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<body id="spring-cloud-stream-binder-kafka-reference" class="book toc2 toc-left">
<div id="header">
<h1>Spring Cloud Stream Kafka Binder Reference Guide</h1>
<div class="details">
<span id="author" class="author">Sabby Anandan, Marius Bogoevici, Eric Bottard, Mark Fisher, Ilayaperumal Gopinathan, Gunnar Hillert, Mark Pollack, Patrick Peralta, Glenn Renfro, Thomas Risberg, Dave Syer, David Turanski, Janne Valkealahti, Benjamin Klein, Henryk Konsek, Gary Russell, Arnaud Jardiné, Soby Chacko</span><br>
</div>
<div id="toc" class="toc2">
<div id="toctitle">Table of Contents</div>
<ul class="sectlevel0">
<li><a href="#_reference_guide">Reference Guide</a>
<ul class="sectlevel1">
<li><a href="#_apache_kafka_binder">1. Apache Kafka Binder</a>
<ul class="sectlevel2">
<li><a href="#_usage">1.1. Usage</a></li>
<li><a href="#_overview">1.2. Overview</a></li>
<li><a href="#_configuration_options">1.3. Configuration Options</a>
<ul class="sectlevel3">
<li><a href="#_kafka_binder_properties">1.3.1. Kafka Binder Properties</a></li>
<li><a href="#kafka-consumer-properties">1.3.2. Kafka Consumer Properties</a></li>
<li><a href="#_consuming_batches">1.3.3. Consuming Batches</a></li>
<li><a href="#kafka-producer-properties">1.3.4. Kafka Producer Properties</a></li>
<li><a href="#_usage_examples">1.3.5. Usage examples</a>
<ul class="sectlevel4">
<li><a href="#_example_setting_autocommitoffset_to_false_and_relying_on_manual_acking">Example: Setting <code>autoCommitOffset</code> to <code>false</code> and Relying on Manual Acking</a></li>
<li><a href="#_example_security_configuration">Example: Security Configuration</a></li>
<li><a href="#pause-resume">Example: Pausing and Resuming the Consumer</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="#kafka-transactional-binder">1.4. Transactional Binder</a></li>
<li><a href="#kafka-error-channels">1.5. Error Channels</a></li>
<li><a href="#kafka-metrics">1.6. Kafka Metrics</a></li>
<li><a href="#kafka-tombstones">1.7. Tombstone Records (null record values)</a></li>
<li><a href="#rebalance-listener">1.8. Using a KafkaRebalanceListener</a></li>
<li><a href="#kafka-dlq-processing">1.9. Dead-Letter Topic Processing</a>
<ul class="sectlevel3">
<li><a href="#dlq-partition-selection">1.9.1. Dead-Letter Topic Partition Selection</a></li>
<li><a href="#dlq-handling">1.9.2. Handling Records in a Dead-Letter Topic</a></li>
</ul>
</li>
<li><a href="#_partitioning_with_the_kafka_binder">1.10. Partitioning with the Kafka Binder</a></li>
</ul>
</li>
<li><a href="#_kafka_streams_binder">2. Kafka Streams Binder</a>
<ul class="sectlevel2">
<li><a href="#_usage_2">2.1. Usage</a></li>
<li><a href="#_overview_2">2.2. Overview</a></li>
<li><a href="#_programming_model">2.3. Programming Model</a>
<ul class="sectlevel3">
<li><a href="#_functional_style">2.3.1. Functional Style</a>
<ul class="sectlevel4">
<li><a href="#_multiple_input_bindings">Multiple Input Bindings</a></li>
<li><a href="#_multiple_output_bindings">Multiple Output Bindings</a></li>
<li><a href="#_summary_of_function_based_programming_styles_for_kafka_streams">Summary of Function based Programming Styles for Kafka Streams</a></li>
</ul>
</li>
<li><a href="#_imperative_programming_model">2.3.2. Imperative programming model.</a></li>
</ul>
</li>
<li><a href="#_ancillaries_to_the_programming_model">2.4. Ancillaries to the programming model</a>
<ul class="sectlevel3">
<li><a href="#_multiple_kafka_streams_processors_within_a_single_application">2.4.1. Multiple Kafka Streams processors within a single application</a></li>
<li><a href="#_kafka_streams_application_id">2.4.2. Kafka Streams Application ID</a>
<ul class="sectlevel5">
<li><a href="#_summary_of_setting_application_id">Summary of setting Application ID</a></li>
</ul>
</li>
<li><a href="#_overriding_the_default_binding_names_generated_by_the_binder_with_the_functional_style">2.4.3. Overriding the default binding names generated by the binder with the functional style</a></li>
<li><a href="#_setting_up_bootstrap_server_configuration">2.4.4. Setting up bootstrap server configuration</a></li>
</ul>
</li>
<li><a href="#_record_serialization_and_deserialization">2.5. Record serialization and deserialization</a>
<ul class="sectlevel3">
<li><a href="#_inbound_deserialization">2.5.1. Inbound deserialization</a></li>
<li><a href="#_outbound_serialization">2.5.2. Outbound serialization</a></li>
</ul>
</li>
<li><a href="#_error_handling">2.6. Error Handling</a>
<ul class="sectlevel3">
<li><a href="#_handling_deserialization_exceptions_in_the_binder">2.6.1. Handling Deserialization Exceptions in the Binder</a></li>
<li><a href="#_dlq_per_input_consumer_binding">2.6.2. DLQ per input consumer binding</a></li>
<li><a href="#_dlq_partitioning">2.6.3. DLQ partitioning</a></li>
<li><a href="#_handling_production_exceptions_in_the_binder">2.6.4. Handling Production Exceptions in the Binder</a></li>
</ul>
</li>
<li><a href="#_state_store">2.7. State Store</a></li>
<li><a href="#_interactive_queries">2.8. Interactive Queries</a></li>
<li><a href="#_health_indicator">2.9. Health Indicator</a></li>
<li><a href="#_accessing_kafka_streams_metrics">2.10. Accessing Kafka Streams Metrics</a></li>
<li><a href="#_mixing_high_level_dsl_and_low_level_processor_api">2.11. Mixing high level DSL and low level Processor API</a></li>
<li><a href="#_partition_support_on_the_outbound">2.12. Partition support on the outbound</a></li>
<li><a href="#_streamsbuilderfactorybean_customizer">2.13. StreamsBuilderFactoryBean customizer</a>
<ul class="sectlevel3">
<li><a href="#_using_customizer_to_register_a_global_state_store">2.13.1. Using Customizer to register a global state store</a></li>
<li><a href="#_using_customizer_to_register_a_production_exception_handler">2.13.2. Using customizer to register a production exception handler</a></li>
</ul>
</li>
<li><a href="#_timestamp_extractor">2.14. Timestamp extractor</a></li>
<li><a href="#_multi_binders_with_kafka_streams_based_binders_and_regular_kafka_binder">2.15. Multi binders with Kafka Streams based binders and regular Kafka Binder</a></li>
<li><a href="#_state_cleanup">2.16. State Cleanup</a></li>
<li><a href="#_configuration_options_2">2.17. Configuration Options</a>
<ul class="sectlevel3">
<li><a href="#_kafka_streams_binder_properties">2.17.1. Kafka Streams Binder Properties</a></li>
<li><a href="#_kafka_streams_producer_properties">2.17.2. Kafka Streams Producer Properties</a></li>
<li><a href="#_kafka_streams_consumer_properties">2.17.3. Kafka Streams Consumer Properties</a></li>
<li><a href="#_special_note_on_concurrency">2.17.4. Special note on concurrency</a></li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
<li><a href="#_appendices">Appendices</a>
<ul class="sectlevel1">
<li><a href="#building">Appendix A: Building</a>
<ul class="sectlevel2">
<li><a href="#_basic_compile_and_test">A.1. Basic Compile and Test</a></li>
<li><a href="#_documentation">A.2. Documentation</a></li>
<li><a href="#_working_with_the_code">A.3. Working with the code</a>
<ul class="sectlevel3">
<li><a href="#_importing_into_eclipse_with_m2eclipse">A.3.1. Importing into eclipse with m2eclipse</a></li>
<li><a href="#_importing_into_eclipse_without_m2eclipse">A.3.2. Importing into eclipse without m2eclipse</a></li>
</ul>
</li>
<li><a href="#_sign_the_contributor_license_agreement">A.4. Sign the Contributor License Agreement</a></li>
<li><a href="#_code_conventions_and_housekeeping">A.5. Code Conventions and Housekeeping</a></li>
</ul>
</li>
</ul>
</li>
</ul>
</div>
</div>
<div id="content">
<div id="preamble">
<div class="sectionbody">
<div class="paragraph">
<p><strong>3.0.2.RELEASE</strong></p>
</div>
</div>
</div>
<h1 id="_reference_guide" class="sect0"><a class="link" href="#_reference_guide">Reference Guide</a></h1>
<div class="openblock partintro">
<div class="content">
<div class="paragraph">
<p>This guide describes the Apache Kafka implementation of the Spring Cloud Stream Binder.
It contains information about its design, usage, and configuration options, as well as information on how the Stream Cloud Stream concepts map onto Apache Kafka specific constructs.
In addition, this guide explains the Kafka Streams binding capabilities of Spring Cloud Stream.</p>
</div>
</div>
</div>
<div class="sect1">
<h2 id="_apache_kafka_binder"><a class="link" href="#_apache_kafka_binder">1. Apache Kafka Binder</a></h2>
<div class="sectionbody">
<div class="sect2">
<h3 id="_usage"><a class="link" href="#_usage">1.1. Usage</a></h3>
<div class="paragraph">
<p>To use Apache Kafka binder, you need to add <code>spring-cloud-stream-binder-kafka</code> as a dependency to your Spring Cloud Stream application, as shown in the following example for Maven:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-xml hljs" data-lang="xml">&lt;dependency&gt;
&lt;groupId&gt;org.springframework.cloud&lt;/groupId&gt;
&lt;artifactId&gt;spring-cloud-stream-binder-kafka&lt;/artifactId&gt;
&lt;/dependency&gt;</code></pre>
</div>
</div>
<div class="paragraph">
<p>Alternatively, you can also use the Spring Cloud Stream Kafka Starter, as shown in the following example for Maven:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-xml hljs" data-lang="xml">&lt;dependency&gt;
&lt;groupId&gt;org.springframework.cloud&lt;/groupId&gt;
&lt;artifactId&gt;spring-cloud-starter-stream-kafka&lt;/artifactId&gt;
&lt;/dependency&gt;</code></pre>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_overview"><a class="link" href="#_overview">1.2. Overview</a></h3>
<div class="paragraph">
<p>The following image shows a simplified diagram of how the Apache Kafka binder operates:</p>
</div>
<div class="imageblock">
<div class="content">
<img src="https://raw.github.com/spring-cloud/spring-cloud-stream-binder-kafka/master/docs/src/main/asciidoc/images/kafka-binder.png" alt="kafka binder" width="300">
</div>
<div class="title">Figure 1. Kafka Binder</div>
</div>
<div class="paragraph">
<p>The Apache Kafka Binder implementation maps each destination to an Apache Kafka topic.
The consumer group maps directly to the same Apache Kafka concept.
Partitioning also maps directly to Apache Kafka partitions as well.</p>
</div>
<div class="paragraph">
<p>The binder currently uses the Apache Kafka <code>kafka-clients</code> version <code>2.3.1</code>.
This client can communicate with older brokers (see the Kafka documentation), but certain features may not be available.
For example, with versions earlier than 0.11.x.x, native headers are not supported.
Also, 0.11.x.x does not support the <code>autoAddPartitions</code> property.</p>
</div>
</div>
<div class="sect2">
<h3 id="_configuration_options"><a class="link" href="#_configuration_options">1.3. Configuration Options</a></h3>
<div class="paragraph">
<p>This section contains the configuration options used by the Apache Kafka binder.</p>
</div>
<div class="paragraph">
<p>For common configuration options and properties pertaining to binder, see the <a href="#binding-properties">core documentation</a>.</p>
</div>
<div class="sect3">
<h4 id="_kafka_binder_properties"><a class="link" href="#_kafka_binder_properties">1.3.1. Kafka Binder Properties</a></h4>
<div class="dlist">
<dl>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.brokers</dt>
<dd>
<p>A list of brokers to which the Kafka binder connects.</p>
<div class="paragraph">
<p>Default: <code>localhost</code>.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.defaultBrokerPort</dt>
<dd>
<p><code>brokers</code> allows hosts specified with or without port information (for example, <code>host1,host2:port2</code>).
This sets the default port when no port is configured in the broker list.</p>
<div class="paragraph">
<p>Default: <code>9092</code>.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.configuration</dt>
<dd>
<p>Key/Value map of client properties (both producers and consumer) passed to all clients created by the binder.
Due to the fact that these properties are used by both producers and consumers, usage should be restricted to common properties&#8201;&#8212;&#8201;for example, security settings.
Unknown Kafka producer or consumer properties provided through this configuration are filtered out and not allowed to propagate.
Properties here supersede any properties set in boot.</p>
<div class="paragraph">
<p>Default: Empty map.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.consumerProperties</dt>
<dd>
<p>Key/Value map of arbitrary Kafka client consumer properties.
In addition to support known Kafka consumer properties, unknown consumer properties are allowed here as well.
Properties here supersede any properties set in boot and in the <code>configuration</code> property above.</p>
<div class="paragraph">
<p>Default: Empty map.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.headers</dt>
<dd>
<p>The list of custom headers that are transported by the binder.
Only required when communicating with older applications (&#8656; 1.3.x) with a <code>kafka-clients</code> version &lt; 0.11.0.0. Newer versions support headers natively.</p>
<div class="paragraph">
<p>Default: empty.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.healthTimeout</dt>
<dd>
<p>The time to wait to get partition information, in seconds.
Health reports as down if this timer expires.</p>
<div class="paragraph">
<p>Default: 10.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.requiredAcks</dt>
<dd>
<p>The number of required acks on the broker.
See the Kafka documentation for the producer <code>acks</code> property.</p>
<div class="paragraph">
<p>Default: <code>1</code>.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.minPartitionCount</dt>
<dd>
<p>Effective only if <code>autoCreateTopics</code> or <code>autoAddPartitions</code> is set.
The global minimum number of partitions that the binder configures on topics on which it produces or consumes data.
It can be superseded by the <code>partitionCount</code> setting of the producer or by the value of <code>instanceCount * concurrency</code> settings of the producer (if either is larger).</p>
<div class="paragraph">
<p>Default: <code>1</code>.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.producerProperties</dt>
<dd>
<p>Key/Value map of arbitrary Kafka client producer properties.
In addition to support known Kafka producer properties, unknown producer properties are allowed here as well.
Properties here supersede any properties set in boot and in the <code>configuration</code> property above.</p>
<div class="paragraph">
<p>Default: Empty map.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.replicationFactor</dt>
<dd>
<p>The replication factor of auto-created topics if <code>autoCreateTopics</code> is active.
Can be overridden on each binding.</p>
<div class="paragraph">
<p>Default: <code>1</code>.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.autoCreateTopics</dt>
<dd>
<p>If set to <code>true</code>, the binder creates new topics automatically.
If set to <code>false</code>, the binder relies on the topics being already configured.
In the latter case, if the topics do not exist, the binder fails to start.</p>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
This setting is independent of the <code>auto.create.topics.enable</code> setting of the broker and does not influence it.
If the server is set to auto-create topics, they may be created as part of the metadata retrieval request, with default broker settings.
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>Default: <code>true</code>.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.autoAddPartitions</dt>
<dd>
<p>If set to <code>true</code>, the binder creates new partitions if required.
If set to <code>false</code>, the binder relies on the partition size of the topic being already configured.
If the partition count of the target topic is smaller than the expected value, the binder fails to start.</p>
<div class="paragraph">
<p>Default: <code>false</code>.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix</dt>
<dd>
<p>Enables transactions in the binder. See <code>transaction.id</code> in the Kafka documentation and <a href="https://docs.spring.io/spring-kafka/reference/html/_reference.html#transactions">Transactions</a> in the <code>spring-kafka</code> documentation.
When transactions are enabled, individual <code>producer</code> properties are ignored and all producers use the <code>spring.cloud.stream.kafka.binder.transaction.producer.*</code> properties.</p>
<div class="paragraph">
<p>Default <code>null</code> (no transactions)</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.transaction.producer.*</dt>
<dd>
<p>Global producer properties for producers in a transactional binder.
See <code>spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix</code> and <a href="#kafka-producer-properties">Kafka Producer Properties</a> and the general producer properties supported by all binders.</p>
<div class="paragraph">
<p>Default: See individual producer properties.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.headerMapperBeanName</dt>
<dd>
<p>The bean name of a <code>KafkaHeaderMapper</code> used for mapping <code>spring-messaging</code> headers to and from Kafka headers.
Use this, for example, if you wish to customize the trusted packages in a <code>BinderHeaderMapper</code> bean that uses JSON deserialization for the headers.
If this custom <code>BinderHeaderMapper</code> bean is not made available to the binder using this property, then the binder will look for a header mapper bean with the name <code>kafkaBinderHeaderMapper</code> that is of type <code>BinderHeaderMapper</code> before falling back to a default <code>BinderHeaderMapper</code> created by the binder.</p>
<div class="paragraph">
<p>Default: none.</p>
</div>
</dd>
</dl>
</div>
</div>
<div class="sect3">
<h4 id="kafka-consumer-properties"><a class="link" href="#kafka-consumer-properties">1.3.2. Kafka Consumer Properties</a></h4>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
To avoid repetition, Spring Cloud Stream supports setting values for all channels, in the format of <code>spring.cloud.stream.kafka.default.consumer.&lt;property&gt;=&lt;value&gt;</code>.
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>The following properties are available for Kafka consumers only and
must be prefixed with <code>spring.cloud.stream.kafka.bindings.&lt;channelName&gt;.consumer.</code>.</p>
</div>
<div class="dlist">
<dl>
<dt class="hdlist1">admin.configuration</dt>
<dd>
<p>Since version 2.1.1, this property is deprecated in favor of <code>topic.properties</code>, and support for it will be removed in a future version.</p>
</dd>
<dt class="hdlist1">admin.replicas-assignment</dt>
<dd>
<p>Since version 2.1.1, this property is deprecated in favor of <code>topic.replicas-assignment</code>, and support for it will be removed in a future version.</p>
</dd>
<dt class="hdlist1">admin.replication-factor</dt>
<dd>
<p>Since version 2.1.1, this property is deprecated in favor of <code>topic.replication-factor</code>, and support for it will be removed in a future version.</p>
</dd>
<dt class="hdlist1">autoRebalanceEnabled</dt>
<dd>
<p>When <code>true</code>, topic partitions is automatically rebalanced between the members of a consumer group.
When <code>false</code>, each consumer is assigned a fixed set of partitions based on <code>spring.cloud.stream.instanceCount</code> and <code>spring.cloud.stream.instanceIndex</code>.
This requires both the <code>spring.cloud.stream.instanceCount</code> and <code>spring.cloud.stream.instanceIndex</code> properties to be set appropriately on each launched instance.
The value of the <code>spring.cloud.stream.instanceCount</code> property must typically be greater than 1 in this case.</p>
<div class="paragraph">
<p>Default: <code>true</code>.</p>
</div>
</dd>
<dt class="hdlist1">ackEachRecord</dt>
<dd>
<p>When <code>autoCommitOffset</code> is <code>true</code>, this setting dictates whether to commit the offset after each record is processed.
By default, offsets are committed after all records in the batch of records returned by <code>consumer.poll()</code> have been processed.
The number of records returned by a poll can be controlled with the <code>max.poll.records</code> Kafka property, which is set through the consumer <code>configuration</code> property.
Setting this to <code>true</code> may cause a degradation in performance, but doing so reduces the likelihood of redelivered records when a failure occurs.
Also, see the binder <code>requiredAcks</code> property, which also affects the performance of committing offsets.</p>
<div class="paragraph">
<p>Default: <code>false</code>.</p>
</div>
</dd>
<dt class="hdlist1">autoCommitOffset</dt>
<dd>
<p>Whether to autocommit offsets when a message has been processed.
