src-kafka/docs/streams/upgrade-guide.html

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    <h1>Upgrade Guide and API Changes</h1>
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    <p>
        If you want to upgrade from 1.1.x to 1.2.0 and you have customized window store implementations on the <code>ReadOnlyWindowStore</code> interface
        you'd need to update your code to incorporate the newly added public APIs; otherwise you don't need to make any code changes.
        See <a href="#streams_api_changes_120">below</a> for a complete list of 1.2.0 API and semantic changes that allow you to advance your application and/or simplify your code base.
    </p>

    <p>
        If you want to upgrade from 1.0.x to 1.1.0 and you have customized window store implementations on the <code>ReadOnlyWindowStore</code> interface
        you'd need to update your code to incorporate the newly added public APIs.
        Otherwise, if you are using Java 7 you don't need to make any code changes as the public API is fully backward compatible;
        but if you are using Java 8 method references in your Kafka Streams code you might need to update your code to resolve method ambiguities.
        Hot-swaping the jar-file only might not work for this case.
        See <a href="#streams_api_changes_110">below</a> for a complete list of 1.1.0 API and semantic changes that allow you to advance your application and/or simplify your code base.
    </p>

    <p>
        If you want to upgrade from 0.11.0.x to 1.0.0 you don't need to make any code changes as the public API is fully backward compatible.
        However, some public APIs were deprecated and thus it is recommended to update your code eventually to allow for future upgrades.
        See <a href="#streams_api_changes_100">below</a> for a complete list of 1.0.0 API and semantic changes that allow you to advance your application and/or simplify your code base.
    </p>

    <p>
        If you want to upgrade from 0.10.2.x to 0.11.0 you don't need to make any code changes as the public API is fully backward compatible.
        However, some configuration parameters were deprecated and thus it is recommended to update your code eventually to allow for future upgrades.
        See <a href="#streams_api_changes_0110">below</a> for a complete list of 0.11.0 API and semantic changes that allow you to advance your application and/or simplify your code base.
    </p>

    <p>
        If you want to upgrade from 0.10.1.x to 0.10.2, see the <a href="/{{version}}/documentation/#upgrade_1020_streams"><b>Upgrade Section for 0.10.2</b></a>.
        It highlights incompatible changes you need to consider to upgrade your code and application.
        See <a href="#streams_api_changes_0102">below</a> for a complete list of 0.10.2 API and semantic changes that allow you to advance your application and/or simplify your code base.
    </p>

    <p>
        If you want to upgrade from 0.10.0.x to 0.10.1, see the <a href="/{{version}}/documentation/#upgrade_1010_streams"><b>Upgrade Section for 0.10.1</b></a>.
        It highlights incompatible changes you need to consider to upgrade your code and application.
        See <a href="#streams_api_changes_0101">below</a> a complete list of 0.10.1 API changes that allow you to advance your application and/or simplify your code base, including the usage of new features.
    </p>

    <!-- TODO: verify release verion and update `id` and `href` attributes (also at other places that link to this headline) -->
    <h3><a id="streams_api_changes_120" href="#streams_api_changes_120">Streams API changes in 1.2.0</a></h3>
    <p>
        We have added support for methods in <code>ReadOnlyWindowStore</code> which allows for querying a single window's key-value pair.
        For users who have customized window store implementations on the above interface, they'd need to update their code to implement the newly added method as well.
        For more details, see <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-261%3A+Add+Single+Value+Fetch+in+Window+Stores">KIP-261</a>.
    </p>

    <p>
        We have added public <code>WindowedSerdes</code> to allow users to read from / write to a topic storing windowed table changelogs directly.
        In addition, in <code>StreamsConfig</code> we have also added <code>default.windowed.key.serde.inner</code> and <code>default.windowed.value.serde.inner</code>
        to let users specify inner serdes if the default serde classes are windowed serdes.
        For more details, see <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-265%3A+Make+Windowed+Serde+to+public+APIs">KIP-265</a>.
    /<p>
    
