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  <div class="section" id="configuring-a-streams-application">
    <span id="streams-developer-guide-configuration"></span><h1>Configuring a Streams Application<a class="headerlink" href="#configuring-a-streams-application" title="Permalink to this headline"></a></h1>
    <p>Kafka and Kafka Streams configuration options must be configured before using Streams. You can configure Kafka Streams by specifying parameters in a <code class="docutils literal"><span class="pre">java.util.Properties</span></code> instance.</p>
    <ol class="arabic">
      <li><p class="first">Create a <code class="docutils literal"><span class="pre">java.util.Properties</span></code> instance.</p>
      </li>
      <li><p class="first">Set the <a class="reference internal" href="#streams-developer-guide-required-configs"><span class="std std-ref">parameters</span></a>. For example:</p>
        <div class="highlight-java"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">java.util.Properties</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.kafka.streams.StreamsConfig</span><span class="o">;</span>

<span class="n">Properties</span> <span class="n">settings</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Properties</span><span class="o">();</span>
<span class="c1">// Set a few key parameters</span>
<span class="n">settings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">APPLICATION_ID_CONFIG</span><span class="o">,</span> <span class="s">&quot;my-first-streams-application&quot;</span><span class="o">);</span>
<span class="n">settings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">BOOTSTRAP_SERVERS_CONFIG</span><span class="o">,</span> <span class="s">&quot;kafka-broker1:9092&quot;</span><span class="o">);</span>
<span class="c1">// Any further settings</span>
<span class="n">settings</span><span class="o">.</span><span class="na">put</span><span class="o">(...</span> <span class="o">,</span> <span class="o">...);</span></code></pre></div>
        </div>
      </li>
    </ol>
    <div class="section" id="configuration-parameter-reference">
      <span id="streams-developer-guide-required-configs"></span><h2>Configuration parameter reference<a class="headerlink" href="#configuration-parameter-reference" title="Permalink to this headline"></a></h2>
      <p>This section contains the most common Streams configuration parameters. For a full reference, see the <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/StreamsConfig.html">Streams</a> Javadocs.</p>
      <div class="contents local topic" id="contents">
        <ul class="simple">
          <li><a class="reference internal" href="#required-configuration-parameters" id="id3">Required configuration parameters</a><ul>
            <li><a class="reference internal" href="#application-id" id="id4">application.id</a></li>
            <li><a class="reference internal" href="#bootstrap-servers" id="id5">bootstrap.servers</a></li>
          </ul>
          </li>
          <li><a class="reference internal" href="#optional-configuration-parameters" id="id6">Optional configuration parameters</a><ul>
            <li><a class="reference internal" href="#default-deserialization-exception-handler" id="id7">default.deserialization.exception.handler</a></li>
            <li><a class="reference internal" href="#default-production-exception-handler" id="id24">default.production.exception.handler</a></li>
            <li><a class="reference internal" href="#default-key-serde" id="id8">default.key.serde</a></li>
            <li><a class="reference internal" href="#default-value-serde" id="id9">default.value.serde</a></li>
            <li><a class="reference internal" href="#num-standby-replicas" id="id10">num.standby.replicas</a></li>
            <li><a class="reference internal" href="#num-stream-threads" id="id11">num.stream.threads</a></li>
            <li><a class="reference internal" href="#partition-grouper" id="id12">partition.grouper</a></li>
            <li><a class="reference internal" href="#processing-guarantee" id="id25">processing.guarantee</a></li>
            <li><a class="reference internal" href="#replication-factor" id="id13">replication.factor</a></li>
            <li><a class="reference internal" href="#state-dir" id="id14">state.dir</a></li>
            <li><a class="reference internal" href="#timestamp-extractor" id="id15">timestamp.extractor</a></li>
          </ul>
          </li>
          <li><a class="reference internal" href="#kafka-consumers-and-producer-configuration-parameters" id="id16">Kafka consumers and producer configuration parameters</a><ul>
            <li><a class="reference internal" href="#naming" id="id17">Naming</a></li>
            <li><a class="reference internal" href="#default-values" id="id18">Default Values</a></li>
            <li><a class="reference internal" href="#enable-auto-commit" id="id19">enable.auto.commit</a></li>
            <li><a class="reference internal" href="#rocksdb-config-setter" id="id20">rocksdb.config.setter</a></li>
          </ul>
          </li>
          <li><a class="reference internal" href="#recommended-configuration-parameters-for-resiliency" id="id21">Recommended configuration parameters for resiliency</a><ul>
            <li><a class="reference internal" href="#acks" id="id22">acks</a></li>
            <li><a class="reference internal" href="#id2" id="id23">replication.factor</a></li>
          </ul>
          </li>
        </ul>
      </div>
      <div class="section" id="required-configuration-parameters">
        <h3><a class="toc-backref" href="#id3">Required configuration parameters</a><a class="headerlink" href="#required-configuration-parameters" title="Permalink to this headline"></a></h3>
        <p>Here are the required Streams configuration parameters.</p>
        <table border="1" class="non-scrolling-table docutils">
          <thead valign="bottom">
          <tr class="row-odd"><th class="head">Parameter Name</th>
            <th class="head">Importance</th>
            <th class="head" colspan="2">Description</th>
            <th class="head">Default Value</th>
          </tr>
          </thead>
          <tbody valign="top">
          <tr class="row-even"><td>application.id</td>
            <td>Required</td>
            <td colspan="2">An identifier for the stream processing application.  Must be unique within the Kafka cluster.</td>
            <td>None</td>
          </tr>
          <tr class="row-odd"><td>bootstrap.servers</td>
            <td>Required</td>
            <td colspan="2">A list of host/port pairs to use for establishing the initial connection to the Kafka cluster.</td>
            <td>None</td>
          </tr>
          </tbody>
        </table>
        <div class="section" id="application-id">
          <h4><a class="toc-backref" href="#id4">application.id</a><a class="headerlink" href="#application-id" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div><p>(Required) The application ID. Each stream processing application must have a unique ID. The same ID must be given to
              all instances of the application.  It is recommended to use only alphanumeric characters, <code class="docutils literal"><span class="pre">.</span></code> (dot), <code class="docutils literal"><span class="pre">-</span></code> (hyphen), and <code class="docutils literal"><span class="pre">_</span></code> (underscore). Examples: <code class="docutils literal"><span class="pre">&quot;hello_world&quot;</span></code>, <code class="docutils literal"><span class="pre">&quot;hello_world-v1.0.0&quot;</span></code></p>
              <p>This ID is used in the following places to isolate resources used by the application from others:</p>
              <ul class="simple">
                <li>As the default Kafka consumer and producer <code class="docutils literal"><span class="pre">client.id</span></code> prefix</li>
                <li>As the Kafka consumer <code class="docutils literal"><span class="pre">group.id</span></code> for coordination</li>
                <li>As the name of the subdirectory in the state directory (cf. <code class="docutils literal"><span class="pre">state.dir</span></code>)</li>
                <li>As the prefix of internal Kafka topic names</li>
              </ul>
              <dl class="docutils">
                <dt>Tip:</dt>
                <dd>When an application is updated, the <code class="docutils literal"><span class="pre">application.id</span></code> should be changed unless you want to reuse the existing data in internal topics and state stores.
                  For example, you could embed the version information within <code class="docutils literal"><span class="pre">application.id</span></code>, as <code class="docutils literal"><span class="pre">my-app-v1.0.0</span></code> and <code class="docutils literal"><span class="pre">my-app-v1.0.2</span></code>.</dd>
              </dl>
            </div></blockquote>
        </div>
        <div class="section" id="bootstrap-servers">
          <h4><a class="toc-backref" href="#id5">bootstrap.servers</a><a class="headerlink" href="#bootstrap-servers" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div><p>(Required) The Kafka bootstrap servers. This is the same <a class="reference external" href="http://kafka.apache.org/documentation.html#producerconfigs">setting</a> that is used by the underlying producer and consumer clients to connect to the Kafka cluster.
              Example: <code class="docutils literal"><span class="pre">&quot;kafka-broker1:9092,kafka-broker2:9092&quot;</span></code>.</p>
              <dl class="docutils">
                <dt>Tip:</dt>
                <dd>Kafka Streams applications can only communicate with a single Kafka cluster specified by this config value.
                  Future versions of Kafka Streams will support connecting to different Kafka clusters for reading input
                  streams and writing output streams.</dd>
              </dl>
            </div></blockquote>
        </div>
      </div>
      <div class="section" id="optional-configuration-parameters">
        <span id="streams-developer-guide-optional-configs"></span><h3><a class="toc-backref" href="#id6">Optional configuration parameters</a><a class="headerlink" href="#optional-configuration-parameters" title="Permalink to this headline"></a></h3>
        <p>Here are the optional <a href="/{{version}}/javadoc/org/apache/kafka/streams/StreamsConfig.html">Streams</a> javadocs, sorted by level of importance:</p>
        <blockquote>
          <div><ul class="simple">
            <li>High: These parameters can have a significant impact on performance. Take care when deciding the values of these parameters.</li>
            <li>Medium: These parameters can have some impact on performance. Your specific environment will determine how much tuning effort should be focused on these parameters.</li>