If set to <code>false</code>, a header with the key <code>kafka_acknowledgment</code> of the type <code>org.springframework.kafka.support.Acknowledgment</code> header is present in the inbound message.
Applications may use this header for acknowledging messages.
See the examples section for details.
When this property is set to <code>false</code>, Kafka binder sets the ack mode to <code>org.springframework.kafka.listener.AbstractMessageListenerContainer.AckMode.MANUAL</code> and the application is responsible for acknowledging records.
Also see <code>ackEachRecord</code>.</p>
<div class="paragraph">
<p>Default: <code>true</code>.</p>
</div>
</dd>
<dt class="hdlist1">autoCommitOnError</dt>
<dd>
<p>Effective only if <code>autoCommitOffset</code> is set to <code>true</code>.
If set to <code>false</code>, it suppresses auto-commits for messages that result in errors and commits only for successful messages. It allows a stream to automatically replay from the last successfully processed message, in case of persistent failures.
If set to <code>true</code>, it always auto-commits (if auto-commit is enabled).
If not set (the default), it effectively has the same value as <code>enableDlq</code>, auto-committing erroneous messages if they are sent to a DLQ and not committing them otherwise.</p>
<div class="paragraph">
<p>Default: not set.</p>
</div>
</dd>
<dt class="hdlist1">resetOffsets</dt>
<dd>
<p>Whether to reset offsets on the consumer to the value provided by startOffset.
Must be false if a <code>KafkaRebalanceListener</code> is provided; see <a href="#rebalance-listener">Using a KafkaRebalanceListener</a>.</p>
<div class="paragraph">
<p>Default: <code>false</code>.</p>
</div>
</dd>
<dt class="hdlist1">startOffset</dt>
<dd>
<p>The starting offset for new groups.
Allowed values: <code>earliest</code> and <code>latest</code>.
If the consumer group is set explicitly for the consumer 'binding' (through <code>spring.cloud.stream.bindings.&lt;channelName&gt;.group</code>), 'startOffset' is set to <code>earliest</code>. Otherwise, it is set to <code>latest</code> for the <code>anonymous</code> consumer group.
Also see <code>resetOffsets</code> (earlier in this list).</p>
<div class="paragraph">
<p>Default: null (equivalent to <code>earliest</code>).</p>
</div>
</dd>
<dt class="hdlist1">enableDlq</dt>
<dd>
<p>When set to true, it enables DLQ behavior for the consumer.
By default, messages that result in errors are forwarded to a topic named <code>error.&lt;destination&gt;.&lt;group&gt;</code>.
The DLQ topic name can be configurable by setting the <code>dlqName</code> property.
This provides an alternative option to the more common Kafka replay scenario for the case when the number of errors is relatively small and replaying the entire original topic may be too cumbersome.
See <a href="#kafka-dlq-processing">Dead-Letter Topic Processing</a> processing for more information.
Starting with version 2.0, messages sent to the DLQ topic are enhanced with the following headers: <code>x-original-topic</code>, <code>x-exception-message</code>, and <code>x-exception-stacktrace</code> as <code>byte[]</code>.
By default, a failed record is sent to the same partition number in the DLQ topic as the original record.
See <a href="#dlq-partition-selection">Dead-Letter Topic Partition Selection</a> for how to change that behavior.
<strong>Not allowed when <code>destinationIsPattern</code> is <code>true</code>.</strong></p>
<div class="paragraph">
<p>Default: <code>false</code>.</p>
</div>
</dd>
<dt class="hdlist1">dlqPartitions</dt>
<dd>
<p>When <code>enableDlq</code> is true, and this property is not set, a dead letter topic with the same number of partitions as the primary topic(s) is created.
Usually, dead-letter records are sent to the same partition in the dead-letter topic as the original record.
This behavior can be changed; see <a href="#dlq-partition-selection">Dead-Letter Topic Partition Selection</a>.
If this property is set to <code>1</code> and there is no <code>DqlPartitionFunction</code> bean, all dead-letter records will be written to partition <code>0</code>.
If this property is greater than <code>1</code>, you <strong>MUST</strong> provide a <code>DlqPartitionFunction</code> bean.
Note that the actual partition count is affected by the binder&#8217;s <code>minPartitionCount</code> property.</p>
<div class="paragraph">
<p>Default: <code>none</code></p>
</div>
</dd>
<dt class="hdlist1">configuration</dt>
<dd>
<p>Map with a key/value pair containing generic Kafka consumer properties.
In addition to having Kafka consumer properties, other configuration properties can be passed here.
For example some properties needed by the application such as <code>spring.cloud.stream.kafka.bindings.input.consumer.configuration.foo=bar</code>.</p>
<div class="paragraph">
<p>Default: Empty map.</p>
</div>
</dd>
<dt class="hdlist1">dlqName</dt>
<dd>
<p>The name of the DLQ topic to receive the error messages.</p>
<div class="paragraph">
<p>Default: null (If not specified, messages that result in errors are forwarded to a topic named <code>error.&lt;destination&gt;.&lt;group&gt;</code>).</p>
</div>
</dd>
<dt class="hdlist1">dlqProducerProperties</dt>
<dd>
<p>Using this, DLQ-specific producer properties can be set.
All the properties available through kafka producer properties can be set through this property.
When native decoding is enabled on the consumer (i.e., useNativeDecoding: true) , the application must provide corresponding key/value serializers for DLQ.
This must be provided in the form of <code>dlqProducerProperties.configuration.key.serializer</code> and <code>dlqProducerProperties.configuration.value.serializer</code>.</p>
<div class="paragraph">
<p>Default: Default Kafka producer properties.</p>
</div>
</dd>
<dt class="hdlist1">standardHeaders</dt>
<dd>
<p>Indicates which standard headers are populated by the inbound channel adapter.
Allowed values: <code>none</code>, <code>id</code>, <code>timestamp</code>, or <code>both</code>.
Useful if using native deserialization and the first component to receive a message needs an <code>id</code> (such as an aggregator that is configured to use a JDBC message store).</p>
<div class="paragraph">
<p>Default: <code>none</code></p>
</div>
</dd>
<dt class="hdlist1">converterBeanName</dt>
<dd>
<p>The name of a bean that implements <code>RecordMessageConverter</code>. Used in the inbound channel adapter to replace the default <code>MessagingMessageConverter</code>.</p>
<div class="paragraph">
<p>Default: <code>null</code></p>
</div>
</dd>
<dt class="hdlist1">idleEventInterval</dt>
<dd>
<p>The interval, in milliseconds, between events indicating that no messages have recently been received.
Use an <code>ApplicationListener&lt;ListenerContainerIdleEvent&gt;</code> to receive these events.
See <a href="#pause-resume">Example: Pausing and Resuming the Consumer</a> for a usage example.</p>
<div class="paragraph">
<p>Default: <code>30000</code></p>
</div>
</dd>
<dt class="hdlist1">destinationIsPattern</dt>
<dd>
<p>When true, the destination is treated as a regular expression <code>Pattern</code> used to match topic names by the broker.
When true, topics are not provisioned, and <code>enableDlq</code> is not allowed, because the binder does not know the topic names during the provisioning phase.
Note, the time taken to detect new topics that match the pattern is controlled by the consumer property <code>metadata.max.age.ms</code>, which (at the time of writing) defaults to 300,000ms (5 minutes).
This can be configured using the <code>configuration</code> property above.</p>
<div class="paragraph">
<p>Default: <code>false</code></p>
</div>
</dd>
<dt class="hdlist1">topic.properties</dt>
<dd>
<p>A <code>Map</code> of Kafka topic properties used when provisioning new topics&#8201;&#8212;&#8201;for example, <code>spring.cloud.stream.kafka.bindings.input.consumer.topic.properties.message.format.version=0.9.0.0</code></p>
<div class="paragraph">
<p>Default: none.</p>
</div>
</dd>
<dt class="hdlist1">topic.replicas-assignment</dt>
<dd>
<p>A Map&lt;Integer, List&lt;Integer&gt;&gt; of replica assignments, with the key being the partition and the value being the assignments.
Used when provisioning new topics.
See the <code>NewTopic</code> Javadocs in the <code>kafka-clients</code> jar.</p>
<div class="paragraph">
<p>Default: none.</p>
</div>
</dd>
<dt class="hdlist1">topic.replication-factor</dt>
<dd>
<p>The replication factor to use when provisioning topics. Overrides the binder-wide setting.
Ignored if <code>replicas-assignments</code> is present.</p>
<div class="paragraph">
<p>Default: none (the binder-wide default of 1 is used).</p>
</div>
</dd>
<dt class="hdlist1">pollTimeout</dt>
<dd>
<p>Timeout used for polling in pollable consumers.</p>
<div class="paragraph">
<p>Default: 5 seconds.</p>
</div>
</dd>
</dl>
</div>
</div>
<div class="sect3">
<h4 id="_consuming_batches"><a class="link" href="#_consuming_batches">1.3.3. Consuming Batches</a></h4>
<div class="paragraph">
<p>Starting with version 3.0, when <code>spring.cloud.stream.binding.&lt;name&gt;.consumer.batch-mode</code> is set to <code>true</code>, all of the records received by polling the Kafka <code>Consumer</code> will be presented as a <code>List&lt;?&gt;</code> to the listener method.
Otherwise, the method will be called with one record at a time.
The size of the batch is controlled by Kafka consumer properties <code>max.poll.records</code>, <code>min.fetch.bytes</code>, <code>fetch.max.wait.ms</code>; refer to the Kafka documentation for more information.</p>
</div>
<div class="admonitionblock important">
<table>
<tr>
<td class="icon">
<i class="fa icon-important" title="Important"></i>
</td>
<td class="content">
Retry within the binder is not supported when using batch mode, so <code>maxAttempts</code> will be overridden to 1.
You can configure a <code>SeekToCurrentBatchErrorHandler</code> (using a <code>ListenerContainerCustomizer</code>) to achieve similar functionality to retry in the binder.
You can also use a manual <code>AckMode</code> and call <code>Ackowledgment.nack(index, sleep)</code> to commit the offsets for a partial batch and have the remaining records redelivered.
Refer to the <a href="https://docs.spring.io/spring-kafka/docs/2.3.0.BUILD-SNAPSHOT/reference/html/#committing-offsets">Spring for Apache Kafka documentation</a> for more information about these techniques.
</td>
</tr>
</table>
</div>
</div>
<div class="sect3">
<h4 id="kafka-producer-properties"><a class="link" href="#kafka-producer-properties">1.3.4. Kafka Producer Properties</a></h4>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
To avoid repetition, Spring Cloud Stream supports setting values for all channels, in the format of <code>spring.cloud.stream.kafka.default.producer.&lt;property&gt;=&lt;value&gt;</code>.
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>The following properties are available for Kafka producers only and
must be prefixed with <code>spring.cloud.stream.kafka.bindings.&lt;channelName&gt;.producer.</code>.</p>
</div>
<div class="dlist">
<dl>
<dt class="hdlist1">admin.configuration</dt>
<dd>
<p>Since version 2.1.1, this property is deprecated in favor of <code>topic.properties</code>, and support for it will be removed in a future version.</p>
</dd>
<dt class="hdlist1">admin.replicas-assignment</dt>
<dd>
<p>Since version 2.1.1, this property is deprecated in favor of <code>topic.replicas-assignment</code>, and support for it will be removed in a future version.</p>
</dd>
<dt class="hdlist1">admin.replication-factor</dt>
<dd>
<p>Since version 2.1.1, this property is deprecated in favor of <code>topic.replication-factor</code>, and support for it will be removed in a future version.</p>
</dd>
<dt class="hdlist1">bufferSize</dt>
<dd>
<p>Upper limit, in bytes, of how much data the Kafka producer attempts to batch before sending.</p>
<div class="paragraph">
<p>Default: <code>16384</code>.</p>
</div>
</dd>
<dt class="hdlist1">sync</dt>
<dd>
<p>Whether the producer is synchronous.</p>
<div class="paragraph">
<p>Default: <code>false</code>.</p>
</div>
</dd>
<dt class="hdlist1">sendTimeoutExpression</dt>
<dd>
<p>A SpEL expression evaluated against the outgoing message used to evaluate the time to wait for ack when synchronous publish is enabled&#8201;&#8212;&#8201;for example, <code>headers['mySendTimeout']</code>.
The value of the timeout is in milliseconds.
With versions before 3.0, the payload could not be used unless native encoding was being used because, by the time this expression was evaluated, the payload was already in the form of a <code>byte[]</code>.
Now, the expression is evaluated before the payload is converted.</p>
<div class="paragraph">
<p>Default: <code>none</code>.</p>
</div>
</dd>
<dt class="hdlist1">batchTimeout</dt>
<dd>
<p>How long the producer waits to allow more messages to accumulate in the same batch before sending the messages.
(Normally, the producer does not wait at all and simply sends all the messages that accumulated while the previous send was in progress.) A non-zero value may increase throughput at the expense of latency.</p>
<div class="paragraph">
<p>Default: <code>0</code>.</p>
</div>
</dd>
<dt class="hdlist1">messageKeyExpression</dt>
<dd>
<p>A SpEL expression evaluated against the outgoing message used to populate the key of the produced Kafka message&#8201;&#8212;&#8201;for example, <code>headers['myKey']</code>.
With versions before 3.0, the payload could not be used unless native encoding was being used because, by the time this expression was evaluated, the payload was already in the form of a <code>byte[]</code>.
Now, the expression is evaluated before the payload is converted.</p>
<div class="paragraph">
<p>Default: <code>none</code>.</p>
</div>
</dd>
<dt class="hdlist1">headerPatterns</dt>
<dd>
<p>A comma-delimited list of simple patterns to match Spring messaging headers to be mapped to the Kafka <code>Headers</code> in the <code>ProducerRecord</code>.
Patterns can begin or end with the wildcard character (asterisk).
Patterns can be negated by prefixing with <code>!</code>.
Matching stops after the first match (positive or negative).
For example <code>!ask,as*</code> will pass <code>ash</code> but not <code>ask</code>.
<code>id</code> and <code>timestamp</code> are never mapped.</p>
<div class="paragraph">
<p>Default: <code>*</code> (all headers - except the <code>id</code> and <code>timestamp</code>)</p>
</div>
</dd>
<dt class="hdlist1">configuration</dt>
<dd>
<p>Map with a key/value pair containing generic Kafka producer properties.</p>
<div class="paragraph">
<p>Default: Empty map.</p>
</div>
</dd>
<dt class="hdlist1">topic.properties</dt>
<dd>
<p>A <code>Map</code> of Kafka topic properties used when provisioning new topics&#8201;&#8212;&#8201;for example, <code>spring.cloud.stream.kafka.bindings.output.producer.topic.properties.message.format.version=0.9.0.0</code></p>
</dd>
<dt class="hdlist1">topic.replicas-assignment</dt>
<dd>
<p>A Map&lt;Integer, List&lt;Integer&gt;&gt; of replica assignments, with the key being the partition and the value being the assignments.
Used when provisioning new topics.
See the <code>NewTopic</code> Javadocs in the <code>kafka-clients</code> jar.</p>
<div class="paragraph">
<p>Default: none.</p>
</div>
</dd>
<dt class="hdlist1">topic.replication-factor</dt>
<dd>
<p>The replication factor to use when provisioning topics. Overrides the binder-wide setting.
Ignored if <code>replicas-assignments</code> is present.</p>
<div class="paragraph">
<p>Default: none (the binder-wide default of 1 is used).</p>
</div>
</dd>
<dt class="hdlist1">useTopicHeader</dt>
<dd>
<p>Set to <code>true</code> to override the default binding destination (topic name) with the value of the <code>KafkaHeaders.TOPIC</code> message header in the outbound message.
If the header is not present, the default binding destination is used.
Default: <code>false</code>.</p>
</dd>
<dt class="hdlist1">recordMetadataChannel</dt>
<dd>
<p>The bean name of a <code>MessageChannel</code> to which successful send results should be sent; the bean must exist in the application context.
The message sent to the channel is the sent message (after conversion, if any) with an additional header <code>KafkaHeaders.RECORD_METADATA</code>.
The header contains a <code>RecordMetadata</code> object provided by the Kafka client; it includes the partition and offset where the record was written in the topic.</p>
</dd>
</dl>
</div>
<div class="paragraph">
<p><code>ResultMetadata meta = sendResultMsg.getHeaders().get(KafkaHeaders.RECORD_METADATA, RecordMetadata.class)</code></p>
</div>
<div class="paragraph">
<p>Failed sends go the producer error channel (if configured); see <a href="#kafka-error-channels">Error Channels</a>.
Default: null</p>
</div>
<div class="paragraph">
<p>+</p>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
The Kafka binder uses the <code>partitionCount</code> setting of the producer as a hint to create a topic with the given partition count (in conjunction with the <code>minPartitionCount</code>, the maximum of the two being the value being used).
Exercise caution when configuring both <code>minPartitionCount</code> for a binder and <code>partitionCount</code> for an application, as the larger value is used.
If a topic already exists with a smaller partition count and <code>autoAddPartitions</code> is disabled (the default), the binder fails to start.
If a topic already exists with a smaller partition count and <code>autoAddPartitions</code> is enabled, new partitions are added.
If a topic already exists with a larger number of partitions than the maximum of (<code>minPartitionCount</code> or <code>partitionCount</code>), the existing partition count is used.
</td>
</tr>
</table>
</div>
<div class="dlist">
<dl>
<dt class="hdlist1">compression</dt>
<dd>
<p>Set the <code>compression.type</code> producer property.
Supported values are <code>none</code>, <code>gzip</code>, <code>snappy</code> and <code>lz4</code>.
If you override the <code>kafka-clients</code> jar to 2.1.0 (or later), as discussed in the <a href="https://docs.spring.io/spring-kafka/docs/2.2.x/reference/html/deps-for-21x.html">Spring for Apache Kafka documentation</a>, and wish to use <code>zstd</code> compression, use <code>spring.cloud.stream.kafka.bindings.&lt;binding-name&gt;.producer.configuration.compression.type=zstd</code>.</p>
<div class="paragraph">
<p>Default: <code>none</code>.</p>
</div>
</dd>
</dl>
</div>
</div>
<div class="sect3">
<h4 id="_usage_examples"><a class="link" href="#_usage_examples">1.3.5. Usage examples</a></h4>
<div class="paragraph">
<p>In this section, we show the use of the preceding properties for specific scenarios.</p>
</div>
<div class="sect4">
<h5 id="_example_setting_autocommitoffset_to_false_and_relying_on_manual_acking"><a class="link" href="#_example_setting_autocommitoffset_to_false_and_relying_on_manual_acking">Example: Setting <code>autoCommitOffset</code> to <code>false</code> and Relying on Manual Acking</a></h5>
<div class="paragraph">
<p>This example illustrates how one may manually acknowledge offsets in a consumer application.</p>
</div>
<div class="paragraph">
<p>This example requires that <code>spring.cloud.stream.kafka.bindings.input.consumer.autoCommitOffset</code> be set to <code>false</code>.
Use the corresponding input channel name for your example.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@SpringBootApplication
@EnableBinding(Sink.class)
public class ManuallyAcknowdledgingConsumer {
public static void main(String[] args) {
SpringApplication.run(ManuallyAcknowdledgingConsumer.class, args);
}
@StreamListener(Sink.INPUT)
public void process(Message&lt;?&gt; message) {
Acknowledgment acknowledgment = message.getHeaders().get(KafkaHeaders.ACKNOWLEDGMENT, Acknowledgment.class);
if (acknowledgment != null) {
System.out.println("Acknowledgment provided");
acknowledgment.acknowledge();
}
}
}</code></pre>
</div>
</div>
</div>
<div class="sect4">
<h5 id="_example_security_configuration"><a class="link" href="#_example_security_configuration">Example: Security Configuration</a></h5>
<div class="paragraph">
<p>Apache Kafka 0.9 supports secure connections between client and brokers.