    <p>
      Kafka 1.2.0 allows to manipulate timestamps of output records using the Processor API (<a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-251%3A+Allow+timestamp+manipulation+in+Processor+API">KIP-251</a>).
      To enable this new feature, <code>ProcessorContext#forward(...)</code> was modified.
      The two existing overloads <code>#forward(Object key, Object value, String childName)</code> and <code>#forward(Object key, Object value, int childIndex)</code> were deprecated and a new overload <code>#forward(Object key, Object value, To to)</code> was added.
      The new class <code>To</code> allows you to send records to all or specific downstream processors by name and to set the timestamp for the output record.
      Forwarding based on child index is not supported in the new API any longer.
    </p>

    <h3><a id="streams_api_changes_110" href="#streams_api_changes_110">Streams API changes in 1.1.0</a></h3>
    <p>
        We have added support for methods in <code>ReadOnlyWindowStore</code> which allows for querying <code>WindowStore</code>s without the necessity of providing keys.
        For users who have customized window store implementations on the above interface, they'd need to update their code to implement the newly added method as well.
        For more details, see <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-205%3A+Add+all%28%29+and+range%28%29+API+to+ReadOnlyWindowStore">KIP-205</a>.
    </p>

    <p>
	There is a new artifact <code>kafka-streams-test-utils</code> providing a <code>TopologyTestDriver</code>, <code>ConsumerRecordFactory</code>, and <code>OutputVerifier</code> class.
	You can include the new artifact as a regular dependency to your unit tests and use the test driver to test your business logic of your Kafka Streams application.
	For more details, see <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-247%3A+Add+public+test+utils+for+Kafka+Streams">KIP-247</a>.
    </p>

    <p>
        The introduction of <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-220%3A+Add+AdminClient+into+Kafka+Streams%27+ClientSupplier">KIP-220</a>
        enables you to provide configuration parameters for the embedded admin client created by Kafka Streams, similar to the embedded producer and consumer clients.
        You can provide the configs via <code>StreamsConfig</code> by adding the configs with the prefix <code>admin.</code> as defined by <code>StreamsConfig#adminClientPrefix(String)</code>
        to distinguish them from configurations of other clients that share the same config names.
    </p>

    <p>
	New method in <code>GlobalKTable</code>
    </p>
    <ul>
	<li> A method has been provided such that it will return the store name associated with the <code>GlobalKTable</code> or <code>null</code> if the store name is non-queryable. </li>
    </ul>

    <p>
        New methods in <code>KafkaStreams</code>:
    </p>
    <ul>
        <li> added overload for the constructor that allows overriding the <code>Time</code> object used for tracking system wall-clock time; this is useful for unit testing your application code. </li>
    </ul>

    <p> New methods in <code>KafkaClientSupplier</code>: </p>
    <ul>
        <li> added <code>getAdminClient(config)</code> that allows to override an <code>AdminClient</code> used for administrative requests such as internal topic creations, etc. </li>
    </ul>

    <p>New error handling for exceptions during production:</p>
    <ul>
        <li>added interface <code>ProductionExceptionHandler</code> that allows implementors to decide whether or not Streams should <code>FAIL</code> or <code>CONTINUE</code> when certain exception occur while trying to produce.</li>
        <li>provided an implementation, <code>DefaultProductionExceptionHandler</code> that always fails, preserving the existing behavior by default.</li>
        <li>changing which implementation is used can be done by settings <code>default.production.exception.handler</code> to the fully qualified name of a class implementing this interface.</li>
    </ul>

    <p> Changes in <code>StreamsResetter</code>: </p>
    <ul>
        <li> added options to specify input topics offsets to reset according to <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-171+-+Extend+Consumer+Group+Reset+Offset+for+Stream+Application">KIP-171</a></li>
    </ul>

    <h3><a id="streams_api_changes_100" href="#streams_api_changes_100">Streams API changes in 1.0.0</a></h3>

