LQXshane commented on code in PR #14446:
URL: https://github.com/apache/kafka/pull/14446#discussion_r1568199337
##########
streams/src/main/java/org/apache/kafka/streams/kstream/KStream.java:
##########
@@ -685,6 +685,47 @@ <VR> KStream<K, VR> flatMapValues(final ValueMapper<?
super V, ? extends Iterabl
<VR> KStream<K, VR> flatMapValues(final ValueMapperWithKey<? super K, ?
super V, ? extends Iterable<? extends VR>> mapper,
final Named named);
+ /**
+ * Marking the {@code KStream} as partitioned signals the stream is
partitioned as intended,
+ * and does not require further repartitioning in downstream key changing
operations.
+ *
+ * <p><em>
+ * Note that {@link KStream#markAsPartitioned()} SHOULD NOT be used
with interactive query(IQ) or {@link KStream#join}.
+ * For reasons that when repartitions happen, records are physically
shuffled by a composite key defined in the stateful operation.
+ * However, if the repartitions were cancelled, records stayed in
their original partition by its original key. IQ or joins
+ * assumes and uses the composite key instead of the original key.
+ * </p></em>
+ *
+ *
+ * This method will overwrite a default behavior as described below.
+ * <p>
+ * By default, Kafka Streams always automatically repartition the
records to prepare for a stateful operation,
+ * however, it is not always required when input stream is partitioned
as intended. As an example,
+ * if a input stream is partitioned by a String key1, calling the
below function will trigger a repartition:
+ * <pre>{@code
+ * KStream<String, String> inputStream = builder.stream("topic");
+ * stream
+ * .selectKey( ... => (key1, metric))
+ * .groupByKey()
+ * .aggregate()
+ * }</pre>
+ * </p>
+ *
+ * <p>
+ * You can then overwrite the default behavior by calling this method:
+ * <pre>{@code
+ * stream
+ * .selectKey( ... => (key1, metric))
+ * .markAsPartitioned()
+ * .groupByKey()
+ * .aggregate()
+ * }</pre>
+ * </p>
+ *
+ * @return a new {@code KStream} instance that will not repartition in
subsequent operations: {@link KStream#selectKey(KeyValueMapper)}, {@link
KStream#map(KeyValueMapper)}, {@link KStream#flatTransform(TransformerSupplier,
String...)}.
Review Comment:
Removed for simplicity
##########
streams/src/main/java/org/apache/kafka/streams/kstream/KStream.java:
##########
@@ -685,6 +685,47 @@ <VR> KStream<K, VR> flatMapValues(final ValueMapper<?
super V, ? extends Iterabl
<VR> KStream<K, VR> flatMapValues(final ValueMapperWithKey<? super K, ?
super V, ? extends Iterable<? extends VR>> mapper,
final Named named);
+ /**
+ * Marking the {@code KStream} as partitioned signals the stream is
partitioned as intended,
+ * and does not require further repartitioning in downstream key changing
operations.
+ *
+ * <p><em>
+ * Note that {@link KStream#markAsPartitioned()} SHOULD NOT be used
with interactive query(IQ) or {@link KStream#join}.
+ * For reasons that when repartitions happen, records are physically
shuffled by a composite key defined in the stateful operation.
+ * However, if the repartitions were cancelled, records stayed in
their original partition by its original key. IQ or joins
+ * assumes and uses the composite key instead of the original key.
+ * </p></em>
+ *
+ *
+ * This method will overwrite a default behavior as described below.
+ * <p>
+ * By default, Kafka Streams always automatically repartition the
records to prepare for a stateful operation,
+ * however, it is not always required when input stream is partitioned
as intended. As an example,
+ * if a input stream is partitioned by a String key1, calling the
below function will trigger a repartition:
+ * <pre>{@code
+ * KStream<String, String> inputStream = builder.stream("topic");
+ * stream
+ * .selectKey( ... => (key1, metric))
+ * .groupByKey()
+ * .aggregate()
+ * }</pre>
+ * </p>
+ *
+ * <p>
+ * You can then overwrite the default behavior by calling this method:
+ * <pre>{@code
+ * stream
+ * .selectKey( ... => (key1, metric))
+ * .markAsPartitioned()
+ * .groupByKey()
+ * .aggregate()
+ * }</pre>
+ * </p>
+ *
+ * @return a new {@code KStream} instance that will not repartition in
subsequent operations: {@link KStream#selectKey(KeyValueMapper)}, {@link
KStream#map(KeyValueMapper)}, {@link KStream#flatTransform(TransformerSupplier,
String...)}.
Review Comment:
Removed for simplicity in the new PR
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