To take advantage of this feature, follow the guidelines in the <a href="https://kafka.apache.org/090/documentation.html#security_configclients">Apache Kafka Documentation</a> as well as the Kafka 0.9 <a href="https://docs.confluent.io/2.0.0/kafka/security.html">security guidelines from the Confluent documentation</a>.
Use the <code>spring.cloud.stream.kafka.binder.configuration</code> option to set security properties for all clients created by the binder.</p>
</div>
<div class="paragraph">
<p>For example, to set <code>security.protocol</code> to <code>SASL_SSL</code>, set the following property:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.binder.configuration.security.protocol=SASL_SSL</code></pre>
</div>
</div>
<div class="paragraph">
<p>All the other security properties can be set in a similar manner.</p>
</div>
<div class="paragraph">
<p>When using Kerberos, follow the instructions in the <a href="https://kafka.apache.org/090/documentation.html#security_sasl_clientconfig">reference documentation</a> for creating and referencing the JAAS configuration.</p>
</div>
<div class="paragraph">
<p>Spring Cloud Stream supports passing JAAS configuration information to the application by using a JAAS configuration file and using Spring Boot properties.</p>
</div>
<div class="sect5">
<h6 id="_using_jaas_configuration_files"><a class="link" href="#_using_jaas_configuration_files">Using JAAS Configuration Files</a></h6>
<div class="paragraph">
<p>The JAAS and (optionally) krb5 file locations can be set for Spring Cloud Stream applications by using system properties.
The following example shows how to launch a Spring Cloud Stream application with SASL and Kerberos by using a JAAS configuration file:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash"> java -Djava.security.auth.login.config=/path.to/kafka_client_jaas.conf -jar log.jar \
--spring.cloud.stream.kafka.binder.brokers=secure.server:9092 \
--spring.cloud.stream.bindings.input.destination=stream.ticktock \
--spring.cloud.stream.kafka.binder.configuration.security.protocol=SASL_PLAINTEXT</code></pre>
</div>
</div>
</div>
<div class="sect5">
<h6 id="_using_spring_boot_properties"><a class="link" href="#_using_spring_boot_properties">Using Spring Boot Properties</a></h6>
<div class="paragraph">
<p>As an alternative to having a JAAS configuration file, Spring Cloud Stream provides a mechanism for setting up the JAAS configuration for Spring Cloud Stream applications by using Spring Boot properties.</p>
</div>
<div class="paragraph">
<p>The following properties can be used to configure the login context of the Kafka client:</p>
</div>
<div class="dlist">
<dl>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.jaas.loginModule</dt>
<dd>
<p>The login module name. Not necessary to be set in normal cases.</p>
<div class="paragraph">
<p>Default: <code>com.sun.security.auth.module.Krb5LoginModule</code>.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.jaas.controlFlag</dt>
<dd>
<p>The control flag of the login module.</p>
<div class="paragraph">
<p>Default: <code>required</code>.</p>
</div>
</dd>
<dt class="hdlist1">spring.cloud.stream.kafka.binder.jaas.options</dt>
<dd>
<p>Map with a key/value pair containing the login module options.</p>
<div class="paragraph">
<p>Default: Empty map.</p>
</div>
</dd>
</dl>
</div>
<div class="paragraph">
<p>The following example shows how to launch a Spring Cloud Stream application with SASL and Kerberos by using Spring Boot configuration properties:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash"> java --spring.cloud.stream.kafka.binder.brokers=secure.server:9092 \
--spring.cloud.stream.bindings.input.destination=stream.ticktock \
--spring.cloud.stream.kafka.binder.autoCreateTopics=false \
--spring.cloud.stream.kafka.binder.configuration.security.protocol=SASL_PLAINTEXT \
--spring.cloud.stream.kafka.binder.jaas.options.useKeyTab=true \
--spring.cloud.stream.kafka.binder.jaas.options.storeKey=true \
--spring.cloud.stream.kafka.binder.jaas.options.keyTab=/etc/security/keytabs/kafka_client.keytab \
--spring.cloud.stream.kafka.binder.jaas.options.principal=kafka-client-1@EXAMPLE.COM</code></pre>
</div>
</div>
<div class="paragraph">
<p>The preceding example represents the equivalent of the following JAAS file:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>KafkaClient {
com.sun.security.auth.module.Krb5LoginModule required
useKeyTab=true
storeKey=true
keyTab="/etc/security/keytabs/kafka_client.keytab"
principal="kafka-client-1@EXAMPLE.COM";
};</code></pre>
</div>
</div>
<div class="paragraph">
<p>If the topics required already exist on the broker or will be created by an administrator, autocreation can be turned off and only client JAAS properties need to be sent.</p>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
Do not mix JAAS configuration files and Spring Boot properties in the same application.
If the <code>-Djava.security.auth.login.config</code> system property is already present, Spring Cloud Stream ignores the Spring Boot properties.
</td>
</tr>
</table>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
Be careful when using the <code>autoCreateTopics</code> and <code>autoAddPartitions</code> with Kerberos.
Usually, applications may use principals that do not have administrative rights in Kafka and Zookeeper.
Consequently, relying on Spring Cloud Stream to create/modify topics may fail.
In secure environments, we strongly recommend creating topics and managing ACLs administratively by using Kafka tooling.
</td>
</tr>
</table>
</div>
</div>
</div>
<div class="sect4">
<h5 id="pause-resume"><a class="link" href="#pause-resume">Example: Pausing and Resuming the Consumer</a></h5>
<div class="paragraph">
<p>If you wish to suspend consumption but not cause a partition rebalance, you can pause and resume the consumer.
This is facilitated by adding the <code>Consumer</code> as a parameter to your <code>@StreamListener</code>.
To resume, you need an <code>ApplicationListener</code> for <code>ListenerContainerIdleEvent</code> instances.
The frequency at which events are published is controlled by the <code>idleEventInterval</code> property.
Since the consumer is not thread-safe, you must call these methods on the calling thread.</p>
</div>
<div class="paragraph">
<p>The following simple application shows how to pause and resume:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-java hljs" data-lang="java">@SpringBootApplication
@EnableBinding(Sink.class)
public class Application {
public static void main(String[] args) {
SpringApplication.run(Application.class, args);
}
@StreamListener(Sink.INPUT)
public void in(String in, @Header(KafkaHeaders.CONSUMER) Consumer&lt;?, ?&gt; consumer) {
System.out.println(in);
consumer.pause(Collections.singleton(new TopicPartition("myTopic", 0)));
}
@Bean
public ApplicationListener&lt;ListenerContainerIdleEvent&gt; idleListener() {
return event -&gt; {
System.out.println(event);
if (event.getConsumer().paused().size() &gt; 0) {
event.getConsumer().resume(event.getConsumer().paused());
}
};
}
}</code></pre>
</div>
</div>
</div>
</div>
</div>
<div class="sect2">
<h3 id="kafka-transactional-binder"><a class="link" href="#kafka-transactional-binder">1.4. Transactional Binder</a></h3>
<div class="paragraph">
<p>Enable transactions by setting <code>spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix</code> to a non-empty value, e.g. <code>tx-</code>.
When used in a processor application, the consumer starts the transaction; any records sent on the consumer thread participate in the same transaction.
When the listener exits normally, the listener container will send the offset to the transaction and commit it.
A common producer factory is used for all producer bindings configured using <code>spring.cloud.stream.kafka.binder.transaction.producer.*</code> properties; individual binding Kafka producer properties are ignored.</p>
</div>
<div class="admonitionblock important">
<table>
<tr>
<td class="icon">
<i class="fa icon-important" title="Important"></i>
</td>
<td class="content">
Normal binder retries (and dead lettering) are not supported with transactions because the retries will run in the original transaction, which may be rolled back and any published records will be rolled back too.
When retries are enabled (the common property <code>maxAttempts</code> is greater than zero) the retry properties are used to configure a <code>DefaultAfterRollbackProcessor</code> to enable retries at the container level.
Similarly, instead of publishing dead-letter records within the transaction, this functionality is moved to the listener container, again via the <code>DefaultAfterRollbackProcessor</code> which runs after the main transaction has rolled back.
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>If you wish to use transactions in a source application, or from some arbitrary thread for producer-only transaction (e.g. <code>@Scheduled</code> method), you must get a reference to the transactional producer factory and define a <code>KafkaTransactionManager</code> bean using it.</p>
</div>
<div class="exampleblock">
<div class="content">
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-java hljs" data-lang="java">@Bean
public PlatformTransactionManager transactionManager(BinderFactory binders) {
ProducerFactory&lt;byte[], byte[]&gt; pf = ((KafkaMessageChannelBinder) binders.getBinder(null,
MessageChannel.class)).getTransactionalProducerFactory();
return new KafkaTransactionManager&lt;&gt;(pf);
}</code></pre>
</div>
</div>
</div>
</div>
<div class="paragraph">
<p>Notice that we get a reference to the binder using the <code>BinderFactory</code>; use <code>null</code> in the first argument when there is only one binder configured.
If more than one binder is configured, use the binder name to get the reference.
Once we have a reference to the binder, we can obtain a reference to the <code>ProducerFactory</code> and create a transaction manager.</p>
</div>
<div class="paragraph">
<p>Then you would use normal Spring transaction support, e.g. <code>TransactionTemplate</code> or <code>@Transactional</code>, for example:</p>
</div>
<div class="exampleblock">
<div class="content">
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-java hljs" data-lang="java">public static class Sender {
@Transactional
public void doInTransaction(MessageChannel output, List&lt;String&gt; stuffToSend) {
stuffToSend.forEach(stuff -&gt; output.send(new GenericMessage&lt;&gt;(stuff)));
}
}</code></pre>
</div>
</div>
</div>
</div>
<div class="paragraph">
<p>If you wish to synchronize producer-only transactions with those from some other transaction manager, use a <code>ChainedTransactionManager</code>.</p>
</div>
</div>
<div class="sect2">
<h3 id="kafka-error-channels"><a class="link" href="#kafka-error-channels">1.5. Error Channels</a></h3>
<div class="paragraph">
<p>Starting with version 1.3, the binder unconditionally sends exceptions to an error channel for each consumer destination and can also be configured to send async producer send failures to an error channel.
See <a href="#spring-cloud-stream-overview-error-handling">[spring-cloud-stream-overview-error-handling]</a> for more information.</p>
</div>
<div class="paragraph">
<p>The payload of the <code>ErrorMessage</code> for a send failure is a <code>KafkaSendFailureException</code> with properties:</p>
</div>
<div class="ulist">
<ul>
<li>
<p><code>failedMessage</code>: The Spring Messaging <code>Message&lt;?&gt;</code> that failed to be sent.</p>
</li>
<li>
<p><code>record</code>: The raw <code>ProducerRecord</code> that was created from the <code>failedMessage</code></p>
</li>
</ul>
</div>
<div class="paragraph">
<p>There is no automatic handling of producer exceptions (such as sending to a <a href="#kafka-dlq-processing">Dead-Letter queue</a>).
You can consume these exceptions with your own Spring Integration flow.</p>
</div>
</div>
<div class="sect2">
<h3 id="kafka-metrics"><a class="link" href="#kafka-metrics">1.6. Kafka Metrics</a></h3>
<div class="paragraph">
<p>Kafka binder module exposes the following metrics:</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.binder.kafka.offset</code>: This metric indicates how many messages have not been yet consumed from a given binder&#8217;s topic by a given consumer group.
The metrics provided are based on the Mircometer metrics library. The metric contains the consumer group information, topic and the actual lag in committed offset from the latest offset on the topic.
This metric is particularly useful for providing auto-scaling feedback to a PaaS platform.</p>
</div>
</div>
<div class="sect2">
<h3 id="kafka-tombstones"><a class="link" href="#kafka-tombstones">1.7. Tombstone Records (null record values)</a></h3>
<div class="paragraph">
<p>When using compacted topics, a record with a <code>null</code> value (also called a tombstone record) represents the deletion of a key.
To receive such messages in a <code>@StreamListener</code> method, the parameter must be marked as not required to receive a <code>null</code> value argument.</p>
</div>
<div class="exampleblock">
<div class="content">
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-java hljs" data-lang="java">@StreamListener(Sink.INPUT)
public void in(@Header(KafkaHeaders.RECEIVED_MESSAGE_KEY) byte[] key,
@Payload(required = false) Customer customer) {
// customer is null if a tombstone record
...
}</code></pre>
</div>
</div>
</div>
</div>
</div>
<div class="sect2">
<h3 id="rebalance-listener"><a class="link" href="#rebalance-listener">1.8. Using a KafkaRebalanceListener</a></h3>
<div class="paragraph">
<p>Applications may wish to seek topics/partitions to arbitrary offsets when the partitions are initially assigned, or perform other operations on the consumer.
Starting with version 2.1, if you provide a single <code>KafkaRebalanceListener</code> bean in the application context, it will be wired into all Kafka consumer bindings.</p>
</div>
<div class="exampleblock">
<div class="content">
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-java hljs" data-lang="java">public interface KafkaBindingRebalanceListener {
/**
* Invoked by the container before any pending offsets are committed.
* @param bindingName the name of the binding.
* @param consumer the consumer.
* @param partitions the partitions.
*/
default void onPartitionsRevokedBeforeCommit(String bindingName, Consumer&lt;?, ?&gt; consumer,
Collection&lt;TopicPartition&gt; partitions) {
}
/**
* Invoked by the container after any pending offsets are committed.
* @param bindingName the name of the binding.
* @param consumer the consumer.
* @param partitions the partitions.
*/
default void onPartitionsRevokedAfterCommit(String bindingName, Consumer&lt;?, ?&gt; consumer, Collection&lt;TopicPartition&gt; partitions) {
}
/**
* Invoked when partitions are initially assigned or after a rebalance.
* Applications might only want to perform seek operations on an initial assignment.
* @param bindingName the name of the binding.
* @param consumer the consumer.
* @param partitions the partitions.
* @param initial true if this is the initial assignment.
*/
default void onPartitionsAssigned(String bindingName, Consumer&lt;?, ?&gt; consumer, Collection&lt;TopicPartition&gt; partitions,
boolean initial) {
}
}</code></pre>
</div>
</div>
</div>
</div>
<div class="paragraph">
<p>You cannot set the <code>resetOffsets</code> consumer property to <code>true</code> when you provide a rebalance listener.</p>
</div>
</div>
<div class="sect2">
<h3 id="kafka-dlq-processing"><a class="link" href="#kafka-dlq-processing">1.9. Dead-Letter Topic Processing</a></h3>
<div class="sect3">
<h4 id="dlq-partition-selection"><a class="link" href="#dlq-partition-selection">1.9.1. Dead-Letter Topic Partition Selection</a></h4>
<div class="paragraph">
<p>By default, records are published to the Dead-Letter topic using the same partition as the original record.
This means the Dead-Letter topic must have at least as many partitions as the original record.</p>
</div>
<div class="paragraph">
<p>To change this behavior, add a <code>DlqPartitionFunction</code> implementation as a <code>@Bean</code> to the application context.
Only one such bean can be present.
The function is provided with the consumer group, the failed <code>ConsumerRecord</code> and the exception.
For example, if you always want to route to partition 0, you might use:</p>
</div>
<div class="exampleblock">
<div class="content">
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-java hljs" data-lang="java">@Bean
public DlqPartitionFunction partitionFunction() {
return (group, record, ex) -&gt; 0;
}</code></pre>
</div>
</div>
</div>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
If you set a consumer binding&#8217;s <code>dlqPartitions</code> property to 1 (and the binder&#8217;s <code>minPartitionCount</code> is equal to <code>1</code>), there is no need to supply a <code>DlqPartitionFunction</code>; the framework will always use partition 0.
If you set a consumer binding&#8217;s <code>dlqPartitions</code> property to a value greater than <code>1</code> (or the binder&#8217;s <code>minPartitionCount</code> is greater than <code>1</code>), you <strong>must</strong> provide a <code>DlqPartitionFunction</code> bean, even if the partition count is the same as the original topic&#8217;s.
</td>
</tr>
</table>
</div>
</div>
<div class="sect3">
<h4 id="dlq-handling"><a class="link" href="#dlq-handling">1.9.2. Handling Records in a Dead-Letter Topic</a></h4>
<div class="paragraph">
<p>Because the framework cannot anticipate how users would want to dispose of dead-lettered messages, it does not provide any standard mechanism to handle them.
If the reason for the dead-lettering is transient, you may wish to route the messages back to the original topic.
However, if the problem is a permanent issue, that could cause an infinite loop.
The sample Spring Boot application within this topic is an example of how to route those messages back to the original topic, but it moves them to a &#8220;parking lot&#8221; topic after three attempts.
The application is another spring-cloud-stream application that reads from the dead-letter topic.
It terminates when no messages are received for 5 seconds.</p>
</div>
<div class="paragraph">
<p>The examples assume the original destination is <code>so8400out</code> and the consumer group is <code>so8400</code>.</p>
</div>
<div class="paragraph">
<p>There are a couple of strategies to consider:</p>
</div>
<div class="ulist">
<ul>
<li>
<p>Consider running the rerouting only when the main application is not running.
Otherwise, the retries for transient errors are used up very quickly.</p>
</li>
<li>
<p>Alternatively, use a two-stage approach: Use this application to route to a third topic and another to route from there back to the main topic.</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>The following code listings show the sample application:</p>
</div>
<div class="listingblock">
<div class="title">application.properties</div>
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.bindings.input.group=so8400replay
spring.cloud.stream.bindings.input.destination=error.so8400out.so8400
spring.cloud.stream.bindings.output.destination=so8400out
spring.cloud.stream.bindings.parkingLot.destination=so8400in.parkingLot
spring.cloud.stream.kafka.binder.configuration.auto.offset.reset=earliest
spring.cloud.stream.kafka.binder.headers=x-retries</code></pre>
</div>
</div>
<div class="listingblock">
<div class="title">Application</div>
<div class="content">
<pre class="highlightjs highlight"><code class="language-java hljs" data-lang="java">@SpringBootApplication
@EnableBinding(TwoOutputProcessor.class)
public class ReRouteDlqKApplication implements CommandLineRunner {
private static final String X_RETRIES_HEADER = "x-retries";
public static void main(String[] args) {
SpringApplication.run(ReRouteDlqKApplication.class, args).close();
}
private final AtomicInteger processed = new AtomicInteger();
@Autowired
private MessageChannel parkingLot;
@StreamListener(Processor.INPUT)
@SendTo(Processor.OUTPUT)
public Message&lt;?&gt; reRoute(Message&lt;?&gt; failed) {
processed.incrementAndGet();
Integer retries = failed.getHeaders().get(X_RETRIES_HEADER, Integer.class);
if (retries == null) {
System.out.println("First retry for " + failed);
return MessageBuilder.fromMessage(failed)
.setHeader(X_RETRIES_HEADER, new Integer(1))
.setHeader(BinderHeaders.PARTITION_OVERRIDE,
failed.getHeaders().get(KafkaHeaders.RECEIVED_PARTITION_ID))
.build();
}
else if (retries.intValue() &lt; 3) {
System.out.println("Another retry for " + failed);
return MessageBuilder.fromMessage(failed)
.setHeader(X_RETRIES_HEADER, new Integer(retries.intValue() + 1))
.setHeader(BinderHeaders.PARTITION_OVERRIDE,
failed.getHeaders().get(KafkaHeaders.RECEIVED_PARTITION_ID))
.build();
}
else {
System.out.println("Retries exhausted for " + failed);
parkingLot.send(MessageBuilder.fromMessage(failed)
.setHeader(BinderHeaders.PARTITION_OVERRIDE,
failed.getHeaders().get(KafkaHeaders.RECEIVED_PARTITION_ID))
.build());
}
return null;
}
@Override
public void run(String... args) throws Exception {
while (true) {
int count = this.processed.get();
Thread.sleep(5000);
if (count == this.processed.get()) {
System.out.println("Idle, terminating");
return;
}
}
}
public interface TwoOutputProcessor extends Processor {
@Output("parkingLot")
MessageChannel parkingLot();
}
}</code></pre>
</div>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_partitioning_with_the_kafka_binder"><a class="link" href="#_partitioning_with_the_kafka_binder">1.10. Partitioning with the Kafka Binder</a></h3>
<div class="paragraph">
<p>Apache Kafka supports topic partitioning natively.</p>
</div>
<div class="paragraph">
<p>Sometimes it is advantageous to send data to specific partitions&#8201;&#8212;&#8201;for example, when you want to strictly order message processing (all messages for a particular customer should go to the same partition).</p>
</div>
<div class="paragraph">
<p>The following example shows how to configure the producer and consumer side:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-java hljs" data-lang="java">@SpringBootApplication
@EnableBinding(Source.class)
public class KafkaPartitionProducerApplication {
private static final Random RANDOM = new Random(System.currentTimeMillis());
private static final String[] data = new String[] {
"foo1", "bar1", "qux1",
"foo2", "bar2", "qux2",
"foo3", "bar3", "qux3",
"foo4", "bar4", "qux4",
};
public static void main(String[] args) {
new SpringApplicationBuilder(KafkaPartitionProducerApplication.class)
.web(false)
.run(args);
}
@InboundChannelAdapter(channel = Source.OUTPUT, poller = @Poller(fixedRate = "5000"))
public Message&lt;?&gt; generate() {
String value = data[RANDOM.nextInt(data.length)];
System.out.println("Sending: " + value);
return MessageBuilder.withPayload(value)
.setHeader("partitionKey", value)
.build();
}
}</code></pre>
</div>
</div>
<div class="listingblock">
<div class="title">application.yml</div>
<div class="content">
<pre class="highlightjs highlight"><code class="language-yaml hljs" data-lang="yaml">spring:
cloud:
stream:
bindings:
output:
destination: partitioned.topic
producer:
partition-key-expression: headers['partitionKey']
partition-count: 12</code></pre>
</div>
</div>
<div class="admonitionblock important">
<table>
<tr>
<td class="icon">
<i class="fa icon-important" title="Important"></i>
</td>
<td class="content">
The topic must be provisioned to have enough partitions to achieve the desired concurrency for all consumer groups.