            <li>Low: These parameters have a less general or less significant impact on performance.</li>
          </ul>
          </div></blockquote>
        <table border="1" class="non-scrolling-table docutils">
          <thead valign="bottom">
          <tr class="row-odd"><th class="head">Parameter Name</th>
            <th class="head">Importance</th>
            <th class="head" colspan="2">Description</th>
            <th class="head">Default Value</th>
          </tr>
          </thead>
          <tbody valign="top">
          <tr class="row-even"><td>application.server</td>
            <td>Low</td>
            <td colspan="2">A host:port pair pointing to an embedded user defined endpoint that can be used for discovering the locations of
              state stores within a single Kafka Streams application. The value of this must be different for each instance
              of the application.</td>
            <td>the empty string</td>
          </tr>
          <tr class="row-odd"><td>buffered.records.per.partition</td>
            <td>Low</td>
            <td colspan="2">The maximum number of records to buffer per partition.</td>
            <td>1000</td>
          </tr>
          <tr class="row-even"><td>cache.max.bytes.buffering</td>
            <td>Medium</td>
            <td colspan="2">Maximum number of memory bytes to be used for record caches across all threads.</td>
            <td>10485760 bytes</td>
          </tr>
          <tr class="row-odd"><td>client.id</td>
            <td>Medium</td>
            <td colspan="2">An ID string to pass to the server when making requests.
              (This setting is passed to the consumer/producer clients used internally by Kafka Streams.)</td>
            <td>the empty string</td>
          </tr>
          <tr class="row-even"><td>commit.interval.ms</td>
            <td>Low</td>
            <td colspan="2">The frequency with which to save the position (offsets in source topics) of tasks.</td>
            <td>30000 milliseconds</td>
          </tr>
          <tr class="row-odd"><td>default.deserialization.exception.handler</td>
            <td>Medium</td>
            <td colspan="2">Exception handling class that implements the <code class="docutils literal"><span class="pre">DeserializationExceptionHandler</span></code> interface.</td>
            <td><code class="docutils literal"><span class="pre">LogAndContinueExceptionHandler</span></code></td>
          </tr>
          <tr class="row-even"><td>default.production.exception.handler</td>
            <td>Medium</td>
            <td colspan="2">Exception handling class that implements the <code class="docutils literal"><span class="pre">ProductionExceptionHandler</span></code> interface.</td>
            <td><code class="docutils literal"><span class="pre">DefaultProductionExceptionHandler</span></code></td>
          </tr>
          <tr class="row-odd"><td>key.serde</td>
            <td>Medium</td>
            <td colspan="2">Default serializer/deserializer class for record keys, implements the <code class="docutils literal"><span class="pre">Serde</span></code> interface (see also value.serde).</td>
            <td><code class="docutils literal"><span class="pre">Serdes.ByteArray().getClass().getName()</span></code></td>
          </tr>
          <tr class="row-even"><td>metric.reporters</td>
            <td>Low</td>
            <td colspan="2">A list of classes to use as metrics reporters.</td>
            <td>the empty list</td>
          </tr>
          <tr class="row-odd"><td>metrics.num.samples</td>
            <td>Low</td>
            <td colspan="2">The number of samples maintained to compute metrics.</td>
            <td>2</td>
          </tr>
          <tr class="row-even"><td>metrics.recording.level</td>
            <td>Low</td>
            <td colspan="2">The highest recording level for metrics.</td>
            <td><code class="docutils literal"><span class="pre">INFO</span></code></td>
          </tr>
          <tr class="row-odd"><td>metrics.sample.window.ms</td>
            <td>Low</td>
            <td colspan="2">The window of time a metrics sample is computed over.</td>
            <td>30000 milliseconds</td>
          </tr>
          <tr class="row-even"><td>num.standby.replicas</td>
            <td>Medium</td>
            <td colspan="2">The number of standby replicas for each task.</td>
            <td>0</td>
          </tr>
          <tr class="row-odd"><td>num.stream.threads</td>
            <td>Medium</td>
            <td colspan="2">The number of threads to execute stream processing.</td>
            <td>1</td>
          </tr>
          <tr class="row-even"><td>partition.grouper</td>
            <td>Low</td>
            <td colspan="2">Partition grouper class that implements the <code class="docutils literal"><span class="pre">PartitionGrouper</span></code> interface.</td>
            <td>See <a class="reference internal" href="#streams-developer-guide-partition-grouper"><span class="std std-ref">Partition Grouper</span></a></td>
          </tr>
          <tr class="row-even"><td>processing.guarantee</td>
            <td>Medium</td>
            <td colspan="2">The processing mode. Can be either <code class="docutils literal"><span class="pre">"at_least_once"</span></code> (default),
              <code class="docutils literal"><span class="pre">"exactly_once"</span></code>, or <code class="docutils literal"><span class="pre">"exactly_once_beta"</span></code>.
            <td>See <a class="reference internal" href="#streams-developer-guide-processing-guarantedd"><span class="std std-ref">Processing Guarantee</span></a></td>
          </tr>
          <tr class="row-odd"><td>poll.ms</td>
            <td>Low</td>
            <td colspan="2">The amount of time in milliseconds to block waiting for input.</td>
            <td>100 milliseconds</td>
          </tr>
          <tr class="row-even"><td>replication.factor</td>
            <td>High</td>
            <td colspan="2">The replication factor for changelog topics and repartition topics created by the application.</td>
            <td>1</td>
          </tr>
          <tr class="row-even"><td>retries</td>
              <td>Medium</td>
              <td colspan="2">The number of retries for broker requests that return a retryable error. </td>
              <td>0</td>
          </tr>
          <tr class="row-even"><td>retry.backoff.ms</td>
              <td>Medium</td>
              <td colspan="2">The amount of time in milliseconds, before a request is retried. This applies if the <code class="docutils literal"><span class="pre">retries</span></code> parameter is configured to be greater than 0. </td>
              <td>100</td>
          </tr>
          <tr class="row-even"><td>rocksdb.config.setter</td>
            <td>Medium</td>
            <td colspan="2">The RocksDB configuration.</td>
            <td></td>
          </tr>
          <tr class="row-odd"><td>state.cleanup.delay.ms</td>
            <td>Low</td>
            <td colspan="2">The amount of time in milliseconds to wait before deleting state when a partition has migrated.</td>
            <td>600000 milliseconds</td>
          </tr>
          <tr class="row-even"><td>state.dir</td>
            <td>High</td>
            <td colspan="2">Directory location for state stores.</td>
            <td><code class="docutils literal"><span class="pre">/tmp/kafka-streams</span></code></td>
          </tr>
          <tr class="row-odd"><td>timestamp.extractor</td>
            <td>Medium</td>
            <td colspan="2">Timestamp extractor class that implements the <code class="docutils literal"><span class="pre">TimestampExtractor</span></code> interface.</td>
            <td>See <a class="reference internal" href="#streams-developer-guide-timestamp-extractor"><span class="std std-ref">Timestamp Extractor</span></a></td>
          </tr>
          <tr class="row-even"><td>upgrade.from</td>
            <td>Medium</td>
            <td colspan="2">The version you are upgrading from during a rolling upgrade.</td>
            <td>See <a class="reference internal" href="#streams-developer-guide-upgrade-from"><span class="std std-ref">Upgrade From</span></a></td>
          </tr>
          <tr class="row-odd"><td>value.serde</td>
            <td>Medium</td>
            <td colspan="2">Default serializer/deserializer class for record values, implements the <code class="docutils literal"><span class="pre">Serde</span></code> interface (see also key.serde).</td>
            <td><code class="docutils literal"><span class="pre">Serdes.ByteArray().getClass().getName()</span></code></td>
          </tr>
          <tr class="row-even"><td>windowstore.changelog.additional.retention.ms</td>
            <td>Low</td>
            <td colspan="2">Added to a windows maintainMs to ensure data is not deleted from the log prematurely. Allows for clock drift.</td>
            <td>86400000 milliseconds = 1 day</td>
          </tr>
          </tbody>
        </table>
        <div class="section" id="default-deserialization-exception-handler">
          <span id="streams-developer-guide-deh"></span><h4><a class="toc-backref" href="#id7">default.deserialization.exception.handler</a><a class="headerlink" href="#default-deserialization-exception-handler" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div><p>The default deserialization exception handler allows you to manage record exceptions that fail to deserialize. This
              can be caused by corrupt data, incorrect serialization logic, or unhandled record types. The implemented exception
              handler needs to return a <code>FAIL</code> or <code>CONTINUE</code> depending on the record and the exception thrown. Returning
              <code>FAIL</code> will signal that Streams should shut down and <code>CONTINUE</code> will signal that Streams should ignore the issue
              and continue processing. The following library built-in exception handlers are available:</p>
              <ul class="simple">
                <li><a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/errors/LogAndContinueExceptionHandler.html">LogAndContinueExceptionHandler</a>:
                  This handler logs the deserialization exception and then signals the processing pipeline to continue processing more records.
                  This log-and-skip strategy allows Kafka Streams to make progress instead of failing if there are records that fail
                  to deserialize.</li>
                <li><a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/errors/LogAndFailExceptionHandler.html">LogAndFailExceptionHandler</a>.
                  This handler logs the deserialization exception and then signals the processing pipeline to stop processing more records.</li>
              </ul>

