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    <div class="section" id="processor-api">
        <span id="streams-developer-guide-processor-api"></span><h1>Processor API<a class="headerlink" href="#processor-api" title="Permalink to this headline"></a></h1>
        <p>The Processor API allows developers to define and connect custom processors and to interact with state stores. With the
            Processor API, you can define arbitrary stream processors that process one received record at a time, and connect these
            processors with their associated state stores to compose the processor topology that represents a customized processing
            logic.</p>
        <div class="contents local topic" id="table-of-contents">
            <p class="topic-title first"><b>Table of Contents</b></p>
            <ul class="simple">
                <li><a class="reference internal" href="#overview" id="id1">Overview</a></li>
                <li><a class="reference internal" href="#defining-a-stream-processor" id="id2">Defining a Stream
                    Processor</a></li>
                <li><a class="reference internal" href="#unit-testing-processors" id="id9">Unit Testing Processors</a></li>
                <li><a class="reference internal" href="#state-stores" id="id3">State Stores</a>
                    <ul>
                        <li><a class="reference internal" href="#defining-and-creating-a-state-store" id="id4">Defining and creating a State Store</a></li>
                        <li><a class="reference internal" href="#fault-tolerant-state-stores" id="id5">Fault-tolerant State Stores</a></li>
                        <li><a class="reference internal" href="#enable-or-disable-fault-tolerance-of-state-stores-store-changelogs" id="id6">Enable or Disable Fault Tolerance of State Stores (Store Changelogs)</a></li>
                        <li><a class="reference internal" href="#timestamped-state-stores" id="id11">Timestamped State Stores</a></li>
                        <li><a class="reference internal" href="#versioned-state-stores" id="id12">Versioned Key-Value State Stores</a></li>
                        <li><a class="reference internal" href="#readonly-state-stores" id="id12">Readonly State Stores</a></li>
                        <li><a class="reference internal" href="#implementing-custom-state-stores" id="id7">Implementing Custom State Stores</a></li>
                    </ul>
                </li>
                <li><a class="reference internal" href="#connecting-processors-and-state-stores" id="id8">Connecting Processors and State Stores</a></li>
                <li><a class="reference internal" href="#accessing-processor-context" id="id10">Accessing Processor Context</a></li>
            </ul>
        </div>
        <div class="section" id="overview">
            <h2><a class="toc-backref" href="#id1">Overview</a><a class="headerlink" href="#overview" title="Permalink to this headline"></a></h2>
            <p>The Processor API can be used to implement both <strong>stateless</strong> as well as <strong>stateful</strong> operations, where the latter is
                achieved through the use of <a class="reference internal" href="#streams-developer-guide-state-store"><span class="std std-ref">state stores</span></a>.</p>
            <div class="admonition tip">
                <p><b>Tip</b></p>
                <p class="last"><strong>Combining the DSL and the Processor API:</strong>
                    You can combine the convenience of the DSL with the power and flexibility of the Processor API as described in the
                    section <a class="reference internal" href="dsl-api.html#streams-developer-guide-dsl-process"><span class="std std-ref">Applying processors and transformers (Processor API integration)</span></a>.</p>
            </div>
            <p>For a complete list of available API functionality, see the <a href="/{{version}}/javadoc/org/apache/kafka/streams/package-summary.html">Streams</a> API docs.</p>
        </div>
        <div class="section" id="defining-a-stream-processor">
            <span id="streams-developer-guide-stream-processor"></span><h2><a class="toc-backref" href="#id2">Defining a Stream Processor</a><a class="headerlink" href="#defining-a-stream-processor" title="Permalink to this headline"></a></h2>
            <p>A <a class="reference internal" href="../core-concepts.html#streams_processor_node"><span class="std std-ref">stream processor</span></a> is a node in the processor topology that represents a single processing step.
                With the Processor API, you can define arbitrary stream processors that processes one received record at a time, and connect
                these processors with their associated state stores to compose the processor topology.</p>
            <p>You can define a customized stream processor by implementing the <code class="docutils literal"><span class="pre">Processor</span></code> interface, which provides the <code class="docutils literal"><span class="pre">process()</span></code> API method.
                The <code class="docutils literal"><span class="pre">process()</span></code> method is called on each of the received records.</p>
            <p>The <code class="docutils literal"><span class="pre">Processor</span></code> interface also has an <code class="docutils literal"><span class="pre">init()</span></code> method, which is called by the Kafka Streams library during task construction
                phase. Processor instances should perform any required initialization in this method. The <code class="docutils literal"><span class="pre">init()</span></code> method passes in a <code class="docutils literal"><span class="pre">ProcessorContext</span></code>
                instance, which provides access to the metadata of the currently processed record, including its source Kafka topic and partition,
                its corresponding message offset, and further such information. You can also use this context instance to schedule a punctuation
                function (via <code class="docutils literal"><span class="pre">ProcessorContext#schedule()</span></code>), to forward a new record as a key-value pair to the downstream processors (via <code class="docutils literal"><span class="pre">ProcessorContext#forward()</span></code>),
              and to commit the current processing progress (via <code class="docutils literal"><span class="pre">ProcessorContext#commit()</span></code>).
              Any resources you set up in <code class="docutils literal"><span class="pre">init()</span></code> can be cleaned up in the
              <code class="docutils literal"><span class="pre">close()</span></code> method. Note that Kafka Streams may re-use a single
              <code class="docutils literal"><span class="pre">Processor</span></code> object by calling
              <code class="docutils literal"><span class="pre">init()</span></code> on it again after <code class="docutils literal"><span class="pre">close()</span></code>.</p>
          <p>
            The <code class="docutils literal"><span class="pre">Processor</span></code> interface takes two sets of generic parameters:
            <code class="docutils literal"><span class="pre">KIn, VIn, KOut, VOut</span></code>. These define the input and output types
            that the processor implementation can handle. <code class="docutils literal"><span class="pre">KIn</span></code> and
            <code class="docutils literal"><span class="pre">VIn</span></code> define the key and value types that will be passed
            to <code class="docutils literal"><span class="pre">process()</span></code>.