The above configuration supports up to 12 consumer instances (6 if their <code>concurrency</code> is 2, 4 if their concurrency is 3, and so on).
It is generally best to &#8220;over-provision&#8221; the partitions to allow for future increases in consumers or concurrency.
</td>
</tr>
</table>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
The preceding configuration uses the default partitioning (<code>key.hashCode() % partitionCount</code>).
This may or may not provide a suitably balanced algorithm, depending on the key values.
You can override this default by using the <code>partitionSelectorExpression</code> or <code>partitionSelectorClass</code> properties.
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>Since partitions are natively handled by Kafka, no special configuration is needed on the consumer side.
Kafka allocates partitions across the instances.</p>
</div>
<div class="paragraph">
<p>The following Spring Boot application listens to a Kafka stream and prints (to the console) the partition ID to which each message goes:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-java hljs" data-lang="java">@SpringBootApplication
@EnableBinding(Sink.class)
public class KafkaPartitionConsumerApplication {
public static void main(String[] args) {
new SpringApplicationBuilder(KafkaPartitionConsumerApplication.class)
.web(false)
.run(args);
}
@StreamListener(Sink.INPUT)
public void listen(@Payload String in, @Header(KafkaHeaders.RECEIVED_PARTITION_ID) int partition) {
System.out.println(in + " received from partition " + partition);
}
}</code></pre>
</div>
</div>
<div class="listingblock">
<div class="title">application.yml</div>
<div class="content">
<pre class="highlightjs highlight"><code class="language-yaml hljs" data-lang="yaml">spring:
cloud:
stream:
bindings:
input:
destination: partitioned.topic
group: myGroup</code></pre>
</div>
</div>
<div class="paragraph">
<p>You can add instances as needed.
Kafka rebalances the partition allocations.
If the instance count (or <code>instance count * concurrency</code>) exceeds the number of partitions, some consumers are idle.</p>
</div>
</div>
</div>
</div>
<div class="sect1">
<h2 id="_kafka_streams_binder"><a class="link" href="#_kafka_streams_binder">2. Kafka Streams Binder</a></h2>
<div class="sectionbody">
<div class="sect2">
<h3 id="_usage_2"><a class="link" href="#_usage_2">2.1. Usage</a></h3>
<div class="paragraph">
<p>For using the Kafka Streams binder, you just need to add it to your Spring Cloud Stream application, using the following maven coordinates:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-xml hljs" data-lang="xml">&lt;dependency&gt;
&lt;groupId&gt;org.springframework.cloud&lt;/groupId&gt;
&lt;artifactId&gt;spring-cloud-stream-binder-kafka-streams&lt;/artifactId&gt;
&lt;/dependency&gt;</code></pre>
</div>
</div>
<div class="paragraph">
<p>A quick way to bootstrap a new project for Kafka Streams binder is to use <a href="http://start.spring.io">Spring Initializr</a> and then select "Cloud Streams" and "Spring for Kafka Streams" as shown below</p>
</div>
<div class="imageblock text-center">
<div class="content">
<img src="https://raw.github.com/spring-cloud/spring-cloud-stream-binder-kafka/master/docs/src/main/asciidoc/images/spring-initializr-kafka-streams.png" alt="spring initializr kafka streams" width="800">
</div>
</div>
</div>
<div class="sect2">
<h3 id="_overview_2"><a class="link" href="#_overview_2">2.2. Overview</a></h3>
<div class="paragraph">
<p>Spring Cloud Stream includes a binder implementation designed explicitly for <a href="https://kafka.apache.org/documentation/streams/">Apache Kafka Streams</a> binding.
With this native integration, a Spring Cloud Stream "processor" application can directly use the
<a href="https://kafka.apache.org/documentation/streams/developer-guide">Apache Kafka Streams</a> APIs in the core business logic.</p>
</div>
<div class="paragraph">
<p>Kafka Streams binder implementation builds on the foundations provided by the <a href="https://docs.spring.io/spring-kafka/reference/html/#kafka-streams">Spring for Apache Kafka</a> project.</p>
</div>
<div class="paragraph">
<p>Kafka Streams binder provides binding capabilities for the three major types in Kafka Streams - <code>KStream</code>, <code>KTable</code> and <code>GlobalKTable</code>.</p>
</div>
<div class="paragraph">
<p>Kafka Streams applications typically follow a model in which the records are read from an inbound topic, apply business logic, and then write the transformed records to an outbound topic.
Alternatively, a Processor application with no outbound destination can be defined as well.</p>
</div>
<div class="paragraph">
<p>In the following sections, we are going to look at the details of Spring Cloud Stream&#8217;s integration with Kafka Streams.</p>
</div>
</div>
<div class="sect2">
<h3 id="_programming_model"><a class="link" href="#_programming_model">2.3. Programming Model</a></h3>
<div class="paragraph">
<p>When using the programming model provided by Kafka Streams binder, both the high-level <a href="https://docs.confluent.io/current/streams/developer-guide/dsl-api.html">Streams DSL</a> and a mix of both the higher level and the lower level <a href="https://docs.confluent.io/current/streams/developer-guide/processor-api.html">Processor-API</a> can be used as options.
When mixing both higher and lower level API&#8217;s, this is usually achieved by invoking <code>transform</code> or <code>process</code> API methods on <code>KStream</code>.</p>
</div>
<div class="sect3">
<h4 id="_functional_style"><a class="link" href="#_functional_style">2.3.1. Functional Style</a></h4>
<div class="paragraph">
<p>Starting with Spring Cloud Stream <code>3.0.0</code>, Kafka Streams binder allows the applications to be designed and developed using the functional programming style that is available in Java 8.
This means that the applications can be concisely represented as a lambda expression of types <code>java.util.function.Function</code> or <code>java.util.function.Consumer</code>.</p>
</div>
<div class="paragraph">
<p>Let&#8217;s take a very basic example.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@SpringBootApplication
public class SimpleConsumerApplication {
@Bean
public java.util.function.Consumer&lt;KStream&lt;Object, String&gt;&gt; process() {
return input -&gt;
input.foreach((key, value) -&gt; {
System.out.println("Key: " + key + " Value: " + value);
});
}
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Albeit simple, this is a complete standalone Spring Boot application that is leveraging Kafka Streams for stream processing.
This is a consumer application with no outbound binding and only a single inbound binding.
The application consumes data and it simply logs the information from the <code>KStream</code> key and value on the standard output.
The application contains the <code>SpringBootApplication</code> annotation and a method that is marked as <code>Bean</code>.
The bean method is of type <code>java.util.function.Consumer</code> which is parameterized with <code>KStream</code>.
Then in the implementation, we are returning a Consumer object that is essentially a lambda expression.
Inside the lambda expression, the code for processing the data is provided.</p>
</div>
<div class="paragraph">
<p>In this application, there is a single input binding that is of type <code>KStream</code>.
The binder creates this binding for the application with a name <code>process-in-0</code>, i.e. the name of the function bean name followed by a dash character (<code>-</code>) and the literal <code>in</code> followed by another dash and then the ordinal position of the parameter.
You use this binding name to set other properties such as destination.
For example, <code>spring.cloud.stream.bindings.process-in-0.destination=my-topic</code>.</p>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
If the destination property is not set on the binding, a topic is created with the same name as the binding (if there are sufficient privileges for the application) or that topic is expected to be already available.
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>Once built as a uber-jar (e.g., <code>kstream-consumer-app.jar</code>), you can run the above example like the following.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>java -jar kstream-consumer-app.jar --spring.cloud.stream.bindings.process-in-0.destination=my-topic</code></pre>
</div>
</div>
<div class="paragraph">
<p>Here is another example, where it is a full processor with both input and output bindings.
This is the classic word-count example in which the application receives data from a topic, the number of occurrences for each word is then computed in a tumbling time-window.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@SpringBootApplication
public class WordCountProcessorApplication {
@Bean
public Function&lt;KStream&lt;Object, String&gt;, KStream&lt;?, WordCount&gt;&gt; process() {
return input -&gt; input
.flatMapValues(value -&gt; Arrays.asList(value.toLowerCase().split("\\W+")))
.map((key, value) -&gt; new KeyValue&lt;&gt;(value, value))
.groupByKey(Serialized.with(Serdes.String(), Serdes.String()))
.windowedBy(TimeWindows.of(5000))
.count(Materialized.as("word-counts-state-store"))
.toStream()
.map((key, value) -&gt; new KeyValue&lt;&gt;(key.key(), new WordCount(key.key(), value,
new Date(key.window().start()), new Date(key.window().end()))));
}
public static void main(String[] args) {
SpringApplication.run(WordCountProcessorApplication.class, args);
}
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Here again, this is a complete Spring Boot application. The difference here from the first application is that the bean method is of type <code>java.util.function.Function</code>.
The first parameterized type for the <code>Function</code> is for the input <code>KStream</code> and the second one is for the output.
In the method body, a lambda expression is provided that is of type <code>Function</code> and as implementation, the actual business logic is given.
Similar to the previously discussed Consumer based application, the input binding here is named as <code>process-in-0</code> by default. For the output, the binding name is automatically also set to <code>process-out-0</code>.</p>
</div>
<div class="paragraph">
<p>Once built as an uber-jar (e.g., <code>wordcount-processor.jar</code>), you can run the above example like the following.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>java -jar wordcount-processor.jar --spring.cloud.stream.bindings.process-in-0.destination=words --spring.cloud.stream.bindings.process-out-0.destination=counts</code></pre>
</div>
</div>
<div class="paragraph">
<p>This application will consume messages from the Kafka topic <code>words</code> and the computed results are published to an output
topic <code>counts</code>.</p>
</div>
<div class="paragraph">
<p>Spring Cloud Stream will ensure that the messages from both the incoming and outgoing topics are automatically bound as
KStream objects. As a developer, you can exclusively focus on the business aspects of the code, i.e. writing the logic
required in the processor. Setting up Kafka Streams specific configuration required by the Kafka Streams infrastructure
is automatically handled by the framework.</p>
</div>
<div class="paragraph">
<p>The two examples we saw above have a single <code>KStream</code> input binding. In both cases, the bindings received the records from a single topic.
If you want to multiplex multiple topics into a single <code>KStream</code> binding, you can provide comma separated Kafka topics as destinations below.</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.bindings.process-in-0.destination=topic-1,topic-2,topic-3</code></p>
</div>
<div class="paragraph">
<p>In addition, you can also provide topic patterns as destinations if you want to match topics against a regular exression.</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.bindings.process-in-0.destination=input.*</code></p>
</div>
<div class="sect4">
<h5 id="_multiple_input_bindings"><a class="link" href="#_multiple_input_bindings">Multiple Input Bindings</a></h5>
<div class="paragraph">
<p>Many non-trivial Kafka Streams applications often consume data from more than one topic through multiple bindings.
For instance, one topic is consumed as <code>Kstream</code> and another as <code>KTable</code> or <code>GlobalKTable</code>.
There are many reasons why an application might want to receive data as a table type.
Think of a use-case where the underlying topic is populated through a change data capture (CDC) mechanism from a database or perhaps the application only cares about the latest updates for downstream processing.
If the application specifies that the data needs to be bound as <code>KTable</code> or <code>GlobalKTable</code>, then Kafka Streams binder will properly bind the destination to a <code>KTable</code> or <code>GlobalKTable</code> and make them available for the application to operate upon.
We will look at a few different scenarios how multiple input bindings are handled in the Kafka Streams binder.</p>
</div>
<div class="sect5">
<h6 id="_bifunction_in_kafka_streams_binder"><a class="link" href="#_bifunction_in_kafka_streams_binder">BiFunction in Kafka Streams Binder</a></h6>
<div class="paragraph">
<p>Here is an example where we have two inputs and an output. In this case, the application can leverage on <code>java.util.function.BiFunction</code>.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public BiFunction&lt;KStream&lt;String, Long&gt;, KTable&lt;String, String&gt;, KStream&lt;String, Long&gt;&gt; process() {
return (userClicksStream, userRegionsTable) -&gt; (userClicksStream
.leftJoin(userRegionsTable, (clicks, region) -&gt; new RegionWithClicks(region == null ?
"UNKNOWN" : region, clicks),
Joined.with(Serdes.String(), Serdes.Long(), null))
.map((user, regionWithClicks) -&gt; new KeyValue&lt;&gt;(regionWithClicks.getRegion(),
regionWithClicks.getClicks()))
.groupByKey(Grouped.with(Serdes.String(), Serdes.Long()))
.reduce(Long::sum)
.toStream());
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Here again, the basic theme is the same as in the previous examples, but here we have two inputs.
Java&#8217;s <code>BiFunction</code> support is used to bind the inputs to the desired destinations.
The default binding names generated by the binder for the inputs are <code>process-in-0</code> and <code>process-in-1</code> respectively. The default output binding is <code>process-out-0</code>.
In this example, the first parameter of <code>BiFunction</code> is bound as a <code>KStream</code> for the first input and the second parameter is bound as a <code>KTable</code> for the second input.</p>
</div>
</div>
<div class="sect5">
<h6 id="_biconsumer_in_kafka_streams_binder"><a class="link" href="#_biconsumer_in_kafka_streams_binder">BiConsumer in Kafka Streams Binder</a></h6>
<div class="paragraph">
<p>If there are two inputs, but no outputs, in that case we can use <code>java.util.function.BiConsumer</code> as shown below.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public BiConsumer&lt;KStream&lt;String, Long&gt;, KTable&lt;String, String&gt;&gt; process() {
return (userClicksStream, userRegionsTable) -&gt; {}
}</code></pre>
</div>
</div>
</div>
<div class="sect5">
<h6 id="_beyond_two_inputs"><a class="link" href="#_beyond_two_inputs">Beyond two inputs</a></h6>
<div class="paragraph">
<p>What if you have more than two inputs?
There are situations in which you need more than two inputs. In that case, the binder allows you to chain partial functions.
In functional programming jargon, this technique is generally known as currying.
With the functional programming support added as part of Java 8, Java now enables you to write curried functions.
Spring Cloud Stream Kafka Streams binder can make use of this feature to enable multiple input bindings.</p>
</div>
<div class="paragraph">
<p>Let&#8217;s see an example.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public Function&lt;KStream&lt;Long, Order&gt;,
Function&lt;GlobalKTable&lt;Long, Customer&gt;,
Function&lt;GlobalKTable&lt;Long, Product&gt;, KStream&lt;Long, EnrichedOrder&gt;&gt;&gt;&gt; enrichOrder() {
return orders -&gt; (
customers -&gt; (
products -&gt; (
orders.join(customers,
(orderId, order) -&gt; order.getCustomerId(),
(order, customer) -&gt; new CustomerOrder(customer, order))
.join(products,
(orderId, customerOrder) -&gt; customerOrder
.productId(),
(customerOrder, product) -&gt; {
EnrichedOrder enrichedOrder = new EnrichedOrder();
enrichedOrder.setProduct(product);
enrichedOrder.setCustomer(customerOrder.customer);
enrichedOrder.setOrder(customerOrder.order);
return enrichedOrder;
})
)
)
);
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Let&#8217;s look at the details of the binding model presented above.
In this model, we have 3 partially applied functions on the inbound. Let&#8217;s call them as <code>f(x)</code>, <code>f(y)</code> and <code>f(z)</code>.
If we expand these functions in the sense of true mathematical functions, it will look like these: <code>f(x) &#8594; (fy) &#8594; f(z) &#8594; KStream&lt;Long, EnrichedOrder&gt;</code>.
The <code>x</code> variable stands for <code>KStream&lt;Long, Order&gt;</code>, the <code>y</code> variable stands for <code>GlobalKTable&lt;Long, Customer&gt;</code> and the <code>z</code> variable stands for <code>GlobalKTable&lt;Long, Product&gt;</code>.
The first function <code>f(x)</code> has the first input binding of the application (<code>KStream&lt;Long, Order&gt;</code>) and its output is the function, f(y).
The function <code>f(y)</code> has the second input binding for the application (<code>GlobalKTable&lt;Long, Customer&gt;</code>) and its output is yet another function, <code>f(z)</code>.
The input for the function <code>f(z)</code> is the third input for the application (<code>GlobalKTable&lt;Long, Product&gt;</code>) and its output is <code>KStream&lt;Long, EnrichedOrder&gt;</code> which is the final output binding for the application.
The input from the three partial functions which are <code>KStream</code>, <code>GlobalKTable</code>, <code>GlobalKTable</code> respectively are available for you in the method body for implementing the business logic as part of the lambda expression.</p>
</div>
<div class="paragraph">
<p>Input bindings are named as <code>enrichOrder-in-0</code>, <code>enrichOrder-in-1</code> and <code>enrichOrder-in-2</code> respectively. Output binding is named as <code>enrichOrder-out-0</code>.</p>
</div>
<div class="paragraph">
<p>With curried functions, you can virtually have any number of inputs. However, keep in mind that, anything more than a smaller number of inputs and partially applied functions for them as above in Java might lead to unreadable code.
Therefore if your Kafka Streams application requires more than a reasonably smaller number of input bindings and you want to use this functional model, then you may want to rethink your design and decompose the application appropriately.</p>
</div>
</div>
</div>
<div class="sect4">
<h5 id="_multiple_output_bindings"><a class="link" href="#_multiple_output_bindings">Multiple Output Bindings</a></h5>
<div class="paragraph">
<p>Kafka Streams allows to write outbound data into multiple topics. This feature is known as branching in Kafka Streams.
