KAFKA-12823 Remove Deprecated method KStream#through (#16761)

Implements KIP-1087

Reviewers: Matthias J. Sax <[email protected]>, Lucas Brutschy <[email protected]>, Anna Sophie Blee-Goldman <[email protected]>

core/src/main/scala/kafka/tools/StreamsResetter.java

@@ -16,7 +16,6 @@
  */
 package kafka.tools;
 
-
 import java.lang.reflect.InvocationTargetException;
 import java.lang.reflect.Method;
 

docs/streams/developer-guide/app-reset-tool.html

@@ -36,12 +36,11 @@
         <p>You can reset an application and force it to reprocess its data from scratch by using the application reset tool.
             This can be useful for development and testing, or when fixing bugs.</p>
         <p>The application reset tool handles the Kafka Streams <a class="reference internal" href="manage-topics.html#streams-developer-guide-topics-user"><span class="std std-ref">user topics</span></a> (input,
-            output, and intermediate topics) and <a class="reference internal" href="manage-topics.html#streams-developer-guide-topics-internal"><span class="std std-ref">internal topics</span></a> differently
+            and output) and <a class="reference internal" href="manage-topics.html#streams-developer-guide-topics-internal"><span class="std std-ref">internal topics</span></a> differently
             when resetting the application.</p>
         <p>Here&#8217;s what the application reset tool does for each topic type:</p>
         <ul class="simple">
             <li>Input topics: Reset offsets to specified position (by default to the beginning of the topic).</li>
-            <li>Intermediate topics: Skip to the end of the topic, i.e., set the application&#8217;s committed consumer offsets for all partitions to each partition&#8217;s <code class="docutils literal"><span class="pre">logSize</span></code> (for consumer group <code class="docutils literal"><span class="pre">application.id</span></code>).</li>
             <li>Internal topics: Delete the internal topic (this automatically deletes any committed offsets).</li>
         </ul>
         <p>The application reset tool does not:</p>
@@ -61,16 +60,6 @@
                 </li>
                 <li><p class="first">Use this tool with care and double-check its parameters: If you provide wrong parameter values (e.g., typos in <code class="docutils literal"><span class="pre">application.id</span></code>) or specify parameters inconsistently (e.g., specify the wrong input topics for the application), this tool might invalidate the application&#8217;s state or even impact other applications, consumer groups, or your Kafka topics.</p>
                 </li>
-                <li><p class="first">You should manually delete and re-create any intermediate topics before running the application reset tool. This will free up disk space in Kafka brokers.</p>
-                </li>
-                <li><p class="first">You should delete and recreate intermediate topics before running the application reset tool, unless the following applies:</p>
-                    <blockquote>
-                        <div><ul class="simple">
-                            <li>You have external downstream consumers for the application&#8217;s intermediate topics.</li>
-                            <li>You are in a development environment where manually deleting and re-creating intermediate topics is unnecessary.</li>
-                        </ul>
-                        </div></blockquote>
-                </li>
             </ul>
             </dd>
         </dl>
@@ -106,10 +95,6 @@ <h2>Step 1: Run the application reset tool<a class="headerlink" href="#step-1-ru
                                         topics. For these topics, the tool will
                                         reset the offset to the earliest
                                         available offset.
---intermediate-topics &lt;String: list&gt;  Comma-separated list of intermediate user
-                                        topics (topics used in the through()
-                                        method). For these topics, the tool
-                                        will skip to the end.
 --internal-topics &lt;String: list&gt;      Comma-separated list of internal topics
                                         to delete. Must be a subset of the
                                         internal topics marked for deletion by

streams/src/main/java/org/apache/kafka/streams/kstream/KStream.java

@@ -797,46 +797,6 @@ <VR> KStream<K, VR> flatMapValues(final ValueMapperWithKey<? super K, ? super V,
      */
     KStream<K, V> merge(final KStream<K, V> stream, final Named named);
 