    <p>
        With 1.0 a major API refactoring was accomplished and the new API is cleaner and easier to use.
        This change includes the five main classes <code>KafkaStreams</code>, <code>KStreamBuilder</code>,
        <code>KStream</code>, <code>KTable</code>, and <code>TopologyBuilder</code> (and some more others).
        All changes are fully backward compatible as old API is only deprecated but not removed.
        We recommend to move to the new API as soon as you can.
        We will summarize all API changes in the next paragraphs.
    </p>

    <p>
        The two main classes to specify a topology via the DSL (<code>KStreamBuilder</code>)
        or the Processor API (<code>TopologyBuilder</code>) were deprecated and replaced by
        <code>StreamsBuilder</code> and <code>Topology</code> (both new classes are located in
        package <code>org.apache.kafka.streams</code>).
        Note, that <code>StreamsBuilder</code> does not extend <code>Topology</code>, i.e.,
        the class hierarchy is different now.
        The new classes have basically the same methods as the old ones to build a topology via DSL or Processor API.
        However, some internal methods that were public in <code>KStreamBuilder</code>
        and <code>TopologyBuilder</code> but not part of the actual API are not present
        in the new classes any longer.
        Furthermore, some overloads were simplified compared to the original classes.
        See <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-120%3A+Cleanup+Kafka+Streams+builder+API">KIP-120</a>
        and <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-182%3A+Reduce+Streams+DSL+overloads+and+allow+easier+use+of+custom+storage+engines">KIP-182</a>
        for full details.
    </p>

    <p>
        Changing how a topology is specified also affects <code>KafkaStreams</code> constructors,
        that now only accept a <code>Topology</code>.
        Using the DSL builder class <code>StreamsBuilder</code> one can get the constructed
        <code>Topology</code> via <code>StreamsBuilder#build()</code>.
        Additionally, a new class <code>org.apache.kafka.streams.TopologyDescription</code>
        (and some more dependent classes) were added.
        Those can be used to get a detailed description of the specified topology
        and can be obtained by calling <code>Topology#describe()</code>.
        An example using this new API is shown in the <a href="/{{version}}/documentation/streams/quickstart">quickstart section</a>.
    </p>

    <p>
        New methods in <code>KStream</code>:
    </p>
    <ul>
        <li>With the introduction of <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-202+Move+merge%28%29+from+StreamsBuilder+to+KStream">KIP-202</a>
            a new method <code>merge()</code> has been created in <code>KStream</code> as the StreamsBuilder class's <code>StreamsBuilder#merge()</code> has been removed.
            The method signature was also changed, too: instead of providing multiple <code>KStream</code>s into the method at the once, only a single <code>KStream</code> is accepted.
        </li>
    </ul>

    <p>
        New methods in <code>KafkaStreams</code>:
    </p>
    <ul>
        <li>retrieve the current runtime information about the local threads via <code>localThreadsMetadata()</code> </li>
        <li>observe the restoration of all state stores via <code>setGlobalStateRestoreListener()</code>, in which users can provide their customized implementation of the <code>org.apache.kafka.streams.processor.StateRestoreListener</code> interface</li>
    </ul>

    <p>
        Deprecated / modified methods in <code>KafkaStreams</code>:
    </p>
    <ul>
        <li>
            <code>toString()</code>, <code>toString(final String indent)</code> were previously used to return static and runtime information.
            They have been deprecated in favor of using the new classes/methods <code>localThreadsMetadata()</code> / <code>ThreadMetadata</code> (returning runtime information) and
            <code>TopologyDescription</code> / <code>Topology#describe()</code> (returning static information).
        </li>
        <li>
            With the introduction of <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-182%3A+Reduce+Streams+DSL+overloads+and+allow+easier+use+of+custom+storage+engines">KIP-182</a>
            you should no longer pass in <code>Serde</code> to <code>KStream#print</code> operations.
            If you can't rely on using <code>toString</code> to print your keys an values, you should instead you provide a custom <code>KeyValueMapper</code> via the <code>Printed#withKeyValueMapper</code> call.
        </li>
        <li>
            <code>setStateListener()</code> now can only be set before the application start running, i.e. before <code>KafkaStreams.start()</code> is called.
        </li>
    </ul>