When using multiple output bindings, you need to provide an array of KStream (<code>KStream[]</code>) as the outbound return type.</p>
</div>
<div class="paragraph">
<p>Here is an example:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public Function&lt;KStream&lt;Object, String&gt;, KStream&lt;?, WordCount&gt;[]&gt; process() {
Predicate&lt;Object, WordCount&gt; isEnglish = (k, v) -&gt; v.word.equals("english");
Predicate&lt;Object, WordCount&gt; isFrench = (k, v) -&gt; v.word.equals("french");
Predicate&lt;Object, WordCount&gt; isSpanish = (k, v) -&gt; v.word.equals("spanish");
return input -&gt; input
.flatMapValues(value -&gt; Arrays.asList(value.toLowerCase().split("\\W+")))
.groupBy((key, value) -&gt; value)
.windowedBy(TimeWindows.of(5000))
.count(Materialized.as("WordCounts-branch"))
.toStream()
.map((key, value) -&gt; new KeyValue&lt;&gt;(null, new WordCount(key.key(), value,
new Date(key.window().start()), new Date(key.window().end()))))
.branch(isEnglish, isFrench, isSpanish);
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>The programming model remains the same, however the outbound parameterized type is <code>KStream[]</code>.
The default output binding names are <code>process-out-0</code>, <code>process-out-1</code>, <code>process-out-2</code> respectively.
The reason why the binder generates three output bindings is because it detects the length of the returned <code>KStream</code> array.</p>
</div>
</div>
<div class="sect4">
<h5 id="_summary_of_function_based_programming_styles_for_kafka_streams"><a class="link" href="#_summary_of_function_based_programming_styles_for_kafka_streams">Summary of Function based Programming Styles for Kafka Streams</a></h5>
<div class="paragraph">
<p>In summary, the following table shows the various options that can be used in the functional paradigm.</p>
</div>
<table class="tableblock frame-all grid-all stretch">
<colgroup>
<col style="width: 33.3333%;">
<col style="width: 33.3333%;">
<col style="width: 33.3334%;">
</colgroup>
<thead>
<tr>
<th class="tableblock halign-left valign-top">Number of Inputs</th>
<th class="tableblock halign-left valign-top">Number of Outputs</th>
<th class="tableblock halign-left valign-top">Component to use</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tableblock halign-left valign-top"><p class="tableblock">1</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">0</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">java.util.function.Consumer</p></td>
</tr>
<tr>
<td class="tableblock halign-left valign-top"><p class="tableblock">2</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">0</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">java.util.function.BiConsumer</p></td>
</tr>
<tr>
<td class="tableblock halign-left valign-top"><p class="tableblock">1</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">1..n</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">java.util.function.Function</p></td>
</tr>
<tr>
<td class="tableblock halign-left valign-top"><p class="tableblock">2</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">1..n</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">java.util.function.BiFunction</p></td>
</tr>
<tr>
<td class="tableblock halign-left valign-top"><p class="tableblock">&gt;= 3</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">0..n</p></td>
<td class="tableblock halign-left valign-top"><p class="tableblock">Use curried functions</p></td>
</tr>
</tbody>
</table>
<div class="ulist">
<ul>
<li>
<p>In the case of more than one output in this table, the type simply becomes <code>KStream[]</code>.</p>
</li>
</ul>
</div>
</div>
</div>
<div class="sect3">
<h4 id="_imperative_programming_model"><a class="link" href="#_imperative_programming_model">2.3.2. Imperative programming model.</a></h4>
<div class="paragraph">
<p>Although the functional programming model outlined above is the preferred approach, you can still use the classic <code>StreamListener</code> based approach if you prefer.</p>
</div>
<div class="paragraph">
<p>Here are some examples.</p>
</div>
<div class="paragraph">
<p>Following is the equivalent of the Word count example using <code>StreamListener</code>.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@SpringBootApplication
@EnableBinding(KafkaStreamsProcessor.class)
public class WordCountProcessorApplication {
@StreamListener("input")
@SendTo("output")
public KStream&lt;?, WordCount&gt; process(KStream&lt;?, String&gt; input) {
return input
.flatMapValues(value -&gt; Arrays.asList(value.toLowerCase().split("\\W+")))
.groupBy((key, value) -&gt; value)
.windowedBy(TimeWindows.of(5000))
.count(Materialized.as("WordCounts-multi"))
.toStream()
.map((key, value) -&gt; new KeyValue&lt;&gt;(null, new WordCount(key.key(), value, new Date(key.window().start()), new Date(key.window().end()))));
}
public static void main(String[] args) {
SpringApplication.run(WordCountProcessorApplication.class, args);
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>As you can see, this is a bit more verbose since you need to provide <code>EnableBinding</code> and the other extra annotations like <code>StreamListener</code> and <code>SendTo</code> to make it a complete application.
<code>EnableBinding</code> is where you specify your binding interface that contains your bindings.
In this case, we are using the stock <code>KafkaStreamsProcessor</code> binding interface that has the following contracts.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>public interface KafkaStreamsProcessor {
@Input("input")
KStream&lt;?, ?&gt; input();
@Output("output")
KStream&lt;?, ?&gt; output();
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Binder will create bindings for the input <code>KStream</code> and output <code>KStream</code> since you are using a binding interface that contains those declarations.</p>
</div>
<div class="paragraph">
<p>In addition to the obvious differences in the programming model offered in the functional style, one particular thing that needs to be mentioned here is that the binding names are what you specify in the binding interface.
For example, in the above application, since we are using <code>KafkaStreamsProcessor</code>, the binding names are <code>input</code> and <code>output</code>.
Binding properties need to use those names. For instance <code>spring.cloud.stream.bindings.input.destination</code>, <code>spring.cloud.stream.bindings.output.destination</code> etc.
Keep in mind that this is fundamentally different from the functional style since there the binder generates binding names for the application.
This is because the application does not provide any binding interfaces in the functional model using <code>EnableBinding</code>.</p>
</div>
<div class="paragraph">
<p>Here is another example of a sink where we have two inputs.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@EnableBinding(KStreamKTableBinding.class)
.....
.....
@StreamListener
public void process(@Input("inputStream") KStream&lt;String, PlayEvent&gt; playEvents,
@Input("inputTable") KTable&lt;Long, Song&gt; songTable) {
....
....
}
interface KStreamKTableBinding {
@Input("inputStream")
KStream&lt;?, ?&gt; inputStream();
@Input("inputTable")
KTable&lt;?, ?&gt; inputTable();
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Following is the <code>StreamListener</code> equivalent of the same <code>BiFunction</code> based processor that we saw above.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@EnableBinding(KStreamKTableBinding.class)
....
....
@StreamListener
@SendTo("output")
public KStream&lt;String, Long&gt; process(@Input("input") KStream&lt;String, Long&gt; userClicksStream,
@Input("inputTable") KTable&lt;String, String&gt; userRegionsTable) {
....
....
}
interface KStreamKTableBinding extends KafkaStreamsProcessor {
@Input("inputX")
KTable&lt;?, ?&gt; inputTable();
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Finally, here is the <code>StreamListener</code> equivalent of the application with three inputs and curried functions.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@EnableBinding(CustomGlobalKTableProcessor.class)
...
...
@StreamListener
@SendTo("output")
public KStream&lt;Long, EnrichedOrder&gt; process(
@Input("input-1") KStream&lt;Long, Order&gt; ordersStream,
@Input("input-"2) GlobalKTable&lt;Long, Customer&gt; customers,
@Input("input-3") GlobalKTable&lt;Long, Product&gt; products) {
KStream&lt;Long, CustomerOrder&gt; customerOrdersStream = ordersStream.join(
customers, (orderId, order) -&gt; order.getCustomerId(),
(order, customer) -&gt; new CustomerOrder(customer, order));
return customerOrdersStream.join(products,
(orderId, customerOrder) -&gt; customerOrder.productId(),
(customerOrder, product) -&gt; {
EnrichedOrder enrichedOrder = new EnrichedOrder();
enrichedOrder.setProduct(product);
enrichedOrder.setCustomer(customerOrder.customer);
enrichedOrder.setOrder(customerOrder.order);
return enrichedOrder;
});
}
interface CustomGlobalKTableProcessor {
@Input("input-1")
KStream&lt;?, ?&gt; input1();
@Input("input-2")
GlobalKTable&lt;?, ?&gt; input2();
@Input("input-3")
GlobalKTable&lt;?, ?&gt; input3();
@Output("output")
KStream&lt;?, ?&gt; output();
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>You might notice that the above two examples are even more verbose since in addition to provide <code>EnableBinding</code>, you also need to write your own custom binding interface as well.
Using the functional model, you can avoid all those ceremonial details.</p>
</div>
<div class="paragraph">
<p>Before we move on from looking at the general programming model offered by Kafka Streams binder, here is the <code>StreamListener</code> version of multiple output bindings.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>EnableBinding(KStreamProcessorWithBranches.class)
public static class WordCountProcessorApplication {
@Autowired
private TimeWindows timeWindows;
@StreamListener("input")
@SendTo({"output1","output2","output3"})
public KStream&lt;?, WordCount&gt;[] process(KStream&lt;Object, String&gt; input) {
Predicate&lt;Object, WordCount&gt; isEnglish = (k, v) -&gt; v.word.equals("english");
Predicate&lt;Object, WordCount&gt; isFrench = (k, v) -&gt; v.word.equals("french");
Predicate&lt;Object, WordCount&gt; isSpanish = (k, v) -&gt; v.word.equals("spanish");
return input
.flatMapValues(value -&gt; Arrays.asList(value.toLowerCase().split("\\W+")))
.groupBy((key, value) -&gt; value)
.windowedBy(timeWindows)
.count(Materialized.as("WordCounts-1"))
.toStream()
.map((key, value) -&gt; new KeyValue&lt;&gt;(null, new WordCount(key.key(), value, new Date(key.window().start()), new Date(key.window().end()))))
.branch(isEnglish, isFrench, isSpanish);
}
interface KStreamProcessorWithBranches {
@Input("input")
KStream&lt;?, ?&gt; input();
@Output("output1")
KStream&lt;?, ?&gt; output1();
@Output("output2")
KStream&lt;?, ?&gt; output2();
@Output("output3")
KStream&lt;?, ?&gt; output3();
}
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>To recap, we have reviewed the various programming model choices when using the Kafka Streams binder.</p>
</div>
<div class="paragraph">
<p>The binder provides binding capabilities for <code>KStream</code>, <code>KTable</code> and <code>GlobalKTable</code> on the input.
<code>KTable</code> and <code>GlobalKTable</code> bindings are only available on the input.
Binder supports both input and output bindings for <code>KStream</code>.</p>
</div>
<div class="paragraph">
<p>The upshot of the programming model of Kafka Streams binder is that the binder provides you the flexibility of going with a fully functional programming model or using the <code>StreamListener</code> based imperative approach.</p>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_ancillaries_to_the_programming_model"><a class="link" href="#_ancillaries_to_the_programming_model">2.4. Ancillaries to the programming model</a></h3>
<div class="sect3">
<h4 id="_multiple_kafka_streams_processors_within_a_single_application"><a class="link" href="#_multiple_kafka_streams_processors_within_a_single_application">2.4.1. Multiple Kafka Streams processors within a single application</a></h4>
<div class="paragraph">
<p>Binder allows to have multiple Kafka Streams processors within a single Spring Cloud Stream application.
You can have an application as below.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public java.util.function.Function&lt;KStream&lt;Object, String&gt;, KStream&lt;Object, String&gt;&gt; process() {
...
}
@Bean
public java.util.function.Consumer&lt;KStream&lt;Object, String&gt;&gt; anotherProcess() {
...
}
@Bean
public java.util.function.BiFunction&lt;KStream&lt;Object, String&gt;, KTable&lt;Integer, String&gt;, KStream&lt;Object, String&gt;&gt; yetAnotherProcess() {
...
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>In this case, the binder will create 3 separate Kafka Streams objects with different application ID&#8217;s (more on this below).
However, if you have more than one processor in the application, you have to tell Spring Cloud Stream, which functions need to be activated.
Here is how you activate the functions.</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.function.definition: process;anotherProcess;yetAnotherProcess</code></p>
</div>
<div class="paragraph">
<p>If you want certain functions to be not activated right away, you can remove that from this list.</p>
</div>
<div class="paragraph">
<p>This is also true when you have a single Kafka Streams processor and other types of <code>Function</code> beans in the same application that is handled through a different binder (for e.g., a function bean that is based on the regular Kafka Message Channel binder)</p>
</div>
</div>
<div class="sect3">
<h4 id="_kafka_streams_application_id"><a class="link" href="#_kafka_streams_application_id">2.4.2. Kafka Streams Application ID</a></h4>
<div class="paragraph">
<p>Application id is a mandatory property that you need to provide for a Kafka Streams application.
Spring Cloud Stream Kafka Streams binder allows you to configure this application id in multiple ways.</p>
</div>
<div class="paragraph">
<p>If you only have one single processor or <code>StreamListener</code> in the application, then you can set this at the binder level using the following property:</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.kafka.streams.binder.applicationId</code>.</p>
</div>
<div class="paragraph">
<p>As a convenience, if you only have a single processor, you can also use <code>spring.application.name</code> as the property to delegate the application id.</p>
</div>
<div class="paragraph">
<p>If you have multiple Kafka Streams processors in the application, then you need to set the application id per processor.
In the case of the functional model, you can attach it to each function as a property.</p>
</div>
<div class="paragraph">
<p>For e.g. imagine that you have the following functions.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public java.util.function.Consumer&lt;KStream&lt;Object, String&gt;&gt; process() {
...
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>and</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public java.util.function.Consumer&lt;KStream&lt;Object, String&gt;&gt; anotherProcess() {
...
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Then you can set the application id for each, using the following binder level properties.</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.kafka.streams.binder.functions.process.applicationId</code></p>
</div>
<div class="paragraph">
<p>and</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.kafka.streams.binder.functions.anotherProcess.applicationId</code></p>
</div>
<div class="paragraph">
<p>In the case of <code>StreamListener</code>, you need to set this on the first input binding on the processor.</p>
</div>
<div class="paragraph">
<p>For e.g. imagine that you have the following two <code>StreamListener</code> based processors.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@StreamListener
@SendTo("output")
public KStream&lt;String, String&gt; process(@Input("input") &lt;KStream&lt;Object, String&gt;&gt; input) {
...
}
@StreamListener
@SendTo("anotherOutput")
public KStream&lt;String, String&gt; anotherProcess(@Input("anotherInput") &lt;KStream&lt;Object, String&gt;&gt; input) {
...
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Then you must set the application id for this using the following binding property.</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.kafka.streams.bindings.input.applicationId</code></p>
</div>
<div class="paragraph">
<p>and</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.kafka.streams.bindings.anotherInput.applicationId</code></p>
</div>
<div class="paragraph">
<p>For function based model also, this approach of setting application id at the binding level will work.
However, setting per function at the binder level as we have seen above is much easier if you are using the functional model.</p>
</div>
<div class="paragraph">
<p>For production deployments, it is highly recommended to explicitly specify the application ID through configuration.
This is especially going to be very critical if you are auto scaling your application in which case you need to make sure that you are deploying each instance with the same application ID.</p>
</div>
<div class="paragraph">
<p>If the application does not provide an application ID, then in that case the binder will auto generate a static application ID for you.
This is convenient in development scenarios as it avoids the need for explicitly providing the application ID.
The generated application ID in this manner will be static over application restarts.
In the case of functional model, the generated application ID will be the function bean name followed by the literal <code>applicationID</code>, for e.g <code>process-applicationID</code> if <code>process</code> if the function bean name.
In the case of <code>StreamListener</code>, instead of using the function bean name, the generated application ID will be use the containing class name followed by the method name followed by the literal <code>applicationId</code>.</p>
</div>
<div class="sect5">
<h6 id="_summary_of_setting_application_id"><a class="link" href="#_summary_of_setting_application_id">Summary of setting Application ID</a></h6>
<div class="ulist">
<ul>
<li>
<p>By default, binder will auto generate the application ID per function or <code>StreamListener</code> methods.</p>
</li>
<li>
<p>If you have a single processor, then you can use <code>spring.kafka.streams.applicationId</code>, <code>spring.application.name</code> or <code>spring.cloud.stream.kafka.streams.binder.applicationId</code>.</p>
</li>
<li>
<p>If you have multiple processors, then application ID can be set per function using the property - <code>spring.cloud.stream.kafka.streams.binder.functions.&lt;function-name&gt;.applicationId</code>.
In the case of <code>StreamListener</code>, this can be done using <code>spring.cloud.stream.kafka.streams.bindings.input.applicationId</code>, assuming that the input binding name is <code>input</code>.</p>
</li>
</ul>
</div>
</div>
</div>
<div class="sect3">
<h4 id="_overriding_the_default_binding_names_generated_by_the_binder_with_the_functional_style"><a class="link" href="#_overriding_the_default_binding_names_generated_by_the_binder_with_the_functional_style">2.4.3. Overriding the default binding names generated by the binder with the functional style</a></h4>
<div class="paragraph">
<p>By default, the binder uses the strategy discussed above to generate the binding name when using the functional style, i.e. &lt;function-bean-name&gt;-&lt;in&gt;|&lt;out&gt;-[0..n], for e.g. process-in-0, process-out-0 etc.
If you want to override those binding names, you can do that by specifying the following properties.</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.function.bindings.&lt;default binding name&gt;</code>. Default binding name is the original binding name generated by the binder.</p>
</div>
<div class="paragraph">
<p>For e.g. lets say, you have this function.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public BiFunction&lt;KStream&lt;String, Long&gt;, KTable&lt;String, String&gt;, KStream&lt;String, Long&gt;&gt; process() {
...
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Binder will generate bindings with names, <code>process-in-0</code>, <code>process-in-1</code> and <code>process-out-0</code>.
Now, if you want to change them to something else completely, maybe more domain specific binding names, then you can do so as below.</p>
</div>
<div class="paragraph">
<p><code>springc.cloud.stream.function.bindings.process-in-0=users</code></p>
</div>
<div class="paragraph">
<p><code>springc.cloud.stream.function.bindings.process-in-0=regions</code></p>
</div>
<div class="paragraph">
<p>and</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.function.bindings.process-out-0=clicks</code></p>
</div>
<div class="paragraph">
<p>After that, you must set all the binding level properties on these new binding names.</p>
</div>
<div class="paragraph">
<p>Please keep in mind that with the functional programming model described above, adhering to the default binding names make sense in most situations.
The only reason you may still want to do this overriding is when you have larger number of configuration properties and you want to map the bindings to something more domain friendly.</p>
</div>
</div>
<div class="sect3">
<h4 id="_setting_up_bootstrap_server_configuration"><a class="link" href="#_setting_up_bootstrap_server_configuration">2.4.4. Setting up bootstrap server configuration</a></h4>
<div class="paragraph">
<p>When running Kafka Streams applications, you must provide the Kafka broker server information.
If you don&#8217;t provide this information, the binder expects that you are running the broker at the default <code>localhost:9092</code>.
If that is not the case, then you need to override that. There are a couple of ways to do that.</p>
</div>
<div class="ulist">
<ul>
<li>
<p>Using the boot property - <code>spring.kafka.bootstrapServers</code></p>
</li>
<li>
<p>Binder level property - <code>spring.cloud.stream.kafka.streams.binder.brokers</code></p>
</li>
</ul>
</div>
<div class="paragraph">
<p>When it comes to the binder level property, it doesn&#8217;t matter if you use the broker property provided through the regular Kafka binder - <code>spring.cloud.stream.kafka.binder.brokers</code>.
Kafka Streams binder will first check if Kafka Streams binder specific broker property is set (<code>spring.cloud.stream.kafka.streams.binder.brokers</code>) and if not found, it looks for <code>spring.cloud.stream.kafka.binder.brokers</code>.</p>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_record_serialization_and_deserialization"><a class="link" href="#_record_serialization_and_deserialization">2.5. Record serialization and deserialization</a></h3>
<div class="paragraph">
<p>Kafka Streams binder allows you to serialize and deserialize records in two ways.
One is the native serialization and deserialization facilities provided by Kafka and the other one is the message conversion capabilities of Spring Cloud Stream framework.
Lets look at some details.</p>
</div>
<div class="sect3">
<h4 id="_inbound_deserialization"><a class="link" href="#_inbound_deserialization">2.5.1. Inbound deserialization</a></h4>
<div class="paragraph">
<p>Keys are always deserialized using native Serdes.</p>
</div>
<div class="paragraph">
<p>For values, by default, deserialization on the inbound is natively performed by Kafka.