            Likewise, <code class="docutils literal"><span class="pre">KOut</span></code> and <code class="docutils literal"><span class="pre">VOut</span></code>
            define the forwarded key and value types that <code class="docutils literal"><span class="pre">ProcessorContext#forward()</span></code>
            will accept. If your processor does not forward any records at all (or if it only forwards
            <code class="docutils literal"><span class="pre">null</span></code> keys or values),
            a best practice is to set the output generic type argument to
            <code class="docutils literal"><span class="pre">Void</span></code>.
            If it needs to forward multiple types that don't share a common superclass, you will
            have to set the output generic type argument to <code class="docutils literal"><span class="pre">Object</span></code>.
          </p>
          <p>
            Both the <code class="docutils literal"><span class="pre">Processor#process()</span></code>
            and the <code class="docutils literal"><span class="pre">ProcessorContext#forward()</span></code>
            methods handle records in the form of the <code class="docutils literal"><span class="pre">Record&lt;K, V&gt;</span></code>
            data class. This class gives you access to the main components of a Kafka record:
            the key, value, timestamp and headers. When forwarding records, you can use the
            constructor to create a new <code class="docutils literal"><span class="pre">Record</span></code>
            from scratch, or you can use the convenience builder methods to replace one of the
            <code class="docutils literal"><span class="pre">Record</span></code>'s properties
            and copy over the rest. For example,
            <code class="docutils literal"><span class="pre">inputRecord.withValue(newValue)</span></code>
            would copy the key, timestamp, and headers from
            <code class="docutils literal"><span class="pre">inputRecord</span></code> while
            setting the output record's value to <code class="docutils literal"><span class="pre">newValue</span></code>.
            Note that this does not mutate <code class="docutils literal"><span class="pre">inputRecord</span></code>,
            but instead creates a shallow copy. Beware that this is only a shallow copy, so if you
            plan to mutate the key, value, or headers elsewhere in the program, you will want to
            create a deep copy of those fields yourself.
          </p>
            <p>
              In addition to handling incoming records via
              <code class="docutils literal"><span class="pre">Processor#process()</span></code>,
              you have the option to schedule periodic invocation (called "punctuation")
              in your processor's <code class="docutils literal"><span class="pre">init()</span></code>
              method by calling  <code class="docutils literal"><span class="pre">ProcessorContext#schedule()</span></code>
              and passing it a  <code class="docutils literal"><span class="pre">Punctuator</span></code>.
                The <code class="docutils literal"><span class="pre">PunctuationType</span></code> determines what notion of time is used
                for the punctuation scheduling: either <a class="reference internal" href="../core-concepts.html#streams_time"><span class="std std-ref">stream-time</span></a> or wall-clock-time (by default, stream-time
                is configured to represent event-time via <code class="docutils literal"><span class="pre">TimestampExtractor</span></code>). When stream-time is used, <code class="docutils literal"><span class="pre">punctuate()</span></code> is triggered purely
                by data because stream-time is determined (and advanced forward) by the timestamps derived from the input data. When there
                is no new input data arriving, stream-time is not advanced and thus <code class="docutils literal"><span class="pre">punctuate()</span></code> is not called.</p>
            <p>For example, if you schedule a <code class="docutils literal"><span class="pre">Punctuator</span></code> function every 10 seconds based on <code class="docutils literal"><span class="pre">PunctuationType.STREAM_TIME</span></code> and if you
                process a stream of 60 records with consecutive timestamps from 1 (first record) to 60 seconds (last record),
                then <code class="docutils literal"><span class="pre">punctuate()</span></code> would be called 6 times. This happens regardless of the time required to actually process those records. <code class="docutils literal"><span class="pre">punctuate()</span></code>
                would be called 6 times regardless of whether processing these 60 records takes a second, a minute, or an hour.</p>
            <p>When wall-clock-time (i.e. <code class="docutils literal"><span class="pre">PunctuationType.WALL_CLOCK_TIME</span></code>) is used, <code class="docutils literal"><span class="pre">punctuate()</span></code> is triggered purely by the wall-clock time.
                Reusing the example above, if the <code class="docutils literal"><span class="pre">Punctuator</span></code> function is scheduled based on <code class="docutils literal"><span class="pre">PunctuationType.WALL_CLOCK_TIME</span></code>, and if these
                60 records were processed within 20 seconds, <code class="docutils literal"><span class="pre">punctuate()</span></code> is called 2 times (one time every 10 seconds). If these 60 records
                were processed within 5 seconds, then no <code class="docutils literal"><span class="pre">punctuate()</span></code> is called at all. Note that you can schedule multiple <code class="docutils literal"><span class="pre">Punctuator</span></code>
                callbacks with different <code class="docutils literal"><span class="pre">PunctuationType</span></code> types within the same processor by calling <code class="docutils literal"><span class="pre">ProcessorContext#schedule()</span></code> multiple
                times inside <code class="docutils literal"><span class="pre">init()</span></code> method.</p>
            <div class="admonition attention">
                <p class="first admonition-title"><b>Attention</b></p>
                <p class="last">Stream-time is only advanced when Streams processes records.
                  If there are no records to process, or if Streams is waiting for new records
                  due to the <a class="reference internal" href="/documentation/#streamsconfigs_max.task.idle.ms">Task Idling</a>
                  configuration, then the stream time will not advance and <code class="docutils literal"><span class="pre">punctuate()</span></code> will not be triggered if <code class="docutils literal"><span class="pre">PunctuationType.STREAM_TIME</span></code> was specified.