    <p>
        Deprecated methods in <code>KGroupedStream</code>
    </p>
    <ul>
        <li>
            Windowed aggregations have been deprecated from <code>KGroupedStream</code> and moved to <code>WindowedKStream</code>.
            You can now perform a windowed aggregation by, for example, using <code>KGroupedStream#windowedBy(Windows)#reduce(Reducer)</code>.
        </li>
    </ul>

    <p>
        Modified methods in <code>Processor</code>:
    </p>
    <ul>
        <li>
            <p>
                The Processor API was extended to allow users to schedule <code>punctuate</code> functions either based on data-driven <b>stream time</b> or wall-clock time.
                As a result, the original <code>ProcessorContext#schedule</code> is deprecated with a new overloaded function that accepts a user customizable <code>Punctuator</code> callback interface, which triggers its <code>punctuate</code> API method periodically based on the <code>PunctuationType</code>.
                The <code>PunctuationType</code> determines what notion of time is used for the punctuation scheduling: either <a href="/{{version}}/documentation/streams/core-concepts#streams_time">stream time</a> or wall-clock time (by default, <b>stream time</b> is configured to represent event time via <code>TimestampExtractor</code>).
                In addition, the <code>punctuate</code> function inside <code>Processor</code> is also deprecated.
            </p>
            <p>
                Before this, users could only schedule based on stream time (i.e. <code>PunctuationType.STREAM_TIME</code>) and hence the <code>punctuate</code> function was data-driven only because stream time is determined (and advanced forward) by the timestamps derived from the input data.
                If there is no data arriving at the processor, the stream time would not advance and hence punctuation will not be triggered.
                On the other hand, When wall-clock time (i.e. <code>PunctuationType.WALL_CLOCK_TIME</code>) is used, <code>punctuate</code> will be triggered purely based on wall-clock time.
                So for example if the <code>Punctuator</code> function is scheduled based on <code>PunctuationType.WALL_CLOCK_TIME</code>, if these 60 records were processed within 20 seconds,
                <code>punctuate</code> would be called 2 times (one time every 10 seconds);
                if these 60 records were processed within 5 seconds, then no <code>punctuate</code> would be called at all.
                Users can schedule multiple <code>Punctuator</code> callbacks with different <code>PunctuationType</code>s within the same processor by simply calling <code>ProcessorContext#schedule</code> multiple times inside processor's <code>init()</code> method.
            </p>
        </li>
    </ul>

    <p>
        If you are monitoring on task level or processor-node / state store level Streams metrics, please note that the metrics sensor name and hierarchy was changed:
        The task ids, store names and processor names are no longer in the sensor metrics names, but instead are added as tags of the sensors to achieve consistent metrics hierarchy.
        As a result you may need to make corresponding code changes on your metrics reporting and monitoring tools when upgrading to 1.0.0.
        Detailed metrics sensor can be found in the <a href="#kafka_streams_monitoring">Streams Monitoring</a> section.
    </p>

    <p>
        The introduction of <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+deserialization+exception+handlers">KIP-161</a>
        enables you to provide a default exception handler for deserialization errors when reading data from Kafka rather than throwing the exception all the way out of your streams application.
        You can provide the configs via the <code>StreamsConfig</code> as <code>StreamsConfig#DEFAULT_DESERIALIZATION_EXCEPTION_HANDLER_CLASS_CONFIG</code>.
        The specified handler must implement the <code>org.apache.kafka.streams.errors.DeserializationExceptionHandler</code> interface.
    </p>