Please note that this is a major change on default behavior from previous versions of Kafka Streams binder where the deserialization was done by the framework.</p>
</div>
<div class="paragraph">
<p>Kafka Streams binder will try to infer matching <code>Serde</code> types by looking at the type signature of <code>java.util.function.Function|Consumer</code> or <code>StreamListener</code>.
Here is the order that it matches the Serdes.</p>
</div>
<div class="ulist">
<ul>
<li>
<p>If the application provides a bean of type <code>Serde</code> and if the return type is parameterized with the actual type of the incoming key or value type, then it will use that <code>Serde</code> for inbound deserialization.
For e.g. if you have the following in the application, the binder detects that the incoming value type for the <code>KStream</code> matches with a type that is parameterized on a <code>Serde</code> bean.
It will use that for inbound deserialization.</p>
</li>
</ul>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public Serde&lt;Foo() customSerde{
...
}
@Bean
public Function&lt;KStream&lt;String, Foo&gt;, KStream&lt;String, Foo&gt;&gt; process() {
}</code></pre>
</div>
</div>
<div class="ulist">
<ul>
<li>
<p>Next, it looks at the types and see if they are one of the types exposed by Kafka Streams. If so, use them.
Here are the Serde types that the binder will try to match from Kafka Streams.</p>
<div class="literalblock">
<div class="content">
<pre>Integer, Long, Short, Double, Float, byte[], UUID and String.</pre>
</div>
</div>
</li>
<li>
<p>If none of the Serdes provided by Kafka Streams don&#8217;t match the types, then it will use JsonSerde provided by Spring Kafka. In this case, the binder assumes that the types are JSON friendly.
This is useful if you have multiple value objects as inputs since the binder will internally infer them to correct Java types.
Before falling back to the <code>JsonSerde</code> though, the binder checks at the default <code>Serde`s set in the Kafka Streams configuration to see if it is a `Serde</code> that it can match with the incoming KStream&#8217;s types.</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>If none of the above strategies worked, then the applications must provide the `Serde`s through configuration.
This can be configured in two ways - binding or default.</p>
</div>
<div class="paragraph">
<p>First the binder will look if a <code>Serde</code> is provided at the binding level.
For e.g. if you have the following processor,</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public BiFunction&lt;KStream&lt;CustomKey, AvroIn1&gt;, KTable&lt;CustomKey, AvroIn2&gt;, KStream&lt;CustomKey, AvroOutput&gt;&gt; process() {...}</code></pre>
</div>
</div>
<div class="paragraph">
<p>then, you can provide a binding level <code>Serde</code> using the following:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.bindings.process-in-0.consumer.keySerde=CustomKeySerde
spring.cloud.stream.kafka.streams.bindings.process-in-0.consumer.valueSerde=io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde
spring.cloud.stream.kafka.streams.bindings.process-in-1.consumer.keySerde=CustomKeySerde
spring.cloud.stream.kafka.streams.bindings.process-in-1.consumer.valueSerde=io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde</code></pre>
</div>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
If you provide <code>Serde</code> as abover per input binding, then that will takes higher precedence and the binder will stay away from any <code>Serde</code> inference.
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>If you want the default key/value Serdes to be used for inbound deserialization, you can do so at the binder level.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.binder.configuration.default.key.serde
spring.cloud.stream.kafka.streams.binder.configuration.default.value.serde</code></pre>
</div>
</div>
<div class="paragraph">
<p>If you don&#8217;t want the native decoding provided by Kafka, you can rely on the message conversion features that Spring Cloud Stream provides.
Since native decoding is the default, in order to let Spring Cloud Stream deserialize the inbound value object, you need to explicitly disable native decoding.</p>
</div>
<div class="paragraph">
<p>For e.g. if you have the same BiFunction processor as above, then <code>spring.cloud.stream.bindings.process-in-0.consumer.nativeDecoding: false</code>
You need to disable native decoding for all the inputs individually. Otherwise, native decoding will still be applied for those you do not disable.</p>
</div>
<div class="paragraph">
<p>By default, Spring Cloud Stream will use <code>application/json</code> as the content type and use an appropriate json message converter.
You can use custom message converters by using the following property and an appropriate <code>MessageConverter</code> bean.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.bindings.process-in-0.contentType</code></pre>
</div>
</div>
</div>
<div class="sect3">
<h4 id="_outbound_serialization"><a class="link" href="#_outbound_serialization">2.5.2. Outbound serialization</a></h4>
<div class="paragraph">
<p>Outbound serialization pretty much follows the same rules as above for inbound deserialization.
As with the inbound deserialization, one major change from the previous versions of Spring Cloud Stream is that the serialization on the outbound is handled by Kafka natively.
Before 3.0 versions of the binder, this was done by the framework itself.</p>
</div>
<div class="paragraph">
<p>Keys on the outbound are always serialized by Kafka using a matching <code>Serde</code> that is inferred by the binder.
If it can&#8217;t infer the type of the key, then that needs to be specified using configuration.</p>
</div>
<div class="paragraph">
<p>Value serdes are inferred using the same rules used for inbound deserialization.
First it matches to see if the outbound type is from a provided bean in the application.
If not, it checks to see if it matches with a <code>Serde</code> exposed by Kafka such as - <code>Integer</code>, <code>Long</code>, <code>Short</code>, <code>Double</code>, <code>Float</code>, <code>byte[]</code>, <code>UUID</code> and <code>String</code>.
If that doesnt&#8217;t work, then it falls back to <code>JsonSerde</code> provided by the Spring Kafka project, but first look at the default <code>Serde</code> configuration to see if there is a match.
Keep in mind that all these happen transparently to the application.
If none of these work, then the user has to provide the <code>Serde</code> to use by configuration.</p>
</div>
<div class="paragraph">
<p>Lets say you are using the same <code>BiFunction</code> processor as above. Then you can configure outbound key/value Serdes as following.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.bindings.process-out-0.producer.keySerde=CustomKeySerde
spring.cloud.stream.kafka.streams.bindings.process-out-0.producer.valueSerde=io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde</code></pre>
</div>
</div>
<div class="paragraph">
<p>If Serde inference fails, and no binding level Serdes are provided, then the binder falls back to the <code>JsonSerde</code>, but look at the default Serdes for a match.</p>
</div>
<div class="paragraph">
<p>Default serdes are configured in the same way as above where it is described under deserialization.</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.kafka.streams.binder.configuration.default.key.serde</code>
<code>spring.cloud.stream.kafka.streams.binder.configuration.default.value.serde</code></p>
</div>
<div class="paragraph">
<p>If your application uses the branching feature and has multiple output bindings, then these have to be configured per binding.
Once again, if the binder is capable of inferring the <code>Serde</code> types, you don&#8217;t need to do this configuration.</p>
</div>
<div class="paragraph">
<p>If you don&#8217;t want the native encoding provided by Kafka, but want to use the framework provided message conversion, then you need to explicitly disable native encoding since since native encoding is the default.
For e.g. if you have the same BiFunction processor as above, then <code>spring.cloud.stream.bindings.process-out-0.producer.nativeEncoding: false</code>
You need to disable native encoding for all the output individually in the case of branching. Otherwise, native encoding will still be applied for those you don&#8217;t disable.</p>
</div>
<div class="paragraph">
<p>When conversion is done by Spring Cloud Stream, by default, it will use <code>application/json</code> as the content type and use an appropriate json message converter.
You can use custom message converters by using the following property and a corresponding <code>MessageConverter</code> bean.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.bindings.process-out-0.contentType</code></pre>
</div>
</div>
<div class="paragraph">
<p>When native encoding/decoding is disabled, binder will not do any inference as in the case of native Serdes.
Applications need to explicitly provide all the configuration options.
For that reason, it is generally advised to stay with the default options for de/serialization and stick with native de/serialization provided by Kafka Streams when you write Spring Cloud Stream Kafka Streams applications.
The one scenario in which you must use message conversion capabilities provided by the framework is when your upstream producer is using a specific serialization strategy.
In that case, you want to use a matching deserialization strategy as native mechanisms may fail.
When relying on the default <code>Serde</code> mechanism, the applications must ensure that the binder has a way forward with correctly map the inbound and outbound with a proper <code>Serde</code>, as otherwise things might fail.</p>
</div>
<div class="paragraph">
<p>It is worth to mention that the data de/serialization approaches outlined above are only applicable on the edges of your processors, i.e. - inbound and outbound.
Your business logic might still need to call Kafka Streams API&#8217;s that explicitly need <code>Serde</code> objects.
Those are still the responsibility of the application and must be handled accordingly by the developer.</p>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_error_handling"><a class="link" href="#_error_handling">2.6. Error Handling</a></h3>
<div class="paragraph">
<p>Apache Kafka Streams provides the capability for natively handling exceptions from deserialization errors.
For details on this support, please see <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-161%3A+streams+deserialization+exception+handlers">this</a>.
Out of the box, Apache Kafka Streams provides two kinds of deserialization exception handlers - <code>LogAndContinueExceptionHandler</code> and <code>LogAndFailExceptionHandler</code>.
As the name indicates, the former will log the error and continue processing the next records and the latter will log the error and fail. <code>LogAndFailExceptionHandler</code> is the default deserialization exception handler.</p>
</div>
<div class="sect3">
<h4 id="_handling_deserialization_exceptions_in_the_binder"><a class="link" href="#_handling_deserialization_exceptions_in_the_binder">2.6.1. Handling Deserialization Exceptions in the Binder</a></h4>
<div class="paragraph">
<p>Kafka Streams binder allows to specify the deserialization exception handlers above using the following property.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.binder.deserializationExceptionHandler: logAndContinue</code></pre>
</div>
</div>
<div class="paragraph">
<p>or</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.binder.deserializationExceptionHandler: logAndFail</code></pre>
</div>
</div>
<div class="paragraph">
<p>In addition to the above two deserialization exception handlers, the binder also provides a third one for sending the erroneous records (poison pills) to a DLQ (dead letter queue) topic.
Here is how you enable this DLQ exception handler.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.binder.deserializationExceptionHandler: sendToDlq</code></pre>
</div>
</div>
<div class="paragraph">
<p>When the above property is set, all the records in deserialization error are automatically sent to the DLQ topic.</p>
</div>
<div class="paragraph">
<p>You can set the topic name where the DLQ messages are published as below.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.bindings.process-in-0.consumer.dlqName: custom-dlq (Change the binding name accordingly)</code></pre>
</div>
</div>
<div class="paragraph">
<p>If this is set, then the error records are sent to the topic <code>custom-dlq</code>.
If this is not set, then it will create a DLQ topic with the name <code>error.&lt;input-topic-name&gt;.&lt;application-id&gt;</code>.
For instance, if your binding&#8217;s destination topic is <code>inputTopic</code> and the application ID is <code>process-applicationId</code>, then the default DLQ topic is <code>error.inputTopic.process-applicationId</code>.
It is always recommended to explicitly create a DLQ topic for each input binding if it is your intention to enable DLQ.</p>
</div>
</div>
<div class="sect3">
<h4 id="_dlq_per_input_consumer_binding"><a class="link" href="#_dlq_per_input_consumer_binding">2.6.2. DLQ per input consumer binding</a></h4>
<div class="paragraph">
<p>The property <code>spring.cloud.stream.kafka.streams.binder.deserializationExceptionHandler</code> is applicable for the entire application.
This implies that if there are multiple functions or <code>StreamListener</code> methods in the same application, this property is applied to all of them.
However, if you have multiple processors or multiple input bindings within a single processor, then you can use the finer-grained DLQ control that the binder provides per input consumer binding.</p>
</div>
<div class="paragraph">
<p>If you have the following processor,</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public BiFunction&lt;KStream&lt;String, Long&gt;, KTable&lt;String, String&gt;, KStream&lt;String, Long&gt;&gt; process() {
...
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>and you only want to enable DLQ on the first input binding and logAndSkip on the second binding, then you can do so on the consumer as below.</p>
</div>
<div class="paragraph">
<p><code>spring.cloud.stream.kafka.streams.bindings.process-in-0.consumer.deserializationExceptionHandler: sendToDlq</code>
<code>spring.cloud.stream.kafka.streams.bindings.process-in-1.consumer.deserializationExceptionHandler: logAndSkip</code></p>
</div>
<div class="paragraph">
<p>Setting deserialization exception handlers this way has a higher precedence than setting at the binder level.</p>
</div>
</div>
<div class="sect3">
<h4 id="_dlq_partitioning"><a class="link" href="#_dlq_partitioning">2.6.3. DLQ partitioning</a></h4>
<div class="paragraph">
<p>By default, records are published to the Dead-Letter topic using the same partition as the original record.
This means the Dead-Letter topic must have at least as many partitions as the original record.</p>
</div>
<div class="paragraph">
<p>To change this behavior, add a <code>DlqPartitionFunction</code> implementation as a <code>@Bean</code> to the application context.
Only one such bean can be present.
The function is provided with the consumer group (which is the same as the application ID in most situations), the failed <code>ConsumerRecord</code> and the exception.
For example, if you always want to route to partition 0, you might use:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-java hljs" data-lang="java">@Bean
public DlqPartitionFunction partitionFunction() {
return (group, record, ex) -&gt; 0;
}</code></pre>
</div>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
If you set a consumer binding&#8217;s <code>dlqPartitions</code> property to 1 (and the binder&#8217;s <code>minPartitionCount</code> is equal to <code>1</code>), there is no need to supply a <code>DlqPartitionFunction</code>; the framework will always use partition 0.
If you set a consumer binding&#8217;s <code>dlqPartitions</code> property to a value greater than <code>1</code> (or the binder&#8217;s <code>minPartitionCount</code> is greater than <code>1</code>), you <strong>must</strong> provide a <code>DlqPartitionFunction</code> bean, even if the partition count is the same as the original topic&#8217;s.
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>A couple of things to keep in mind when using the exception handling feature in Kafka Streams binder.</p>
</div>
<div class="ulist">
<ul>
<li>
<p>The property <code>spring.cloud.stream.kafka.streams.binder.deserializationExceptionHandler</code> is applicable for the entire application.
This implies that if there are multiple functions or <code>StreamListener</code> methods in the same application, this property is applied to all of them.</p>
</li>
<li>
<p>The exception handling for deserialization works consistently with native deserialization and framework provided message conversion.</p>
</li>
</ul>
</div>
</div>
<div class="sect3">
<h4 id="_handling_production_exceptions_in_the_binder"><a class="link" href="#_handling_production_exceptions_in_the_binder">2.6.4. Handling Production Exceptions in the Binder</a></h4>
<div class="paragraph">
<p>Unlike the support for deserialization exception handlers as described above, the binder does not provide such first class mechanisms for handling production exceptions.
However, you still can configure production exception handlers using the <code>StreamsBuilderFactoryBean</code> customizer which you can find more details about, in a subsequent section below.</p>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_state_store"><a class="link" href="#_state_store">2.7. State Store</a></h3>
<div class="paragraph">
<p>State stores are created automatically by Kafka Streams when the high level DSL is used and appropriate calls are made those trigger a state store.</p>
</div>
<div class="paragraph">
<p>If you want to materialize an incoming <code>KTable</code> binding as a named state store, then you can do so by using the following strategy.</p>
</div>
<div class="paragraph">
<p>Lets say you have the following function.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public BiFunction&lt;KStream&lt;String, Long&gt;, KTable&lt;String, String&gt;, KStream&lt;String, Long&gt;&gt; process() {
...
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Then by setting the following property, the incoming <code>KTable</code> data will be materialized in to the named state store.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.bindings.process-in-1.consumer.materializedAs: incoming-store</code></pre>
</div>
</div>
<div class="paragraph">
<p>You can define custom state stores as beans in your application and those will be detected and added to the Kafka Streams builder by the binder.
Especially when the processor API is used, you need to register a state store manually.
In order to do so, you can create the StateStore as a bean in the application.
Here are examples of defining such beans.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public StoreBuilder myStore() {
return Stores.keyValueStoreBuilder(
Stores.persistentKeyValueStore("my-store"), Serdes.Long(),
Serdes.Long());
}
@Bean
public StoreBuilder otherStore() {
return Stores.windowStoreBuilder(
Stores.persistentWindowStore("other-store",
1L, 3, 3L, false), Serdes.Long(),
Serdes.Long());
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>These state stores can be then accessed by the applications directly.</p>
</div>
<div class="paragraph">
<p>During the bootstrap, the above beans will be processed by the binder and passed on to the Streams builder object.</p>
</div>
<div class="paragraph">
<p>Accessing the state store:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>Processor&lt;Object, Product&gt;() {
WindowStore&lt;Object, String&gt; state;
@Override
public void init(ProcessorContext processorContext) {
state = (WindowStore)processorContext.getStateStore("mystate");
}
...
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>This will not work when it comes to registering global state stores.
In order to register a global state store, please see the section below on customizing <code>StreamsBuilderFactoryBean</code>.</p>
</div>
</div>
<div class="sect2">
<h3 id="_interactive_queries"><a class="link" href="#_interactive_queries">2.8. Interactive Queries</a></h3>
<div class="paragraph">
<p>Kafka Streams binder API exposes a class called <code>InteractiveQueryService</code> to interactively query the state stores.
You can access this as a Spring bean in your application. An easy way to get access to this bean from your application is to <code>autowire</code> the bean.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Autowired
private InteractiveQueryService interactiveQueryService;</code></pre>
</div>
</div>
<div class="paragraph">
<p>Once you gain access to this bean, then you can query for the particular state-store that you are interested. See below.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>ReadOnlyKeyValueStore&lt;Object, Object&gt; keyValueStore =
interactiveQueryService.getQueryableStoreType("my-store", QueryableStoreTypes.keyValueStore());</code></pre>
</div>
</div>
<div class="paragraph">
<p>During the startup, the above method call to retrieve the store might fail.
For e.g it might still be in the middle of initializing the state store.
In such cases, it will be useful to retry this operation.
Kafka Streams binder provides a simple retry mechanism to accommodate this.</p>
</div>
<div class="paragraph">
<p>Following are the two properties that you can use to control this retrying.</p>
</div>
<div class="ulist">
<ul>
<li>
<p>spring.cloud.stream.kafka.streams.binder.stateStoreRetry.maxAttempts - Default is <code>1</code> .</p>
</li>
<li>
<p>spring.cloud.stream.kafka.streams.binder.stateStoreRetry.backOffInterval - Default is <code>1000</code> milliseconds.</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>If there are multiple instances of the kafka streams application running, then before you can query them interactively, you need to identify which application instance hosts the particular key that you are querying.
<code>InteractiveQueryService</code> API provides methods for identifying the host information.</p>
</div>
<div class="paragraph">
<p>In order for this to work, you must configure the property <code>application.server</code> as below:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.binder.configuration.application.server: &lt;server&gt;:&lt;port&gt;</code></pre>
</div>
</div>
<div class="paragraph">
<p>Here are some code snippets:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>org.apache.kafka.streams.state.HostInfo hostInfo = interactiveQueryService.getHostInfo("store-name",
key, keySerializer);
if (interactiveQueryService.getCurrentHostInfo().equals(hostInfo)) {
//query from the store that is locally available
}
else {
//query from the remote host
}</code></pre>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_health_indicator"><a class="link" href="#_health_indicator">2.9. Health Indicator</a></h3>
<div class="paragraph">
<p>The health indicator requires the dependency <code>spring-boot-starter-actuator</code>. For maven use:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code class="language-xml hljs" data-lang="xml">&lt;dependency&gt;
&lt;groupId&gt;org.springframework.boot&lt;/groupId&gt;
&lt;artifactId&gt;spring-boot-starter-actuator&lt;/artifactId&gt;
&lt;/dependency&gt;</code></pre>
</div>
</div>
<div class="paragraph">
<p>Spring Cloud Stream Kafka Streams Binder provides a health indicator to check the state of the underlying streams threads.