-    /**
-     * Materialize this stream to a topic and creates a new {@code KStream} from the topic using default serializers,
-     * deserializers, and producer's default partitioning strategy.
-     * The specified topic should be manually created before it is used (i.e., before the Kafka Streams application is
-     * started).
-     * <p>
-     * This is similar to calling {@link #to(String) #to(someTopicName)} and
-     * {@link StreamsBuilder#stream(String) StreamsBuilder#stream(someTopicName)}.
-     * Note that {@code through()} uses a hard coded {@link org.apache.kafka.streams.processor.FailOnInvalidTimestamp
-     * timestamp extractor} and does not allow to customize it, to ensure correct timestamp propagation.
-     *
-     * @param topic the topic name
-     * @return a {@code KStream} that contains the exact same (and potentially repartitioned) records as this {@code KStream}
-     * @deprecated since 2.6; use {@link #repartition()} instead
-     */
-    // TODO: when removed, update `StreamsResetter` description of --intermediate-topics
-    @Deprecated
-    KStream<K, V> through(final String topic);
-
-    /**
-     * Materialize this stream to a topic and creates a new {@code KStream} from the topic using the
-     * {@link Produced} instance for configuration of the {@link Serde key serde}, {@link Serde value serde},
-     * and {@link StreamPartitioner}.
-     * The specified topic should be manually created before it is used (i.e., before the Kafka Streams application is
-     * started).
-     * <p>
-     * This is similar to calling {@link #to(String, Produced) to(someTopic, Produced.with(keySerde, valueSerde)}
-     * and {@link StreamsBuilder#stream(String, Consumed) StreamsBuilder#stream(someTopicName, Consumed.with(keySerde, valueSerde))}.
-     * Note that {@code through()} uses a hard coded {@link org.apache.kafka.streams.processor.FailOnInvalidTimestamp
-     * timestamp extractor} and does not allow to customize it, to ensure correct timestamp propagation.
-     *
-     * @param topic     the topic name
-     * @param produced  the options to use when producing to the topic
-     * @return a {@code KStream} that contains the exact same (and potentially repartitioned) records as this {@code KStream}
-     * @deprecated since 2.6; use {@link #repartition(Repartitioned)} instead
-     */
-    @Deprecated
-    KStream<K, V> through(final String topic,
-                          final Produced<K, V> produced);
-
     /**
      * Materialize this stream to an auto-generated repartition topic and create a new {@code KStream}
      * from the auto-generated topic using default serializers, deserializers, and producer's default partitioning strategy.

streams/src/main/java/org/apache/kafka/streams/kstream/internals/KStreamImpl.java

@@ -55,7 +55,6 @@
 import org.apache.kafka.streams.kstream.internals.graph.StreamToTableNode;
 import org.apache.kafka.streams.kstream.internals.graph.UnoptimizableRepartitionNode;
 import org.apache.kafka.streams.kstream.internals.graph.UnoptimizableRepartitionNode.UnoptimizableRepartitionNodeBuilder;
-import org.apache.kafka.streams.processor.FailOnInvalidTimestamp;
 import org.apache.kafka.streams.processor.StreamPartitioner;
 import org.apache.kafka.streams.processor.TopicNameExtractor;
 import org.apache.kafka.streams.processor.api.FixedKeyProcessorSupplier;
@@ -498,39 +497,6 @@ private KStream<K, V> merge(final InternalStreamsBuilder builder,
             builder);
     }
 
-    @Deprecated
-    @Override
-    public KStream<K, V> through(final String topic) {
-        return through(topic, Produced.with(keySerde, valueSerde, null));
-    }
-
-    @Deprecated
-    @Override
-    public KStream<K, V> through(final String topic,
-                                 final Produced<K, V> produced) {
-        Objects.requireNonNull(topic, "topic can't be null");
-        Objects.requireNonNull(produced, "produced can't be null");
-
-        final ProducedInternal<K, V> producedInternal = new ProducedInternal<>(produced);
-        if (producedInternal.keySerde() == null) {
-            producedInternal.withKeySerde(keySerde);
-        }
-        if (producedInternal.valueSerde() == null) {
-            producedInternal.withValueSerde(valueSerde);
-        }
-        to(topic, producedInternal);
-
-        return builder.stream(
-            Collections.singleton(topic),
-            new ConsumedInternal<>(
-                producedInternal.keySerde(),
-                producedInternal.valueSerde(),
-                new FailOnInvalidTimestamp(),
-                null
-            )
-        );
-    }
-
     @Override
     public KStream<K, V> repartition() {
         return doRepartition(Repartitioned.as(null));