                    This behavior is independent of the configured timestamp extractor, i.e., using <code class="docutils literal"><span class="pre">WallclockTimestampExtractor</span></code> does not enable wall-clock triggering of <code class="docutils literal"><span class="pre">punctuate()</span></code>.</p>
            </div>
            <p><b>Example</b></p>
            <p>The following example <code class="docutils literal"><span class="pre">Processor</span></code> defines a simple word-count algorithm and the following actions are performed:</p>
            <ul class="simple">
                <li>In the <code class="docutils literal"><span class="pre">init()</span></code> method, schedule the punctuation every 1000 time units (the time unit is normally milliseconds, which in this example would translate to punctuation every 1 second) and retrieve the local state store by its name &#8220;Counts&#8221;.</li>
                <li>In the <code class="docutils literal"><span class="pre">process()</span></code> method, upon each received record, split the value string into words, and update their counts into the state store (we will talk about this later in this section).</li>
                <li>In the <code class="docutils literal"><span class="pre">punctuate()</span></code> method, iterate the local state store and send the aggregated counts to the downstream processor (we will talk about downstream processors later in this section), and commit the current stream state.</li>
            </ul>
            <pre class="line-numbers"><code class="language-java">public class WordCountProcessor implements Processor&lt;String, String, String, String&gt; {
    private KeyValueStore&lt;String, Integer&gt; kvStore;

    @Override
    public void init(final ProcessorContext&lt;String, String> context) {
        context.schedule(Duration.ofSeconds(1), PunctuationType.STREAM_TIME, timestamp -&gt; {
            try (final KeyValueIterator&lt;String, Integer&gt; iter = kvStore.all()) {
                while (iter.hasNext()) {
                    final KeyValue&lt;String, Integer&gt; entry = iter.next();
                    context.forward(new Record&lt;&gt;(entry.key, entry.value.toString(), timestamp));
                }
            }
        });
        kvStore = context.getStateStore("Counts");
    }

    @Override
    public void process(final Record&lt;String, String&gt; record) {
        final String[] words = record.value().toLowerCase(Locale.getDefault()).split("\\W+");

        for (final String word : words) {
            final Integer oldValue = kvStore.get(word);

            if (oldValue == null) {
                kvStore.put(word, 1);
            } else {
                kvStore.put(word, oldValue + 1);
            }
        }
    }

    @Override
    public void close() {
        // close any resources managed by this processor
        // Note: Do not close any StateStores as these are managed by the library
    }
}</code></pre>
            <div class="admonition note">
                <p><b>Note</b></p>
                <p class="last"><strong>Stateful processing with state stores:</strong>
                    The <code class="docutils literal"><span class="pre">WordCountProcessor</span></code> defined above can access the currently received record in its <code class="docutils literal"><span class="pre">process()</span></code> method, and it can
                    leverage <a class="reference internal" href="#streams-developer-guide-state-store"><span class="std std-ref">state stores</span></a> to maintain processing states to, for example, remember recently
                    arrived records for stateful processing needs like aggregations and joins. For more information, see the <a class="reference internal" href="#streams-developer-guide-state-store"><span class="std std-ref">state stores</span></a> documentation.</p>
            </div>
        </div>
        <div class="section" id="unit-testing-processors">
            <h2>
                <a class="toc-backref" href="#id9">Unit Testing Processors</a>
                <a class="headerlink" href="#unit-testing-processors" title="Permalink to this headline"></a>
            </h2>
            <p>
                Kafka Streams comes with a <code>test-utils</code> module to help you write unit tests for your
                processors <a href="testing.html#unit-testing-processors">here</a>.
            </p>
        </div>
        <div class="section" id="state-stores">
            <span id="streams-developer-guide-state-store"></span><h2><a class="toc-backref" href="#id3">State Stores</a><a class="headerlink" href="#state-stores" title="Permalink to this headline"></a></h2>
            <p>To implement a <strong>stateful</strong> <code class="docutils literal"><span class="pre">Processor</span></code> or <code class="docutils literal"><span class="pre">Transformer</span></code>, you must provide one or more state stores to the processor
                or transformer (<em>stateless</em> processors or transformers do not need state stores).  State stores can be used to remember
                recently received input records, to track rolling aggregates, to de-duplicate input records, and more.
                Another feature of state stores is that they can be
                <a class="reference internal" href="interactive-queries.html#streams-developer-guide-interactive-queries"><span class="std std-ref">interactively queried</span></a> from other applications, such as a
                NodeJS-based dashboard or a microservice implemented in Scala or Go.</p>
            <p>The
                <a class="reference internal" href="#streams-developer-guide-state-store-defining"><span class="std std-ref">available state store types</span></a> in Kafka Streams have
                <a class="reference internal" href="#streams-developer-guide-state-store-fault-tolerance"><span class="std std-ref">fault tolerance</span></a> enabled by default.</p>
            <div class="section" id="defining-and-creating-a-state-store">
                <span id="streams-developer-guide-state-store-defining"></span><h3><a class="toc-backref" href="#id4">Defining and creating a State Store</a><a class="headerlink" href="#defining-and-creating-a-state-store" title="Permalink to this headline"></a></h3>
                <p>You can either use one of the available store types or
                    <a class="reference internal" href="#streams-developer-guide-state-store-custom"><span class="std std-ref">implement your own custom store type</span></a>.
                    It&#8217;s common practice to leverage an existing store type via the <code class="docutils literal"><span class="pre">Stores</span></code> factory.</p>
                <p>Note that, when using Kafka Streams, you normally don&#8217;t create or instantiate state stores directly in your code.