Spring Cloud Stream defines a property <code>management.health.binders.enabled</code> to enable the health indicator. See the
<a href="https://docs.spring.io/spring-cloud-stream/docs/current/reference/htmlsingle/#_health_indicator">Spring Cloud Stream documentation</a>.</p>
</div>
<div class="paragraph">
<p>The health indicator provides the following details for each stream thread&#8217;s metadata:</p>
</div>
<div class="ulist">
<ul>
<li>
<p>Thread name</p>
</li>
<li>
<p>Thread state: <code>CREATED</code>, <code>RUNNING</code>, <code>PARTITIONS_REVOKED</code>, <code>PARTITIONS_ASSIGNED</code>, <code>PENDING_SHUTDOWN</code> or <code>DEAD</code></p>
</li>
<li>
<p>Active tasks: task ID and partitions</p>
</li>
<li>
<p>Standby tasks: task ID and partitions</p>
</li>
</ul>
</div>
<div class="paragraph">
<p>By default, only the global status is visible (<code>UP</code> or <code>DOWN</code>). To show the details, the property <code>management.endpoint.health.show-details</code> must be set to <code>ALWAYS</code> or <code>WHEN_AUTHORIZED</code>.
For more details about the health information, see the
<a href="https://docs.spring.io/spring-boot/docs/current/reference/html/production-ready-endpoints.html#production-ready-health">Spring Boot Actuator documentation</a>.</p>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
The status of the health indicator is <code>UP</code> if all the Kafka threads registered are in the <code>RUNNING</code> state.
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>Since there are three individual binders in Kafka Streams binder (<code>KStream</code>, <code>KTable</code> and <code>GlobalKTable</code>), all of them will report the health status.
When enabling <code>show-details</code>, some of the information reported may be redundant.</p>
</div>
<div class="paragraph">
<p>When there are multiple Kafka Streams processors present in the same application, then the health checks will be reported for all of them and will be categorized by the application ID of Kafka Streams.</p>
</div>
</div>
<div class="sect2">
<h3 id="_accessing_kafka_streams_metrics"><a class="link" href="#_accessing_kafka_streams_metrics">2.10. Accessing Kafka Streams Metrics</a></h3>
<div class="paragraph">
<p>Spring Cloud Stream Kafka Streams binder provides a basic mechanism for accessing Kafka Streams metrics exported through a Micrometer <code>MeterRegistry</code>.
Kafka Streams metrics that are available through <code>KafkaStreams#metrics()</code> are exported to this meter registry by the binder.
The metrics exported are from the consumers, producers, admin-client and the stream itself.</p>
</div>
<div class="paragraph">
<p>The metrics exported by the binder are exported with the format of metrics group name followed by a dot and then the actual metric name.
All dashes in the original metric information is replaced with dots.</p>
</div>
<div class="paragraph">
<p>For e.g. the metric name <code>network-io-total</code> from the metric group <code>consumer-metrics</code> is available in the micrometer registry as <code>consumer.metrics.network.io.total</code>.
Similarly, the metric <code>commit-total</code> from <code>stream-metrics</code> is available as <code>stream.metrics.commit.total</code>.</p>
</div>
<div class="paragraph">
<p>If you have multiple Kafka Streams processors in the same application, then the metric name will be prepended with the corresponding application ID of the Kafka Streams.
The application ID in this case will be preserved as is, i.e. no dashes will be converted to dots etc.
For example, if the application ID of the first processor is <code>processor-1</code>, then the metric name <code>network-io-total</code> from the metric group <code>consumer-metrics</code> is available in the micrometer registry as <code>processor-1.consumer.metrics.network.io.total</code>.</p>
</div>
<div class="paragraph">
<p>You can either programmatically access the Micrometer <code>MeterRegistry</code> in the application and then iterate through the available gauges or use Spring Boot actuator to access the metrics through a REST endpoint.
When accessing through the Boot actuator endpoint, make sure to add <code>metrics</code> to the property <code>management.endpoints.web.exposure.include</code>.
Then you can access <code>/acutator/metrics</code> to get a list of all the available metrics which then can be individually accessed through the same URI (<code>/actuator/metrics/&lt;metric-name&gt;</code>).</p>
</div>
<div class="paragraph">
<p>Anything beyond the info level metrics available through <code>KafkaStreams#metrics()</code>, (for e.g. the debugging level metrics) are still only available through JMX after you set the <code>metrics.recording.level</code> to <code>DEBUG</code>.
Kafka Streams, by default, set this level to <code>INFO</code>.
<a href="https://kafka.apache.org/documentation/#kafka_streams_monitoring">Please see this section</a> from Kafka Streams documentation for more details.
In a future release, binder may support exporting these DEBUG level metrics through Micrometer.</p>
</div>
</div>
<div class="sect2">
<h3 id="_mixing_high_level_dsl_and_low_level_processor_api"><a class="link" href="#_mixing_high_level_dsl_and_low_level_processor_api">2.11. Mixing high level DSL and low level Processor API</a></h3>
<div class="paragraph">
<p>Kafka Streams provides two variants of APIs.
It has a higher level DSL like API where you can chain various operations that maybe familiar to a lot of functional programmers.
Kafka Streams also gives access to a low level Processor API.
The processor API, although very powerful and gives the ability to control things in a much lower level, is imperative in nature.
Kafka Streams binder for Spring Cloud Stream, allows you to use either the high level DSL or mixing both the DSL and the processor API.
Mixing both of these variants give you a lot of options to control various use cases in an application.
Applications can use the <code>transform</code> or <code>process</code> method API calls to get access to the processor API.</p>
</div>
<div class="paragraph">
<p>Here is a look at how one may combine both the DSL and the processor API in a Spring Cloud Stream application using the <code>process</code> API.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public Consumer&lt;KStream&lt;Object, String&gt;&gt; process() {
return input -&gt;
input.process(() -&gt; new Processor&lt;Object, String&gt;() {
@Override
@SuppressWarnings("unchecked")
public void init(ProcessorContext context) {
this.context = context;
}
@Override
public void process(Object key, String value) {
//business logic
}
@Override
public void close() {
});
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Here is an example using the <code>transform</code> API.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public Consumer&lt;KStream&lt;Object, String&gt;&gt; process() {
return (input, a) -&gt;
input.transform(() -&gt; new Transformer&lt;Object, String, KeyValue&lt;Object, String&gt;&gt;() {
@Override
public void init(ProcessorContext context) {
}
@Override
public void close() {
}
@Override
public KeyValue&lt;Object, String&gt; transform(Object key, String value) {
// business logic - return transformed KStream;
}
});
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>The <code>process</code> API method call is a terminal operation while the <code>transform</code> API is non terminal and gives you a potentially transformed <code>KStream</code> using which you can continue further processing using either the DSL or the processor API.</p>
</div>
</div>
<div class="sect2">
<h3 id="_partition_support_on_the_outbound"><a class="link" href="#_partition_support_on_the_outbound">2.12. Partition support on the outbound</a></h3>
<div class="paragraph">
<p>A Kafka Streams processor usually sends the processed output into an outbound Kafka topic.
If the outbound topic is partitioned and the processor needs to send the outgoing data into particular partitions, the applications needs to provide a bean of type <code>StreamPartitioner</code>.
See <a href="https://kafka.apache.org/23/javadoc/org/apache/kafka/streams/processor/StreamPartitioner.html">StreamPartitioner</a> for more details.
Let&#8217;s see some examples.</p>
</div>
<div class="paragraph">
<p>This is the same processor we already saw multiple times,</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public Function&lt;KStream&lt;Object, String&gt;, KStream&lt;?, WordCount&gt;&gt; process() {
...
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Here is the output binding destination:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.bindings.process-out-0.destination: outputTopic</code></pre>
</div>
</div>
<div class="paragraph">
<p>If the topic <code>outputTopic</code> has 4 partitions, if you don&#8217;t provide a partitioning strategy, Kafka Streams will use default partitioning strategy which may not be the outcome you want depending on the particular use case.
Let&#8217;s say, you want to send any key that matches to <code>spring</code> to partition 0, <code>cloud</code> to partition 1, <code>stream</code> to partition 2, and everything else to partition 3.
This is what you need to do in the application.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public StreamPartitioner&lt;String, WordCount&gt; streamPartitioner() {
return (t, k, v, n) -&gt; {
if (k.equals("spring")) {
return 0;
}
else if (k.equals("cloud")) {
return 1;
}
else if (k.equals("stream")) {
return 2;
}
else {
return 3;
}
};
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>This is a rudimentary implementation, however, you have access to the key/value of the record, the topic name and the total number of partitions.
Therefore, you can implement complex partitioning strategies if need be.</p>
</div>
<div class="paragraph">
<p>You also need to provide this bean name along with the application configuration.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.bindings.process-out-0.producer.streamPartitionerBeanName: streamPartitioner</code></pre>
</div>
</div>
<div class="paragraph">
<p>Each output topic in the application needs to be configured separately like this.</p>
</div>
</div>
<div class="sect2">
<h3 id="_streamsbuilderfactorybean_customizer"><a class="link" href="#_streamsbuilderfactorybean_customizer">2.13. StreamsBuilderFactoryBean customizer</a></h3>
<div class="paragraph">
<p>It is often required to customize the <code>StreamsBuilderFactoryBean</code> that creates the <code>KafkaStreams</code> objects.
Based on the underlying support provided by Spring Kafka, the binder allows you to customize the <code>StreamsBuilderFactoryBean</code>.
You can use the <code>StreamsBuilderFactoryBeanCustomizer</code> to customize the <code>StreamsBuilderFactoryBean</code> itself.
Then, once you get access to the <code>StreamsBuilderFactoryBean</code> through this customizer, you can customize the corresponding <code>KafkaStreams</code> using <code>KafkaStreamsCustomzier</code>.
Both of these customizers are part of the Spring for Apache Kafka project.</p>
</div>
<div class="paragraph">
<p>Here is an example of using the <code>StreamsBuilderFactoryBeanCustomizer</code>.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public StreamsBuilderFactoryBeanCustomizer streamsBuilderFactoryBeanCustomizer() {
return sfb -&gt; sfb.setStateListener((newState, oldState) -&gt; {
//Do some action here!
});
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>The above is shown as an illustration of the things you can do to customize the <code>StreamsBuilderFactoryBean</code>.
You can essentially call any available mutation operations from <code>StreamsBuilderFactoryBean</code> to customize it.
This customizer will be invoked by the binder right before the factory bean is started.</p>
</div>
<div class="paragraph">
<p>Once you get access to the <code>StreamsBuilderFactoryBean</code>, you can also customize the underlying <code>KafkaStreams</code> object.
Here is a blueprint for doing so.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public StreamsBuilderFactoryBeanCustomizer streamsBuilderFactoryBeanCustomizer() {
return factoryBean -&gt; {
factoryBean.setKafkaStreamsCustomizer(new KafkaStreamsCustomizer() {
@Override
public void customize(KafkaStreams kafkaStreams) {
kafkaStreams.setUncaughtExceptionHandler((t, e) -&gt; {
});
}
});
};
}</code></pre>
</div>
</div>
<div class="paragraph">
<p><code>KafkaStreamsCustomizer</code> will be called by the <code>StreamsBuilderFactoryBeabn</code> right before the underlying <code>KafkaStreams</code> gets started.</p>
</div>
<div class="paragraph">
<p>There can only be one <code>StreamsBuilderFactoryBeanCustomizer</code> in the entire application.
Then how do we account for multiple Kafka Streams processors as each of them are backed up by individual <code>StreamsBuilderFactoryBean</code> objects?
In that case, if the customization needs to be different for those processors, then the application needs to apply some filter based on the application ID.</p>
</div>
<div class="paragraph">
<p>For e.g,</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public StreamsBuilderFactoryBeanCustomizer streamsBuilderFactoryBeanCustomizer() {
return factoryBean -&gt; {
if (factoryBean.getStreamsConfiguration().getProperty(StreamsConfig.APPLICATION_ID_CONFIG)
.equals("processor1-application-id")) {
factoryBean.setKafkaStreamsCustomizer(new KafkaStreamsCustomizer() {
@Override
public void customize(KafkaStreams kafkaStreams) {
kafkaStreams.setUncaughtExceptionHandler((t, e) -&gt; {
});
}
});
}
};</code></pre>
</div>
</div>
<div class="sect3">
<h4 id="_using_customizer_to_register_a_global_state_store"><a class="link" href="#_using_customizer_to_register_a_global_state_store">2.13.1. Using Customizer to register a global state store</a></h4>
<div class="paragraph">
<p>As mentioned above, the binder does not provide a first class way to register global state stores as a feature.
For that, you need to use the customizer.
Here is how that can be done.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public StreamsBuilderFactoryBeanCustomizer customizer() {
return fb -&gt; {
try {
final StreamsBuilder streamsBuilder = fb.getObject();
streamsBuilder.addGlobalStore(...);
}
catch (Exception e) {
}
};
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Again, if you have multiple processors, you want to attach the global state store to the right <code>StreamsBuilder</code> by filtering out the other <code>StreamsBuilderFactoryBean</code> objects using the application id as outlined above.</p>
</div>
</div>
<div class="sect3">
<h4 id="_using_customizer_to_register_a_production_exception_handler"><a class="link" href="#_using_customizer_to_register_a_production_exception_handler">2.13.2. Using customizer to register a production exception handler</a></h4>
<div class="paragraph">
<p>In the error handling section, we indicated that the binder does not provide a first class way to deal with production exceptions.
Though that is the case, you can still use the <code>StreamsBuilderFacotryBean</code> customizer to register production exception handlers. See below.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public StreamsBuilderFactoryBeanCustomizer customizer() {
return fb -&gt; {
fb.getStreamsConfiguration().put(StreamsConfig.DEFAULT_PRODUCTION_EXCEPTION_HANDLER_CLASS_CONFIG,
CustomProductionExceptionHandler.class);
};
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Once again, if you have multiple processors, you may want to set it appropriately against the correct <code>StreamsBuilderFactoryBean</code>.
You may also add such production exception handlers using the configuration property (See below for more on that), but this is an option if you choose to go with a programmatic approach.</p>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_timestamp_extractor"><a class="link" href="#_timestamp_extractor">2.14. Timestamp extractor</a></h3>
<div class="paragraph">
<p>Kafka Streams allows you to control the processing of the consumer records based on various notions of timestamp.
By default, Kafka Streams extracts the timestamp metadata embedded in the consumer record.
You can change this default behavior by providing a different <code>TimestampExtractor</code> implementation per input binding.
Here are some details on how that can be done.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public Function&lt;KStream&lt;Long, Order&gt;,
Function&lt;KTable&lt;Long, Customer&gt;,
Function&lt;GlobalKTable&lt;Long, Product&gt;, KStream&lt;Long, Order&gt;&gt;&gt;&gt; process() {
return orderStream -&gt;
customers -&gt;
products -&gt; orderStream;
}
@Bean
public TimestampExtractor timestampExtractor() {
return new WallclockTimestampExtractor();
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>Then you set the above <code>TimestampExtractor</code> bean name per consumer binding.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.bindings.process-in-0.consumer.timestampExtractorBeanName=timestampExtractor
spring.cloud.stream.kafka.streams.bindings.process-in-1.consumer.timestampExtractorBeanName=timestampExtractor
spring.cloud.stream.kafka.streams.bindings.process-in-2.consumer.timestampExtractorBeanName=timestampExtractor"</code></pre>
</div>
</div>
<div class="paragraph">
<p>If you skip an input consumer binding for setting a custom timestamp extractor, that consumer will use the default settings.</p>
</div>
</div>
<div class="sect2">
<h3 id="_multi_binders_with_kafka_streams_based_binders_and_regular_kafka_binder"><a class="link" href="#_multi_binders_with_kafka_streams_based_binders_and_regular_kafka_binder">2.15. Multi binders with Kafka Streams based binders and regular Kafka Binder</a></h3>
<div class="paragraph">
<p>You can have an application where you have both a function/consumer/supplier that is based on the regular Kafka binder and a Kafka Streams based processor.
However, you cannot mix both of them within a single function or consumer.</p>
</div>
<div class="paragraph">
<p>Here is an example, where you have both binder based components within the same application.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public Function&lt;String, String&gt; process() {
return s -&gt; s;
}
@Bean
public Function&lt;KStream&lt;Object, String&gt;, KStream&lt;?, WordCount&gt;&gt; kstreamProcess() {
return input -&gt; input;
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>This is the relevant parts from the configuration:</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.function.definition=process;kstreamProcess
spring.cloud.stream.bindings.process-in-0.destination=foo
spring.cloud.stream.bindings.process-out-0.destination=bar
spring.cloud.stream.bindings.kstreamProcess-in-0.destination=bar
spring.cloud.stream.bindings.kstreamProcess-out-0.destination=foobar</code></pre>
</div>
</div>
<div class="paragraph">
<p>Things become a bit more complex if you have the same application as above, but is dealing with two different Kafka clusters, for e.g. the regular <code>process</code> is acting upon both Kafka cluster 1 and cluster 2 (receiving data from cluster-1 and sending to cluster-2) and the Kafka Streams processor is acting upon Kafka cluster 2.
Then you have to use the <a href="https://cloud.spring.io/spring-cloud-stream/reference/html/spring-cloud-stream.html#multiple-binders">multi binder</a> facilities provided by Spring Cloud Stream.</p>
</div>
<div class="paragraph">
<p>Here is how your configuration may change in that scenario.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code># multi binder configuration
spring.cloud.stream.binders.kafka1.type: kafka
spring.cloud.stream.binders.kafka1.environment.spring.cloud.stream.kafka.streams.binder.brokers=${kafkaCluster-1} #Replace kafkaCluster-1 with the approprate IP of the cluster
spring.cloud.stream.binders.kafka2.type: kafka
spring.cloud.stream.binders.kafka2.environment.spring.cloud.stream.kafka.streams.binder.brokers=${kafkaCluster-2} #Replace kafkaCluster-2 with the approprate IP of the cluster
spring.cloud.stream.binders.kafka3.type: kstream
spring.cloud.stream.binders.kafka3.environment.spring.cloud.stream.kafka.streams.binder.brokers=${kafkaCluster-2} #Replace kafkaCluster-2 with the approprate IP of the cluster
spring.cloud.stream.function.definition=process;kstreamProcess
# From cluster 1 to cluster 2 with regular process function
spring.cloud.stream.bindings.process-in-0.destination=foo
spring.cloud.stream.bindings.process-in-0.binder=kafka1 # source from cluster 1
spring.cloud.stream.bindings.process-out-0.destination=bar
spring.cloud.stream.bindings.process-out-0.binder=kafka2 # send to cluster 2
# Kafka Streams processor on cluster 2
spring.cloud.stream.bindings.kstreamProcess-in-0.destination=bar
spring.cloud.stream.bindings.kstreamProcess-in-0.binder=kafka3
spring.cloud.stream.bindings.kstreamProcess-out-0.destination=foobar
spring.cloud.stream.bindings.kstreamProcess-out-0.binder=kafka3</code></pre>
</div>
</div>
<div class="paragraph">
<p>Pay attention to the above configuration.
We have two kinds of binders, but 3 binders all in all, first one is the regular Kafka binder based on cluster 1 (<code>kafka1</code>), then another Kafka binder based on cluster 2 (<code>kafka2</code>) and finally the <code>kstream</code> one (<code>kafka3</code>).
The first processor in the application receives data from <code>kafka1</code> and publishes to <code>kafka2</code> where both binders are based on regular Kafka binder but differnt clusters.
The second processor, which is a Kafka Streams processor consumes data from <code>kafka3</code> which is the same cluster as <code>kafka2</code>, but a different binder type.</p>
</div>
<div class="paragraph">
<p>Since there are three different binder types available in the Kafka Streams family of binders - <code>kstream</code>, <code>ktable</code> and <code>globalktable</code> - if your application has multiple bindings based on any of these binders, that needs to be explicitly provided as the binder type.</p>
</div>
<div class="paragraph">
<p>For e.g if you have a processor as below,</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>@Bean
public Function&lt;KStream&lt;Long, Order&gt;,
Function&lt;KTable&lt;Long, Customer&gt;,
Function&lt;GlobalKTable&lt;Long, Product&gt;, KStream&lt;Long, EnrichedOrder&gt;&gt;&gt;&gt; enrichOrder() {
...