    <p>
        The introduction of <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-173%3A+Add+prefix+to+StreamsConfig+to+enable+setting+default+internal+topic+configs">KIP-173</a>
        enables you to provide topic configuration parameters for any topics created by Kafka Streams.
        This includes repartition and changelog topics.
        You can provide the configs via the <code>StreamsConfig</code> by adding the configs with the prefix as defined by <code>StreamsConfig#topicPrefix(String)</code>.
        Any properties in the <code>StreamsConfig</code> with the prefix will be applied when creating internal topics.
        Any configs that aren't topic configs will be ignored.
        If you already use <code>StateStoreSupplier</code> or <code>Materialized</code> to provide configs for changelogs, then they will take precedence over those supplied in the config.
    </p>

    <h3><a id="streams_api_changes_0110" href="#streams_api_changes_0110">Streams API changes in 0.11.0.0</a></h3>

    <p> Updates in <code>StreamsConfig</code>: </p>
    <ul>
        <li> new configuration parameter <code>processing.guarantee</code> is added </li>
        <li> configuration parameter <code>key.serde</code> was deprecated and replaced by <code>default.key.serde</code> </li>
        <li> configuration parameter <code>value.serde</code> was deprecated and replaced by <code>default.value.serde</code> </li>
        <li> configuration parameter <code>timestamp.extractor</code> was deprecated and replaced by <code>default.timestamp.extractor</code> </li>
        <li> method <code>keySerde()</code> was deprecated and replaced by <code>defaultKeySerde()</code> </li>
        <li> method <code>valueSerde()</code> was deprecated and replaced by <code>defaultValueSerde()</code> </li>
        <li> new method <code>defaultTimestampExtractor()</code> was added </li>
    </ul>

    <p> New methods in <code>TopologyBuilder</code>: </p>
    <ul>
        <li> added overloads for <code>addSource()</code> that allow to define a <code>TimestampExtractor</code> per source node </li>
        <li> added overloads for <code>addGlobalStore()</code> that allow to define a <code>TimestampExtractor</code> per source node associated with the global store </li>
    </ul>

    <p> New methods in <code>KStreamBuilder</code>: </p>
    <ul>
        <li> added overloads for <code>stream()</code> that allow to define a <code>TimestampExtractor</code> per input stream </li>
        <li> added overloads for <code>table()</code> that allow to define a <code>TimestampExtractor</code> per input table </li>
        <li> added overloads for <code>globalKTable()</code> that allow to define a <code>TimestampExtractor</code> per global table </li>
    </ul>

    <p> Deprecated methods in <code>KTable</code>: </p>
    <ul>
        <li> <code>void foreach(final ForeachAction&lt;? super K, ? super V&gt; action)</code> </li>
        <li> <code>void print()</code> </li>
        <li> <code>void print(final String streamName)</code> </li>
        <li> <code>void print(final Serde&lt;K&gt; keySerde, final Serde&lt;V&gt; valSerde)</code> </li>
        <li> <code>void print(final Serde&lt;K&gt; keySerde, final Serde&lt;V&gt; valSerde, final String streamName)</code> </li>
        <li> <code>void writeAsText(final String filePath)</code> </li>
        <li> <code>void writeAsText(final String filePath, final String streamName)</code> </li>
        <li> <code>void writeAsText(final String filePath, final Serde&lt;K&gt; keySerde, final Serde&lt;V&gt; valSerde)</code> </li>
        <li> <code>void writeAsText(final String filePath, final String streamName, final Serde&lt;K&gt; keySerde, final Serde&lt;V&gt; valSerde)</code> </li>
    </ul>

    <p>
        The above methods have been deprecated in favor of using the Interactive Queries API.
        If you want to query the current content of the state store backing the KTable, use the following approach:
    </p>
    <ul>
        <li> Make a call to <code>KafkaStreams.store(final String storeName, final QueryableStoreType&lt;T&gt; queryableStoreType)</code> </li>
        <li> Then make a call to <code>ReadOnlyKeyValueStore.all()</code> to iterate over the keys of a <code>KTable</code>. </li>
    </ul>
    <p>
        If you want to view the changelog stream of the <code>KTable</code> then you could call <code>KTable.toStream().print(Printed.toSysOut)</code>.
    </p>

    <p> Metrics using exactly-once semantics: </p>
    <p>
        If exactly-once processing is enabled via the <code>processing.guarantees</code> parameter, internally Streams switches from a producer per thread to a producer per task runtime model.
        In order to distinguish the different producers, the producer's <code>client.id</code> additionally encodes the task-ID for this case.
        Because the producer's <code>client.id</code> is used to report JMX metrics, it might be required to update tools that receive those metrics.

