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'Distinct' operation is common in data processing, e. g.

• SQL DISTINCT keyword,
• In standard libraries for programming languages
  • .NET LINQ Distinct method,
  • Java Stream distinct(),
  • Scala Seq distinct(),
• In data processing frameworks:

  • Apache Spark's distinct(),

  • Apache Flink's distinct(),
  • Apache Beam's Distinct(),
  • Hazelcast Jet's distinct(), etc
• java.util.stream.Stream has distinct() method,
• SQL has DISTINCT keyword
  • .

Hence it is natural to expect the similar functionality from Kafka Streams.

Although Kafka Streams Tutorials contains an example of how distinct can be emulated, but this example is complicated: it involves low-level coding with local state store and a custom transformer. It might be much more convenient to have distinct as a first-class DSL operation.

Due to 'infinite' nature of KStream, distinct operation should be windowed, similar to windowed joins and aggregations for KStreams.

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• distinct() parameterless DSLOperation on

  • TimeWindowedKStream<K,V>  DSLObject which returns KStream<Windowed<K>,V> 
  • SessionWindowedKStream<K,V> DSLObject which returns KStream<Windowed<K>,V>

The following methods are added to the corresponding interfaces:

KTable<Windowed<K>, V> distinct(final Named named);
KTable<Windowed<K>, V> distinct(final Materialized<K, V, WindowStore<Bytes, byte[]>> materialized);
KTable<Windowed<K>, V> distinct(final Named named,
                                    final Materialized<K, V, WindowStore<Bytes, byte[]>> materialized);

The distinct operation returns only a first record that falls into a new window, and filters out all the other records that fall into an already existing window.

The records are considered to be duplicates iff serialized forms of their keys are equal.

Usage Examples

Consider the following example (record times are in seconds):

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