              <p>You can also provide your own customized exception handler besides the library provided ones to meet your needs. For example, you can choose to forward corrupt
                records into a quarantine topic (think: a "dead letter queue") for further processing. To do this, use the Producer API to write a corrupted record directly to
                the quarantine topic. To be more concrete, you can create a separate <code>KafkaProducer</code> object outside the Streams client, and pass in this object
                as well as the dead letter queue topic name into the <code>Properties</code> map, which then can be retrieved from the <code>configure</code> function call.
                The drawback of this approach is that "manual" writes are side effects that are invisible to the Kafka Streams runtime library,
                so they do not benefit from the end-to-end processing guarantees of the Streams API:</p>

              <pre class="line-numbers"><code class="language-java">              public class SendToDeadLetterQueueExceptionHandler implements DeserializationExceptionHandler {
                  KafkaProducer&lt;byte[], byte[]&gt; dlqProducer;
                  String dlqTopic;

                  @Override
                  public DeserializationHandlerResponse handle(final ProcessorContext context,
                                                               final ConsumerRecord&lt;byte[], byte[]&gt; record,
                                                               final Exception exception) {

                      log.warn("Exception caught during Deserialization, sending to the dead queue topic; " +
                          "taskId: {}, topic: {}, partition: {}, offset: {}",
                          context.taskId(), record.topic(), record.partition(), record.offset(),
                          exception);

                      dlqProducer.send(new ProducerRecord&lt;&gt;(dlqTopic, record.timestamp(), record.key(), record.value(), record.headers())).get();

                      return DeserializationHandlerResponse.CONTINUE;
                  }

                  @Override
                  public void configure(final Map&lt;String, ?&gt; configs) {
                      dlqProducer = .. // get a producer from the configs map
                      dlqTopic = .. // get the topic name from the configs map
                  }
              }</code></pre>

            </div></blockquote>
        </div>
        <div class="section" id="default-production-exception-handler">
          <span id="streams-developer-guide-peh"></span><h4><a class="toc-backref" href="#id24">default.production.exception.handler</a><a class="headerlink" href="#default-production-exception-handler" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div><p>The default production exception handler allows you to manage exceptions triggered when trying to interact with a broker
              such as attempting to produce a record that is too large. By default, Kafka provides and uses the <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/errors/DefaultProductionExceptionHandler.html">DefaultProductionExceptionHandler</a>
              that always fails when these exceptions occur.</p>

            <p>Each exception handler can return a <code>FAIL</code> or <code>CONTINUE</code> depending on the record and the exception thrown. Returning <code>FAIL</code> will signal that Streams should shut down and <code>CONTINUE</code> will signal that Streams
            should ignore the issue and continue processing. If you want to provide an exception handler that always ignores records that are too large, you could implement something like the following:</p>

            <pre class="line-numbers"><code class="language-java">
            import java.util.Properties;
            import org.apache.kafka.streams.StreamsConfig;
            import org.apache.kafka.common.errors.RecordTooLargeException;
            import org.apache.kafka.streams.errors.ProductionExceptionHandler;
            import org.apache.kafka.streams.errors.ProductionExceptionHandler.ProductionExceptionHandlerResponse;

            public class IgnoreRecordTooLargeHandler implements ProductionExceptionHandler {
                public void configure(Map&lt;String, Object&gt; config) {}

                public ProductionExceptionHandlerResponse handle(final ProducerRecord&lt;byte[], byte[]&gt; record,
                                                                 final Exception exception) {
                    if (exception instanceof RecordTooLargeException) {
                        return ProductionExceptionHandlerResponse.CONTINUE;
                    } else {
                        return ProductionExceptionHandlerResponse.FAIL;
                    }
                }
            }

            Properties settings = new Properties();