    </p>

    <p> Producer's <code>client.id</code> naming schema: </p>
    <ul>
        <li> at-least-once (default): <code>[client.Id]-StreamThread-[sequence-number]</code> </li>
        <li> exactly-once: <code>[client.Id]-StreamThread-[sequence-number]-[taskId]</code> </li>
    </ul>
    <p> <code>[client.Id]</code> is either set via Streams configuration parameter <code>client.id</code> or defaults to <code>[application.id]-[processId]</code> (<code>[processId]</code> is a random UUID). </p>

    <h3><a id="streams_api_changes_01021" href="#streams_api_changes_01021">Notable changes in 0.10.2.1</a></h3>

    <p>
        Parameter updates in <code>StreamsConfig</code>:
    </p>
    <ul>
        <li> The default config values of embedded producer's <code>retries</code> and consumer's <code>max.poll.interval.ms</code> have been changed to improve the resiliency of a Kafka Streams application </li>
    </ul>

    <h3><a id="streams_api_changes_0102" href="#streams_api_changes_0102">Streams API changes in 0.10.2.0</a></h3>

    <p>
        New methods in <code>KafkaStreams</code>:
    </p>
    <ul>
        <li> set a listener to react on application state change via <code>setStateListener(StateListener listener)</code> </li>
        <li> retrieve the current application state via <code>state()</code> </li>
        <li> retrieve the global metrics registry via <code>metrics()</code> </li>
        <li> apply a timeout when closing an application via <code>close(long timeout, TimeUnit timeUnit)</code> </li>
        <li> specify a custom indent when retrieving Kafka Streams information via <code>toString(String indent)</code> </li>
    </ul>

    <p>
        Parameter updates in <code>StreamsConfig</code>:
    </p>
    <ul>
        <li> parameter <code>zookeeper.connect</code> was deprecated; a Kafka Streams application does no longer interact with ZooKeeper for topic management but uses the new broker admin protocol
            (cf. <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-4+-+Command+line+and+centralized+administrative+operations#KIP-4-Commandlineandcentralizedadministrativeoperations-TopicAdminSchema.1">KIP-4, Section "Topic Admin Schema"</a>) </li>
        <li> added many new parameters for metrics, security, and client configurations </li>
    </ul>

    <p> Changes in <code>StreamsMetrics</code> interface: </p>
    <ul>
        <li> removed methods: <code>addLatencySensor()</code> </li>
        <li> added methods: <code>addLatencyAndThroughputSensor()</code>, <code>addThroughputSensor()</code>, <code>recordThroughput()</code>,
            <code>addSensor()</code>, <code>removeSensor()</code> </li>
    </ul>

    <p> New methods in <code>TopologyBuilder</code>: </p>
    <ul>
        <li> added overloads for <code>addSource()</code> that allow to define a <code>auto.offset.reset</code> policy per source node </li>
        <li> added methods <code>addGlobalStore()</code> to add global <code>StateStore</code>s </li>
    </ul>

    <p> New methods in <code>KStreamBuilder</code>: </p>
    <ul>
        <li> added overloads for <code>stream()</code> and <code>table()</code> that allow to define a <code>auto.offset.reset</code> policy per input stream/table </li>
        <li> added method <code>globalKTable()</code> to create a <code>GlobalKTable</code> </li>
    </ul>

