The way we consume services from the Internet today includes many instances of streaming data, both downloading from a service as well as uploading to it or peer-to-peer data transfers. Regarding data as a stream of elements instead of in its entirety is very useful because it matches the way computers send and receive them (for example via TCP), but it is often also a necessity because data sets frequently become too large to be handled as a whole. We spread computations or analyses over large clusters and call it “big data”, where the whole principle of processing them is by feeding those data sequentially—as a stream—through some CPUs.

Actors can be seen as dealing with streams as well: they send and receive series of messages in order to transfer knowledge (or data) from one place to another. We have found it tedious and error-prone to implement all the proper measures in order to achieve stable streaming between actors, since in addition to sending and receiving we also need to take care to not overflow any buffers or mailboxes in the process. Another pitfall is that Actor messages can be lost and must be retransmitted in that case. Failure to do so would lead to holes at the receiving side.

For these reasons we decided to bundle up a solution to these problems as an Akka Streams API. The purpose is to offer an intuitive and safe way to formulate stream processing setups such that we can then execute them efficiently and with bounded resource usage—no more OutOfMemoryErrors. In order to achieve this our streams need to be able to limit the buffering that they employ, they need to be able to slow down producers if the consumers cannot keep up. This feature is called back-pressure and is at the core of the Reactive Streams initiative of which Akka is a founding member. For you this means that the hard problem of propagating and reacting to back-pressure has been incorporated in the design of Akka Streams already, so you have one less thing to worry about; it also means that Akka Streams interoperate seamlessly with all other Reactive Streams implementations (where Reactive Streams interfaces define the interoperability SPI while implementations like Akka Streams offer a nice user API).

Relationship with Reactive Streams

The Akka Streams API is completely decoupled from the Reactive Streams interfaces. While Akka Streams focus on the formulation of transformations on data streams the scope of Reactive Streams is to define a common mechanism of how to move data across an asynchronous boundary without losses, buffering or resource exhaustion.

The relationship between these two is that the Akka Streams API is geared towards end-users while the Akka Streams implementation uses the Reactive Streams interfaces internally to pass data between the different operators. For this reason you will not find any resemblance between the Reactive Streams interfaces and the Akka Streams API. This is in line with the expectations of the Reactive Streams project, whose primary purpose is to define interfaces such that different streaming implementation can interoperate; it is not the purpose of Reactive Streams to describe an end-user API.

How to read these docs

Stream processing is a different paradigm to the Actor Model or to Future composition, therefore it may take some careful study of this subject until you feel familiar with the tools and techniques. The documentation is here to help and for best results we recommend the following approach:

  • Read the Quick Start Guide to get a feel for how streams look like and what they can do.
  • The top-down learners may want to peruse the Design Principles behind Akka Streams at this point.
  • The bottom-up learners may feel more at home rummaging through the Streams Cookbook.
  • For a complete overview of the built-in processing operators you can look at the operator index
  • The other sections can be read sequentially or as needed during the previous steps, each digging deeper into specific topics.
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