            // other various kafka streams settings, e.g. bootstrap servers, application id, etc

            settings.put(StreamsConfig.DEFAULT_PRODUCTION_EXCEPTION_HANDLER_CLASS_CONFIG,
                         IgnoreRecordTooLargeHandler.class);</code></pre></div>
          </blockquote>
        </div>
        <div class="section" id="default-key-serde">
          <h4><a class="toc-backref" href="#id8">default.key.serde</a><a class="headerlink" href="#default-key-serde" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div><p>The default Serializer/Deserializer class for record keys. Serialization and deserialization in Kafka Streams happens
              whenever data needs to be materialized, for example:</p>
              <blockquote>
                <div><ul class="simple">
                  <li>Whenever data is read from or written to a <em>Kafka topic</em> (e.g., via the <code class="docutils literal"><span class="pre">StreamsBuilder#stream()</span></code> and <code class="docutils literal"><span class="pre">KStream#to()</span></code> methods).</li>
                  <li>Whenever data is read from or written to a <em>state store</em>.</li>
                </ul>
                  <p>This is discussed in more detail in <a class="reference internal" href="datatypes.html#streams-developer-guide-serdes"><span class="std std-ref">Data types and serialization</span></a>.</p>
                </div></blockquote>
            </div></blockquote>
        </div>
        <div class="section" id="default-value-serde">
          <h4><a class="toc-backref" href="#id9">default.value.serde</a><a class="headerlink" href="#default-value-serde" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div><p>The default Serializer/Deserializer class for record values. Serialization and deserialization in Kafka Streams
              happens whenever data needs to be materialized, for example:</p>
              <ul class="simple">
                <li>Whenever data is read from or written to a <em>Kafka topic</em> (e.g., via the <code class="docutils literal"><span class="pre">StreamsBuilder#stream()</span></code> and <code class="docutils literal"><span class="pre">KStream#to()</span></code> methods).</li>
                <li>Whenever data is read from or written to a <em>state store</em>.</li>
              </ul>
              <p>This is discussed in more detail in <a class="reference internal" href="datatypes.html#streams-developer-guide-serdes"><span class="std std-ref">Data types and serialization</span></a>.</p>
            </div></blockquote>
        </div>
        <div class="section" id="num-standby-replicas">
          <span id="streams-developer-guide-standby-replicas"></span><h4><a class="toc-backref" href="#id10">num.standby.replicas</a><a class="headerlink" href="#num-standby-replicas" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div>The number of standby replicas. Standby replicas are shadow copies of local state stores. Kafka Streams attempts to create the
              specified number of replicas per store and keep them up to date as long as there are enough instances running.
              Standby replicas are used to minimize the latency of task failover.  A task that was previously running on a failed instance is
              preferred to restart on an instance that has standby replicas so that the local state store restoration process from its
              changelog can be minimized.  Details about how Kafka Streams makes use of the standby replicas to minimize the cost of
              resuming tasks on failover can be found in the <a class="reference internal" href="../architecture.html#streams_architecture_state"><span class="std std-ref">State</span></a> section.</div></blockquote>
            </div>
            <div class="admonition note">
              <p class="first admonition-title">Note</p>
              <p class="last">If you enable <cite>n</cite> standby tasks, you need to provision <cite>n+1</cite> <code class="docutils literal"><span class="pre">KafkaStreams</span></code>
              instances.</p>
              </div>
        <div class="section" id="num-stream-threads">
          <h4><a class="toc-backref" href="#id11">num.stream.threads</a><a class="headerlink" href="#num-stream-threads" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div>This specifies the number of stream threads in an instance of the Kafka Streams application. The stream processing code runs in these thread.
              For more information about Kafka Streams threading model, see <a class="reference internal" href="../architecture.html#streams_architecture_threads"><span class="std std-ref">Threading Model</span></a>.</div></blockquote>
        </div>
        <div class="section" id="partition-grouper">
          <span id="streams-developer-guide-partition-grouper"></span><h4><a class="toc-backref" href="#id12">partition.grouper</a><a class="headerlink" href="#partition-grouper" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div>A partition grouper creates a list of stream tasks from the partitions of source topics, where each created task is assigned with a group of source topic partitions.
              The default implementation provided by Kafka Streams is <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/processor/DefaultPartitionGrouper.html">DefaultPartitionGrouper</a>.
              It assigns each task with one partition for each of the source topic partitions. The generated number of tasks equals the largest
              number of partitions among the input topics. Usually an application does not need to customize the partition grouper.</div></blockquote>
        </div>
        <div class="section" id="processing-guarantee">
          <span id="streams-developer-guide-processing-guarantee"></span><h4><a class="toc-backref" href="#id25">processing.guarantee</a><a class="headerlink" href="#processing-guarantee" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div>The processing guarantee that should be used.
                 Possible values are <code class="docutils literal"><span class="pre">"at_least_once"</span></code> (default),
                 <code class="docutils literal"><span class="pre">"exactly_once"</span></code>,
                 and <code class="docutils literal"><span class="pre">"exactly_once_beta"</span></code>.
                 Using <code class="docutils literal"><span class="pre">"exactly_once"</span></code> requires broker
                 version 0.11.0 or newer, while using <code class="docutils literal"><span class="pre">"exactly_once_beta"</span></code>
                 requires broker version 2.5 or newer.
                 Note that if exactly-once processing is enabled, the default for parameter
                 <code class="docutils literal"><span class="pre">commit.interval.ms</span></code> changes to 100ms.
                 Additionally, consumers are configured with <code class="docutils literal"><span class="pre">isolation.level="read_committed"</span></code>
                 and producers are configured with <code class="docutils literal"><span class="pre">enable.idempotence=true</span></code> per default.
                 Note that by default exactly-once processing requires a cluster of at least three brokers what is the recommended setting for production.
                 For development, you can change this configuration by adjusting broker setting
                 <code class="docutils literal"><span class="pre">transaction.state.log.replication.factor</span></code>
                 and <code class="docutils literal"><span class="pre">transaction.state.log.min.isr</span></code>
                 to the number of brokers you want to use.
                 For more details see <a href="../core-concepts#streams_processing_guarantee">Processing Guarantees</a>.
            </div></blockquote>
        </div>
        <div class="section" id="replication-factor">
          <span id="replication-factor-parm"></span><h4><a class="toc-backref" href="#id13">replication.factor</a><a class="headerlink" href="#replication-factor" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div><p>This specifies the replication factor of internal topics that Kafka Streams creates when local states are used or a stream is
              repartitioned for aggregation. Replication is important for fault tolerance. Without replication even a single broker failure
              may prevent progress of the stream processing application. It is recommended to use a similar replication factor as source topics.</p>
              <dl class="docutils">
                <dt>Recommendation:</dt>
                <dd>Increase the replication factor to 3 to ensure that the internal Kafka Streams topic can tolerate up to 2 broker failures.
                  Note that you will require more storage space as well (3 times more with the replication factor of 3).</dd>
              </dl>
            </div></blockquote>
        </div>
        <div class="section" id="rocksdb-config-setter">
          <span id="streams-developer-guide-rocksdb-config"></span><h4><a class="toc-backref" href="#id20">rocksdb.config.setter</a><a class="headerlink" href="#rocksdb-config-setter" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div><p>The RocksDB configuration. Kafka Streams uses RocksDB as the default storage engine for persistent stores. To change the default
              configuration for RocksDB, you can implement <code class="docutils literal"><span class="pre">RocksDBConfigSetter</span></code> and provide your custom class via <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/state/RocksDBConfigSetter.html">rocksdb.config.setter</a>.</p>
              <p>Here is an example that adjusts the memory size consumed by RocksDB.</p>
              <div class="highlight-java"><div class="highlight"><pre><span></span>    <span class="kd">public</span> <span class="kd">static</span> <span class="kd">class</span> <span class="nc">CustomRocksDBConfig</span> <span class="kd">implements</span> <span class="n">RocksDBConfigSetter</span> <span class="o">{</span>
                    <span class="c1">// This object should be a member variable so it can be closed in RocksDBConfigSetter#close.</span>
                    <span class="kd">private</span> <span class="n">org.rocksdb.Cache</span> <span class="n">cache</span> <span class="o">=</span> <span class="k">new</span> <span class="n">org</span><span class="o">.</span><span class="na">rocksdb</span><span class="o">.</span><span class="na">LRUCache</span><span class="o">(</span><span class="mi">16</span> <span class="o">*</span> <span class="mi">1024L</span> <span class="o">*</span> <span class="mi">1024L</span><span class="o">);</span>

