Lightbend Activator

Reactive Kafka with Scala

Activator will be EOL-ed on May 24, 2017.

We’re making it easier and simpler for developers to get started with Lightbend technologies. This unfortunately means that future releases of Play, Akka and Scala will no longer include Activator support, and Lightbend’s Activator server will be decommissioned by the end of 2017. Instead of supporting Activator to create and set up development projects, we'll be supporting standard Giter8 templates for sbt users and Maven archetypes for Maven users. So going forward,

To create new Lightbend projects

Instead of using the Activator command, make sure you have sbt 0.13.13 (or higher), and use the “sbt new” command, providing the name of the template. For example, “$ sbt new akka/hello-akka.g8”. You can find a list of templates here.

Also, as a convenience, the Lightbend Project Starter allows you to quickly create a variety of example projects that you just unzip and run.

To create new templates

If you want to create new templates, you can now do that in Giter8.

To migrate templates from Activator to Giter8

If you created Activator templates in the past, please consider migrating them to Giter8 with this simple process.

Reactive Kafka with Scala

SoftwareMill
Source
November 22, 2016
akka scala kafka streams sample

Demonstrates Reactive Kafka for wrapping Apache Kafka as a reactive stream

How to get "Reactive Kafka with Scala" on your computer

There are several ways to get this template.

Option 1: Choose reactive-kafka-scala in the Lightbend Activator UI.

Already have Lightbend Activator (get it here)? Launch the UI then search for reactive-kafka-scala in the list of templates.

Option 2: Download the reactive-kafka-scala project as a zip archive

If you haven't installed Activator, you can get the code by downloading the template bundle for reactive-kafka-scala.

  1. Download the Template Bundle for "Reactive Kafka with Scala"
  2. Extract the downloaded zip file to your system
  3. The bundle includes a small bootstrap script that can start Activator. To start Lightbend Activator's UI:

    In your File Explorer, navigate into the directory that the template was extracted to, right-click on the file named "activator.bat", then select "Open", and if prompted with a warning, click to continue:

    Or from a command line:

     C:\Users\typesafe\reactive-kafka-scala> activator ui 
    This will start Lightbend Activator and open this template in your browser.

Option 3: Create a reactive-kafka-scala project from the command line

If you have Lightbend Activator, use its command line mode to create a new project from this template. Type activator new PROJECTNAME reactive-kafka-scala on the command line.

Option 4: View the template source

The creator of this template maintains it at https://github.com/softwaremill/activator-reactive-kafka-scala#master.

Option 5: Preview the tutorial below

We've included the text of this template's tutorial below, but it may work better if you view it inside Activator on your computer. Activator tutorials are often designed to be interactive.

Preview the tutorial

Introduction

This tutorial contains a sample app that demonstrates akka-stream-kafka.

akka-stream-kafka is a wrapper that allows interacting with Apache Kafka like with a Reactive Stream, which is a standard for asynchronous stream processing with non-blocking backpressure.

akka-stream-kafka allows reading from Kafka like from a reactive Source, or writing to its representation as a Sink. The library offers a simple DSL for constructing reactive Sinks/Streams/Flows with various configuration options for the underlying Kafka queue. It also supports manual commit to achieve at-least-once delivery.

The application

What is our example application's logic?

Our flow begins with a random number generator which publishes numbers to a Kafka queue using a reactive stream. At the same time, two consumer streams read from this topic and expose read data in two different ways. The LoggingConsumer starts with application and writes all the numbers to a log. The HomeController represents a Play web endpoint which initializes another stream and emits read numbers via a WebSocket.

Let's now take a look at all the individual components and how they work together.

DemoLifecycle

Starts embedded Kafka service and actors: RandomNumberWriter + LoggingConsumer. Also maintains a shutdown hook responsible for graceful shutdown.

RandomNumberWriter

An actor which creates and runs the producer stream. This stream uses Kafka as a sink and sends generated numbers to it, using default ack settings. Materialized value of type `Control` is used for graceful shutdown of the stream. In case of stream failure, the exception will handled by restarting the actor which will re-initialize the stream.

LoggingConsumer

This actor creates a consumer stream which reads data from our topic and writes the numbers to log (which is configured as STDOUT). Our processing can be parallelized, which is demonstrated with mapAsync(2)(processMessage). We consider data as "processed" after it gets written to the log, then all the messages flow further into a grouping stage (groupWithin). This stage finishes grouping after it collects 10 messages or 15 seconds pass, whatever happens first. Such group gets then committed to Kafka.

Error handling and shutdown control are managed in the same way as in case of `RandomNumberWriter`. When our actor restarts, it will resume the stream which should start consuming from last committed offset. Since we commit messages in batches after processing, our approach guarantees at-least-once delivery semantics.

WebSocket

There may be more than one consumer for a topic. Our HomeController allows connecting using, for example, a web browser. Such request will open a websocket emitting numbers read by its own stream. This consumer stream has a different groupId than our logging consumer.

Error handling and shutdown are maintained by Play framework, so we can just create a `Source` and pass it further to the framework.

Shutdown

It is important that we close the consumers and publishers before actor system goes down. The DemoLifecycle uses Play shutdown hook which sends appropriate messages to writer/reader actors. These actor will then use `Control` objects materialized by streams to close the flows and all underlying Kafka connections.

Running the application

To start the app, you just need to execute run in the sbt console. Next, open localhost:9000 in a web browser. The console output should look similar to following:


09:52:54.941 INFO  akka.event.slf4j.Slf4jLogger - Slf4jLogger started
09:52:54.945 INFO  reactivekafka.DemoLifecycle - Starting embedded Kafka
09:52:55.556 INFO  reactivekafka.DemoLifecycle - Embedded Kafka ready
09:52:55.686 INFO  reactivekafka.RandomNumberWriter - Initializing writer
09:52:55.746 INFO  reactivekafka.RandomNumberWriter - Writer now running, writing random numbers to topic RandomNumbers
09:52:57.562 INFO  reactivekafka.LoggingConsumer - Initializing logging consumer
09:52:57.589 INFO  reactivekafka.LoggingConsumer - Logging consumer started
09:52:57.672 INFO  play.api.Play - Application started (Dev)
09:52:58.761 INFO  reactivekafka.LoggingConsumer - Consumed number: -881358456
09:52:58.762 INFO  reactivekafka.LoggingConsumer - Consumed number: -1211016723
09:52:58.762 INFO  reactivekafka.LoggingConsumer - Consumed number: 82910467
09:52:59.737 INFO  reactivekafka.LoggingConsumer - Consumed number: -1250504005
        
Output in the browser should display same numbers. If you now push ctrl+D in the console, you will see indications that the clean shutdown hooks are doing their job:


09:54:32.453 INFO  reactivekafka.DemoLifecycle - Shutting down application...
09:54:32.454 INFO  reactivekafka.RandomNumberWriter - Stopping Kafka producer stream and actor
09:54:32.455 INFO  reactivekafka.LoggingConsumer - Shutting down logging consumer stream and actor

You may notice that the logback.xml configuration file contains directives that silence some errors and warnings. These messages are related to running Kafka/Zookeeper in embedded mode and do not really affect our application, so we don't want them to pollute our output in this example. You can always try to run the app on your local ZK/Kafka instance and disable usage of EmbeddedKafka in the DemoLifecycle actor.

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