From 8b0437784fceec427c5ec7f72a077b2b873f5bde Mon Sep 17 00:00:00 2001 From: buildmaster Date: Tue, 13 Aug 2019 07:58:48 +0000 Subject: [PATCH] Sync docs from v3.0.0.M3 to gh-pages --- .../3.0.0.M3/reference/html/Guardfile | 20 + .../3.0.0.M3/reference/html/README.html | 1285 +++++++ .../3.0.0.M3/reference/html/appendix.html | 106 + .../3.0.0.M3/reference/html/building.html | 244 ++ .../3.0.0.M3/reference/html/contributing.html | 185 + .../3.0.0.M3/reference/html/css/spring.css | 1 + .../3.0.0.M3/reference/html/dlq.html | 227 ++ .../3.0.0.M3/reference/html/favicon.ico | Bin 0 -> 111804 bytes .../reference/html/images/kafka-binder.png | Bin 0 -> 9469 bytes .../html/images/kafka-streams-initializr.png | Bin 0 -> 121574 bytes .../spring-initializr-kafka-streams.png | Bin 0 -> 119579 bytes .../html/js/highlight/highlight.min.js | 2 + .../js/highlight/styles/a11y-dark.min.css | 99 + .../js/highlight/styles/an-old-hope.min.css | 89 + .../styles/atom-one-dark-reasonable.min.css | 77 + .../js/highlight/styles/atom-one-dark.min.css | 96 + .../highlight/styles/atom-one-light.min.css | 96 + .../html/js/highlight/styles/dracula.min.css | 76 + .../html/js/highlight/styles/github.min.css | 99 + .../highlight/styles/monokai-sublime.min.css | 83 + .../html/js/highlight/styles/monokai.min.css | 70 + .../highlight/styles/solarized-light.min.css | 84 + .../html/js/highlight/styles/zenburn.min.css | 80 + .../3.0.0.M3/reference/html/js/toc.js | 107 + .../reference/html/js/tocbot/tocbot.css | 1 + .../reference/html/js/tocbot/tocbot.min.js | 1 + .../reference/html/kafka-streams.html | 1497 +++++++++ .../3.0.0.M3/reference/html/overview.html | 1060 ++++++ .../3.0.0.M3/reference/html/partitions.html | 241 ++ ...g-cloud-stream-binder-kafka-aggregate.html | 1295 +++++++ .../spring-cloud-stream-binder-kafka.html | 2987 +++++++++++++++++ .../3.0.0.M3/reference/htmlsingle/Guardfile | 20 + .../reference/htmlsingle/css/spring.css | 1 + .../3.0.0.M3/reference/htmlsingle/favicon.ico | Bin 0 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b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/Guardfile new file mode 100644 index 00000000..bdd4d729 --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/Guardfile @@ -0,0 +1,20 @@ +require 'asciidoctor' +require 'erb' + +guard 'shell' do + watch(/.*\.adoc$/) {|m| + Asciidoctor.render_file('index.adoc', \ + :in_place => true, \ + :safe => Asciidoctor::SafeMode::UNSAFE, \ + :attributes=> { \ + 'source-highlighter' => 'prettify', \ + 'icons' => 'font', \ + 'linkcss'=> 'true', \ + 'copycss' => 'true', \ + 'doctype' => 'book'}) + } +end + +guard 'livereload' do + watch(%r{^.+\.(css|js|html)$}) +end diff --git a/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/README.html b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/README.html new file mode 100644 index 00000000..80003d81 --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/README.html @@ -0,0 +1,1285 @@ + + + + + + + +Apache Kafka Binder + + + + + + + + + + +
+
+
+
+
+CircleCI +
+
+
+
+codecov +
+
+
+
+Gitter +
+
+ +
+
+
+

Apache Kafka Binder

+
+
+

Usage

+
+

To use Apache Kafka binder, you need to add spring-cloud-stream-binder-kafka as a dependency to your Spring Cloud Stream application, as shown in the following example for Maven:

+
+
+
+
<dependency>
+  <groupId>org.springframework.cloud</groupId>
+  <artifactId>spring-cloud-stream-binder-kafka</artifactId>
+</dependency>
+
+
+
+

Alternatively, you can also use the Spring Cloud Stream Kafka Starter, as shown inn the following example for Maven:

+
+
+
+
<dependency>
+  <groupId>org.springframework.cloud</groupId>
+  <artifactId>spring-cloud-starter-stream-kafka</artifactId>
+</dependency>
+
+
+
+
+

Overview

+
+

The following image shows a simplified diagram of how the Apache Kafka binder operates:

+
+
+
+kafka binder +
+
Figure 1. Kafka Binder
+
+
+

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.

+
+
+

The binder currently uses the Apache Kafka kafka-clients 1.0.0 jar and is designed to be used with a broker of at least that version. +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 autoAddPartitions property.

+
+
+
+

Configuration Options

+
+

This section contains the configuration options used by the Apache Kafka binder.

+
+
+

For common configuration options and properties pertaining to binder, see the core documentation.

+
+
+

Kafka Binder Properties

+
+
+
spring.cloud.stream.kafka.binder.brokers
+
+

A list of brokers to which the Kafka binder connects.

+
+

Default: localhost.

+
+
+
spring.cloud.stream.kafka.binder.defaultBrokerPort
+
+

brokers allows hosts specified with or without port information (for example, host1,host2:port2). +This sets the default port when no port is configured in the broker list.

+
+

Default: 9092.

+
+
+
spring.cloud.stream.kafka.binder.configuration
+
+

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 — 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.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.consumerProperties
+
+

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 configuration property above.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.headers
+
+

The list of custom headers that are transported by the binder. +Only required when communicating with older applications (⇐ 1.3.x) with a kafka-clients version < 0.11.0.0. Newer versions support headers natively.

+
+

Default: empty.

+
+
+
spring.cloud.stream.kafka.binder.healthTimeout
+
+

The time to wait to get partition information, in seconds. +Health reports as down if this timer expires.

+
+

Default: 10.

+
+
+
spring.cloud.stream.kafka.binder.requiredAcks
+
+

The number of required acks on the broker. +See the Kafka documentation for the producer acks property.

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.minPartitionCount
+
+

Effective only if autoCreateTopics or autoAddPartitions 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 partitionCount setting of the producer or by the value of instanceCount * concurrency settings of the producer (if either is larger).

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.producerProperties
+
+

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 configuration property above.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.replicationFactor
+
+

The replication factor of auto-created topics if autoCreateTopics is active. +Can be overridden on each binding.

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.autoCreateTopics
+
+

If set to true, the binder creates new topics automatically. +If set to false, the binder relies on the topics being already configured. +In the latter case, if the topics do not exist, the binder fails to start.

+
+ + + + + +
+ + +This setting is independent of the auto.create.topics.enable 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. +
+
+
+

Default: true.

+
+
+
spring.cloud.stream.kafka.binder.autoAddPartitions
+
+

If set to true, the binder creates new partitions if required. +If set to false, 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.

+
+

Default: false.

+
+
+
spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix
+
+

Enables transactions in the binder. See transaction.id in the Kafka documentation and Transactions in the spring-kafka documentation. +When transactions are enabled, individual producer properties are ignored and all producers use the spring.cloud.stream.kafka.binder.transaction.producer.* properties.

+
+

Default null (no transactions)

+
+
+
spring.cloud.stream.kafka.binder.transaction.producer.*
+
+

Global producer properties for producers in a transactional binder. +See spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix and Kafka Producer Properties and the general producer properties supported by all binders.

+
+

Default: See individual producer properties.

+
+
+
spring.cloud.stream.kafka.binder.headerMapperBeanName
+
+

The bean name of a KafkaHeaderMapper used for mapping spring-messaging headers to and from Kafka headers. +Use this, for example, if you wish to customize the trusted packages in a DefaultKafkaHeaderMapper that uses JSON deserialization for the headers.

+
+

Default: none.

+
+
+
+
+
+
+

Kafka Consumer Properties

+
+ + + + + +
+ + +To avoid repetition, Spring Cloud Stream supports setting values for all channels, in the format of spring.cloud.stream.default.<property>=<value>. +
+
+
+

The following properties are available for Kafka consumers only and +must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.consumer..

+
+
+
+
admin.configuration
+
+

Since version 2.1.1, this property is deprecated in favor of topic.properties, and support for it will be removed in a future version.

+
+
admin.replicas-assignment
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replicas-assignment, and support for it will be removed in a future version.

+
+
admin.replication-factor
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replication-factor, and support for it will be removed in a future version.

+
+
autoRebalanceEnabled
+
+

When true, topic partitions is automatically rebalanced between the members of a consumer group. +When false, each consumer is assigned a fixed set of partitions based on spring.cloud.stream.instanceCount and spring.cloud.stream.instanceIndex. +This requires both the spring.cloud.stream.instanceCount and spring.cloud.stream.instanceIndex properties to be set appropriately on each launched instance. +The value of the spring.cloud.stream.instanceCount property must typically be greater than 1 in this case.

+
+

Default: true.

+
+
+
ackEachRecord
+
+

When autoCommitOffset is true, 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 consumer.poll() have been processed. +The number of records returned by a poll can be controlled with the max.poll.records Kafka property, which is set through the consumer configuration property. +Setting this to true may cause a degradation in performance, but doing so reduces the likelihood of redelivered records when a failure occurs. +Also, see the binder requiredAcks property, which also affects the performance of committing offsets.

+
+

Default: false.

+
+
+
autoCommitOffset
+
+

Whether to autocommit offsets when a message has been processed. +If set to false, a header with the key kafka_acknowledgment of the type org.springframework.kafka.support.Acknowledgment 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 false, Kafka binder sets the ack mode to org.springframework.kafka.listener.AbstractMessageListenerContainer.AckMode.MANUAL and the application is responsible for acknowledging records. +Also see ackEachRecord.

+
+

Default: true.

+
+
+
autoCommitOnError
+
+

Effective only if autoCommitOffset is set to true. +If set to false, 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 true, it always auto-commits (if auto-commit is enabled). +If not set (the default), it effectively has the same value as enableDlq, auto-committing erroneous messages if they are sent to a DLQ and not committing them otherwise.

+
+

Default: not set.

+
+
+
resetOffsets
+
+

Whether to reset offsets on the consumer to the value provided by startOffset. +Must be false if a KafkaRebalanceListener is provided; see Using a KafkaRebalanceListener.

+
+

Default: false.

+
+
+
startOffset
+
+

The starting offset for new groups. +Allowed values: earliest and latest. +If the consumer group is set explicitly for the consumer 'binding' (through spring.cloud.stream.bindings.<channelName>.group), 'startOffset' is set to earliest. Otherwise, it is set to latest for the anonymous consumer group. +Also see resetOffsets (earlier in this list).

+
+

Default: null (equivalent to earliest).