                    <span class="nd">@Override</span>
                    <span class="kd">public</span> <span class="kt">void</span> <span class="nf">setConfig</span><span class="o">(</span><span class="kd">final</span> <span class="n">String</span> <span class="n">storeName</span><span class="o">,</span> <span class="kd">final</span> <span class="n">Options</span> <span class="n">options</span><span class="o">,</span> <span class="kd">final</span> <span class="n">Map</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Object</span><span class="o">&gt;</span> <span class="n">configs</span><span class="o">)</span> <span class="o">{</span>
                      <span class="c1">// See #1 below.</span>
                      <span class="n">BlockBasedTableConfig</span> <span class="n">tableConfig</span> <span class="o">=</span> <span class="k">(BlockBasedTableConfig)</span> <span class="n">options</span><span><span class="o">.</span><span class="na">tableFormatConfig</span><span class="o">();</span>
                      <span class="n">tableConfig</span><span class="o">.</span><span class="na">setBlockCache</span><span class="o">(</span><span class="mi">cache</span></span><span class="o">);</span>
                      <span class="c1">// See #2 below.</span>
                      <span class="n">tableConfig</span><span class="o">.</span><span class="na">setBlockSize</span><span class="o">(</span><span class="mi">16</span> <span class="o">*</span> <span class="mi">1024L</span><span class="o">);</span>
                      <span class="c1">// See #3 below.</span>
                      <span class="n">tableConfig</span><span class="o">.</span><span class="na">setCacheIndexAndFilterBlocks</span><span class="o">(</span><span class="kc">true</span><span class="o">);</span>
                      <span class="n">options</span><span class="o">.</span><span class="na">setTableFormatConfig</span><span class="o">(</span><span class="n">tableConfig</span><span class="o">);</span>
                      <span class="c1">// See #4 below.</span>
                      <span class="n">options</span><span class="o">.</span><span class="na">setMaxWriteBufferNumber</span><span class="o">(</span><span class="mi">2</span><span class="o">);</span>
                    <span class="o">}</span>

                    <span class="nd">@Override</span>
                    <span class="kd">public</span> <span class="kt">void</span> <span class="nf">close</span><span class="o">(</span><span class="kd">final</span> <span class="n">String</span> <span class="n">storeName</span><span class="o">,</span> <span class="kd">final</span> <span class="n">Options</span> <span class="n">options</span><span class="o">)</span> <span class="o">{</span>
                      <span class="c1">// See #5 below.</span>
                      <span class="n">cache</span><span class="o">.</span><span class="na">close</span><span class="o">();</span>
                    <span class="o">}</span>
                    <span class="o">}</span>

                    <span class="n">Properties</span> <span class="n">streamsSettings</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Properties</span><span class="o">();</span>
                    <span class="n">streamsConfig</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">ROCKSDB_CONFIG_SETTER_CLASS_CONFIG</span><span class="o">,</span> <span class="n">CustomRocksDBConfig</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
                    </pre></div>
                    </div>
                    <dl class="docutils">
                      <dt>Notes for example:</dt>
                      <dd><ol class="first last arabic simple">
                        <li><code class="docutils literal"><span class="pre">BlockBasedTableConfig tableConfig = (BlockBasedTableConfig) options.tableFormatConfig();</span></code> Get a reference to the existing table config rather than create a new one, so you don't accidentally overwrite defaults such as the <code class="docutils literal"><span class="pre">BloomFilter</span></code>, which is an important optimization.
                        <li><code class="docutils literal"><span class="pre">tableConfig.setBlockSize(16</span> <span class="pre">*</span> <span class="pre">1024L);</span></code> Modify the default <a class="reference external" href="https://github.com/apache/kafka/blob/2.3/streams/src/main/java/org/apache/kafka/streams/state/internals/RocksDBStore.java#L79">block size</a> per these instructions from the <a class="reference external" href="https://github.com/facebook/rocksdb/wiki/Memory-usage-in-RocksDB#indexes-and-filter-blocks">RocksDB GitHub</a>.</li>
                        <li><code class="docutils literal"><span class="pre">tableConfig.setCacheIndexAndFilterBlocks(true);</span></code> Do not let the index and filter blocks grow unbounded. For more information, see the <a class="reference external" href="https://github.com/facebook/rocksdb/wiki/Block-Cache#caching-index-and-filter-blocks">RocksDB GitHub</a>.</li>
                        <li><code class="docutils literal"><span class="pre">options.setMaxWriteBufferNumber(2);</span></code> See the advanced options in the <a class="reference external" href="https://github.com/facebook/rocksdb/blob/8dee8cad9ee6b70fd6e1a5989a8156650a70c04f/include/rocksdb/advanced_options.h#L103">RocksDB GitHub</a>.</li>
                        <li><code class="docutils literal"><span class="pre">cache.close();</span></code> To avoid memory leaks, you must close any objects you constructed that extend org.rocksdb.RocksObject. See  <a class="reference external" href="https://github.com/facebook/rocksdb/wiki/RocksJava-Basics#memory-management">RocksJava docs</a> for more details.</li>
                      </ol>
                      </dd>
                    </dl>
                  </div></blockquote>
              </div>
            </div>
          </blockquote>
        </div>
        <div class="section" id="state-dir">
          <h4><a class="toc-backref" href="#id14">state.dir</a><a class="headerlink" href="#state-dir" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div>The state directory. Kafka Streams persists local states under the state directory. Each application has a subdirectory on its hosting
              machine that is located under the state directory. The name of the subdirectory is the application ID. The state stores associated
              with the application are created under this subdirectory. When running multiple instances of the same application on a single machine,
              this path must be unique for each such instance.</div>
          </blockquote>
        </div>
        <div class="section" id="timestamp-extractor">
          <span id="streams-developer-guide-timestamp-extractor"></span><h4><a class="toc-backref" href="#id15">timestamp.extractor</a><a class="headerlink" href="#timestamp-extractor" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div><p>A timestamp extractor pulls a timestamp from an instance of <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/clients/consumer/ConsumerRecord.html">ConsumerRecord</a>.
              Timestamps are used to control the progress of streams.</p>
              <p>The default extractor is
                <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/processor/FailOnInvalidTimestamp.html">FailOnInvalidTimestamp</a>.
                This extractor retrieves built-in timestamps that are automatically embedded into Kafka messages by the Kafka producer
                client since
                <a class="reference external" href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-32+-+Add+timestamps+to+Kafka+message">Kafka version 0.10</a>.
                Depending on the setting of Kafka&#8217;s server-side <code class="docutils literal"><span class="pre">log.message.timestamp.type</span></code> broker and <code class="docutils literal"><span class="pre">message.timestamp.type</span></code> topic parameters,
                this extractor provides you with:</p>
              <ul class="simple">
                <li><strong>event-time</strong> processing semantics if <code class="docutils literal"><span class="pre">log.message.timestamp.type</span></code> is set to <code class="docutils literal"><span class="pre">CreateTime</span></code> aka &#8220;producer time&#8221;
                  (which is the default).  This represents the time when a Kafka producer sent the original message.  If you use Kafka&#8217;s
                  official producer client, the timestamp represents milliseconds since the epoch.</li>
                <li><strong>ingestion-time</strong> processing semantics if <code class="docutils literal"><span class="pre">log.message.timestamp.type</span></code> is set to <code class="docutils literal"><span class="pre">LogAppendTime</span></code> aka &#8220;broker
                  time&#8221;.  This represents the time when the Kafka broker received the original message, in milliseconds since the epoch.</li>
              </ul>
              <p>The <code class="docutils literal"><span class="pre">FailOnInvalidTimestamp</span></code> extractor throws an exception if a record contains an invalid (i.e. negative) built-in
                timestamp, because Kafka Streams would not process this record but silently drop it.  Invalid built-in timestamps can
                occur for various reasons:  if for example, you consume a topic that is written to by pre-0.10 Kafka producer clients
                or by third-party producer clients that don&#8217;t support the new Kafka 0.10 message format yet;  another situation where
                this may happen is after upgrading your Kafka cluster from <code class="docutils literal"><span class="pre">0.9</span></code> to <code class="docutils literal"><span class="pre">0.10</span></code>, where all the data that was generated
                with <code class="docutils literal"><span class="pre">0.9</span></code> does not include the <code class="docutils literal"><span class="pre">0.10</span></code> message timestamps.</p>
              <p>If you have data with invalid timestamps and want to process it, then there are two alternative extractors available.
                Both work on built-in timestamps, but handle invalid timestamps differently.</p>
              <ul class="simple">
                <li><a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/processor/LogAndSkipOnInvalidTimestamp.html">LogAndSkipOnInvalidTimestamp</a>:
                  This extractor logs a warn message and returns the invalid timestamp to Kafka Streams, which will not process but
                  silently drop the record.
                  This log-and-skip strategy allows Kafka Streams to make progress instead of failing if there are records with an
                  invalid built-in timestamp in your input data.</li>
                <li><a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/processor/UsePartitionTimeOnInvalidTimestamp.html">UsePartitionTimeOnInvalidTimestamp</a>.
                  This extractor returns the record&#8217;s built-in timestamp if it is valid (i.e. not negative).  If the record does not
                  have a valid built-in timestamps, the extractor returns the previously extracted valid timestamp from a record of the
                  same topic partition as the current record as a timestamp estimation.  In case that no timestamp can be estimated, it
                  throws an exception.</li>
              </ul>
              <p>Another built-in extractor is
                <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/streams/processor/WallclockTimestampExtractor.html">WallclockTimestampExtractor</a>.
                This extractor does not actually &#8220;extract&#8221; a timestamp from the consumed record but rather returns the current time in
                milliseconds from the system clock (think: <code class="docutils literal"><span class="pre">System.currentTimeMillis()</span></code>), which effectively means Streams will operate
                on the basis of the so-called <strong>processing-time</strong> of events.</p>
              <p>You can also provide your own timestamp extractors, for instance to retrieve timestamps embedded in the payload of
                messages.  If you cannot extract a valid timestamp, you can either throw an exception, return a negative timestamp, or
                estimate a timestamp.  Returning a negative timestamp will result in data loss &#8211; the corresponding record will not be
                processed but silently dropped.  If you want to estimate a new timestamp, you can use the value provided via
                <code class="docutils literal"><span class="pre">previousTimestamp</span></code> (i.e., a Kafka Streams timestamp estimation).  Here is an example of a custom
                <code class="docutils literal"><span class="pre">TimestampExtractor</span></code> implementation:</p>
              <div class="highlight-java"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">org.apache.kafka.clients.consumer.ConsumerRecord</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.kafka.streams.processor.TimestampExtractor</span><span class="o">;</span>