streams/src/test/java/org/apache/kafka/streams/StreamsBuilderTest.java

@@ -332,28 +332,6 @@ public void shouldProcessingFromSinkTopic() {
                      processorSupplier.theCapturedProcessor().processed());
     }
 
-    @Deprecated
-    @Test
-    public void shouldProcessViaThroughTopic() {
-        final KStream<String, String> source = builder.stream("topic-source");
-        final KStream<String, String> through = source.through("topic-sink");
-
-        final MockApiProcessorSupplier<String, String, Void, Void> sourceProcessorSupplier = new MockApiProcessorSupplier<>();
-        source.process(sourceProcessorSupplier);
-
-        final MockApiProcessorSupplier<String, String, Void, Void> throughProcessorSupplier = new MockApiProcessorSupplier<>();
-        through.process(throughProcessorSupplier);
-
-        try (final TopologyTestDriver driver = new TopologyTestDriver(builder.build(), props)) {
-            final TestInputTopic<String, String> inputTopic =
-                driver.createInputTopic("topic-source", new StringSerializer(), new StringSerializer(), Instant.ofEpochMilli(0L), Duration.ZERO);
-            inputTopic.pipeInput("A", "aa");
-        }
-
-        assertEquals(Collections.singletonList(new KeyValueTimestamp<>("A", "aa", 0)), sourceProcessorSupplier.theCapturedProcessor().processed());
-        assertEquals(Collections.singletonList(new KeyValueTimestamp<>("A", "aa", 0)), throughProcessorSupplier.theCapturedProcessor().processed());
-    }
-
     @Test
     public void shouldProcessViaRepartitionTopic() {
         final KStream<String, String> source = builder.stream("topic-source");

streams/src/test/java/org/apache/kafka/streams/kstream/internals/KStreamImplTest.java

@@ -515,33 +515,6 @@ public void shouldNotAllowNullNamedOnMerge() {
         assertThat(exception.getMessage(), equalTo("named can't be null"));
     }
 
-    @Deprecated // specifically testing the deprecated variant
-    @Test
-    public void shouldNotAllowNullTopicOnThrough() {
-        final NullPointerException exception = assertThrows(
-            NullPointerException.class,
-            () -> testStream.through(null));
-        assertThat(exception.getMessage(), equalTo("topic can't be null"));
-    }
-
-    @Deprecated // specifically testing the deprecated variant
-    @Test
-    public void shouldNotAllowNullTopicOnThroughWithProduced() {
-        final NullPointerException exception = assertThrows(
-            NullPointerException.class,
-            () -> testStream.through(null, Produced.as("through")));
-        assertThat(exception.getMessage(), equalTo("topic can't be null"));
-    }
-
-    @Deprecated // specifically testing the deprecated variant
-    @Test
-    public void shouldNotAllowNullProducedOnThrough() {
-        final NullPointerException exception = assertThrows(
-            NullPointerException.class,
-            () -> testStream.through("topic", null));
-        assertThat(exception.getMessage(), equalTo("produced can't be null"));
-    }
-
     @Test
     public void shouldNotAllowNullTopicOnTo() {
         final NullPointerException exception = assertThrows(
@@ -1277,10 +1250,6 @@ public void shouldPreserveSerdesForOperators() {
         assertNull(((AbstractStream) stream1.merge(stream1)).keySerde());
         assertNull(((AbstractStream) stream1.merge(stream1)).valueSerde());
 