    <p> New joins for <code>KStream</code>: </p>
    <ul>
        <li> added overloads for <code>join()</code> to join with <code>KTable</code> </li>
        <li> added overloads for <code>join()</code> and <code>leftJoin()</code> to join with <code>GlobalKTable</code> </li>
        <li> note, join semantics in 0.10.2 were improved and thus you might see different result compared to 0.10.0.x and 0.10.1.x
            (cf. <a href="https://cwiki.apache.org/confluence/display/KAFKA/Kafka+Streams+Join+Semantics">Kafka Streams Join Semantics</a> in the Apache Kafka wiki)
    </ul>

    <p> Aligned <code>null</code>-key handling for <code>KTable</code> joins: </p>
    <ul>
        <li> like all other KTable operations, <code>KTable-KTable</code> joins do not throw an exception on <code>null</code> key records anymore, but drop those records silently </li>
    </ul>

    <p> New window type <em>Session Windows</em>: </p>
    <ul>
        <li> added class <code>SessionWindows</code> to specify session windows </li>
        <li> added overloads for <code>KGroupedStream</code> methods <code>count()</code>, <code>reduce()</code>, and <code>aggregate()</code>
            to allow session window aggregations </li>
    </ul>

    <p> Changes to <code>TimestampExtractor</code>: </p>
    <ul>
        <li> method <code>extract()</code> has a second parameter now </li>
        <li> new default timestamp extractor class <code>FailOnInvalidTimestamp</code>
            (it gives the same behavior as old (and removed) default extractor <code>ConsumerRecordTimestampExtractor</code>) </li>
        <li> new alternative timestamp extractor classes <code>LogAndSkipOnInvalidTimestamp</code> and <code>UsePreviousTimeOnInvalidTimestamps</code> </li>
    </ul>

    <p> Relaxed type constraints of many DSL interfaces, classes, and methods (cf. <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-100+-+Relax+Type+constraints+in+Kafka+Streams+API">KIP-100</a>). </p>

    <h3><a id="streams_api_changes_0101" href="#streams_api_changes_0101">Streams API changes in 0.10.1.0</a></h3>

    <p> Stream grouping and aggregation split into two methods: </p>
    <ul>
        <li> old: KStream #aggregateByKey(), #reduceByKey(), and #countByKey() </li>
        <li> new: KStream#groupByKey() plus KGroupedStream #aggregate(), #reduce(), and #count() </li>
        <li> Example: stream.countByKey() changes to stream.groupByKey().count() </li>
    </ul>

    <p> Auto Repartitioning: </p>
    <ul>
        <li> a call to through() after a key-changing operator and before an aggregation/join is no longer required </li>
        <li> Example: stream.selectKey(...).through(...).countByKey() changes to stream.selectKey().groupByKey().count() </li>
    </ul>

    <p> TopologyBuilder: </p>
    <ul>
        <li> methods #sourceTopics(String applicationId) and #topicGroups(String applicationId) got simplified to #sourceTopics() and #topicGroups() </li>
    </ul>

    <p> DSL: new parameter to specify state store names: </p>
    <ul>
        <li> The new Interactive Queries feature requires to specify a store name for all source KTables and window aggregation result KTables (previous parameter "operator/window name" is now the storeName) </li>
        <li> KStreamBuilder#table(String topic) changes to #topic(String topic, String storeName) </li>
        <li> KTable#through(String topic) changes to #through(String topic, String storeName) </li>
        <li> KGroupedStream #aggregate(), #reduce(), and #count() require additional parameter "String storeName"</li>
        <li> Example: stream.countByKey(TimeWindows.of("windowName", 1000)) changes to stream.groupByKey().count(TimeWindows.of(1000), "countStoreName") </li>
    </ul>

    <p> Windowing: </p>
    <ul>
        <li> Windows are not named anymore: TimeWindows.of("name", 1000) changes to TimeWindows.of(1000) (cf. DSL: new parameter to specify state store names) </li>
        <li> JoinWindows has no default size anymore: JoinWindows.of("name").within(1000) changes to JoinWindows.of(1000) </li>
    </ul>

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