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gitter-badger narayana-glassbeam niedzwiedzislaw mlosiewicz-pl fperezp jlescanciano schrepfler bracanareactive-system's Issues
Review Client/Server API to make it easy to use. Produce a design document for the new API.
Simplifying the Reactive System Algebra from Future[Xor[Error, A]] to a Future[A]
Refactor ReactiveClient code
Currently the ReactiveClient is using actor base implementation and the KafkaProducer is blocking the thread which is not acceptable.
As part of this task evaluate the possibility to implement the client using Akka Stream.
- Implement the client using Akka Stream
- Refactor tests
- Document the Client Design document in paradox website
- Implement One Way Message
Add Scaladocs in order to generate API documentation
Implement Secure Messages
In order to implement secure services encryption must be implemented.
Messages should be encrypted. Investigate possible usage of JWT.
- Investigation, produce high level design document in the wiki
Add Semantic At-Least-Once to Client
The current implementation of the Reactive-System client has a Kafka Producer to read the response messages. It uses a strategy which commit the offset to Kafka immediately after the message is read.
We should also offer an implementation where through an API the user library get a Future[Committable[Out]].
i.e.:
val client = ReactiveClient.atLeastOnce(...)
val futureResult: Future[Committable[String]] = client.request("echoService")("kafka:echoTopic/echo", "hello world")
futureResult.map { cr: Committable[String] =>
val result: String = cr.value
cr.commit()
}
Note:
I was tempted to make Committable composable but it isn't, only if the messages are coming from the same topic/partition they can compose, in all other scenarios there is no easy implementation.
As conclusion do not attempt to make Committable a monad.
Server Refactoring to use concurrency
The Server side of the library is using already akka stream however the parallelism of mapAsync stages is hard coded and we are using the same dispatcher as the stream for kafka producer/consumer, serialization and deserialization.
- Make the concurrency value of the mapAsync configurable
- Use dedicated dispatcher for kafka producer/consumer through configuration
- Make dispatcher for serialization/deserialization configurable
- Write Server Design page in the documentation
Modify Reactive system in order to use backpressure
Reactive system Server Component graph is not using the right Source and Sink in order to use back pressure.
In order to use back pressure we should use mapAsync from a Source[KafkaRequestEnvelope, _], call our ReactiveService and close into a Sink[KafkaResponseEnvelope, _].
The reactive system should also now accept a max concurrency level for our route which would be nice to wrap into a Flow.
As part of this ticket, investigate a possible solution for both message semantics (at-most-once and at-least-once) and document in the wiki the solution.
- Produce Design Wiki Docs
- Implement Solution
Create Document Website
In order for others to use the framework we should have an update documentation generated and published.
Using sbt-paradox (similar to akka http) and github pages the site should be autogenerated and published.
Implement Batching in Kafka Consumer Server
We would like to increase the performance of the Kafka Consumer in order to fetch batch of messages rather than pull 1 message at the time.
This will improve the overall performance for both: at-most-once and at-least-once semantics as we don't need to acknowledge each single message.
Design an API for Kafka Source and Sink to support batching.
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