+
+
+
enableDlq
+
+

When set to true, it enables DLQ behavior for the consumer. +By default, messages that result in errors are forwarded to a topic named error.<destination>.<group>. +The DLQ topic name can be configurable by setting the dlqName 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 [kafka-dlq-processing] processing for more information. +Starting with version 2.0, messages sent to the DLQ topic are enhanced with the following headers: x-original-topic, x-exception-message, and x-exception-stacktrace as byte[]. +Not allowed when destinationIsPattern is true.

+
+

Default: false.

+
+
+
configuration
+
+

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 spring.cloud.stream.kafka.bindings.input.consumer.configuration.foo=bar.

+
+

Default: Empty map.

+
+
+
dlqName
+
+

The name of the DLQ topic to receive the error messages.

+
+

Default: null (If not specified, messages that result in errors are forwarded to a topic named error.<destination>.<group>).

+
+
+
dlqProducerProperties
+
+

Using this, DLQ-specific producer properties can be set. +All the properties available through kafka producer properties can be set through this property.

+
+

Default: Default Kafka producer properties.

+
+
+
standardHeaders
+
+

Indicates which standard headers are populated by the inbound channel adapter. +Allowed values: none, id, timestamp, or both. +Useful if using native deserialization and the first component to receive a message needs an id (such as an aggregator that is configured to use a JDBC message store).

+
+

Default: none

+
+
+
converterBeanName
+
+

The name of a bean that implements RecordMessageConverter. Used in the inbound channel adapter to replace the default MessagingMessageConverter.

+
+

Default: null

+
+
+
idleEventInterval
+
+

The interval, in milliseconds, between events indicating that no messages have recently been received. +Use an ApplicationListener<ListenerContainerIdleEvent> to receive these events. +See Example: Pausing and Resuming the Consumer for a usage example.

+
+

Default: 30000

+
+
+
destinationIsPattern
+
+

When true, the destination is treated as a regular expression Pattern used to match topic names by the broker. +When true, topics are not provisioned, and enableDlq 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 metadata.max.age.ms, which (at the time of writing) defaults to 300,000ms (5 minutes). +This can be configured using the configuration property above.

+
+

Default: false

+
+
+
topic.properties
+
+

A Map of Kafka topic properties used when provisioning new topics — for example, spring.cloud.stream.kafka.bindings.input.consumer.topic.properties.message.format.version=0.9.0.0

+
+

Default: none.

+
+
+
topic.replicas-assignment
+
+

A Map<Integer, List<Integer>> of replica assignments, with the key being the partition and the value being the assignments. +Used when provisioning new topics. +See the NewTopic Javadocs in the kafka-clients jar.

+
+

Default: none.

+
+
+
topic.replication-factor
+
+

The replication factor to use when provisioning topics. Overrides the binder-wide setting. +Ignored if replicas-assignments is present.

+
+

Default: none (the binder-wide default of 1 is used).

+
+
+
+
+
+
+

Kafka Producer Properties

+
+ + + + + +
+ + +To avoid repetition, Spring Cloud Stream supports setting values for all channels, in the format of spring.cloud.stream.default.<property>=<value>. +
+
+
+

The following properties are available for Kafka producers only and +must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.producer..

+
+
+
+
admin.configuration
+
+

Since version 2.1.1, this property is deprecated in favor of topic.properties, and support for it will be removed in a future version.

+
+
admin.replicas-assignment
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replicas-assignment, and support for it will be removed in a future version.

+
+
admin.replication-factor
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replication-factor, and support for it will be removed in a future version.

+
+
bufferSize
+
+

Upper limit, in bytes, of how much data the Kafka producer attempts to batch before sending.

+
+

Default: 16384.

+
+
+
sync
+
+

Whether the producer is synchronous.

+
+

Default: false.

+
+
+
batchTimeout
+
+

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.

+
+

Default: 0.

+
+
+
messageKeyExpression
+
+

A SpEL expression evaluated against the outgoing message used to populate the key of the produced Kafka message — for example, headers['myKey']. +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 byte[]. +Now, the expression is evaluated before the payload is converted.

+
+

Default: none.

+
+
+
headerPatterns
+
+

A comma-delimited list of simple patterns to match Spring messaging headers to be mapped to the Kafka Headers in the ProducerRecord. +Patterns can begin or end with the wildcard character (asterisk). +Patterns can be negated by prefixing with !. +Matching stops after the first match (positive or negative). +For example !ask,as* will pass ash but not ask. +id and timestamp are never mapped.

+
+

Default: * (all headers - except the id and timestamp)

+
+
+
configuration
+
+

Map with a key/value pair containing generic Kafka producer properties.

+
+

Default: Empty map.

+
+
+
topic.properties
+
+

A Map of Kafka topic properties used when provisioning new topics — for example, spring.cloud.stream.kafka.bindings.output.producer.topic.properties.message.format.version=0.9.0.0

+
+
topic.replicas-assignment
+
+

A Map<Integer, List<Integer>> of replica assignments, with the key being the partition and the value being the assignments. +Used when provisioning new topics. +See the NewTopic Javadocs in the kafka-clients jar.

+
+

Default: none.

+
+
+
topic.replication-factor
+
+

The replication factor to use when provisioning topics. Overrides the binder-wide setting. +Ignored if replicas-assignments is present.

+
+

Default: none (the binder-wide default of 1 is used).

+
+
+
useTopicHeader
+
+

Set to true to override the default binding destination (topic name) with the value of the KafkaHeaders.TOPIC message header in the outbound message. +If the header is not present, the default binding destination is used. +Default: false.

+
+
recordMetadataChannel
+
+

The bean name of a MessageChannel 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 KafkaHeaders.RECORD_METADATA. +The header contains a RecordMetadata object provided by the Kafka client; it includes the partition and offset where the record was written in the topic.

+
+
+
+
+

ResultMetadata meta = sendResultMsg.getHeaders().get(KafkaHeaders.RECORD_METADATA, RecordMetadata.class)

+
+
+

Failed sends go the producer error channel (if configured); see Error Channels. +Default: null

+
+
+

+

+
+
+ + + + + +
+ + +The Kafka binder uses the partitionCount setting of the producer as a hint to create a topic with the given partition count (in conjunction with the minPartitionCount, the maximum of the two being the value being used). +Exercise caution when configuring both minPartitionCount for a binder and partitionCount for an application, as the larger value is used. +If a topic already exists with a smaller partition count and autoAddPartitions is disabled (the default), the binder fails to start. +If a topic already exists with a smaller partition count and autoAddPartitions is enabled, new partitions are added. +If a topic already exists with a larger number of partitions than the maximum of (minPartitionCount or partitionCount), the existing partition count is used. +
+
+
+
+
compression
+
+

Set the compression.type producer property. +Supported values are none, gzip, snappy and lz4. +If you override the kafka-clients jar to 2.1.0 (or later), as discussed in the Spring for Apache Kafka documentation, and wish to use zstd compression, use spring.cloud.stream.kafka.bindings.<binding-name>.producer.configuration.compression.type=zstd.

+
+

Default: none.

+
+
+
+
+
+
+

Usage examples

+
+

In this section, we show the use of the preceding properties for specific scenarios.

+
+
+
Example: Setting autoCommitOffset to false and Relying on Manual Acking
+
+

This example illustrates how one may manually acknowledge offsets in a consumer application.

+
+
+

This example requires that spring.cloud.stream.kafka.bindings.input.consumer.autoCommitOffset be set to false. +Use the corresponding input channel name for your example.

+
+
+
+
@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<?> message) {
+     Acknowledgment acknowledgment = message.getHeaders().get(KafkaHeaders.ACKNOWLEDGMENT, Acknowledgment.class);
+     if (acknowledgment != null) {
+         System.out.println("Acknowledgment provided");
+         acknowledgment.acknowledge();
+     }
+ }
+}
+
+
+
+
+
Example: Security Configuration
+
+

Apache Kafka 0.9 supports secure connections between client and brokers. +To take advantage of this feature, follow the guidelines in the Apache Kafka Documentation as well as the Kafka 0.9 security guidelines from the Confluent documentation. +Use the spring.cloud.stream.kafka.binder.configuration option to set security properties for all clients created by the binder.

+
+
+

For example, to set security.protocol to SASL_SSL, set the following property:

+
+
+
+
spring.cloud.stream.kafka.binder.configuration.security.protocol=SASL_SSL
+
+
+
+

All the other security properties can be set in a similar manner.

+
+
+

When using Kerberos, follow the instructions in the reference documentation for creating and referencing the JAAS configuration.

+
+
+

Spring Cloud Stream supports passing JAAS configuration information to the application by using a JAAS configuration file and using Spring Boot properties.

+
+
+
Using JAAS Configuration Files
+
+

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:

+
+
+
+
 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
+
+
+
+
+
Using Spring Boot Properties
+
+

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.

+
+
+

The following properties can be used to configure the login context of the Kafka client:

+
+
+
+
spring.cloud.stream.kafka.binder.jaas.loginModule
+
+

The login module name. Not necessary to be set in normal cases.

+
+

Default: com.sun.security.auth.module.Krb5LoginModule.

+
+
+
spring.cloud.stream.kafka.binder.jaas.controlFlag
+
+

The control flag of the login module.

+
+

Default: required.

+
+
+
spring.cloud.stream.kafka.binder.jaas.options
+
+

Map with a key/value pair containing the login module options.

+
+

Default: Empty map.

+
+
+
+
+
+

The following example shows how to launch a Spring Cloud Stream application with SASL and Kerberos by using Spring Boot configuration properties:

+
+
+
+
 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
+
+
+
+

The preceding example represents the equivalent of the following JAAS file:

+
+
+
+
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";
+};
+
+
+
+

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.

+
+
+ + + + + +
+ + +Do not mix JAAS configuration files and Spring Boot properties in the same application. +If the -Djava.security.auth.login.config system property is already present, Spring Cloud Stream ignores the Spring Boot properties. +
+
+
+ + + + + +
+ + +Be careful when using the autoCreateTopics and autoAddPartitions 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. +
+
+
+
+
+
Example: Pausing and Resuming the Consumer
+
+

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 Consumer as a parameter to your @StreamListener. +To resume, you need an ApplicationListener for ListenerContainerIdleEvent instances. +The frequency at which events are published is controlled by the idleEventInterval property. +Since the consumer is not thread-safe, you must call these methods on the calling thread.

+
+
+

The following simple application shows how to pause and resume:

+
+
+
+
@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<?, ?> consumer) {
+		System.out.println(in);
+		consumer.pause(Collections.singleton(new TopicPartition("myTopic", 0)));
+	}
+
+	@Bean
+	public ApplicationListener<ListenerContainerIdleEvent> idleListener() {
+		return event -> {
+			System.out.println(event);
+			if (event.getConsumer().paused().size() > 0) {
+				event.getConsumer().resume(event.getConsumer().paused());
+			}
+		};
+	}
+
+}
+
+
+
+
+
+
+

Transactional Binder

+
+

Enable transactions by setting spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix to a non-empty value, e.g. tx-. +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 spring.cloud.stream.kafka.binder.transaction.producer.* properties; individual binding Kafka producer properties are ignored.