                    Rather, you define state stores indirectly by creating a so-called <code class="docutils literal"><span class="pre">StoreBuilder</span></code>.  This builder is used by
                    Kafka Streams as a factory to instantiate the actual state stores locally in application instances when and where
                    needed.</p>
                <p>The following store types are available out of the box.</p>
                <table border="1" class="non-scrolling-table width-100-percent docutils">
                    <colgroup>
                        <col width="19%" />
                        <col width="11%" />
                        <col width="18%" />
                        <col width="51%" />
                    </colgroup>
                    <thead valign="bottom">
                    <tr class="row-odd"><th class="head">Store Type</th>
                        <th class="head">Storage Engine</th>
                        <th class="head">Fault-tolerant?</th>
                        <th class="head">Description</th>
                    </tr>
                    </thead>
                    <tbody valign="top">
                    <tr class="row-even"><td>Persistent
                        <code class="docutils literal"><span class="pre">KeyValueStore&lt;K,</span> <span class="pre">V&gt;</span></code></td>
                        <td>RocksDB</td>
                        <td>Yes (enabled by default)</td>
                        <td><ul class="first simple">
                            <li><strong>The recommended store type for most use cases.</strong></li>
                            <li>Stores its data on local disk.</li>
                            <li>Storage capacity:
                                managed local state can be larger than the memory (heap space) of an
                                application instance, but must fit into the available local disk
                                space.</li>
                            <li>RocksDB settings can be fine-tuned, see
                                <a class="reference internal" href="config-streams.html#streams-developer-guide-rocksdb-config"><span class="std std-ref">RocksDB configuration</span></a>.</li>
                            <li>Available <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#persistentKeyValueStore(java.lang.String)">store variants</a>:
                                timestamped key-value store, versioned key-value store, time window key-value store, session window key-value store.</li>
                            <li>Use <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#persistentTimestampedKeyValueStore(java.lang.String)">persistentTimestampedKeyValueStore</a>
                                when you need a persistent key-(value/timestamp) store that supports put/get/delete and range queries.</li>
                            <li>Use <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#persistentVersionedKeyValueStore(java.lang.String,java.time.Duration)">persistentVersionedKeyValueStore</a>
                                when you need a persistent, versioned key-(value/timestamp) store that supports put/get/delete and timestamped get operations.</li>
                            <li>Use <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#persistentWindowStore(java.lang.String,java.time.Duration,java.time.Duration,boolean)">persistentWindowStore</a>
                                or <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#persistentTimestampedWindowStore(java.lang.String,java.time.Duration,java.time.Duration,boolean)">persistentTimestampedWindowStore</a>
                                when you need a persistent timeWindowedKey-value or timeWindowedKey-(value/timestamp) store, respectively.</li>
                            <li>Use <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#persistentSessionStore(java.lang.String,java.time.Duration)">persistentSessionStore</a>
                                when you need a persistent sessionWindowedKey-value store.</li>
                        </ul>
                            <pre class="line-numbers"><code class="language-java">// Creating a persistent key-value store:
// here, we create a `KeyValueStore&lt;String, Long&gt;` named &quot;persistent-counts&quot;.
import org.apache.kafka.streams.state.StoreBuilder;
import org.apache.kafka.streams.state.Stores;

// Using a `KeyValueStoreBuilder` to build a `KeyValueStore`.
StoreBuilder&lt;KeyValueStore&lt;String, Long&gt;&gt; countStoreSupplier =
  Stores.keyValueStoreBuilder(
    Stores.persistentKeyValueStore(&quot;persistent-counts&quot;),
    Serdes.String(),
    Serdes.Long());
KeyValueStore&lt;String, Long&gt; countStore = countStoreSupplier.build();</code></pre>
                        </td>
                    </tr>
                    <tr class="row-odd"><td>In-memory
                        <code class="docutils literal"><span class="pre">KeyValueStore&lt;K,</span> <span class="pre">V&gt;</span></code></td>
                        <td>-</td>
                        <td>Yes (enabled by default)</td>
                        <td><ul class="first simple">
                            <li>Stores its data in memory.</li>
                            <li>Storage capacity:
                                managed local state must fit into memory (heap space) of an
                                application instance.</li>
                            <li>Useful when application instances run in an environment where local
                                disk space is either not available or local disk space is wiped
                                in-between app instance restarts.</li>
                            <li>Available <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#inMemoryKeyValueStore-java.lang.String-">store variants</a>:
                                time window key-value store, session window key-value store.</li>
                            <li>Use <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/TimestampedKeyValueStore.html">TimestampedKeyValueStore</a>
                                when you need a key-(value/timestamp) store that supports put/get/delete and range queries.</li>
                            <li>Use <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/TimestampedWindowStore.html">TimestampedWindowStore</a>
                                when you need to store windowedKey-(value/timestamp) pairs.</li>
                            <li>There is no built-in in-memory, versioned key-value store at this time.</li>
                        </ul>
                            <pre class="line-numbers"><code class="language-java">// Creating an in-memory key-value store:
// here, we create a `KeyValueStore&lt;String, Long&gt;` named &quot;inmemory-counts&quot;.
import org.apache.kafka.streams.state.StoreBuilder;
import org.apache.kafka.streams.state.Stores;

// Using a `KeyValueStoreBuilder` to build a `KeyValueStore`.
