swave vs. Akka-Stream

While swave and Akka-Stream are more closely related than, for example, swave and FS2 they still differ in some important aspects. Apart from the underlying implementations, which are completely unalike, the key differences are:

Scala-only vs. Scala + Java
swave is Scala-only, whereas Akka-Stream comes with a Scala API and a Java API. While the latter addresses a strictly larger user base it also adds a significant amount of development and maintenance effort.
Non-Lifted vs. Lifted Design
All of swave’s key types are non-lifted, single use abstractions. This differs from Akka-Stream which tries to keep its abstraction as reusable as possible (which is not always possible).
Sync + Async vs. Async only
In addition to scheduling tasks onto configurable thread-pools swave allows for synchronous (but still non-blocking!) execution on the caller thread, if the stream setup permits it. Akka-Stream always runs its streams off the caller thread on independent Akka Actors (and thus thread-pools).
Fluent DSL vs. Point-to-point Graph DSL
While swave tries to keep individual transformations named similarly to Akka-Stream the DSL for creating more complex stream setups, with fan-outs, fan-ins, loops, etc. is substantially different between the two.
Full vs. Limited Stream-of-Streams Support
swave fully supports streams of streams, where as Akka limits stream nesting to streams that handle all nested streams in the same way. The latter does have certain benefits (like inspectability of transformations on nested streams) but is strictly less powerful.
In our latest benchmarks swave achieves between 1.3 and 4.8 times as much throughput as Akka-Stream for quasi-identical streaming graphs, depending on the exact mix of transformations in the “hot” parts of the setup.
Quick Evolution vs. Long-term stability
Being part of Akka Akka-Stream has much tighter requirements on long-term stability and binary compatibility than swave. This means that you can probably rely on Akka-Stream as a more stable and mature foundation going forward. However, the less stringent stability requirements allow us to evolve swave faster and incorporate new learnings more freely.

Non-blocking, backpressured stream processing in Scala is an exciting and promising arena, with most of its potential still untapped. Therefore we don’t see swave as a head-on competitor to Akka-Stream, trying to steal away from its user base. Rather we regards ourselves as two teams that are exploring this still new field together, learning from one another and comparing approaches on numerous levels. The more users whose needs we can jointly address the more the Scala eco-system will benefit as a whole.

Interfacing with Akka-Stream

Since seamless interaction between swave and Akka-Stream is a frequent requirement swave has built-in support for easy interfacing between the two. See the docs for the swave-akka-compat module for more details.