}</code></pre>
</div>
</div>
<div class="paragraph">
<p>then, this has to be configured in a multi binder scenario as the following.
Please note that this is only needed if you have a true multi-binder scenario where there are multiple processors dealing with multiple clusters within a single application.
In that case, the binders need to be explicitly provided with the bindings to distinguish from other processor&#8217;s binder types and clusters.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.binders.kafka1.type: kstream
spring.cloud.stream.binders.kafka1.environment.spring.cloud.stream.kafka.streams.binder.brokers=${kafkaCluster-2}
spring.cloud.stream.binders.kafka2.type: ktable
spring.cloud.stream.binders.kafka2.environment.spring.cloud.stream.kafka.streams.binder.brokers=${kafkaCluster-2}
spring.cloud.stream.binders.kafka3.type: globalktable
spring.cloud.stream.binders.kafka3.environment.spring.cloud.stream.kafka.streams.binder.brokers=${kafkaCluster-2}
spring.cloud.stream.bindings.enrichOrder-in-0.binder=kafka1 #kstream
spring.cloud.stream.bindings.enrichOrder-in-1.binder=kafka2 #ktablr
spring.cloud.stream.bindings.enrichOrder-in-2.binder=kafka3 #globalktable
spring.cloud.stream.bindings.enrichOrder-out-0.binder=kafka1 #kstream
# rest of the configuration is omitted.</code></pre>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_state_cleanup"><a class="link" href="#_state_cleanup">2.16. State Cleanup</a></h3>
<div class="paragraph">
<p>By default, the <code>Kafkastreams.cleanup()</code> method is called when the binding is stopped.
See <a href="https://docs.spring.io/spring-kafka/reference/html/_reference.html#_configuration">the Spring Kafka documentation</a>.
To modify this behavior simply add a single <code>CleanupConfig</code> <code>@Bean</code> (configured to clean up on start, stop, or neither) to the application context; the bean will be detected and wired into the factory bean.</p>
</div>
</div>
<div class="sect2">
<h3 id="_configuration_options_2"><a class="link" href="#_configuration_options_2">2.17. Configuration Options</a></h3>
<div class="paragraph">
<p>This section contains the configuration options used by the Kafka Streams binder.</p>
</div>
<div class="paragraph">
<p>For common configuration options and properties pertaining to binder, refer to the <a href="#binding-properties">core documentation</a>.</p>
</div>
<div class="sect3">
<h4 id="_kafka_streams_binder_properties"><a class="link" href="#_kafka_streams_binder_properties">2.17.1. Kafka Streams Binder Properties</a></h4>
<div class="paragraph">
<p>The following properties are available at the binder level and must be prefixed with <code>spring.cloud.stream.kafka.streams.binder.</code></p>
</div>
<div class="dlist">
<dl>
<dt class="hdlist1">configuration</dt>
<dd>
<p> Map with a key/value pair containing properties pertaining to Apache Kafka Streams API.
This property must be prefixed with <code>spring.cloud.stream.kafka.streams.binder.</code>.
Following are some examples of using this property.</p>
</dd>
</dl>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlightjs highlight"><code>spring.cloud.stream.kafka.streams.binder.configuration.default.key.serde=org.apache.kafka.common.serialization.Serdes$StringSerde
spring.cloud.stream.kafka.streams.binder.configuration.default.value.serde=org.apache.kafka.common.serialization.Serdes$StringSerde
spring.cloud.stream.kafka.streams.binder.configuration.commit.interval.ms=1000</code></pre>
</div>
</div>
<div class="paragraph">
<p>For more information about all the properties that may go into streams configuration, see <code>StreamsConfig</code> JavaDocs in Apache Kafka Streams docs.
All configuration that you can set from <code>StreamsConfig</code> can be set through this.
When using this property, it is applicable against the entire application since this is a binder level property.
If you have more than processors in the application, all of them will acquire these properties.
In the case of properties like <code>application.id</code>, this will become problematic and therefore you have to carefully examine how the properties from <code>StreamsConfig</code> are mapped using this binder level <code>configuration</code> property.</p>
</div>
<div class="dlist">
<dl>
<dt class="hdlist1">functions.&lt;function-bean-name&gt;.applicationId</dt>
<dd>
<p>Applicable only for functional style processors.
This can be used for setting application ID per function in the application.
In the case of multiple functions, this is a handy way to set the application ID.</p>
</dd>
<dt class="hdlist1">functions.&lt;function-bean-name&gt;.configuration</dt>
<dd>
<p>Applicable only for functional style processors.
Map with a key/value pair containing properties pertaining to Apache Kafka Streams API.
This is similar to the binder level <code>configuration</code> property describe above, but this level of <code>configuration</code> property is restricted only against the named function.
When you have multiple processors and you want to restrict access to the configuration based on particular functions, you might want to use this.
All <code>StreamsConfig</code> properties can be used here.</p>
</dd>
<dt class="hdlist1">brokers</dt>
<dd>
<p>Broker URL</p>
<div class="paragraph">
<p>Default: <code>localhost</code></p>
</div>
</dd>
<dt class="hdlist1">zkNodes</dt>
<dd>
<p>Zookeeper URL</p>
<div class="paragraph">
<p>Default: <code>localhost</code></p>
</div>
</dd>
<dt class="hdlist1">deserializationExceptionHandler</dt>
<dd>
<p>Deserialization error handler type.
This handler is applied at the binder level and thus applied against all input binding in the application.
There is a way to control it in a more fine-grained way at the consumer binding level.
Possible values are - <code>logAndContinue</code>, <code>logAndFail</code> or <code>sendToDlq</code></p>
<div class="paragraph">
<p>Default: <code>logAndFail</code></p>
</div>
</dd>
<dt class="hdlist1">applicationId</dt>
<dd>
<p>Convenient way to set the application.id for the Kafka Streams application globally at the binder level.
If the application contains multiple functions or <code>StreamListener</code> methods, then the application id should be set differently.
See above where setting the application id is discussed in detail.</p>
<div class="paragraph">
<p>Default: application will generate a static application ID. See the application ID section for more details.</p>
</div>
</dd>
<dt class="hdlist1">stateStoreRetry.maxAttempts</dt>
<dd>
<p>Max attempts for trying to connect to a state store.</p>
<div class="paragraph">
<p>Default: 1</p>
</div>
</dd>
<dt class="hdlist1">stateStoreRetry.backoffPeriod</dt>
<dd>
<p>Backoff period when trying to connect to a state store on a retry.</p>
<div class="paragraph">
<p>Default: 1000 ms</p>
</div>
</dd>
</dl>
</div>
</div>
<div class="sect3">
<h4 id="_kafka_streams_producer_properties"><a class="link" href="#_kafka_streams_producer_properties">2.17.2. Kafka Streams Producer Properties</a></h4>
<div class="paragraph">
<p>The following properties are <em>only</em> available for Kafka Streams producers and must be prefixed with <code>spring.cloud.stream.kafka.streams.bindings.&lt;binding name&gt;.producer.</code>
For convenience, if there are multiple output bindings and they all require a common value, that can be configured by using the prefix <code>spring.cloud.stream.kafka.streams.default.producer.</code>.</p>
</div>
<div class="dlist">
<dl>
<dt class="hdlist1">keySerde</dt>
<dd>
<p>key serde to use</p>
<div class="paragraph">
<p>Default: See the above discussion on message de/serialization</p>
</div>
</dd>
<dt class="hdlist1">valueSerde</dt>
<dd>
<p>value serde to use</p>
<div class="paragraph">
<p>Default: See the above discussion on message de/serialization</p>
</div>
</dd>
<dt class="hdlist1">useNativeEncoding</dt>
<dd>
<p>flag to enable/disable native encoding</p>
<div class="paragraph">
<p>Default: <code>true</code>.</p>
</div>
</dd>
</dl>
</div>
<div class="paragraph">
<p>streamPartitionerBeanName:
Custom outbound partitioner bean name to be used at the consumer.
Applications can provide custom <code>StreamPartitioner</code> as a Spring bean and the name of this bean can be provided to the producer to use instead of the default one.</p>
</div>
<div class="paragraph">
<p>+
Default: See the discussion above on outbound partition support.</p>
</div>
</div>
<div class="sect3">
<h4 id="_kafka_streams_consumer_properties"><a class="link" href="#_kafka_streams_consumer_properties">2.17.3. Kafka Streams Consumer Properties</a></h4>
<div class="paragraph">
<p>The following properties are available for Kafka Streams consumers and must be prefixed with <code>spring.cloud.stream.kafka.streams.bindings.&lt;binding-name&gt;.consumer.</code>
For convenience, if there are multiple input bindings and they all require a common value, that can be configured by using the prefix <code>spring.cloud.stream.kafka.streams.default.consumer.</code>.</p>
</div>
<div class="dlist">
<dl>
<dt class="hdlist1">applicationId</dt>
<dd>
<p>Setting application.id per input binding. This is only preferred for <code>StreamListener</code> based processors, for function based processors see other approaches outlined above.</p>
<div class="paragraph">
<p>Default: See above.</p>
</div>
</dd>
<dt class="hdlist1">keySerde</dt>
<dd>
<p>key serde to use</p>
<div class="paragraph">
<p>Default: See the above discussion on message de/serialization</p>
</div>
</dd>
<dt class="hdlist1">valueSerde</dt>
<dd>
<p>value serde to use</p>
<div class="paragraph">
<p>Default: See the above discussion on message de/serialization</p>
</div>
</dd>
<dt class="hdlist1">materializedAs</dt>
<dd>
<p>state store to materialize when using incoming KTable types</p>
<div class="paragraph">
<p>Default: <code>none</code>.</p>
</div>
</dd>
<dt class="hdlist1">useNativeDecoding</dt>
<dd>
<p>flag to enable/disable native decoding</p>
<div class="paragraph">
<p>Default: <code>true</code>.</p>
</div>
</dd>
<dt class="hdlist1">dlqName</dt>
<dd>
<p>DLQ topic name.</p>
<div class="paragraph">
<p>Default: See above on the discussion of error handling and DLQ.</p>
</div>
</dd>
<dt class="hdlist1">startOffset</dt>
<dd>
<p>Offset to start from if there is no committed offset to consume from.
This is mostly used when the consumer is consuming from a topic for the first time.
Kafka Streams uses <code>earliest</code> as the default strategy and the binder uses the same default.
This can be overridden to <code>latest</code> using this property.</p>
<div class="paragraph">
<p>Default: <code>earliest</code>.</p>
</div>
</dd>
</dl>
</div>
<div class="paragraph">
<p>Note: Using <code>resetOffsets</code> on the consumer does not have any effect on Kafka Streams binder.
Unlike the message channel based binder, Kafka Streams binder does not seek to beginning or end on demand.</p>
</div>
<div class="dlist">
<dl>
<dt class="hdlist1">deserializationExceptionHandler</dt>
<dd>
<p>Deserialization error handler type.
This handler is applied per consumer binding as opposed to the binder level property described before.
Possible values are - <code>logAndContinue</code>, <code>logAndFail</code> or <code>sendToDlq</code></p>
<div class="paragraph">
<p>Default: <code>logAndFail</code></p>
</div>
</dd>
<dt class="hdlist1">timestampExtractorBeanName</dt>
<dd>
<p>Specific time stamp extractor bean name to be used at the consumer.
Applications can provide <code>TimestampExtractor</code> as a Spring bean and the name of this bean can be provided to the consumer to use instead of the default one.</p>
<div class="paragraph">
<p>Default: See the discussion above on timestamp extractors.</p>
</div>
</dd>
</dl>
</div>
</div>
<div class="sect3">
<h4 id="_special_note_on_concurrency"><a class="link" href="#_special_note_on_concurrency">2.17.4. Special note on concurrency</a></h4>
<div class="paragraph">
<p>In Kafka Streams, you can control of the number of threads a processor can create using the <code>num.stream.threads</code> property.
This, you can do using the various <code>configuration</code> options described above under binder, functions, producer or consumer level.
You can also use the <code>concurrency</code> property that core Spring Cloud Stream provides for this purpose.
When using this, you need to use it on the consumer.
When you have more than one input bindings either in a function or <code>StreamListener</code>, set this on the first input binding.
For e.g. when setting <code>spring.cloud.stream.bindings.process-in-0.consumer.concurrency</code>, it will be translated as <code>num.stream.threads</code> by the binder.</p>
</div>
</div>
</div>
</div>
</div>
<h1 id="_appendices" class="sect0"><a class="link" href="#_appendices">Appendices</a></h1>
<div class="sect1">
<h2 id="building"><a class="link" href="#building">Appendix A: Building</a></h2>
<div class="sectionbody">
<div class="sect2">
<h3 id="_basic_compile_and_test"><a class="link" href="#_basic_compile_and_test">A.1. Basic Compile and Test</a></h3>
<div class="paragraph">
<p>To build the source you will need to install JDK 1.7.</p>
</div>
<div class="paragraph">
<p>The build uses the Maven wrapper so you don&#8217;t have to install a specific
version of Maven. To enable the tests, you should have Kafka server 0.9 or above running
before building. See below for more information on running the servers.</p>
</div>
<div class="paragraph">
<p>The main build command is</p>
</div>
<div class="listingblock">
<div class="content">
<pre>$ ./mvnw clean install</pre>
</div>
</div>
<div class="paragraph">
<p>You can also add '-DskipTests' if you like, to avoid running the tests.</p>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
You can also install Maven (&gt;=3.3.3) yourself and run the <code>mvn</code> command
in place of <code>./mvnw</code> in the examples below. If you do that you also
might need to add <code>-P spring</code> if your local Maven settings do not
contain repository declarations for spring pre-release artifacts.
</td>
</tr>
</table>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
Be aware that you might need to increase the amount of memory
available to Maven by setting a <code>MAVEN_OPTS</code> environment variable with
a value like <code>-Xmx512m -XX:MaxPermSize=128m</code>. We try to cover this in
the <code>.mvn</code> configuration, so if you find you have to do it to make a
build succeed, please raise a ticket to get the settings added to
source control.
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>The projects that require middleware generally include a
<code>docker-compose.yml</code>, so consider using
<a href="https://compose.docker.io/">Docker Compose</a> to run the middeware servers
in Docker containers.</p>
</div>
</div>
<div class="sect2">
<h3 id="_documentation"><a class="link" href="#_documentation">A.2. Documentation</a></h3>
<div class="paragraph">
<p>There is a "full" profile that will generate documentation.</p>
</div>
</div>
<div class="sect2">
<h3 id="_working_with_the_code"><a class="link" href="#_working_with_the_code">A.3. Working with the code</a></h3>
<div class="paragraph">
<p>If you don&#8217;t have an IDE preference we would recommend that you use
<a href="https://www.springsource.com/developer/sts">Spring Tools Suite</a> or
<a href="https://eclipse.org">Eclipse</a> when working with the code. We use the
<a href="https://eclipse.org/m2e/">m2eclipe</a> eclipse plugin for maven support. Other IDEs and tools
should also work without issue.</p>
</div>
<div class="sect3">
<h4 id="_importing_into_eclipse_with_m2eclipse"><a class="link" href="#_importing_into_eclipse_with_m2eclipse">A.3.1. Importing into eclipse with m2eclipse</a></h4>
<div class="paragraph">
<p>We recommend the <a href="https://eclipse.org/m2e/">m2eclipe</a> eclipse plugin when working with
eclipse. If you don&#8217;t already have m2eclipse installed it is available from the "eclipse
marketplace".</p>
</div>
<div class="paragraph">
<p>Unfortunately m2e does not yet support Maven 3.3, so once the projects
are imported into Eclipse you will also need to tell m2eclipse to use
the <code>.settings.xml</code> file for the projects. If you do not do this you
may see many different errors related to the POMs in the
projects. Open your Eclipse preferences, expand the Maven
preferences, and select User Settings. In the User Settings field
click Browse and navigate to the Spring Cloud project you imported
selecting the <code>.settings.xml</code> file in that project. Click Apply and
then OK to save the preference changes.</p>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
Alternatively you can copy the repository settings from <a href="https://github.com/spring-cloud/spring-cloud-build/blob/master/.settings.xml"><code>.settings.xml</code></a> into your own <code>~/.m2/settings.xml</code>.
</td>
</tr>
</table>
</div>
</div>
<div class="sect3">
<h4 id="_importing_into_eclipse_without_m2eclipse"><a class="link" href="#_importing_into_eclipse_without_m2eclipse">A.3.2. Importing into eclipse without m2eclipse</a></h4>
<div class="paragraph">
<p>If you prefer not to use m2eclipse you can generate eclipse project metadata using the
following command:</p>
</div>
<div class="listingblock">
<div class="content">
<pre>$ ./mvnw eclipse:eclipse</pre>
</div>
</div>
<div class="paragraph">
<p>The generated eclipse projects can be imported by selecting <code>import existing projects</code>
from the <code>file</code> menu.</p>
</div>
<div class="paragraph">
<p>[[contributing]
== Contributing</p>
</div>
<div class="paragraph">
<p>Spring Cloud is released under the non-restrictive Apache 2.0 license,
and follows a very standard Github development process, using Github
tracker for issues and merging pull requests into master. If you want
to contribute even something trivial please do not hesitate, but
follow the guidelines below.</p>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_sign_the_contributor_license_agreement"><a class="link" href="#_sign_the_contributor_license_agreement">A.4. Sign the Contributor License Agreement</a></h3>
<div class="paragraph">
<p>Before we accept a non-trivial patch or pull request we will need you to sign the
<a href="https://support.springsource.com/spring_committer_signup">contributor&#8217;s agreement</a>.
Signing the contributor&#8217;s agreement does not grant anyone commit rights to the main
repository, but it does mean that we can accept your contributions, and you will get an
author credit if we do. Active contributors might be asked to join the core team, and
given the ability to merge pull requests.</p>
</div>
</div>
<div class="sect2">
<h3 id="_code_conventions_and_housekeeping"><a class="link" href="#_code_conventions_and_housekeeping">A.5. Code Conventions and Housekeeping</a></h3>
<div class="paragraph">
<p>None of these is essential for a pull request, but they will all help. They can also be
added after the original pull request but before a merge.</p>
</div>
<div class="ulist">
<ul>
<li>
<p>Use the Spring Framework code format conventions. If you use Eclipse
you can import formatter settings using the
<code>eclipse-code-formatter.xml</code> file from the
<a href="https://github.com/spring-cloud/build/tree/master/eclipse-coding-conventions.xml">Spring
Cloud Build</a> project. If using IntelliJ, you can use the
<a href="https://plugins.jetbrains.com/plugin/6546">Eclipse Code Formatter
Plugin</a> to import the same file.</p>
</li>
<li>
<p>Make sure all new <code>.java</code> files to have a simple Javadoc class comment with at least an
<code>@author</code> tag identifying you, and preferably at least a paragraph on what the class is
for.</p>
</li>
<li>
<p>Add the ASF license header comment to all new <code>.java</code> files (copy from existing files
in the project)</p>
</li>
<li>
<p>Add yourself as an <code>@author</code> to the .java files that you modify substantially (more
than cosmetic changes).</p>
</li>
<li>
<p>Add some Javadocs and, if you change the namespace, some XSD doc elements.</p>
</li>
<li>
<p>A few unit tests would help a lot as well&#8201;&#8212;&#8201;someone has to do it.</p>
</li>
<li>
<p>If no-one else is using your branch, please rebase it against the current master (or
other target branch in the main project).</p>
</li>
<li>
<p>When writing a commit message please follow <a href="https://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html">these conventions</a>,
if you are fixing an existing issue please add <code>Fixes gh-XXXX</code> at the end of the commit
message (where XXXX is the issue number).</p>
</li>
</ul>
</div>
</div>
</div>
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