StoreBuilder&lt;KeyValueStore&lt;String, Long&gt;&gt; countStoreSupplier =
  Stores.keyValueStoreBuilder(
    Stores.inMemoryKeyValueStore(&quot;inmemory-counts&quot;),
    Serdes.String(),
    Serdes.Long());
KeyValueStore&lt;String, Long&gt; countStore = countStoreSupplier.build();</code></pre>
                        </td>
                    </tr>
                    </tbody>
                </table>
            </div>
            <div class="section" id="fault-tolerant-state-stores">
                <span id="streams-developer-guide-state-store-fault-tolerance"></span><h3><a class="toc-backref" href="#id5">Fault-tolerant State Stores</a><a class="headerlink" href="#fault-tolerant-state-stores" title="Permalink to this headline"></a></h3>
                <p>To make state stores fault-tolerant and to allow for state store migration without data loss, a state store can be
                    continuously backed up to a Kafka topic behind the scenes. For example, to migrate a stateful stream task from one
                    machine to another when <a class="reference internal" href="running-app.html#streams-developer-guide-execution-scaling"><span class="std std-ref">elastically adding or removing capacity from your application</span></a>.
                    This topic is sometimes referred to as the state store&#8217;s associated <em>changelog topic</em>, or its <em>changelog</em>.  For example, if
                    you experience machine failure, the state store and the application&#8217;s state can be fully restored from its changelog. You can
                    <a class="reference internal" href="#streams-developer-guide-state-store-enable-disable-fault-tolerance"><span class="std std-ref">enable or disable this backup feature</span></a> for a
                    state store.</p>
                <p>Fault-tolerant state stores are backed by a
                    <a class="reference external" href="https://kafka.apache.org/documentation.html#compaction">compacted</a> changelog topic.  The purpose of compacting this
                    topic is to prevent the topic from growing indefinitely, to reduce the storage consumed in the associated Kafka cluster,
                    and to minimize recovery time if a state store needs to be restored from its changelog topic.</p>
                <p>Fault-tolerant windowed state stores are backed by a topic that uses both compaction and
                    deletion. Because of the structure of the message keys that are being sent to the changelog topics, this combination of
                    deletion and compaction is required for the changelog topics of window stores. For window stores, the message keys are
                    composite keys that include the &#8220;normal&#8221; key and window timestamps.  For these types of composite keys it would not
                    be sufficient to only enable compaction to prevent a changelog topic from growing out of bounds.  With deletion
                    enabled, old windows that have expired will be cleaned up by Kafka&#8217;s log cleaner as the log segments expire.  The
                    default retention setting is <code class="docutils literal"><span class="pre">Windows#maintainMs()</span></code> + 1 day.  You can override this setting by specifying
                    <code class="docutils literal"><span class="pre">StreamsConfig.WINDOW_STORE_CHANGE_LOG_ADDITIONAL_RETENTION_MS_CONFIG</span></code> in the <code class="docutils literal"><span class="pre">StreamsConfig</span></code>.</p>
                <p>When you open an <code class="docutils literal"><span class="pre">Iterator</span></code> from a state store you must call <code class="docutils literal"><span class="pre">close()</span></code> on the iterator when you are done working with
                    it to reclaim resources; or you can use the iterator from within a try-with-resources statement. If you do not close an iterator,
                    you may encounter an OOM error.</p>
            </div>
            <div class="section" id="enable-or-disable-fault-tolerance-of-state-stores-store-changelogs">
                <span id="streams-developer-guide-state-store-enable-disable-fault-tolerance"></span><h3><a class="toc-backref" href="#id6">Enable or Disable Fault Tolerance of State Stores (Store Changelogs)</a><a class="headerlink" href="#enable-or-disable-fault-tolerance-of-state-stores-store-changelogs" title="Permalink to this headline"></a></h3>
                <p>You can enable or disable fault tolerance for a state store by enabling or disabling the change logging
                    of the store through <code class="docutils literal"><span class="pre">enableLogging()</span></code> and <code class="docutils literal"><span class="pre">disableLogging()</span></code>.
                    You can also fine-tune the associated topic&#8217;s configuration if needed.</p>
                <p>Example for disabling fault-tolerance:</p>
                <pre class="line-numbers"><code class="language-java">import org.apache.kafka.streams.state.StoreBuilder;
import org.apache.kafka.streams.state.Stores;

StoreBuilder&lt;KeyValueStore&lt;String, Long&gt;&gt; countStoreSupplier = Stores.keyValueStoreBuilder(
  Stores.persistentKeyValueStore(&quot;Counts&quot;),
    Serdes.String(),
    Serdes.Long())
  .withLoggingDisabled(); // disable backing up the store to a changelog topic</code></pre>
                <div class="admonition attention">
                    <p class="first admonition-title">Attention</p>
                    <p class="last">If the changelog is disabled then the attached state store is no longer fault tolerant and it can&#8217;t have any <a class="reference internal" href="config-streams.html#streams-developer-guide-standby-replicas"><span class="std std-ref">standby replicas</span></a>.</p>
                </div>
                <p>Here is an example for enabling fault tolerance, with additional changelog-topic configuration:
                    You can add any log config from <a class="reference external" href="https://github.com/apache/kafka/blob/trunk/core/src/main/scala/kafka/log/LogConfig.scala">kafka.log.LogConfig</a>.
                    Unrecognized configs will be ignored.</p>
                <pre class="line-numbers"><code class="language-java">import org.apache.kafka.streams.state.StoreBuilder;
import org.apache.kafka.streams.state.Stores;

Map&lt;String, String&gt; changelogConfig = new HashMap();
// override min.insync.replicas
changelogConfig.put(TopicConfig.MIN_IN_SYNC_REPLICAS_CONFIG, &quot;1&quot;)

StoreBuilder&lt;KeyValueStore&lt;String, Long&gt;&gt; countStoreSupplier = Stores.keyValueStoreBuilder(
  Stores.persistentKeyValueStore(&quot;Counts&quot;),
    Serdes.String(),
    Serdes.Long())
  .withLoggingEnabled(changelogConfig); // enable changelogging, with custom changelog settings</code></pre>
            </div>
            <div class="section" id="timestamped-state-stores">
                <span id="streams-developer-guide-state-store-timestamps"></span><h3><a class="toc-backref" href="#id11">Timestamped State Stores</a><a class="headerlink" href="#timestamped-state-stores" title="Permalink to this headline"></a></h3>
                <p>KTables always store timestamps by default.