+
+
+

If you wish to use transactions in a source application, or from some arbitrary thread for producer-only transaction (e.g. @Scheduled method), you must get a reference to the transactional producer factory and define a KafkaTransactionManager bean using it.

+
+
+
+
+
+
@Bean
+public PlatformTransactionManager transactionManager(BinderFactory binders) {
+    ProducerFactory<byte[], byte[]> pf = ((KafkaMessageChannelBinder) binders.getBinder(null,
+            MessageChannel.class)).getTransactionalProducerFactory();
+    return new KafkaTransactionManager<>(pf);
+}
+
+
+
+
+
+

Notice that we get a reference to the binder using the BinderFactory; use null 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 ProducerFactory and create a transaction manager.

+
+
+

Then you would use normal Spring transaction support, e.g. TransactionTemplate or @Transactional, for example:

+
+
+
+
+
+
public static class Sender {
+
+    @Transactional
+    public void doInTransaction(MessageChannel output, List<String> stuffToSend) {
+        stuffToSend.forEach(stuff -> output.send(new GenericMessage<>(stuff)));
+    }
+
+}
+
+
+
+
+
+

If you wish to synchronize producer-only transactions with those from some other transaction manager, use a ChainedTransactionManager.

+
+
+
+

Error Channels

+
+

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 [spring-cloud-stream-overview-error-handling] for more information.

+
+
+

The payload of the ErrorMessage for a send failure is a KafkaSendFailureException with properties:

+
+
+
    +
  • +

    failedMessage: The Spring Messaging Message<?> that failed to be sent.

    +
    • +
  • +

    record: The raw ProducerRecord that was created from the failedMessage

    +
    • +
+
+
+

There is no automatic handling of producer exceptions (such as sending to a Dead-Letter queue). +You can consume these exceptions with your own Spring Integration flow.

+
+
+
+

Kafka Metrics

+
+

Kafka binder module exposes the following metrics:

+
+
+

spring.cloud.stream.binder.kafka.offset: This metric indicates how many messages have not been yet consumed from a given binder’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.

+
+
+
+

Tombstone Records (null record values)

+
+

When using compacted topics, a record with a null value (also called a tombstone record) represents the deletion of a key. +To receive such messages in a @StreamListener method, the parameter must be marked as not required to receive a null value argument.

+
+
+
+
+
+
@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
+    ...
+}
+
+
+
+
+
+
+

Using a KafkaRebalanceListener

+
+

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 KafkaRebalanceListener bean in the application context, it will be wired into all Kafka consumer bindings.

+
+
+
+
+
+
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<?, ?> consumer,
+			Collection<TopicPartition> 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<?, ?> consumer, Collection<TopicPartition> 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<?, ?> consumer, Collection<TopicPartition> partitions,
+			boolean initial) {
+
+	}
+
+}
+
+
+
+
+
+

You cannot set the resetOffsets consumer property to true when you provide a rebalance listener.

+
+
+
+
+

Appendices

+
+

Appendix A: Building

+
+
+

Basic Compile and Test

+
+

To build the source you will need to install JDK 1.7.

+
+
+

The build uses the Maven wrapper so you don’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.

+
+
+

The main build command is

+
+
+
+
$ ./mvnw clean install
+
+
+
+

You can also add '-DskipTests' if you like, to avoid running the tests.

+
+
+ + + + + +
+ + +You can also install Maven (>=3.3.3) yourself and run the mvn command +in place of ./mvnw in the examples below. If you do that you also +might need to add -P spring if your local Maven settings do not +contain repository declarations for spring pre-release artifacts. +
+
+
+ + + + + +
+ + +Be aware that you might need to increase the amount of memory +available to Maven by setting a MAVEN_OPTS environment variable with +a value like -Xmx512m -XX:MaxPermSize=128m. We try to cover this in +the .mvn 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. +
+
+
+

The projects that require middleware generally include a +docker-compose.yml, so consider using +Docker Compose to run the middeware servers +in Docker containers.

+
+
+
+

Documentation

+
+

There is a "full" profile that will generate documentation.

+
+
+
+

Working with the code

+
+

If you don’t have an IDE preference we would recommend that you use +Spring Tools Suite or +Eclipse when working with the code. We use the +m2eclipe eclipse plugin for maven support. Other IDEs and tools +should also work without issue.

+
+
+

Importing into eclipse with m2eclipse

+
+

We recommend the m2eclipe eclipse plugin when working with +eclipse. If you don’t already have m2eclipse installed it is available from the "eclipse +marketplace".

+
+
+

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 .settings.xml 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 .settings.xml file in that project. Click Apply and +then OK to save the preference changes.

+
+
+ + + + + +
+ + +Alternatively you can copy the repository settings from .settings.xml into your own ~/.m2/settings.xml. +
+
+
+
+

Importing into eclipse without m2eclipse

+
+

If you prefer not to use m2eclipse you can generate eclipse project metadata using the +following command:

+
+
+
+
$ ./mvnw eclipse:eclipse
+
+
+
+

The generated eclipse projects can be imported by selecting import existing projects +from the file menu. +[[contributing] +== Contributing

+
+
+

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.

+
+
+
+
+

Sign the Contributor License Agreement

+
+

Before we accept a non-trivial patch or pull request we will need you to sign the +contributor’s agreement. +Signing the contributor’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.

+
+
+
+

Code Conventions and Housekeeping

+
+

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.

+
+
+
    +
  • +

    Use the Spring Framework code format conventions. If you use Eclipse +you can import formatter settings using the +eclipse-code-formatter.xml file from the +Spring +Cloud Build project. If using IntelliJ, you can use the +Eclipse Code Formatter +Plugin to import the same file.

    +
    • +
  • +

    Make sure all new .java files to have a simple Javadoc class comment with at least an +@author tag identifying you, and preferably at least a paragraph on what the class is +for.

    +
    • +
  • +

    Add the ASF license header comment to all new .java files (copy from existing files +in the project)

    +
    • +
  • +

    Add yourself as an @author to the .java files that you modify substantially (more +than cosmetic changes).

    +
    • +
  • +

    Add some Javadocs and, if you change the namespace, some XSD doc elements.

    +
    • +
  • +

    A few unit tests would help a lot as well — someone has to do it.

    +
    • +
  • +

    If no-one else is using your branch, please rebase it against the current master (or +other target branch in the main project).

    +
    • +
  • +

    When writing a commit message please follow these conventions, +if you are fixing an existing issue please add Fixes gh-XXXX at the end of the commit +message (where XXXX is the issue number).

    +
    • +
+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/appendix.html b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/appendix.html new file mode 100644 index 00000000..86314ff6 --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/appendix.html @@ -0,0 +1,106 @@ + + + + + + + +Appendices + + + + + + + + + + +
+ +
+ + + + + + + \ No newline at end of file diff --git a/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/building.html b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/building.html new file mode 100644 index 00000000..63c28604 --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/building.html @@ -0,0 +1,244 @@ + + + + + + + +Building + + + + + + + + + + +
+
+

Building

+
+
+

Basic Compile and Test

+
+

To build the source you will need to install JDK 1.7.

+
+
+

The build uses the Maven wrapper so you don’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.

+
+
+

The main build command is

+
+
+
+
$ ./mvnw clean install
+
+
+
+

You can also add '-DskipTests' if you like, to avoid running the tests.

+
+
+ + + + + +
+ + +You can also install Maven (>=3.3.3) yourself and run the mvn command +in place of ./mvnw in the examples below. If you do that you also +might need to add -P spring if your local Maven settings do not +contain repository declarations for spring pre-release artifacts. +
+
+
+ + + + + +
+ + +Be aware that you might need to increase the amount of memory +available to Maven by setting a MAVEN_OPTS environment variable with +a value like -Xmx512m -XX:MaxPermSize=128m. We try to cover this in +the .mvn 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. +
+
+
+

The projects that require middleware generally include a +docker-compose.yml, so consider using +Docker Compose to run the middeware servers +in Docker containers.

+
+
+
+

Documentation

+
+

There is a "full" profile that will generate documentation.

+
+
+
+

Working with the code

+
+

If you don’t have an IDE preference we would recommend that you use +Spring Tools Suite or +Eclipse when working with the code. We use the +m2eclipe eclipse plugin for maven support. Other IDEs and tools +should also work without issue.

+
+
+

Importing into eclipse with m2eclipse

+
+

We recommend the m2eclipe eclipse plugin when working with +eclipse. If you don’t already have m2eclipse installed it is available from the "eclipse +marketplace".

+
+
+

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 .settings.xml 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 .settings.xml file in that project. Click Apply and +then OK to save the preference changes.

+
+
+ + + + + +
+ + +Alternatively you can copy the repository settings from .settings.xml into your own ~/.m2/settings.xml. +
+
+
+
+

Importing into eclipse without m2eclipse

+
+

If you prefer not to use m2eclipse you can generate eclipse project metadata using the +following command:

+
+
+
+
$ ./mvnw eclipse:eclipse
+
+
+
+

The generated eclipse projects can be imported by selecting import existing projects +from the file menu.

+
+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/contributing.html b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/contributing.html new file mode 100644 index 00000000..44f361ab --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/contributing.html @@ -0,0 +1,185 @@ + + + + + + + +Sign the Contributor License Agreement + + + + + + + + + + +
+
+
+
+

[[contributing] +== Contributing

+
+
+

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.

+
+
+
+
+

Sign the Contributor License Agreement

+
+

Before we accept a non-trivial patch or pull request we will need you to sign the +contributor’s agreement. +Signing the contributor’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.

+
+
+
+

Code Conventions and Housekeeping

+
+

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.

+
+
+
    +
  • +

    Use the Spring Framework code format conventions. If you use Eclipse +you can import formatter settings using the +eclipse-code-formatter.xml file from the +Spring +Cloud Build project. If using IntelliJ, you can use the +Eclipse Code Formatter +Plugin to import the same file.

    +
    • +
  • +

    Make sure all new .java files to have a simple Javadoc class comment with at least an +@author tag identifying you, and preferably at least a paragraph on what the class is +for.

    +
    • +
  • +

    Add the ASF license header comment to all new .java files (copy from existing files +in the project)

    +
    • +
  • +

    Add yourself as an @author to the .java files that you modify substantially (more +than cosmetic changes).

    +
    • +
  • +

    Add some Javadocs and, if you change the namespace, some XSD doc elements.

    +
    • +
  • +

    A few unit tests would help a lot as well — someone has to do it.

    +
    • +
  • +

    If no-one else is using your branch, please rebase it against the current master (or +other target branch in the main project).

    +
    • +
  • +

    When writing a commit message please follow these conventions, +if you are fixing an existing issue please add Fixes gh-XXXX at the end of the commit +message (where XXXX is the issue number).