-        assertEquals(((AbstractStream) stream1.through("topic-3")).keySerde(), consumedInternal.keySerde());
-        assertEquals(((AbstractStream) stream1.through("topic-3")).valueSerde(), consumedInternal.valueSerde());
-        assertEquals(((AbstractStream) stream1.through("topic-3", Produced.with(mySerde, mySerde))).keySerde(), mySerde);
-        assertEquals(((AbstractStream) stream1.through("topic-3", Produced.with(mySerde, mySerde))).valueSerde(), mySerde);
 
         assertEquals(((AbstractStream) stream1.repartition()).keySerde(), consumedInternal.keySerde());
         assertEquals(((AbstractStream) stream1.repartition()).valueSerde(), consumedInternal.valueSerde());
@@ -1329,24 +1298,6 @@ public void shouldPreserveSerdesForOperators() {
         assertNull(((AbstractStream) stream1.leftJoin(table2, selector, joiner)).valueSerde());
     }
 
-    @Deprecated
-    @Test
-    public void shouldUseRecordMetadataTimestampExtractorWithThrough() {
-        final StreamsBuilder builder = new StreamsBuilder();
-        final KStream<String, String> stream1 = builder.stream(Arrays.asList("topic-1", "topic-2"), stringConsumed);
-        final KStream<String, String> stream2 = builder.stream(Arrays.asList("topic-3", "topic-4"), stringConsumed);
-
-        stream1.to("topic-5");
-        stream2.through("topic-6");
-
-        final ProcessorTopology processorTopology = TopologyWrapper.getInternalTopologyBuilder(builder.build()).setApplicationId("X").buildTopology();
-        assertThat(processorTopology.source("topic-6").timestampExtractor(), instanceOf(FailOnInvalidTimestamp.class));
-        assertNull(processorTopology.source("topic-4").timestampExtractor());
-        assertNull(processorTopology.source("topic-3").timestampExtractor());
-        assertNull(processorTopology.source("topic-2").timestampExtractor());
-        assertNull(processorTopology.source("topic-1").timestampExtractor());
-    }
-
     @Test
     public void shouldUseRecordMetadataTimestampExtractorWithRepartition() {
         final StreamsBuilder builder = new StreamsBuilder();
@@ -1364,22 +1315,6 @@ public void shouldUseRecordMetadataTimestampExtractorWithRepartition() {
         assertNull(processorTopology.source("topic-1").timestampExtractor());
     }
 
-    @Deprecated
-    @Test
-    public void shouldSendDataThroughTopicUsingProduced() {
-        final StreamsBuilder builder = new StreamsBuilder();
-        final String input = "topic";
-        final KStream<String, String> stream = builder.stream(input, stringConsumed);
-        stream.through("through-topic", Produced.with(Serdes.String(), Serdes.String())).process(processorSupplier);
-
-        try (final TopologyTestDriver driver = new TopologyTestDriver(builder.build(), props)) {
-            final TestInputTopic<String, String> inputTopic =
-                driver.createInputTopic(input, new StringSerializer(), new StringSerializer(), Instant.ofEpochMilli(0L), Duration.ZERO);
-            inputTopic.pipeInput("a", "b");
-        }
-        assertThat(processorSupplier.theCapturedProcessor().processed(), equalTo(Collections.singletonList(new KeyValueTimestamp<>("a", "b", 0))));
-    }
-
     @Test
     public void shouldSendDataThroughRepartitionTopicUsingRepartitioned() {
         final StreamsBuilder builder = new StreamsBuilder();

streams/src/test/java/org/apache/kafka/streams/kstream/internals/graph/StreamsGraphTest.java

@@ -195,19 +195,6 @@ public void shouldNotOptimizeWithValueOrKeyChangingOperatorsAfterInitialKeyChang
         assertEquals(2, getCountOfRepartitionTopicsFound(noOptimization.describe().toString()));
     }
 