                    A timestamped state store improves stream processing semantics and enables
                    handling out-of-order data in source KTables, detecting out-of-order joins and aggregations,
                    and getting the timestamp of the latest update in an Interactive Query.</p>
                <p>You can query timestamped state stores both with and without a timestamp.</p>
                <b>Upgrade note:</b> All users upgrade with a single rolling bounce per instance.
                <ul class="first simple">
                    <li>For Processor API users, nothing changes in existing applications, and you
                    have the option of using the timestamped stores.</li>        
                    <li>For DSL operators, store data is upgraded lazily in the background.</li>
                    <li>No upgrade happens if you provide a custom XxxBytesStoreSupplier, but you can opt-in
                    by implementing the <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/TimestampedBytesStore.html">TimestampedBytesStore</a>
                    interface. In this case, the old format is retained, and Streams uses a proxy store
                    that removes/adds timestamps on read/write.</li>
                </ul>
                </p>
            </div>
            <div class="section" id="versioned-state-stores">
                <span id="streams-developer-guide-state-store-versioned"></span><h3><a class="toc-backref" href="#id12">Versioned Key-Value State Stores</a><a class="headerlink" href="#versioned-state-stores" title="Permalink to this headline"></a></h3>
                <p>Versioned key-value state stores are available since Kafka Streams 3.5.
                    Rather than storing a single record version (value and timestamp) per key,
                    versioned state stores may store multiple record versions per key. This
                    allows versioned state stores to support timestamped retrieval operations
                    to return the latest record (per key) as of a specified timestamp.</p>
                <p>You can create a persistent, versioned state store by passing a
                    <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#persistentVersionedKeyValueStore(java.lang.String,java.time.Duration)">VersionedBytesStoreSupplier</a>
                    to the
                    <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#versionedKeyValueStoreBuilder(java.lang.String,java.time.Duration)">versionedKeyValueStoreBuilder</a>,
                    or by implementing your own
                    <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/VersionedKeyValueStore.html">VersionedKeyValueStore</a>.</p>
                <p>Each versioned store has an associated, fixed-duration <em>history retention</em>
                    parameter which specifies long old record versions should be kept for.
                    In particular, a versioned store guarantees to return accurate results for
                    timestamped retrieval operations where the timestamp being queried is within
                    history retention of the current observed stream time.</p>
                <p>History retention also doubles as its <em>grace period</em>, which determines
                    how far back in time out-of-order writes to the store will be accepted. A
                    versioned store will not accept writes (inserts, updates, or deletions) if
                    the timestamp associated with the write is older than the current observed
                    stream time by more than the grace period. Stream time in this context is
                    tracked per-partition, rather than per-key, which means it's important
                    that grace period (i.e., history retention) be set high enough to
                    accommodate a record with one key arriving out-of-order relative to a
                    record for another key.</p>
                <p>Because the memory footprint of versioned key-value stores is higher than
                    that of non-versioned key-value stores, you may want to adjust your
                    <a class="reference internal" href="memory-mgmt.html#streams-developer-guide-memory-management-rocksdb"><span class="std std-ref">RocksDB memory settings</span></a>
                    accordingly. Benchmarking your application with versioned stores is also
                    advised as performance is expected to be worse than when using non-versioned
                    stores.</p>
                <p>Versioned stores do not support caching or interactive queries at this time.
                    Also, window stores and global tables may not be versioned.</p>
                <b>Upgrade note:</b> Versioned state stores are opt-in only; no automatic
                    upgrades from non-versioned to versioned stores will take place.
                <p>Upgrades are supported from persistent, non-versioned key-value stores
                    to persistent, versioned key-value stores as long as the original store
                    has the same changelog topic format as the versioned store being upgraded
                    to. Both persistent
                    <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#persistentKeyValueStore(java.lang.String)">key-value stores</a>
                    and <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#persistentTimestampedKeyValueStore(java.lang.String)">timestamped key-value stores</a>
                    share the same changelog topic format as
                    <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/Stores.html#persistentVersionedKeyValueStore(java.lang.String,java.time.Duration)">persistent versioned key-value stores</a>,
                    and therefore both are eligible for upgrades.</p>
                <p>If you wish to upgrade an application using persistent, non-versioned
                    key-value stores to use persistent, versioned key-value stores
                    instead, you can perform the following procedure:</p>
                <ul class="first simple">
                    <li>Stop all application instances, and
                        <a class="reference internal" href="app-reset-tool.html#streams-developer-guide-reset-local-environment"><span class="std std-ref">clear any local state directories</span></a>
                        for the store(s) being upgraded.</li>
                    <li>Update your application code to use versioned stores where desired.</li>
                    <li>Update your changelog topic configs, for the relevant state stores,
                        to set the value of <code class="docutils literal"><span class="pre">min.compaction.lag.ms</span></code>
                        to be at least your desired history retention. History retention plus
                        one day is recommended as buffer for the use of broker wall clock time
                        during compaction.</li>
                    <li>Restart your application instances and allow time for the versioned
                        stores to rebuild state from changelog.</li>
                </ul>
                </p>
            <div class="section" id="readonly-state-stores">
                <span id="streams-developer-guide-state-store-readonly"></span><h3><a class="toc-backref" href="#id12">ReadOnly State Stores</a><a class="headerlink" href="#readonly-state-stores" title="Permalink to this headline"></a></h3>
                <p>A read-only state store materialized the data from its input topic. It also uses the input topic
                    for fault-tolerance, and thus does not have an additional changelog topic (the input topic is
                    re-used as changelog). Thus, the input topic should be configured with <a class="reference external" href="https://kafka.apache.org/documentation.html#compaction">log compaction</a>.