<span class="c1">// Extracts the embedded timestamp of a record (giving you &quot;event-time&quot; semantics).</span>
<span class="kd">public</span> <span class="kd">class</span> <span class="nc">MyEventTimeExtractor</span> <span class="kd">implements</span> <span class="n">TimestampExtractor</span> <span class="o">{</span>

  <span class="nd">@Override</span>
  <span class="kd">public</span> <span class="kt">long</span> <span class="nf">extract</span><span class="o">(</span><span class="kd">final</span> <span class="n">ConsumerRecord</span><span class="o">&lt;</span><span class="n">Object</span><span class="o">,</span> <span class="n">Object</span><span class="o">&gt;</span> <span class="n">record</span><span class="o">,</span> <span class="kd">final</span> <span class="kt">long</span> <span class="n">previousTimestamp</span><span class="o">)</span> <span class="o">{</span>
    <span class="c1">// `Foo` is your own custom class, which we assume has a method that returns</span>
    <span class="c1">// the embedded timestamp (milliseconds since midnight, January 1, 1970 UTC).</span>
    <span class="kt">long</span> <span class="n">timestamp</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span><span class="o">;</span>
    <span class="kd">final</span> <span class="n">Foo</span> <span class="n">myPojo</span> <span class="o">=</span> <span class="o">(</span><span class="n">Foo</span><span class="o">)</span> <span class="n">record</span><span class="o">.</span><span class="na">value</span><span class="o">();</span>
    <span class="k">if</span> <span class="o">(</span><span class="n">myPojo</span> <span class="o">!=</span> <span class="kc">null</span><span class="o">)</span> <span class="o">{</span>
      <span class="n">timestamp</span> <span class="o">=</span> <span class="n">myPojo</span><span class="o">.</span><span class="na">getTimestampInMillis</span><span class="o">();</span>
    <span class="o">}</span>
    <span class="k">if</span> <span class="o">(</span><span class="n">timestamp</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="o">)</span> <span class="o">{</span>
      <span class="c1">// Invalid timestamp!  Attempt to estimate a new timestamp,</span>
      <span class="c1">// otherwise fall back to wall-clock time (processing-time).</span>
      <span class="k">if</span> <span class="o">(</span><span class="n">previousTimestamp</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="o">)</span> <span class="o">{</span>
        <span class="k">return</span> <span class="n">previousTimestamp</span><span class="o">;</span>
      <span class="o">}</span> <span class="k">else</span> <span class="o">{</span>
        <span class="k">return</span> <span class="n">System</span><span class="o">.</span><span class="na">currentTimeMillis</span><span class="o">();</span>
      <span class="o">}</span>
    <span class="o">}</span>
  <span class="o">}</span>

<span class="o">}</span>
</pre></div>
              </div>
              <p>You would then define the custom timestamp extractor in your Streams configuration as follows:</p>
              <div class="highlight-java"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">java.util.Properties</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.kafka.streams.StreamsConfig</span><span class="o">;</span>