    +
    • +
+
+
+
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diff --git a/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/dlq.html b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/dlq.html new file mode 100644 index 00000000..30718f70 --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/dlq.html @@ -0,0 +1,227 @@ + + + + + + + +Dead-Letter Topic Processing + + + + + + + + + + +
+
+

Dead-Letter Topic Processing

+
+

Because you cannot anticipate how users would want to dispose of dead-lettered messages, the framework 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 “parking lot” 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.

+
+
+

The examples assume the original destination is so8400out and the consumer group is so8400.

+
+
+

There are a couple of strategies to consider:

+
+
+
    +
  • +

    Consider running the rerouting only when the main application is not running. +Otherwise, the retries for transient errors are used up very quickly.

    +
    • +
  • +

    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.

    +
    • +
+
+
+

The following code listings show the sample application:

+
+
+
application.properties
+
+
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
+
+
+
+
Application
+
+
@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<?> reRoute(Message<?> 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() < 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();
+
+    }
+
+}
+
+
+
+
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n(e){s=e-i,window.scrollTo(0,c.easing(s,r,u,d)),s + + + + + + +Kafka Streams Binder + + + + + + + + + + +
+
+

Kafka Streams Binder

+
+
+

Usage

+
+

For using the Kafka Streams binder, you just need to add it to your Spring Cloud Stream application, using the following +Maven coordinates:

+
+
+
+
<dependency>
+  <groupId>org.springframework.cloud</groupId>
+  <artifactId>spring-cloud-stream-binder-kafka-streams</artifactId>
+</dependency>
+
+
+
+

A quick way to bootstrap a new project for Kafka Streams binder is to use Spring Initializr and then select "Cloud Streams" and "Spring for Kafka Streams" as shown below

+
+
+
+spring initializr kafka streams +
+
+
+
+

Overview

+
+

Spring Cloud Stream includes a binder implementation designed explicitly for Apache Kafka Streams binding. +With this native integration, a Spring Cloud Stream "processor" application can directly use the +Apache Kafka Streams APIs in the core business logic.

+
+
+

Kafka Streams binder implementation builds on the foundations provided by the Spring for Apache Kafka project.

+
+
+

Kafka Streams binder provides binding capabilities for the three major types in Kafka Streams - KStream, KTable and GlobalKTable.

+
+
+

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.

+
+
+

In the following sections, we are going to look at the details of Spring Cloud Stream’s integration with Kafka Streams.

+
+
+
+

Programming Model

+
+

When using the programming model provided by Kafka Streams binder, both the high-level Streams DSL and the lower level Processor-API can be used as options.

+
+
+

Functional Style

+
+

Starting with Spring Cloud Stream 3.0, 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 java.util.function.Function or java.util.function.Consumer.

+
+
+

Let’s take a very basic example.

+
+
+
+
@SpringBootApplication
+public class SimpleConsumerApplication {
+
+    @Bean
+    public java.util.function.Consumer<KStream<Object, String>> process() {
+
+        return input ->
+                input.foreach((key, value) -> {
+                    System.out.println("Key: " + key + " Value: " + value);
+                });
+    }
+}
+
+
+
+

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 transformation as standard output. +The application contains the SpringBootApplication annotation and a method that is marked as Bean. +The bean method is of type java.util.function.Consumer which is parameterized with KStream. +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.

+
+
+

In this application, there is a single input binding that is of type KStream. +The binder creates this binding for the application with a name process_in, i.e. the name of the function bean name followed by an underscore and the literal in. +You use this binding name to set other properties such as destination. +For example, spring.cloud.stream.bindings.process_in.destinaion=my-topic.

+
+
+

Once built as a uber-jar (e.g., kstream-consumer-app.jar), you can run the above example like the following.

+
+
+
+
java -jar `kstream-consumer-app.jar --spring.cloud.stream.bindings.process_in.destinaion=my-topic --spring.cloud.stream.bindings.output.destination=count
+
+
+
+

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.

+
+
+
+
@SpringBootApplication
+public class WordCountProcessorApplication {
+
+  @Bean
+  public Function<KStream<Object, String>, KStream<?, WordCount>> process() {
+
+    return input -> input
+        .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
+        .map((key, value) -> new KeyValue<>(value, value))
+        .groupByKey(Serialized.with(Serdes.String(), Serdes.String()))
+        .windowedBy(TimeWindows.of(5000))
+        .count(Materialized.as("word-counts-state-store"))
+        .toStream()
+        .map((key, value) -> new KeyValue<>(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);
+	}
+}
+
+
+
+

Here again, this is a complete Spring Boot application. The difference here from the first application, though, the bean method is of type java.util.function.Function. +The first parameterized type for the Function is for the input KStream and the second one is for the output. +In the method body, a lambda expression is provided that is of type Function and as implementation, the actual business logic is given. +Similar to the previously discussed Consumer based application, the input binding here is named as process_in by default. For the output, the binding name is automatically also set to process_out.

+
+
+

Once built as a uber-jar (e.g., wordcount-processor.jar), you can run the above example like the following.

+
+
+
+
java -jar wordcount-processor.jar  --spring.cloud.stream.bindings.process_in.destination=words --spring.cloud.stream.bindings.process_out.destination=counts
+
+
+
+

This application will consume messages from the Kafka topic words and the computed results are published to an output +topic counts.

+
+
+

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 the Streams DSL specific configuration required by the Kafka Streams infrastructure +is automatically handled by the framework.

+
+
+

The two examples we saw above have a single KStream input binding. In both cases, the bindings received the records from a single topic. +If you want to multiplex multiple topics into a single KStream binding, you can provide comma separated Kafka topics as destinations below.

+
+
+

spring.cloud.stream.bindings.process_in.destination=topic-1,topic-2,topic-3

+
+
+
Multiple Input Bindings
+
+

Any non-trivial Kafka Streams applications often consume data from more than one topic through multiple bindings. +For instance, one topic is consumed as Kstream and another as KTable or GlobalKTable. +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 KTable or GlobalKTable, then Kafka Streams binder will properly bind the destination to a KTable or GlobalKTable 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.

+
+
+
BiFunction in Kafka Streams Binder
+
+

Here is an example where we have two inputs and an output. In this case, the application can leverage on java.util.function.BiFunction.

+
+
+
+
@Bean
+public BiFunction<KStream<String, Long>, KTable<String, String>, KStream<String, Long>> process() {
+    return (userClicksStream, userRegionsTable) -> (userClicksStream
+            .leftJoin(userRegionsTable, (clicks, region) -> new RegionWithClicks(region == null ?
+                            "UNKNOWN" : region, clicks),
+                    Joined.with(Serdes.String(), Serdes.Long(), null))
+            .map((user, regionWithClicks) -> new KeyValue<>(regionWithClicks.getRegion(),
+                    regionWithClicks.getClicks()))
+            .groupByKey(Serialized.with(Serdes.String(), Serdes.Long()))
+            .reduce(Long::sum)
+            .toStream());
+}
+
+
+
+

Here again, the basic theme is the same as previous examples, the difference, though, you have two inputs and the Java’s BiFunction support is used to bind the inputs to the desired destinations. +The default binding names generated by the binder for the inputs are process_in_0 and process_in_1 respectively. The default output binding remains to be process_out. +In this example, the first parameter of BiFunction is bound as a KStream for the first input and the second parameter is bound as a KTable.

+
+
+
+
BiConsumer in Kafka Streams Binder
+
+

If there are two inputs, but no outputs, in that case we can use java.util.funcion.BiConsumer. +Here is a blueprint for such an application.

+
+
+
+
@Bean
+public BiConsumer<KStream<String, Long>, KTable<String, String>> process() {
+    return (userClicksStream, userRegionsTable) -> {}
+}
+
+
+
+

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. +The Kafka Streams binder can make use of this feature to enable multiple input bindings.

+
+
+

Let’s see an example.

+
+
+
+
@Bean
+public Function<KStream<Long, Order>,
+        Function<GlobalKTable<Long, Customer>,
+                Function<GlobalKTable<Long, Product>, KStream<Long, EnrichedOrder>>>> process() {
+
+    return orders -> (
+              customers -> (
+                    products -> (
+                        orders.join(customers,
+                            (orderId, order) -> order.getCustomerId(),
+                                (order, customer) -> new CustomerOrder(customer, order))
+                                .join(products,
+                                        (orderId, customerOrder) -> customerOrder
+                                                .productId(),
+                                        (customerOrder, product) -> {
+                                            EnrichedOrder enrichedOrder = new EnrichedOrder();
+                                            enrichedOrder.setProduct(product);
+                                            enrichedOrder.setCustomer(customerOrder.customer);
+                                            enrichedOrder.setOrder(customerOrder.order);
+                                            return enrichedOrder;
+                                        })
+                        )
+                )
+    );
+}
+
+
+
+

In this model, we have 3 partial functions as inputs. The first function has the first input binding of the application (Order) and its output is another function. +This output function’s input is the second input binding for the application (Customer) and its output is another function. +This output function’s input is the third input for the application (Product) and its output is a KStream which is final output binding for the application. +The input from the three partial functions which are KStream, GlobalKTable, GlobalKTable respectively are available for you in the method body for implementing the business logic as part of the lambda expression.

+
+
+

Input bindings are named as process_in_0, process_in_1 and process_in_2 respectively. Output binding is named as process_out.

+
+
+

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.

+
+
+
+
+
Multiple Output Bindings
+
+

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 (KStream[]) as the outbound return type.

+
+
+

Here is an example:

+
+
+
+
@Bean
+public Function<KStream<Object, String>, KStream<?, WordCount>[]> process() {
+
+    Predicate<Object, WordCount> isEnglish = (k, v) -> v.word.equals("english");
+    Predicate<Object, WordCount> isFrench = (k, v) -> v.word.equals("french");
+    Predicate<Object, WordCount> isSpanish = (k, v) -> v.word.equals("spanish");
+
+    return input -> input
+            .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
+            .groupBy((key, value) -> value)
+            .windowedBy(TimeWindows.of(5000))
+            .count(Materialized.as("WordCounts-branch"))
+            .toStream()
+            .map((key, value) -> new KeyValue<>(null, new WordCount(key.key(), value,
+                    new Date(key.window().start()), new Date(key.window().end()))))
+            .branch(isEnglish, isFrench, isSpanish);
+}
+
+
+
+

The programming model remains the same, however the outbound parameterized type is KStream[]. +The default output binding names are process_out_0, process_out_1, process_out_2 respectively.

+
+
+
+
Function based Programming Styles for Kafka Streams
+
+

In summary, the following table shows the various options that can be used in the functional paradigm.

+
+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Number of InputsNumber of OutputsComponent to use

1

0

java.util.function.Consumer

2

0

java.util.function.BiConsumer

1

1..n

java.util.function.Function

2

1..n

java.util.function.BiFunction

>= 3

0..n

Use curried functions

+
+
    +
  • +

    In the case of more than one output in this table, the type simply becomes KStream[].