                    Note that no other processor should modify the content of the state store, and the only writer
                    should be the associated "state update processor"; other processors may read the content of the
                    read-only store.</p>

                <p><b>note:</b> beware of the partitioning requirements when using read-only state stores for lookups during
                    processing. You might want to make sure the original changelog topic is co-partitioned with the processors
                    reading the read-only statestore.</p>
            </div>
            <div class="section" id="implementing-custom-state-stores">
                <span id="streams-developer-guide-state-store-custom"></span><h3><a class="toc-backref" href="#id7">Implementing Custom State Stores</a><a class="headerlink" href="#implementing-custom-state-stores" title="Permalink to this headline"></a></h3>
                <p>You can use the <a class="reference internal" href="#streams-developer-guide-state-store-defining"><span class="std std-ref">built-in state store types</span></a> or  implement your own.
                    The primary interface to implement for the store is
                    <code class="docutils literal"><span class="pre">org.apache.kafka.streams.processor.StateStore</span></code>.  Kafka Streams also has a few extended interfaces such
                    as <code class="docutils literal"><span class="pre">KeyValueStore</span></code> and <code class="docutils literal"><span class="pre">VersionedKeyValueStore</span></code>.</p>
                <p>Note that your customized <code class="docutils literal"><span class="pre">org.apache.kafka.streams.processor.StateStore</span></code> implementation also needs to provide the logic on how to restore the state
                    via the <code class="docutils literal"><span class="pre">org.apache.kafka.streams.processor.StateRestoreCallback</span></code> or <code class="docutils literal"><span class="pre">org.apache.kafka.streams.processor.BatchingStateRestoreCallback</span></code> interface.
                    Details on how to instantiate these interfaces can be found in the <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/processor/StateStore.html">javadocs</a>.</p>
                <p>You also need to provide a &#8220;builder&#8221; for the store by implementing the
                    <code class="docutils literal"><span class="pre">org.apache.kafka.streams.state.StoreBuilder</span></code> interface, which Kafka Streams uses to create instances of
                    your store.</p>
            </div>
        </div>
        <div class="section" id="accessing-processor-context">
            <h2><a class="toc-backref" href="#id10">Accessing Processor Context</a><a class="headerlink" href="#accessing-processor-context" title="Permalink to this headline"></a></h2>
            <p>As we have mentioned in the <a href=#defining-a-stream-processor>Defining a Stream Processor</a> section, a <code>ProcessorContext</code> control the processing workflow, such as scheduling a punctuation function, and committing the current processed state.</p>
            <p>This object can also be used to access the metadata related with the application like
            <code class="docutils literal"><span class="pre">applicationId</span></code>, <code class="docutils literal"><span class="pre">taskId</span></code>,
                and <code class="docutils literal"><span class="pre">stateDir</span></code>, and also record related metadata as <code class="docutils literal"><span class="pre">topic</span></code>,
                <code class="docutils literal"><span class="pre">partition</span></code>, <code class="docutils literal"><span class="pre">offset</span></code>, <code class="docutils literal"><span class="pre">timestamp</span></code> and
                <code class="docutils literal"><span class="pre">headers</span></code>.</p>
            <p>Here is an example implementation of how to add a new header to the record:</p>
            <pre class="line-numbers"><code class="language-java">public void process(String key, String value) {

    // add a header to the elements
    context().headers().add.(&quot;key&quot;, &quot;value&quot;);
}</code></pre>
        <div class="section" id="connecting-processors-and-state-stores">
            <h2><a class="toc-backref" href="#id8">Connecting Processors and State Stores</a><a class="headerlink" href="#connecting-processors-and-state-stores" title="Permalink to this headline"></a></h2>
            <p>Now that a <a class="reference internal" href="#streams-developer-guide-stream-processor"><span class="std std-ref">processor</span></a> (WordCountProcessor) and the
                state stores have been defined, you can construct the processor topology by connecting these processors and state stores together by
                using the <code class="docutils literal"><span class="pre">Topology</span></code> instance.  In addition, you can add source processors with the specified Kafka topics
                to generate input data streams into the topology, and sink processors with the specified Kafka topics to generate
                output data streams out of the topology.</p>
            <p>Here is an example implementation:</p>
            <pre class="line-numbers"><code class="language-java">Topology builder = new Topology();
// add the source processor node that takes Kafka topic "source-topic" as input
builder.addSource("Source", "source-topic")
    // add the WordCountProcessor node which takes the source processor as its upstream processor
    .addProcessor("Process", () -> new WordCountProcessor(), "Source")
    // add the count store associated with the WordCountProcessor processor
    .addStateStore(countStoreBuilder, "Process")
    // add the sink processor node that takes Kafka topic "sink-topic" as output
    // and the WordCountProcessor node as its upstream processor
    .addSink("Sink", "sink-topic", "Process");</code></pre>
            <p>Here is a quick explanation of this example:</p>
            <ul class="simple">
                <li>A source processor node named <code class="docutils literal"><span class="pre">&quot;Source&quot;</span></code> is added to the topology using the <code class="docutils literal"><span class="pre">addSource</span></code> method, with one Kafka topic
                    <code class="docutils literal"><span class="pre">&quot;source-topic&quot;</span></code> fed to it.</li>
                <li>A processor node named <code class="docutils literal"><span class="pre">&quot;Process&quot;</span></code> with the pre-defined <code class="docutils literal"><span class="pre">WordCountProcessor</span></code> logic is then added as the downstream
                    processor of the <code class="docutils literal"><span class="pre">&quot;Source&quot;</span></code> node using the <code class="docutils literal"><span class="pre">addProcessor</span></code> method.</li>
                <li>A predefined persistent key-value state store is created and associated with the <code class="docutils literal"><span class="pre">&quot;Process&quot;</span></code> node, using
                    <code class="docutils literal"><span class="pre">countStoreBuilder</span></code>.</li>
                <li>A sink processor node is then added to complete the topology using the <code class="docutils literal"><span class="pre">addSink</span></code> method, taking the <code class="docutils literal"><span class="pre">&quot;Process&quot;</span></code> node
                    as its upstream processor and writing to a separate <code class="docutils literal"><span class="pre">&quot;sink-topic&quot;</span></code> Kafka topic (note that users can also use another overloaded variant of <code class="docutils literal"><span class="pre">addSink</span></code>
                    to dynamically determine the Kafka topic to write to for each received record from the upstream processor).</li>
            </ul>
            <p>In some cases, it may be more convenient to add and connect a state store at the same time as you add the processor to the topology.