<span class="n">Properties</span> <span class="n">streamsConfiguration</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Properties</span><span class="o">();</span>
<span class="n">streamsConfiguration</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">DEFAULT_TIMESTAMP_EXTRACTOR_CLASS_CONFIG</span><span class="o">,</span> <span class="n">MyEventTimeExtractor</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
</pre></div>
              </div>
            </div></blockquote>
        </div>
        <div class="section" id="upgrade-from">
          <h4><a class="toc-backref" href="#id14">upgrade.from</a><a class="headerlink" href="#upgrade-from" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div>
              The version you are upgrading from. It is important to set this config when performing a rolling upgrade to certain versions, as described in the upgrade guide.
              You should set this config to the appropriate version before bouncing your instances and upgrading them to the newer version. Once everyone is on the
              newer version, you should remove this config and do a second rolling bounce. It is only necessary to set this config and follow the two-bounce upgrade path
              when upgrading from below version 2.0, or when upgrading to 2.4+ from any version lower than 2.4.
            </div>
          </blockquote>
        </div>
      </div>
      <div class="section" id="kafka-consumers-and-producer-configuration-parameters">
        <h3><a class="toc-backref" href="#id16">Kafka consumers, producer and admin client configuration parameters</a><a class="headerlink" href="#kafka-consumers-and-producer-configuration-parameters" title="Permalink to this headline"></a></h3>
        <p>You can specify parameters for the Kafka <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/clients/consumer/package-summary.html">consumers</a>, <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/clients/producer/package-summary.html">producers</a>,
            and <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/kafka/clients/admin/package-summary.html">admin client</a> that are used internally.
            The consumer, producer and admin client settings are defined by specifying parameters in a <code class="docutils literal"><span class="pre">StreamsConfig</span></code> instance.</p>
        <p>In this example, the Kafka <a class="reference external" href="/{{version}}/javadoc/org/apache/kafka/clients/consumer/ConsumerConfig.html#SESSION_TIMEOUT_MS_CONFIG">consumer session timeout</a> is configured to be 60000 milliseconds in the Streams settings:</p>
        <div class="highlight-java"><div class="highlight"><pre><span></span><span class="n">Properties</span> <span class="n">streamsSettings</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Properties</span><span class="o">();</span>
<span class="c1">// Example of a &quot;normal&quot; setting for Kafka Streams</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">BOOTSTRAP_SERVERS_CONFIG</span><span class="o">,</span> <span class="s">&quot;kafka-broker-01:9092&quot;</span><span class="o">);</span>
<span class="c1">// Customize the Kafka consumer settings of your Streams application</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">ConsumerConfig</span><span class="o">.</span><span class="na">SESSION_TIMEOUT_MS_CONFIG</span><span class="o">,</span> <span class="mi">60000</span><span class="o">);</span>
</pre></div>
        </div>
        <div class="section" id="naming">
          <h4><a class="toc-backref" href="#id17">Naming</a><a class="headerlink" href="#naming" title="Permalink to this headline"></a></h4>
          <p>Some consumer, producer and admin client configuration parameters use the same parameter name, and Kafka Streams library itself also uses some parameters that share the same name with its embedded client. For example, <code class="docutils literal"><span class="pre">send.buffer.bytes</span></code> and
              <code class="docutils literal"><span class="pre">receive.buffer.bytes</span></code> are used to configure TCP buffers; <code class="docutils literal"><span class="pre">request.timeout.ms</span></code> and <code class="docutils literal"><span class="pre">retry.backoff.ms</span></code> control retries for client request;
              <code class="docutils literal"><span class="pre">retries</span></code> are used to configure how many retries are allowed when handling retriable errors from broker request responses.
              You can avoid duplicate names by prefix parameter names with <code class="docutils literal"><span class="pre">consumer.</span></code>, <code class="docutils literal"><span class="pre">producer.</span></code>, or <code class="docutils literal"><span class="pre">admin.</span></code> (e.g., <code class="docutils literal"><span class="pre">consumer.send.buffer.bytes</span></code> and <code class="docutils literal"><span class="pre">producer.send.buffer.bytes</span></code>).</p>
          <div class="highlight-java"><div class="highlight"><pre><span></span><span class="n">Properties</span> <span class="n">streamsSettings</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Properties</span><span class="o">();</span>
<span class="c1">// same value for consumer, producer, and admin client</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="s">&quot;PARAMETER_NAME&quot;</span><span class="o">,</span> <span class="s">&quot;value&quot;</span><span class="o">);</span>
<span class="c1">// different values for consumer and producer</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="s">&quot;consumer.PARAMETER_NAME&quot;</span><span class="o">,</span> <span class="s">&quot;consumer-value&quot;</span><span class="o">);</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="s">&quot;producer.PARAMETER_NAME&quot;</span><span class="o">,</span> <span class="s">&quot;producer-value&quot;</span><span class="o">);</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="s">&quot;admin.PARAMETER_NAME&quot;</span><span class="o">,</span> <span class="s">&quot;admin-value&quot;</span><span class="o">);</span>
<span class="c1">// alternatively, you can use</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">consumerPrefix</span><span class="o">(</span><span class="s">&quot;PARAMETER_NAME&quot;</span><span class="o">),</span> <span class="s">&quot;consumer-value&quot;</span><span class="o">);</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">producerPrefix</span><span class="o">(</span><span class="s">&quot;PARAMETER_NAME&quot;</span><span class="o">),</span> <span class="s">&quot;producer-value&quot;</span><span class="o">);</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">adminClientPrefix</span><span class="o">(</span><span class="s">&quot;PARAMETER_NAME&quot;</span><span class="o">),</span> <span class="s">&quot;admin-value&quot;</span><span class="o">);</span>
</pre></div>
          <p>You could further separate consumer configuration by adding different prefixes:</p>
          <ul class="simple">
            <li><code class="docutils literal"><span class="pre">main.consumer.</span></code> for main consumer which is the default consumer of stream source.</li>
            <li><code class="docutils literal"><span class="pre">restore.consumer.</span></code> for restore consumer which is in charge of state store recovery.</li>
            <li><code class="docutils literal"><span class="pre">global.consumer.</span></code> for global consumer which is used in global KTable construction.</li>
          </ul>
          <p>For example, if you only want to set restore consumer config without touching other consumers' settings, you could simply use <code class="docutils literal"><span class="pre">restore.consumer.</span></code> to set the config.</p>
          <div class="highlight-java"><div class="highlight"><pre><span></span><span class="n">Properties</span> <span class="n">streamsSettings</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Properties</span><span class="o">();</span>
<span class="c1">// same config value for all consumer types</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="s">&quot;consumer.PARAMETER_NAME&quot;</span><span class="o">,</span> <span class="s">&quot;general-consumer-value&quot;</span><span class="o">);</span>
<span class="c1">// set a different restore consumer config. This would make restore consumer take restore-consumer-value,</span>
<span>// while main consumer and global consumer stay with general-consumer-value</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="s">&quot;restore.consumer.PARAMETER_NAME&quot;</span><span class="o">,</span> <span class="s">&quot;restore-consumer-value&quot;</span><span class="o">);</span>
<span class="c1">// alternatively, you can use</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">restoreConsumerPrefix</span><span class="o">(</span><span class="s">&quot;PARAMETER_NAME&quot;</span><span class="o">),</span> <span class="s">&quot;restore-consumer-value&quot;</span><span class="o">);</span>
</pre></div>
          </div>
          <p> Same applied to <code class="docutils literal"><span class="pre">main.consumer.</span></code> and <code class="docutils literal"><span class="pre">main.consumer.</span></code>, if you only want to specify one consumer type config.</p>
          <p> Additionally, to configure the internal repartition/changelog topics, you could use the <code class="docutils literal"><span class="pre">topic.</span></code> prefix, followed by any of the standard topic configs.</p>
            <div class="highlight-java"><div class="highlight"><pre><span></span><span class="n">Properties</span> <span class="n">streamsSettings</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Properties</span><span class="o">();</span>
<span class="c1">// Override default for both changelog and repartition topics</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="s">&quot;topic.PARAMETER_NAME&quot;</span><span class="o">,</span> <span class="s">&quot;topic-value&quot;</span><span class="o">);</span>
<span class="c1">// alternatively, you can use</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">topicPrefix</span><span class="o">(</span><span class="s">&quot;PARAMETER_NAME&quot;</span><span class="o">),</span> <span class="s">&quot;topic-value&quot;</span><span class="o">);</span>
</pre></div>
            </div>
          </div>
        </div>
        <div class="section" id="default-values">