    +
    • +
+
+
+
+
+

Imperative programming model.

+
+

Although the functional programming model outlined above is the preferred approach, you can still use the classic StreamListener based approach if you prefer.

+
+
+

Here are some examples.

+
+
+

Following is the equivalent of the Word count example using StreamListener.

+
+
+
+
@SpringBootApplication
+@EnableBinding(KafkaStreamsProcessor.class)
+public class WordCountProcessorApplication {
+
+    @StreamListener("input")
+    @SendTo("output")
+    public KStream<?, WordCount> process(KStream<?, String> input) {
+        return input
+                .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
+                .groupBy((key, value) -> value)
+                .windowedBy(TimeWindows.of(5000))
+                .count(Materialized.as("WordCounts-multi"))
+                .toStream()
+                .map((key, value) -> new KeyValue<>(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);
+    }
+
+
+
+

As you can see, this is a bit more verbose since you need to provide EnableBinding and the other extra annotations like StreamListener and SendTo to make it a complete application. +EnableBinding is where you specify your binding interface that contains your bindings. +In this case, we are using the stock KafkaStreamsProcessor binding interface that has the following contracts.

+
+
+
+
public interface KafkaStreamsProcessor {
+
+	@Input("input")
+	KStream<?, ?> input();
+
+	@Output("output")
+	KStream<?, ?> output();
+
+}
+
+
+
+

Binder will create bindings for the input KStream and output KStream since you are using a binding interface that contains those declarations.

+
+
+

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 KafkaStreamsProcessor, the binding names are input and output. +Binding properties need to use those names. For instance spring.cloud.stream.bindings.input.destination, spring.cloud.stream.bindings.output.destination 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 EnableBinding.

+
+
+

Here is another example of a sink where we have two inputs.

+
+
+
+
@EnableBinding(KStreamKTableBinding.class)
+.....
+.....
+@StreamListener
+public void process(@Input("inputStream") KStream<String, PlayEvent> playEvents,
+                    @Input("inputTable") KTable<Long, Song> songTable) {
+                    ....
+                    ....
+}
+
+interface KStreamKTableBinding {
+
+    @Input("inputStream")
+    KStream<?, ?> inputStream();
+
+    @Input("inputTable")
+    KTable<?, ?> inputTable();
+}
+
+
+
+

Following is the StreamListener equivalent of the same BiFunction based processor that we saw above.

+
+
+
+
@EnableBinding(KStreamKTableBinding.class)
+....
+....
+
+@StreamListener
+@SendTo("output")
+public KStream<String, Long> process(@Input("input") KStream<String, Long> userClicksStream,
+                                     @Input("inputTable") KTable<String, String> userRegionsTable) {
+....
+....
+}
+
+interface KStreamKTableBinding extends KafkaStreamsProcessor {
+
+    @Input("inputX")
+    KTable<?, ?> inputTable();
+}
+
+
+
+

Finally, here is the StreamListener equivalent of the application with three inputs and curried functions.

+
+
+
+
@EnableBinding(CustomGlobalKTableProcessor.class)
+...
+...
+    @StreamListener
+		@SendTo("output")
+		public KStream<Long, EnrichedOrder> process(
+				@Input("input-1") KStream<Long, Order> ordersStream,
+				@Input("input-"2) GlobalKTable<Long, Customer> customers,
+				@Input("input-3") GlobalKTable<Long, Product> products) {
+
+			KStream<Long, CustomerOrder> customerOrdersStream = ordersStream.join(
+					customers, (orderId, order) -> order.getCustomerId(),
+					(order, customer) -> new CustomerOrder(customer, order));
+
+			return customerOrdersStream.join(products,
+					(orderId, customerOrder) -> customerOrder.productId(),
+					(customerOrder, product) -> {
+						EnrichedOrder enrichedOrder = new EnrichedOrder();
+						enrichedOrder.setProduct(product);
+						enrichedOrder.setCustomer(customerOrder.customer);
+						enrichedOrder.setOrder(customerOrder.order);
+						return enrichedOrder;
+					});
+		}
+
+    interface CustomGlobalKTableProcessor {
+
+            @Input("input-1")
+            KStream<?, ?> input1();
+
+            @Input("input-2")
+            GlobalKTable<?, ?> input2();
+
+            @Input("input-3")
+            GlobalKTable<?, ?> input3();
+
+            @Output("output")
+            KStream<?, ?> output();
+    }
+
+
+
+

You might notice that the above two examples are even more verbose since in addition to provide EnableBinding, you also need to write your own custom binding interface as well. +Using the functional model, you can avoid all those ceremonial details.

+
+
+

Before we move on looking at the general programming model offered by Kafka Streams binder, here is the StreamListener version of multiple output bindings.

+
+
+
+
EnableBinding(KStreamProcessorWithBranches.class)
+public static class WordCountProcessorApplication {
+
+    @Autowired
+    private TimeWindows timeWindows;
+
+    @StreamListener("input")
+    @SendTo({"output1","output2","output3})
+    public KStream<?, WordCount>[] process(KStream<Object, String> input) {
+
+			Predicate<Object, WordCount> isEnglish = (k, v) -> v.word.equals("english");
+			Predicate<Object, WordCount> isFrench =  (k, v) -> v.word.equals("french");
+			Predicate<Object, WordCount> isSpanish = (k, v) -> v.word.equals("spanish");
+
+			return input
+					.flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
+					.groupBy((key, value) -> value)
+					.windowedBy(timeWindows)
+					.count(Materialized.as("WordCounts-1"))
+					.toStream()
+					.map((key, value) -> new KeyValue<>(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<?, ?> input();
+
+    		@Output("output1")
+    		KStream<?, ?> output1();
+
+    		@Output("output2")
+    		KStream<?, ?> output2();
+
+    		@Output("output3")
+    		KStream<?, ?> output3();
+    	}
+}
+
+
+
+

To recap, we have reviewed the various programming model choices when using the Kafka Streams binder.

+
+
+

The binder provides binding capabilities for KStream, KTable and GlobalKTable on the input. +KTable and GlobalKTable bindings are only available on the input. +Binder supports both input and output bindings for KStream.

+
+
+

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 StreamListener based imperative approach.

+
+
+
+
+

Ancillary to the programming model

+
+

Kafka Streams Application ID

+
+

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.

+
+
+

If you only have one single processor in the application, then you can set this at the binder level using the following property:

+
+
+

spring.cloud.stream.kafka.streams.binder.applicationId.

+
+
+

As a convenience, if you only have a single processor, you can also use spring.application.name as the property to delegate the application id.

+
+
+

If you have multiple Kafka Streams processors in the application, then you need to set the application id per processor. +You can set this on the input binding on each processor.

+
+
+

For e.g. imagine that you have to two following functions.

+
+
+
+
@Bean
+public java.util.function.Consumer<KStream<Object, String>> process() {
+   ...
+}
+
+
+
+

and

+
+
+
+
@Bean
+public java.util.function.Consumer<KStream<Object, String>> anotherProcess() {
+  ...
+}
+
+
+
+

Then you must set the application id for each, using the following binding properties.

+
+
+

spring.cloud.stream.kafka.streams.bindings.process_in.applicationId

+
+
+

and

+
+
+

spring.cloud.stream.kafka.streams.bindings.anotherProcess_in.applicationId

+
+
+

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.

+
+
+

If the application does not provide an application ID, then in that case the binder will auto generate a random application ID for you. +This is convenient in development scenarios as it avoids the need for explicitly providing the application ID. +Please keep in mind that when you rely on this, each time you start the application, it starts with a brand new application id. +In the case of functional model, the generated application ID will be the function bean name followed by a UUID which is then postfixed with the literal applicationID. +In the case of StreamListener, instead of using the function bean name, the generated application ID will be use the containing class name followed by the method name.

+
+
+
Summary of setting Application ID
+
+
    +
  • +

    Auto generated by the binder per processor in the application. This can be overridden by setting at the binding level such as spring.cloud.stream.kafka.streams.bindings.process_in.applicationId. +When you have more than one processor, then you have to choose one of these options - either fall back to the defaults or override per input binding.

    +
    • +
  • +

    If you have a single processor, then you can use spring.kafka.streams.applicationId, spring.application.name or spring.cloud.stream.binder.kafka.streams.applicationId.

    +
    • +
+
+
+
+
+

Custom bindings in the functional style

+
+

By default, the binder uses the strategy discussed out above to generate the binding name when using the functional style, i.e. <function-bean-name>_<in>|<out>_[0..n], for e.g. process_in, process_in_0 etc. +If you want to override those binding names, you can do that by specifying the following properties.

+
+
+

spring.cloud.stream.function.inputBindings.<function-bean-name>.

+
+
+

For e.g. lets say, you have this function.

+
+
+
+
@Bean
+public BiFunction<KStream<String, Long>, KTable<String, String>, KStream<String, Long>> process() {
+...
+}
+
+
+
+

Binder will generate bindings with names, process_in_0, process_in_1 and process_out. +Now, if you want to change them to something else completely, maybe more domain specific binding names. You can do so, as below.

+
+
+

springc.cloud.stream.function.inputBindings.process=users,regions

+
+
+

and

+
+
+

spring.cloud.stream.function.outputBindings.process=clicks

+
+
+

After that, you must set all the binding level properties on these new binding names.

+
+
+

Please keep in mind that with the functional programming model described above, sticking with 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.

+
+
+
+

Setting up bootstrap server configuration

+
+

When running Kafka Streams applications, you must provide the Kafka broker server information. +If you don’t provide this information, the binder expects that you are running the broker at the default localhost:9092. +If that is not the case, then you need to override that. There are a couple of ways to do that.

+
+
+
    +
  • +

    Using the boot property - spring.kafka.bootstrapServers

    +
    • +
  • +

    Binder level property - spring.cloud.stream.kafka.streams.binder.brokers

    +
    • +
+
+
+

When it comes to the binder level property, it doesn’t matter if you use the broker property provided through the regular Kafka binder - spring.cloud.stream.kafka.binder.brokers. +Kafka Streams binder will first check if Kafka Streams binder specific broker property is set (spring.cloud.stream.kafka.streams.binder.brokers) and if nothing found, it looks for spring.cloud.stream.kafka.binder.brokers.

+
+
+
+
+

Record serialization and deserialization

+
+

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.

+
+
+

Inbound deserialization

+
+

Keys are always deserialized by native Serdes.

+
+
+

By default, for values, 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 in which case the deserialization was done by the framework.

+
+
+

Kafka Streams binder will try to infer matching Serde types by looking at the type signature of java.util.function.Function|Consumer or StreamListener. +Here is the order that it matches Serdes.

+
+
+
    +
  • +

    First 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.

    +
    +
    +
    Integer, Long, Short, Double, Float, byte[] and String.
    +
    +
    +
    • +
  • +

    If none of the Serdes provided by Kafka Streams don’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 json Serde objects. Otherwise, you have to configure Serde and target types on them individually.