                This can be done by implementing <code class="docutils literal"><span class="pre">ConnectedStoreProvider#stores()</span></code> on the <code class="docutils literal"><span class="pre">ProcessorSupplier</span></code>
                instead of calling <code class="docutils literal"><span class="pre">Topology#addStateStore()</span></code>, like this:
            </p>
            <pre class="line-numbers"><code class="language-java">Topology builder = new Topology();
// add the source processor node that takes Kafka "source-topic" as input
builder.addSource("Source", "source-topic")
    // add the WordCountProcessor node which takes the source processor as its upstream processor.
    // the ProcessorSupplier provides the count store associated with the WordCountProcessor
    .addProcessor("Process", new ProcessorSupplier&lt;String, String, String, String&gt;() {
        public Processor&lt;String, String, String, String&gt; get() {
            return new WordCountProcessor();
        }

        public Set&lt;StoreBuilder&lt;?&gt;&gt; stores() {
            final StoreBuilder&lt;KeyValueStore&lt;String, Long&gt;&gt; countsStoreBuilder =
                Stores
                    .keyValueStoreBuilder(
                        Stores.persistentKeyValueStore("Counts"),
                        Serdes.String(),
                        Serdes.Long()
                    );
            return Collections.singleton(countsStoreBuilder);
        }
    }, "Source")
    // add the sink processor node that takes Kafka topic "sink-topic" as output
    // and the WordCountProcessor node as its upstream processor
    .addSink("Sink", "sink-topic", "Process");</code></pre>
            <p>This allows for a processor to "own" state stores, effectively encapsulating their usage from the user wiring the topology.
                Multiple processors that share a state store may provide the same store with this technique, as long as the <code class="docutils literal"><span class="pre">StoreBuilder</span></code> is the same <code class="docutils literal"><span class="pre">instance</span></code>.</p>
            <p>In these topologies, the <code class="docutils literal"><span class="pre">&quot;Process&quot;</span></code> stream processor node is considered a downstream processor of the <code class="docutils literal"><span class="pre">&quot;Source&quot;</span></code> node, and an
                upstream processor of the <code class="docutils literal"><span class="pre">&quot;Sink&quot;</span></code> node.  As a result, whenever the <code class="docutils literal"><span class="pre">&quot;Source&quot;</span></code> node forwards a newly fetched record from
                Kafka to its downstream <code class="docutils literal"><span class="pre">&quot;Process&quot;</span></code> node, the <code class="docutils literal"><span class="pre">WordCountProcessor#process()</span></code> method is triggered to process the record and
                update the associated state store. Whenever <code class="docutils literal"><span class="pre">context#forward()</span></code> is called in the
                <code class="docutils literal"><span class="pre">WordCountProcessor#punctuate()</span></code> method, the aggregate key-value pair will be sent via the <code class="docutils literal"><span class="pre">&quot;Sink&quot;</span></code> processor node to
                the Kafka topic <code class="docutils literal"><span class="pre">&quot;sink-topic&quot;</span></code>.  Note that in the <code class="docutils literal"><span class="pre">WordCountProcessor</span></code> implementation, you must refer to the
                same store name <code class="docutils literal"><span class="pre">&quot;Counts&quot;</span></code> when accessing the key-value store, otherwise an exception will be thrown at runtime,
                indicating that the state store cannot be found. If the state store is not associated with the processor
                in the <code class="docutils literal"><span class="pre">Topology</span></code> code, accessing it in the processor&#8217;s <code class="docutils literal"><span class="pre">init()</span></code> method will also throw an exception at
                runtime, indicating the state store is not accessible from this processor.</p>
            <p>Note that the <code class="docutils literal"><span class="pre">Topology#addProcessor</span></code> function takes a <code class="docutils literal"><span class="pre">ProcessorSupplier</span></code> as argument, and that the supplier pattern requires that a new
                <code class="docutils literal"><span class="pre">Processor</span></code> instance is returned each time <code class="docutils literal"><span class="pre">ProcessorSupplier#get()</span></code> is called. Creating a single <code class="docutils literal"><span class="pre">Processor</span></code>
                object and returning the same object reference in <code class="docutils literal"><span class="pre">ProcessorSupplier#get()</span></code> would be a violation of the supplier pattern and leads to runtime exceptions.
                So remember not to provide a singleton <code class="docutils literal"><span class="pre">Processor</span></code> instance to <code class="docutils literal"><span class="pre">Topology</span></code>. The
                <code class="docutils literal"><span class="pre">ProcessorSupplier</span></code> should always generate a new instance each time <code class="docutils literal"><span class="pre">ProcessorSupplier#get()</span></code> gets called.</p>
            <p>Now that you have fully defined your processor topology in your application, you can proceed to
                <a class="reference internal" href="running-app.html#streams-developer-guide-execution"><span class="std std-ref">running the Kafka Streams application</span></a>.</p>
        </div>
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