          <h4><a class="toc-backref" href="#id18">Default Values</a><a class="headerlink" href="#default-values" title="Permalink to this headline"></a></h4>
          <p>Kafka Streams uses different default values for some of the underlying client configs, which are summarized below. For detailed descriptions
            of these configs, see <a class="reference external" href="http://kafka.apache.org/0100/documentation.html#producerconfigs">Producer Configs</a>
            and <a class="reference external" href="http://kafka.apache.org/0100/documentation.html#newconsumerconfigs">Consumer Configs</a>.</p>
          <table border="1" class="non-scrolling-table docutils">
            <thead valign="bottom">
            <tr class="row-odd"><th class="head">Parameter Name</th>
              <th class="head">Corresponding Client</th>
              <th class="head">Streams Default</th>
            </tr>
            </thead>
            <tbody valign="top">
            <tr class="row-even"><td>auto.offset.reset</td>
              <td>Consumer</td>
              <td>earliest</td>
            </tr>
            <tr class="row-even"><td>linger.ms</td>
              <td>Producer</td>
              <td>100</td>
            </tr>
            <tr class="row-odd"><td>max.poll.interval.ms</td>
              <td>Consumer</td>
              <td>Integer.MAX_VALUE</td>
            </tr>
            <tr class="row-even"><td>max.poll.records</td>
              <td>Consumer</td>
              <td>1000</td>
            </tr>
            </tbody>
          </table>
        </div>
        <div class="section" id="parameters-controlled-by-kafka-streams">
          <h3><a class="toc-backref" href="#id26">Parameters controlled by Kafka Streams</a><a class="headerlink" href="#parameters-controlled-by-kafka-streams" title="Permalink to this headline"></a></h3>
          <p>Kafka Streams assigns the following configuration parameters. If you try to change
            <code class="docutils literal"><span class="pre">allow.auto.create.topics</span></code>, your value
            is ignored and setting it has no effect in a Kafka Streams application. You can set the other parameters.
            Kafka Streams sets them to different default values than a plain
            <code class="docutils literal"><span class="pre">KafkaConsumer</span></code>.
          <p>Kafka Streams uses the <code class="docutils literal"><span class="pre">client.id</span></code>
            parameter to compute derived client IDs for internal clients. If you don't set
            <code class="docutils literal"><span class="pre">client.id</span></code>, Kafka Streams sets it to
            <code class="docutils literal"><span class="pre">&lt;application.id&gt;-&lt;random-UUID&gt;</span></code>.
            <table border="1" class="non-scrolling-table docutils">
              <colgroup>
              <col width="50%">
              <col width="19%">
              <col width="31%">
              </colgroup>
              <thead valign="bottom">
              <tr class="row-odd"><th class="head">Parameter Name</th>
              <th class="head">Corresponding Client</th>
              <th class="head">Streams Default</th>
              </tr>
              </thead>
              <tbody valign="top">
              <tr class="row-odd"><td>allow.auto.create.topics</td>
              <td>Consumer</td>
              <td>false</td>
              </tr>
              <tr class="row-even"><td>auto.offset.reset</td>
              <td>Consumer</td>
              <td>earliest</td>
              </tr>
              <tr class="row-odd"><td>linger.ms</td>
              <td>Producer</td>
              <td>100</td>
              </tr>
              <tr class="row-even"><td>max.poll.interval.ms</td>
              <td>Consumer</td>
              <td>300000</td>
              </tr>
              <tr class="row-odd"><td>max.poll.records</td>
              <td>Consumer</td>
              <td>1000</td>
              </tr>
              </tbody>
              </table>
        <div class="section" id="enable-auto-commit">
          <span id="streams-developer-guide-consumer-auto-commit"></span><h4><a class="toc-backref" href="#id19">enable.auto.commit</a><a class="headerlink" href="#enable-auto-commit" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div>The consumer auto commit. To guarantee at-least-once processing semantics and turn off auto commits, Kafka Streams overrides this consumer config
              value to <code class="docutils literal"><span class="pre">false</span></code>.  Consumers will only commit explicitly via <em>commitSync</em> calls when the Kafka Streams library or a user decides
              to commit the current processing state.</div></blockquote>
        </div>
      <div class="section" id="recommended-configuration-parameters-for-resiliency">
        <h3><a class="toc-backref" href="#id21">Recommended configuration parameters for resiliency</a><a class="headerlink" href="#recommended-configuration-parameters-for-resiliency" title="Permalink to this headline"></a></h3>
        <p>There are several Kafka and Kafka Streams configuration options that need to be configured explicitly for resiliency in face of broker failures:</p>
        <table border="1" class="non-scrolling-table docutils">
          <thead valign="bottom">
          <tr class="row-odd"><th class="head">Parameter Name</th>
            <th class="head">Corresponding Client</th>
            <th class="head">Default value</th>
            <th class="head">Consider setting to</th>
          </tr>
          </thead>
          <tbody valign="top">
          <tr class="row-even"><td>acks</td>
            <td>Producer</td>
            <td><code class="docutils literal"><span class="pre">acks=1</span></code></td>
            <td><code class="docutils literal"><span class="pre">acks=all</span></code></td>
          </tr>
          <tr class="row-odd"><td>replication.factor</td>
            <td>Streams</td>
            <td><code class="docutils literal"><span class="pre">1</span></code></td>
            <td><code class="docutils literal"><span class="pre">3</span></code></td>
          </tr>
          <tr class="row-even"><td>min.insync.replicas</td>
            <td>Broker</td>
            <td><code class="docutils literal"><span class="pre">1</span></code></td>
            <td><code class="docutils literal"><span class="pre">2</span></code></td>
          </tr>
          </tbody>
        </table>
        <p>Increasing the replication factor to 3 ensures that the internal Kafka Streams topic can tolerate up to 2 broker failures. Changing the acks setting to &#8220;all&#8221;
          guarantees that a record will not be lost as long as one replica is alive. The tradeoff from moving to the default values to the recommended ones is
          that some performance and more storage space (3x with the replication factor of 3) are sacrificed for more resiliency.</p>
        <div class="section" id="acks">
          <h4><a class="toc-backref" href="#id22">acks</a><a class="headerlink" href="#acks" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div><p>The number of acknowledgments that the leader must have received before considering a request complete. This controls
              the durability of records that are sent. The possible values are:</p>
              <ul class="simple">
                <li><code class="docutils literal"><span class="pre">acks=0</span></code> The producer does not wait for acknowledgment from the server and the record is immediately added to the socket buffer and considered sent. No guarantee can be made that the server has received the record in this case, and the <code class="docutils literal"><span class="pre">retries</span></code> configuration will not take effect (as the client won&#8217;t generally know of any failures). The offset returned for each record will always be set to <code class="docutils literal"><span class="pre">-1</span></code>.</li>
                <li><code class="docutils literal"><span class="pre">acks=1</span></code> The leader writes the record to its local log and responds without waiting for full acknowledgement from all followers. If the leader immediately fails after acknowledging the record, but before the followers have replicated it, then the record will be lost.</li>
                <li><code class="docutils literal"><span class="pre">acks=all</span></code> The leader waits for the full set of in-sync replicas to acknowledge the record. This guarantees that the record will not be lost if there is at least one in-sync replica alive. This is the strongest available guarantee.</li>
              </ul>
              <p>For more information, see the <a class="reference external" href="https://kafka.apache.org/documentation/#producerconfigs">Kafka Producer documentation</a>.</p>
            </div></blockquote>
        </div>
        <div class="section" id="id2">
          <h4><a class="toc-backref" href="#id23">replication.factor</a><a class="headerlink" href="#id2" title="Permalink to this headline"></a></h4>
          <blockquote>
            <div>See the <a class="reference internal" href="#replication-factor-parm"><span class="std std-ref">description here</span></a>.</div></blockquote>
          <div class="highlight-java"><div class="highlight"><pre><span></span><span class="n">Properties</span> <span class="n">streamsSettings</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Properties</span><span class="o">();</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">REPLICATION_FACTOR_CONFIG</span><span class="o">,</span> <span class="mi">3</span><span class="o">);</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">topicPrefix</span><span class="o">(</span><span class="n">TopicConfig</span><span class="o">.</span><span class="na">MIN_IN_SYNC_REPLICAS_CONFIG</span><span class="o">),</span> <span class="mi">2</span><span class="o">);</span>
<span class="n">streamsSettings</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">StreamsConfig</span><span class="o">.</span><span class="na">producerPrefix</span><span class="o">(</span><span class="n">ProducerConfig</span><span class="o">.</span><span class="na">ACKS_CONFIG</span><span class="o">),</span> <span class="s">&quot;all&quot;</span><span class="o">);</span></code></pre></div>
          </div>
</div>
</div>
</div>
</div>


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