    +
    • +
+
+
+

If none of the above strategies worked, then the applications must provide the Serdes through configuration. +This can be configured in two ways - binding or default.

+
+
+

First the binder will look if a Serde is provided at the binding level. +For e.g. if you have the following processor,

+
+
+
+
@Bean
+public BiFunction<KStream<CustomKey, AvroIn1>, KTable<CustomKey, AvroIn2>, KStream<CustomKey, AvroOutput>> process() {...}
+
+
+
+

then, you can provide a binding level Serde using the following:

+
+
+
+
spring.cloud.stream.kafka.streams.bindings.process_in_0.keySerde=CustomKeySerde
+spring.cloud.stream.kafka.streams.bindings.process_in_0.valueSerde=io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde
+
+spring.cloud.stream.kafka.streams.bindings.process_in_1.keySerde=CustomKeySerde
+spring.cloud.stream.kafka.streams.bindings.process_in_1.valueSerde=io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde
+
+
+
+

If you want the default key/value Serdes to be used for inbound deserialization, you can do so at the binder level.

+
+
+
+
spring.cloud.stream.kafka.streams.binder.configuration.default.key.serde
+spring.cloud.stream.kafka.streams.binder.configuration.default.value.serde
+
+
+
+

If you don’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 deserialze the inbound value object, you need to explicitly disable native decoding.

+
+
+

For e.g. if you have the same BiFunction processor as above, then spring.cloud.stream.bindings.process_in_0.nativeDecoding: false +You need to disable native decoding for all the inputs individually. Otherwise, native decoding will still be applied for those you don’t disable.

+
+
+

By default, Spring Cloud Stream will use application/json as the content type and use an appropriate json message converter. +You can use custom message converters by using the following property.

+
+
+
+
spring.cloud.stream.bindings.process_in_0.contentType
+
+
+
+
+

Outbound serialization

+
+

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.

+
+
+

Keys on the outbound are always serialized by Kafka using a matching Serde that is inferred by the binder. +If it can’t infer the type of the key, then that needs to be specified using configuration.

+
+
+

Value serdes are inferred using the same rules used for inbound deserialization. +First it matches to see if the outbound type is of a Serde exposed by Kafka such as - Long, Short, Double, Float, byte[] and String. +If that doesnt’t work, then fall back to JsonSerde provided by the Spring Kafka project. +Keep in mind that all these happen transparently to the application. +If none of these work, then the user has to provide the Serde to use by configuration.

+
+
+

Lets say you are using the same BiFunction processor as above. Then you can configure outbound key/value Serdes as following.

+
+
+
+
spring.cloud.stream.kafka.streams.bindings.process_out.keySerde=CustomKeySerde
+spring.cloud.stream.kafka.streams.bindings.process_out.valueSerde=io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde
+
+
+
+

However, falling back to default Serdes for both input deserialization and output serialization is the last resort. +This may or may not work. Therefore, you need to ensure that you have a path forward for the application to correctly retrive the Serde.

+
+
+

If Serde inference fails, no binding level Serdes are provided, then the binder falls back to the default Serdes.

+
+
+

spring.cloud.stream.kafka.streams.binder.configuration.default.key.serde +spring.cloud.stream.kafka.streams.binder.configuration.default.value.serde

+
+
+

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 Serde types, you don’t need to do this configuration.

+
+
+

If you don’t want the native encoding provided by Kafka, but want to use the framework provided message conversion, then you need to explicitly disable native decoding since since native decoding is the default. +For e.g. if you have the same BiFunction processor as above, then spring.cloud.stream.bindings.process_out.nativeEncoding: false +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’t disable.

+
+
+

By default, Spring Cloud Stream will use application/json as the content type and use an appropriate json message converter. +You can use custom message converters by using the following property.

+
+
+
+
spring.cloud.stream.bindings.process_output.contentType
+
+
+
+

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.

+
+
+
+
+

Configuration Options

+
+

This section contains the configuration options used by the Kafka Streams binder.

+
+
+

For common configuration options and properties pertaining to binder, refer to the core documentation.

+
+
+

Kafka Streams Properties

+
+

The following properties are available at the binder level and must be prefixed with spring.cloud.stream.kafka.streams.binder.

+
+
+
+
configuration
+
+

Map with a key/value pair containing properties pertaining to Apache Kafka Streams API. + This property must be prefixed with spring.cloud.stream.kafka.streams.binder.. +Following are some examples of using this property.

+
+
+
+
+
+
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
+
+
+
+

For more information about all the properties that may go into streams configuration, see StreamsConfig JavaDocs in +Apache Kafka Streams docs.

+
+
+
+
brokers
+
+

Broker URL

+
+

Default: localhost

+
+
+
zkNodes
+
+

Zookeeper URL

+
+

Default: localhost

+
+
+
serdeError
+
+

Deserialization error handler type. +Possible values are - logAndContinue, logAndFail or sendToDlq

+
+

Default: logAndFail

+
+
+
applicationId
+
+

Convenient way to set the application.id for the Kafka Streams application globally at the binder level. +If the application contains multiple functions or StreamListener methods, then the application id should be set at the binding level per input binding. +See above where setting the application id is discussed in detail.

+
+

Default: none

+
+
+
+
+
+

The following properties are only available for Kafka Streams producers and must be prefixed with spring.cloud.stream.kafka.streams.bindings.<binding name>.producer. +For convenience, if there multiple output bindings and they all require a common value, that can be configured by using the prefix spring.cloud.stream.kafka.streams.default.producer..

+
+
+
+
keySerde
+
+

key serde to use

+
+

Default: See the above discussion on message de/serialization

+
+
+
valueSerde
+
+

value serde to use

+
+

Default: See the above discussion on message de/serialization

+
+
+
useNativeEncoding
+
+

flag to enable/disable native encoding

+
+

Default: true.

+
+
+
+
+
+

The following properties are available for Kafka Streams consumers and must be prefixed with spring.cloud.stream.kafka.streams.bindings.<binding-name>.consumer. +For convenience, if there are multiple input bindings and they all require a common value, that can be configured by using the prefix spring.cloud.stream.kafka.streams.default.consumer..

+
+
+
+
applicationId
+
+

Setting application.id per input binding.

+
+

Default: none

+
+
+
keySerde
+
+

key serde to use

+
+

Default: See the above discussion on message de/serialization

+
+
+
valueSerde
+
+

value serde to use

+
+

Default: See the above discussion on message de/serialization

+
+
+
materializedAs
+
+

state store to materialize when using incoming KTable types

+
+

Default: none.

+
+
+
useNativeDecoding
+
+

flag to enable/disable native decoding

+
+

Default: true.

+
+
+
dlqName
+
+

DLQ topic name.

+
+

Default: none.

+
+
+
startOffset
+
+

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 earliest as the default strategy and +the binder uses the same default. This can be overridden to latest using this property.

+
+

Default: earliest.

+
+
+
+
+
+

Note: Using resetOffsets 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.

+
+
+
+
+

Materializing KTable as a State Store.

+
+

Lets say you have the following function.

+
+
+
+
@Bean
+public BiFunction<KStream<String, Long>, KTable<String, String>, KStream<String, Long>> process() {
+   ...
+}
+
+
+
+

In the case of incoming KTable, if you want to materialize the computations to a state store, you have to express it +through the following property.

+
+
+
+
spring.cloud.stream.kafka.streams.bindings.process_in_1.consumer.materializedAs: incoming-store
+
+
+
+
+

Error Handling

+
+

Apache Kafka Streams provide the capability for natively handling exceptions from deserialization errors. +For details on this support, please see this +Out of the box, Apache Kafka Streams provide two kinds of deserialization exception handlers - logAndContinue and logAndFail. +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. LogAndFail is the default deserialization exception handler.

+
+
+
+

Handling Deserialization Exceptions

+
+

Kafka Streams binder supports a selection of exception handlers through the following properties.

+
+
+
+
spring.cloud.stream.kafka.streams.binder.serdeError: logAndContinue
+
+
+
+

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 topic. Here is how you enable this DLQ exception handler.

+
+
+
+
spring.cloud.stream.kafka.streams.binder.serdeError: sendToDlq
+
+
+
+

When the above property is set, all the deserialization error records are automatically sent to the DLQ topic.

+
+
+
+
spring.cloud.stream.kafka.streams.bindings.input.consumer.dlqName: foo-dlq
+
+
+
+

If this is set, then the error records are sent to the topic foo-dlq. If this is not set, then it will create a DLQ +topic with the name error.<input-topic-name>.<group-name>.

+
+
+

A couple of things to keep in mind when using the exception handling feature in Kafka Streams binder.

+
+
+
    +
  • +

    The property spring.cloud.stream.kafka.streams.binder.serdeError is applicable for the entire application. This implies +that if there are multiple StreamListener methods in the same application, this property is applied to all of them.

    +
    • +
  • +

    The exception handling for deserialization works consistently with native deserialization and framework provided message +conversion.

    +
    • +
+
+
+

Handling Non-Deserialization Exceptions

+
+

For general error handling in Kafka Streams binder, it is up to the end user applications to handle application level errors. +As a side effect of providing a DLQ for deserialization exception handlers, Kafka Streams binder provides a way to get +access to the DLQ sending bean directly from your application. +Once you get access to that bean, you can programmatically send any exception records from your application to the DLQ.

+
+
+

It continues to remain hard to robust error handling using the high-level DSL; Kafka Streams doesn’t natively support error +handling yet.

+
+
+

However, when you use the low-level Processor API in your application, there are options to control this behavior. See +below.

+
+
+
+
@Autowired
+private SendToDlqAndContinue dlqHandler;
+
+@StreamListener("input")
+@SendTo("output")
+public KStream<?, WordCount> process(KStream<Object, String> input) {
+
+    input.process(() -> new Processor() {
+    			ProcessorContext context;
+
+    			@Override
+    			public void init(ProcessorContext context) {
+    				this.context = context;
+    			}
+
+    			@Override
+    			public void process(Object o, Object o2) {
+
+    			    try {
+    			        .....
+    			        .....
+    			    }
+    			    catch(Exception e) {
+    			        //explicitly provide the kafka topic corresponding to the input binding as the first argument.
+                        //DLQ handler will correctly map to the dlq topic from the actual incoming destination.
+                        dlqHandler.sendToDlq("topic-name", (byte[]) o1, (byte[]) o2, context.partition());
+    			    }
+    			}
+
+    			.....
+    			.....
+    });
+}
+
+
+
+
+
+

State Store

+
+

State store is created automatically by Kafka Streams when the DSL is used. +When processor API is used, you need to register a state store manually. In order to do so, you can use KafkaStreamsStateStore annotation. +You can specify the name and type of the store, flags to control log and disabling cache, etc. +Once the store is created by the binder during the bootstrapping phase, you can access this state store through the processor API. +Below are some primitives for doing this.

