diff --git a/clients/src/main/java/org/apache/kafka/clients/consumer/RangeAssignor.java b/clients/src/main/java/org/apache/kafka/clients/consumer/RangeAssignor.java index d8d72ee60..5d5a2684e 100644 --- a/clients/src/main/java/org/apache/kafka/clients/consumer/RangeAssignor.java +++ b/clients/src/main/java/org/apache/kafka/clients/consumer/RangeAssignor.java @@ -26,17 +26,20 @@ import java.util.List; import java.util.Map; /** - * The range assignor works on a per-topic basis. For each topic, we lay out the available partitions in numeric order + *

The range assignor works on a per-topic basis. For each topic, we lay out the available partitions in numeric order * and the consumers in lexicographic order. We then divide the number of partitions by the total number of * consumers to determine the number of partitions to assign to each consumer. If it does not evenly * divide, then the first few consumers will have one extra partition. * - * For example, suppose there are two consumers C0 and C1, two topics t0 and t1, and each topic has 3 partitions, - * resulting in partitions t0p0, t0p1, t0p2, t1p0, t1p1, and t1p2. + *

For example, suppose there are two consumers C0 and C1, two topics t0 and + * t1, and each topic has 3 partitions, resulting in partitions t0p0, t0p1, + * t0p2, t1p0, t1p1, and t1p2. * - * The assignment will be: - * C0: [t0p0, t0p1, t1p0, t1p1] - * C1: [t0p2, t1p2] + *

The assignment will be: + *

*/ public class RangeAssignor extends AbstractPartitionAssignor { diff --git a/streams/src/main/java/org/apache/kafka/streams/kstream/Transformer.java b/streams/src/main/java/org/apache/kafka/streams/kstream/Transformer.java index 2eb49219c..308fcadf6 100644 --- a/streams/src/main/java/org/apache/kafka/streams/kstream/Transformer.java +++ b/streams/src/main/java/org/apache/kafka/streams/kstream/Transformer.java @@ -24,7 +24,7 @@ import org.apache.kafka.streams.processor.StateStore; import org.apache.kafka.streams.processor.TimestampExtractor; /** - * The {@code Transformer} interface for stateful mapping of an input record to zero, one, or multiple new output + * The {@code Transformer} interface is for stateful mapping of an input record to zero, one, or multiple new output * records (both key and value type can be altered arbitrarily). * This is a stateful record-by-record operation, i.e, {@link #transform(Object, Object)} is invoked individually for * each record of a stream and can access and modify a state that is available beyond a single call of