#2663 Addressing review remarks;

* Removing superfluous RegisterForReflection configuration
* Adding linebreak and comment

docs/src/main/asciidoc/kafka-streams-guide.adoc

@@ -129,7 +129,9 @@ public class ValuesGenerator {
         return Flowable.interval(500, TimeUnit.MILLISECONDS)    // <2>
                 .map(tick -> {
                     WeatherStation station = stations.get(random.nextInt(stations.size()));
-                    double temperature = new BigDecimal(random.nextGaussian() * 15 + station.averageTemperature)
+                    double temperature = new BigDecimal(
+                                random.nextGaussian() * 15 + station.averageTemperature
+                            )
                             .setScale(1, RoundingMode.HALF_UP)
                             .doubleValue();
 
@@ -174,6 +176,7 @@ For that, add the following to the file `producer/src/main/resources/application
 
 [source]
 ----
+# Configure the Kafka broker location
 kafka.bootstrap.servers=localhost:9092
 
 mp.messaging.outgoing.temperature-values.connector=smallrye-kafka
@@ -220,13 +223,14 @@ package org.acme.quarkus.sample.kafkastreams.model;
 
 import io.quarkus.runtime.annotations.RegisterForReflection;
 
-@RegisterForReflection
+@RegisterForReflection // <1>
 public class WeatherStation {
 
     public int id;
     public String name;
 }
 ----
+<1> By adding the `@RegisterForReflection` annotation, it is ensured that this type can be instantiated reflectively when running the application in native mode.
 
 Then the file `aggregator/src/main/java/org/acme/quarkus/sample/kafkastreams/model/TemperatureMeasurement.java`,
 representing temperature measurements for a given station:
@@ -265,7 +269,6 @@ import java.math.RoundingMode;
 
 import io.quarkus.runtime.annotations.RegisterForReflection;
 
-@RegisterForReflection
 public class Aggregation {
 
     public int stationId;
@@ -293,8 +296,6 @@ public class Aggregation {
 }
 ----
 
-All these types carry the `@RegisterForReflection`, allowing them to be instantiated reflectively when running the application in native mode.
-
 Next, let's create the actual streaming query implementation itself in the `aggregator/src/main/java/org/acme/quarkus/sample/kafkastreams/streams/KafkaStreamsPipeline.java` file:
 
 [source, java]
@@ -459,13 +460,6 @@ public class KafkaStreamsPipeline {
 <7> The results of the pipeline are written out to the `temperatures-aggregated` topic
 <8> The Quarkus `StartupEvent` and `ShutdownEvent` lifecycle events are used for starting and stopping the pipeline; as it may only be started once the topics exist, the Kafka admin client is used to check for their existence continuously, until they have been set up
 
-Lastly, create the Quarkus configuration file `aggregator/src/main/resources/application.properties` with the following contents:
-
-[source]
-----
-org.acme.quarkus.sample.kafkastreams.bootstrap.servers=localhost:9092
-----
-
 == Building and Running the Applications
 
 We now can build the `producer` and `aggregator` applications:
@@ -661,9 +655,6 @@ which represents the actual aggregation result for a weather station:
 ----
 package org.acme.quarkus.sample.kafkastreams.model;
 
-import io.quarkus.runtime.annotations.RegisterForReflection;
-
-@RegisterForReflection
 public class WeatherStationData {
 
     public int stationId;