+
+
+

Creating a state store:

+
+
+
+
@KafkaStreamsStateStore(name="mystate", type= KafkaStreamsStateStoreProperties.StoreType.WINDOW, lengthMs=300000)
+public void process(KStream<Object, Product> input) {
+    ...
+}
+
+
+
+

Accessing the state store:

+
+
+
+
Processor<Object, Product>() {
+
+    WindowStore<Object, String> state;
+
+    @Override
+    public void init(ProcessorContext processorContext) {
+        state = (WindowStore)processorContext.getStateStore("mystate");
+    }
+    ...
+}
+
+
+
+
+

Interactive Queries

+
+

As part of the public Kafka Streams binder API, we expose a class called InteractiveQueryService. +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 "autowire" the bean.

+
+
+
+
@Autowired
+private InteractiveQueryService interactiveQueryService;
+
+
+
+

Once you gain access to this bean, then you can query for the particular state-store that you are interested. See below.

+
+
+
+
ReadOnlyKeyValueStore<Object, Object> keyValueStore =
+						interactiveQueryService.getQueryableStoreType("my-store", QueryableStoreTypes.keyValueStore());
+
+
+
+

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 key. +InteractiveQueryService API provides methods for identifying the host information.

+
+
+

In order for this to work, you must configure the property application.server as below:

+
+
+
+
spring.cloud.stream.kafka.streams.binder.configuration.application.server: <server>:<port>
+
+
+
+

Here are some code snippets:

+
+
+
+
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
+}
+
+
+
+
+

Accessing the underlying KafkaStreams object

+
+

StreamBuilderFactoryBean from spring-kafka that is responsible for constructing the KafkaStreams object can be accessed programmatically. +Each StreamBuilderFactoryBean is registered as stream-builder and appended with the StreamListener method name. +If your StreamListener method is named as process for example, the stream builder bean is named as stream-builder-process. +Since this is a factory bean, it should be accessed by prepending an ampersand (&) when accessing it programmatically. +Following is an example and it assumes the StreamListener method is named as process

+
+
+
+
StreamsBuilderFactoryBean streamsBuilderFactoryBean = context.getBean("&stream-builder-process", StreamsBuilderFactoryBean.class);
+			KafkaStreams kafkaStreams = streamsBuilderFactoryBean.getKafkaStreams();
+
+
+
+
+

State Cleanup

+
+

By default, the Kafkastreams.cleanup() method is called when the binding is stopped. +See the Spring Kafka documentation. +To modify this behavior simply add a single CleanupConfig @Bean (configured to clean up on start, stop, or neither) to the application context; the bean will be detected and wired into the factory bean.

+
+
+
+

Health Indicator

+
+

The health indicator requires the dependency spring-boot-starter-actuator. For maven use:

+
+
+
+
<dependency>
+  <groupId>org.springframework.boot</groupId>
+  <artifactId>spring-boot-starter-actuator</artifactId>
+</dependency>
+
+
+
+

Spring Cloud Stream Binder Kafka Streams provides a health indicator to check the state of the underlying Kafka threads. +Spring Cloud Stream defines a property management.health.binders.enabled to enable the health indicator. See the +Spring Cloud Stream documentation.

+
+
+

The health indicator provides the following details for each Kafka threads:

+
+
+
    +
  • +

    Thread name

    +
    • +
  • +

    Thread state: CREATED, RUNNING, PARTITIONS_REVOKED, PARTITIONS_ASSIGNED, PENDING_SHUTDOWN or DEAD

    +
    • +
  • +

    Active tasks: task ID and partitions

    +
    • +
  • +

    Standby tasks: task ID and partitions

    +
    • +
+
+
+

By default, only the global status is visible (UP or DOWN). To show the details, the property management.endpoint.health.show-details must be set to ALWAYS or WHEN_AUTHORIZED. +For more details about the health information, see the +Spring Boot Actuator documentation.

+
+
+ + + + + +
+ + +The status of the health indicator is UP if all the Kafka threads registered are in the RUNNING state. +
+
+
+

Using custom state stores in functional applications

+
+

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. +Note that, for regular StreamListener based processors, you still need to use the KafkaStreamsStateStore annotation for custom state stores. +Here is an example of using custom state stores with functional style described in this section.

+
+
+
+
@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());
+		}
+
+
+
+

These state stores can be then accessed by the applications directly.

+
+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/overview.html b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/overview.html new file mode 100644 index 00000000..3cd93f00 --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/overview.html @@ -0,0 +1,1060 @@ + + + + + + + +Apache Kafka Binder + + + + + + + + + + +
+
+
+ +
+
+
+

Apache Kafka Binder

+
+
+

Usage

+
+

To use Apache Kafka binder, you need to add spring-cloud-stream-binder-kafka as a dependency to your Spring Cloud Stream application, as shown in the following example for Maven:

+
+
+
+
<dependency>
+  <groupId>org.springframework.cloud</groupId>
+  <artifactId>spring-cloud-stream-binder-kafka</artifactId>
+</dependency>
+
+
+
+

Alternatively, you can also use the Spring Cloud Stream Kafka Starter, as shown inn the following example for Maven:

+
+
+
+
<dependency>
+  <groupId>org.springframework.cloud</groupId>
+  <artifactId>spring-cloud-starter-stream-kafka</artifactId>
+</dependency>
+
+
+
+
+

Overview

+
+

The following image shows a simplified diagram of how the Apache Kafka binder operates:

+
+
+
+kafka binder +
+
Figure 1. Kafka Binder
+
+
+

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.

+
+
+

The binder currently uses the Apache Kafka kafka-clients 1.0.0 jar and is designed to be used with a broker of at least that version. +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 autoAddPartitions property.

+
+
+
+

Configuration Options

+
+

This section contains the configuration options used by the Apache Kafka binder.

+
+
+

For common configuration options and properties pertaining to binder, see the core documentation.

+
+
+

Kafka Binder Properties

+
+
+
spring.cloud.stream.kafka.binder.brokers
+
+

A list of brokers to which the Kafka binder connects.

+
+

Default: localhost.

+
+
+
spring.cloud.stream.kafka.binder.defaultBrokerPort
+
+

brokers allows hosts specified with or without port information (for example, host1,host2:port2). +This sets the default port when no port is configured in the broker list.

+
+

Default: 9092.

+
+
+
spring.cloud.stream.kafka.binder.configuration
+
+

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 — 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.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.consumerProperties
+
+

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 configuration property above.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.headers
+
+

The list of custom headers that are transported by the binder. +Only required when communicating with older applications (⇐ 1.3.x) with a kafka-clients version < 0.11.0.0. Newer versions support headers natively.

+
+

Default: empty.

+
+
+
spring.cloud.stream.kafka.binder.healthTimeout
+
+

The time to wait to get partition information, in seconds. +Health reports as down if this timer expires.

+
+

Default: 10.

+
+
+
spring.cloud.stream.kafka.binder.requiredAcks
+
+

The number of required acks on the broker. +See the Kafka documentation for the producer acks property.

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.minPartitionCount
+
+

Effective only if autoCreateTopics or autoAddPartitions 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 partitionCount setting of the producer or by the value of instanceCount * concurrency settings of the producer (if either is larger).

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.producerProperties
+
+

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 configuration property above.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.replicationFactor
+
+

The replication factor of auto-created topics if autoCreateTopics is active. +Can be overridden on each binding.

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.autoCreateTopics
+
+

If set to true, the binder creates new topics automatically. +If set to false, the binder relies on the topics being already configured. +In the latter case, if the topics do not exist, the binder fails to start.

+
+ + + + + +
+ + +This setting is independent of the auto.create.topics.enable 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. +
+
+
+

Default: true.

+
+
+
spring.cloud.stream.kafka.binder.autoAddPartitions
+
+

If set to true, the binder creates new partitions if required. +If set to false, 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.

+
+

Default: false.

+
+
+
spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix
+
+

Enables transactions in the binder. See transaction.id in the Kafka documentation and Transactions in the spring-kafka documentation. +When transactions are enabled, individual producer properties are ignored and all producers use the spring.cloud.stream.kafka.binder.transaction.producer.* properties.

+
+

Default null (no transactions)

+
+
+
spring.cloud.stream.kafka.binder.transaction.producer.*
+
+

Global producer properties for producers in a transactional binder. +See spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix and Kafka Producer Properties and the general producer properties supported by all binders.

+
+

Default: See individual producer properties.

+
+
+
spring.cloud.stream.kafka.binder.headerMapperBeanName
+
+

The bean name of a KafkaHeaderMapper used for mapping spring-messaging headers to and from Kafka headers. +Use this, for example, if you wish to customize the trusted packages in a DefaultKafkaHeaderMapper that uses JSON deserialization for the headers.

+
+

Default: none.

+
+
+
+
+
+
+

Kafka Consumer Properties

+
+ + + + + +
+ + +To avoid repetition, Spring Cloud Stream supports setting values for all channels, in the format of spring.cloud.stream.default.<property>=<value>. +
+
+
+

The following properties are available for Kafka consumers only and +must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.consumer..

+
+
+
+
admin.configuration
+
+

Since version 2.1.1, this property is deprecated in favor of topic.properties, and support for it will be removed in a future version.

+
+
admin.replicas-assignment
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replicas-assignment, and support for it will be removed in a future version.

+
+
admin.replication-factor
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replication-factor, and support for it will be removed in a future version.

+
+
autoRebalanceEnabled
+
+

When true, topic partitions is automatically rebalanced between the members of a consumer group. +When false, each consumer is assigned a fixed set of partitions based on spring.cloud.stream.instanceCount and spring.cloud.stream.instanceIndex. +This requires both the spring.cloud.stream.instanceCount and spring.cloud.stream.instanceIndex properties to be set appropriately on each launched instance. +The value of the spring.cloud.stream.instanceCount property must typically be greater than 1 in this case.

+
+

Default: true.

+
+
+
ackEachRecord
+
+

When autoCommitOffset is true, 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 consumer.poll() have been processed. +The number of records returned by a poll can be controlled with the max.poll.records Kafka property, which is set through the consumer configuration property. +Setting this to true may cause a degradation in performance, but doing so reduces the likelihood of redelivered records when a failure occurs. +Also, see the binder requiredAcks property, which also affects the performance of committing offsets.

+
+

Default: false.

+
+
+
autoCommitOffset
+
+

Whether to autocommit offsets when a message has been processed. +If set to false, a header with the key kafka_acknowledgment of the type org.springframework.kafka.support.Acknowledgment 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 false, Kafka binder sets the ack mode to org.springframework.kafka.listener.AbstractMessageListenerContainer.AckMode.MANUAL and the application is responsible for acknowledging records. +Also see ackEachRecord.

+
+

Default: true.

+
+
+
autoCommitOnError
+
+

Effective only if autoCommitOffset is set to true. +If set to false, 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 true, it always auto-commits (if auto-commit is enabled). +If not set (the default), it effectively has the same value as enableDlq, auto-committing erroneous messages if they are sent to a DLQ and not committing them otherwise.