-    // no need to optimize as user has already performed the repartitioning manually
-    @Deprecated
-    @Test
-    public void shouldNotOptimizeWhenAThroughOperationIsDone() {
-        final Topology attemptedOptimize = getTopologyWithThroughOperation(StreamsConfig.OPTIMIZE);
-        final Topology noOptimization = getTopologyWithThroughOperation(StreamsConfig.NO_OPTIMIZATION);
-
-        assertEquals(attemptedOptimize.describe().toString(), noOptimization.describe().toString());
-        assertEquals(0, getCountOfRepartitionTopicsFound(attemptedOptimize.describe().toString()));
-        assertEquals(0, getCountOfRepartitionTopicsFound(noOptimization.describe().toString()));
-
-    }
-
     @Test
     public void shouldOptimizeSeveralMergeNodesWithCommonKeyChangingParent() {
         final StreamsBuilder streamsBuilder = new StreamsBuilder();
@@ -256,23 +243,6 @@ private Topology getTopologyWithChangingValuesAfterChangingKey(final String opti
 
     }
 
-    @Deprecated // specifically testing the deprecated variant
-    private Topology getTopologyWithThroughOperation(final String optimizeConfig) {
-
-        final StreamsBuilder builder = new StreamsBuilder();
-        final Properties properties = new Properties();
-        properties.put(StreamsConfig.TOPOLOGY_OPTIMIZATION_CONFIG, optimizeConfig);
-
-        final KStream<String, String> inputStream = builder.stream("input");
-        final KStream<String, String> mappedKeyStream = inputStream.selectKey((k, v) -> k + v).through("through-topic");
-
-        mappedKeyStream.groupByKey().count().toStream().to("output");
-        mappedKeyStream.groupByKey().windowedBy(TimeWindows.ofSizeWithNoGrace(ofMillis(5000))).count().toStream().to("windowed-output");
-
-        return builder.build(properties);
-
-    }
-
     private Topology getTopologyWithRepartitionOperation(final String optimizeConfig) {
         final StreamsBuilder builder = new StreamsBuilder();
         final Properties properties = new Properties();

streams/streams-scala/src/main/scala/org/apache/kafka/streams/scala/kstream/KStream.scala

@@ -349,40 +349,6 @@ class KStream[K, V](val inner: KStreamJ[K, V]) {
   def split(named: Named): BranchedKStream[K, V] =
     new BranchedKStream(inner.split(named))
 
-  /**
-   * Materialize this stream to a topic and creates a new [[KStream]] from the topic using the `Produced` instance for
-   * configuration of the `Serde key serde`, `Serde value serde`, and `StreamPartitioner`
-   * <p>
-   * The user can either supply the `Produced` instance as an implicit in scope or they can also provide implicit
-   * key and value serdes that will be converted to a `Produced` instance implicitly.
-   * <p>
-   * {{{
-   * Example:
-   *
-   * // brings implicit serdes in scope
-   * import Serdes._
-   *
-   * //..
-   * val clicksPerRegion: KStream[String, Long] = //..
-   *
-   * // Implicit serdes in scope will generate an implicit Produced instance, which
-   * // will be passed automatically to the call of through below
-   * clicksPerRegion.through(topic)
-   *
-   * // Similarly you can create an implicit Produced and it will be passed implicitly
-   * // to the through call
-   * }}}
-   *
-   * @param topic    the topic name
-   * @param produced the instance of Produced that gives the serdes and `StreamPartitioner`
-   * @return a [[KStream]] that contains the exact same (and potentially repartitioned) records as this [[KStream]]
-   * @see `org.apache.kafka.streams.kstream.KStream#through`
-   * @deprecated use `repartition()` instead
-   */
-  @deprecated("use `repartition()` instead", "2.6.0")
-  def through(topic: String)(implicit produced: Produced[K, V]): KStream[K, V] =
-    new KStream(inner.through(topic, produced))
-
   /**
    * Materialize this stream to a topic and creates a new [[KStream]] from the topic using the `Repartitioned` instance
    * for configuration of the `Serde key serde`, `Serde value serde`, `StreamPartitioner`, number of partitions, and