | Manual (blocking sends or select/default) | Automatic credit-based demand propagation |
| Error handling | Explicit error channels and coordination logic | ErrorStrategy per stage (FailFast, Resume, Retry) |
| Fan-out | Manual goroutine fan; complex synchronisation | Broadcast, Balance primitives |
| Fan-in | select or sync.WaitGroup coordination | Merge, Combine primitives |
| Windowing | Custom timer goroutines and buffers | Batch[T](n, maxWait) built-in |
| Rate limiting | Custom token bucket / ticker goroutines | Throttle[T](n, per) built-in |
| Cancellation | context.Context plumbing through every goroutine | handle.Stop(ctx) / handle.Abort() |
| Lifecycle | Manual sync.WaitGroup + goroutine tracking | StreamHandle.Done() + supervision tree |
| Observability | Manual counters | Built-in StreamMetrics and Tracer |
RxGo
RxGo is a Go implementation of Reactive Extensions (ReactiveX). It offers a rich operator library and a familiar API for developers coming from RxJS or RxJava.| Aspect | RxGo | GoAkt Streams |
|---|---|---|
| Backpressure | Not supported in v2; the observable push model can overflow without consumer-side control | Pull-only, credit-based; source never outpaces downstream |
| Concurrency model | Goroutines with optional WithPool options | Actor-per-stage; supervised, lifecycle-managed |
| Type safety | interface{} items in v2; generic operators not yet complete | Full generics: Flow[In, Out], Source[T], Sink[T] |
| Actor integration | None | First-class: FromActor, ToActor, actor supervision |
| Error model | Error items in the stream; OnError handler | Typed ErrorStrategy (FailFast, Resume, Retry) per stage |
| Lifecycle | Dispose pattern | StreamHandle with Stop / Abort / Done |
| Operator richness | Very rich (debounce, zip, skip, take, window…) | Core operators; FlatMap, Batch, Throttle, ParallelMap |
| Fan-out | Connectable observables | Broadcast and Balance with automatic backpressure |
| Supervision | None | GoAkt actor supervision tree |
| Status | Maintenance mode | Actively maintained |
Benthos / Redpanda Connect
Benthos (now Redpanda Connect) is a production-grade stream-processing engine focused on connecting data systems. It is configuration-driven and ships with hundreds of connectors.| Aspect | Benthos / Redpanda Connect | GoAkt Streams |
|---|---|---|
| Primary audience | Platform / DevOps teams building data pipelines | Go application developers integrating stream logic into services |
| Configuration model | YAML configuration files | Code-first Go API |
| Backpressure | Ack-based; built into every connector | Credit-based demand propagation |
| Connectors | 200+ built-in (Kafka, S3, HTTP, databases…) | User-defined via Source, Sink wrappers |
| Type safety | Untyped message blobs | Generic Source[T] / Flow[In,Out] |
| Embedding | Supported via Go library | Native — it is a Go library |
| Processing model | Component DAG configured in YAML | Composable Go values assembled in code |
| Testing | Unit tests via YAML fixtures | Standard Go tests; testkit |
| Error handling | Retry / DLQ per connector | ErrorStrategy + OnDrop callback per stage |
| Actor integration | None | First-class GoAkt actor interop |
Apache Kafka Streams
Kafka Streams is a JVM client library for building stateful stream processors on top of Apache Kafka.| Aspect | Kafka Streams | GoAkt Streams |
|---|---|---|
| Language | Java / Kotlin (JVM) | Go |
| Broker dependency | Requires Apache Kafka | No broker; in-process |
| Durability | Persistent topics; replayable | In-memory within the process; ephemeral |
| State stores | RocksDB-backed state stores; fault-tolerant | Stateful stages via actor state; no persistent store |
| Scalability | Horizontal via Kafka partitions | Vertical + actor location transparency |
| Backpressure | Implicit via Kafka consumer offset | Explicit credit-based demand |
| Exactly-once | Exactly-once semantics with transactions | Exactly-once within a local pipeline |
| Windowing | Tumbling, hopping, sliding, session windows | Batch[T](n, maxWait) for simple windowing |
| Table joins | KTable / KStream joins with changelog topics | Not built-in; compose via actor state |
| Operator richness | Filter, map, flatMap, groupBy, aggregate, join | See flow reference table above |
Akka Streams
Akka Streams (Scala / Java) is the direct inspiration for GoAkt Streams. Both implement the Reactive Streams specification.| Aspect | Akka Streams | GoAkt Streams |
|---|---|---|
| Language | Scala / Java (JVM) | Go |
| Backpressure | Reactive Streams specification | Credit-based pull; equivalent semantics |
| Graph DSL | Rich typed graph builder (GraphDSL) | Graph named-node builder |
| Stage fusion | Automatic operator fusion | FuseStateless (default), FuseNone, FuseAggressive |
| Materializer | ActorMaterializer | GoAkt ActorSystem |
| Error handling | SupervisionStrategy per flow | ErrorStrategy per stage |
| Fan-out | Broadcast, Balance, Partition | Broadcast, Balance |
| Fan-in | Merge, MergePreferred, Zip, ZipWith | Merge, Combine |
| Substreams | groupBy, splitWhen, splitAfter, mergeSubstreams, concatSubstreams | GroupBy, SplitWhen, SplitAfter, MergeSubstreams (with per-substream buffer and error strategy) |
| Cross-node refs | SourceRef, SinkRef | SourceRef, SinkRef (single-subscription, grace-period reaped, credit-paced) |
| Actor interop | Source.actorRef, Sink.actorRef | FromActor, ToActor, ToActorNamed |
| Type safety | Full (Scala generics) | Full (Go generics) |
| Operator richness | Very rich | Core operators; sufficient for most workloads |
| Runtime | JVM; Akka actor system | Go runtime; GoAkt actor system |
| Licence | BSL 1.1 (Lightbend) | MIT |
Source, Flow, Sink, demand-driven
backpressure, stage fusion — while adapting them to Go idioms and the GoAkt actor hierarchy. The PullRequest /
PullResponse actor source protocol is analogous to Akka’s Source.actorRefWithBackpressure.
Related
- Observability — Metrics and OpenTelemetry integration
- Event Streams — Internal system and cluster event bus
- PubSub — Application-level topic-based pub/sub
- Actor Scheduling — Timer and cron scheduling used by
TickandBatch - Supervision — Actor failure handling that underpins stream stage recovery