+
+

Default: not set.

+
+
+
resetOffsets
+
+

Whether to reset offsets on the consumer to the value provided by startOffset. +Must be false if a KafkaRebalanceListener is provided; see Using a KafkaRebalanceListener.

+
+

Default: false.

+
+
+
startOffset
+
+

The starting offset for new groups. +Allowed values: earliest and latest. +If the consumer group is set explicitly for the consumer 'binding' (through spring.cloud.stream.bindings.<channelName>.group), 'startOffset' is set to earliest. Otherwise, it is set to latest for the anonymous consumer group. +Also see resetOffsets (earlier in this list).

+
+

Default: null (equivalent to earliest).

+
+
+
enableDlq
+
+

When set to true, it enables DLQ behavior for the consumer. +By default, messages that result in errors are forwarded to a topic named error.<destination>.<group>. +The DLQ topic name can be configurable by setting the dlqName 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 [kafka-dlq-processing] processing for more information. +Starting with version 2.0, messages sent to the DLQ topic are enhanced with the following headers: x-original-topic, x-exception-message, and x-exception-stacktrace as byte[]. +Not allowed when destinationIsPattern is true.

+
+

Default: false.

+
+
+
configuration
+
+

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 spring.cloud.stream.kafka.bindings.input.consumer.configuration.foo=bar.

+
+

Default: Empty map.

+
+
+
dlqName
+
+

The name of the DLQ topic to receive the error messages.

+
+

Default: null (If not specified, messages that result in errors are forwarded to a topic named error.<destination>.<group>).

+
+
+
dlqProducerProperties
+
+

Using this, DLQ-specific producer properties can be set. +All the properties available through kafka producer properties can be set through this property.

+
+

Default: Default Kafka producer properties.

+
+
+
standardHeaders
+
+

Indicates which standard headers are populated by the inbound channel adapter. +Allowed values: none, id, timestamp, or both. +Useful if using native deserialization and the first component to receive a message needs an id (such as an aggregator that is configured to use a JDBC message store).

+
+

Default: none

+
+
+
converterBeanName
+
+

The name of a bean that implements RecordMessageConverter. Used in the inbound channel adapter to replace the default MessagingMessageConverter.

+
+

Default: null

+
+
+
idleEventInterval
+
+

The interval, in milliseconds, between events indicating that no messages have recently been received. +Use an ApplicationListener<ListenerContainerIdleEvent> to receive these events. +See Example: Pausing and Resuming the Consumer for a usage example.

+
+

Default: 30000

+
+
+
destinationIsPattern
+
+

When true, the destination is treated as a regular expression Pattern used to match topic names by the broker. +When true, topics are not provisioned, and enableDlq 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 metadata.max.age.ms, which (at the time of writing) defaults to 300,000ms (5 minutes). +This can be configured using the configuration property above.

+
+

Default: false

+
+
+
topic.properties
+
+

A Map of Kafka topic properties used when provisioning new topics — for example, spring.cloud.stream.kafka.bindings.input.consumer.topic.properties.message.format.version=0.9.0.0

+
+

Default: none.

+
+
+
topic.replicas-assignment
+
+

A Map<Integer, List<Integer>> of replica assignments, with the key being the partition and the value being the assignments. +Used when provisioning new topics. +See the NewTopic Javadocs in the kafka-clients jar.

+
+

Default: none.

+
+
+
topic.replication-factor
+
+

The replication factor to use when provisioning topics. Overrides the binder-wide setting. +Ignored if replicas-assignments is present.

+
+

Default: none (the binder-wide default of 1 is used).

+
+
+
+
+
+
+

Kafka Producer Properties

+
+ + + + + +
+ + +To avoid repetition, Spring Cloud Stream supports setting values for all channels, in the format of spring.cloud.stream.default.<property>=<value>. +
+
+
+

The following properties are available for Kafka producers only and +must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.producer..

+
+
+
+
admin.configuration
+
+

Since version 2.1.1, this property is deprecated in favor of topic.properties, and support for it will be removed in a future version.

+
+
admin.replicas-assignment
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replicas-assignment, and support for it will be removed in a future version.

+
+
admin.replication-factor
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replication-factor, and support for it will be removed in a future version.

+
+
bufferSize
+
+

Upper limit, in bytes, of how much data the Kafka producer attempts to batch before sending.

+
+

Default: 16384.

+
+
+
sync
+
+

Whether the producer is synchronous.

+
+

Default: false.

+
+
+
batchTimeout
+
+

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.

+
+

Default: 0.

+
+
+
messageKeyExpression
+
+

A SpEL expression evaluated against the outgoing message used to populate the key of the produced Kafka message — for example, headers['myKey']. +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 byte[]. +Now, the expression is evaluated before the payload is converted.

+
+

Default: none.

+
+
+
headerPatterns
+
+

A comma-delimited list of simple patterns to match Spring messaging headers to be mapped to the Kafka Headers in the ProducerRecord. +Patterns can begin or end with the wildcard character (asterisk). +Patterns can be negated by prefixing with !. +Matching stops after the first match (positive or negative). +For example !ask,as* will pass ash but not ask. +id and timestamp are never mapped.

+
+

Default: * (all headers - except the id and timestamp)

+
+
+
configuration
+
+

Map with a key/value pair containing generic Kafka producer properties.

+
+

Default: Empty map.

+
+
+
topic.properties
+
+

A Map of Kafka topic properties used when provisioning new topics — for example, spring.cloud.stream.kafka.bindings.output.producer.topic.properties.message.format.version=0.9.0.0

+
+
topic.replicas-assignment
+
+

A Map<Integer, List<Integer>> of replica assignments, with the key being the partition and the value being the assignments. +Used when provisioning new topics. +See the NewTopic Javadocs in the kafka-clients jar.

+
+

Default: none.

+
+
+
topic.replication-factor
+
+

The replication factor to use when provisioning topics. Overrides the binder-wide setting. +Ignored if replicas-assignments is present.

+
+

Default: none (the binder-wide default of 1 is used).

+
+
+
useTopicHeader
+
+

Set to true to override the default binding destination (topic name) with the value of the KafkaHeaders.TOPIC message header in the outbound message. +If the header is not present, the default binding destination is used. +Default: false.

+
+
recordMetadataChannel
+
+

The bean name of a MessageChannel 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 KafkaHeaders.RECORD_METADATA. +The header contains a RecordMetadata object provided by the Kafka client; it includes the partition and offset where the record was written in the topic.

+
+
+
+
+

ResultMetadata meta = sendResultMsg.getHeaders().get(KafkaHeaders.RECORD_METADATA, RecordMetadata.class)

+
+
+

Failed sends go the producer error channel (if configured); see Error Channels. +Default: null

+
+
+

+

+
+
+ + + + + +
+ + +The Kafka binder uses the partitionCount setting of the producer as a hint to create a topic with the given partition count (in conjunction with the minPartitionCount, the maximum of the two being the value being used). +Exercise caution when configuring both minPartitionCount for a binder and partitionCount for an application, as the larger value is used. +If a topic already exists with a smaller partition count and autoAddPartitions is disabled (the default), the binder fails to start. +If a topic already exists with a smaller partition count and autoAddPartitions is enabled, new partitions are added. +If a topic already exists with a larger number of partitions than the maximum of (minPartitionCount or partitionCount), the existing partition count is used. +
+
+
+
+
compression
+
+

Set the compression.type producer property. +Supported values are none, gzip, snappy and lz4. +If you override the kafka-clients jar to 2.1.0 (or later), as discussed in the Spring for Apache Kafka documentation, and wish to use zstd compression, use spring.cloud.stream.kafka.bindings.<binding-name>.producer.configuration.compression.type=zstd.

+
+

Default: none.

+
+
+
+
+
+
+

Usage examples

+
+

In this section, we show the use of the preceding properties for specific scenarios.

+
+
+
Example: Setting autoCommitOffset to false and Relying on Manual Acking
+
+

This example illustrates how one may manually acknowledge offsets in a consumer application.

+
+
+

This example requires that spring.cloud.stream.kafka.bindings.input.consumer.autoCommitOffset be set to false. +Use the corresponding input channel name for your example.

+
+
+
+
@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<?> message) {
+     Acknowledgment acknowledgment = message.getHeaders().get(KafkaHeaders.ACKNOWLEDGMENT, Acknowledgment.class);
+     if (acknowledgment != null) {
+         System.out.println("Acknowledgment provided");
+         acknowledgment.acknowledge();
+     }
+ }
+}
+
+
+
+
+
Example: Security Configuration
+
+

Apache Kafka 0.9 supports secure connections between client and brokers. +To take advantage of this feature, follow the guidelines in the Apache Kafka Documentation as well as the Kafka 0.9 security guidelines from the Confluent documentation. +Use the spring.cloud.stream.kafka.binder.configuration option to set security properties for all clients created by the binder.

+
+
+

For example, to set security.protocol to SASL_SSL, set the following property:

+
+
+
+
spring.cloud.stream.kafka.binder.configuration.security.protocol=SASL_SSL
+
+
+
+

All the other security properties can be set in a similar manner.

+
+
+

When using Kerberos, follow the instructions in the reference documentation for creating and referencing the JAAS configuration.

+
+
+

Spring Cloud Stream supports passing JAAS configuration information to the application by using a JAAS configuration file and using Spring Boot properties.

+
+
+
Using JAAS Configuration Files
+
+

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:

+
+
+
+
 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
+
+
+
+
+
Using Spring Boot Properties
+
+

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.

+
+
+

The following properties can be used to configure the login context of the Kafka client:

+
+
+
+
spring.cloud.stream.kafka.binder.jaas.loginModule
+
+

The login module name. Not necessary to be set in normal cases.

+
+

Default: com.sun.security.auth.module.Krb5LoginModule.

+
+
+
spring.cloud.stream.kafka.binder.jaas.controlFlag
+
+

The control flag of the login module.

+
+

Default: required.

+
+
+
spring.cloud.stream.kafka.binder.jaas.options
+
+

Map with a key/value pair containing the login module options.

+
+

Default: Empty map.

+
+
+
+
+
+

The following example shows how to launch a Spring Cloud Stream application with SASL and Kerberos by using Spring Boot configuration properties:

+
+
+
+
 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
+
+
+
+

The preceding example represents the equivalent of the following JAAS file:

+
+
+
+
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";
+};
+
+
+
+

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.

+
+
+ + + + + +
+ + +Do not mix JAAS configuration files and Spring Boot properties in the same application. +If the -Djava.security.auth.login.config system property is already present, Spring Cloud Stream ignores the Spring Boot properties. +
+
+
+ + + + + +
+ + +Be careful when using the autoCreateTopics and autoAddPartitions 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. +
+
+
+
+
+
Example: Pausing and Resuming the Consumer
+
+

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 Consumer as a parameter to your @StreamListener. +To resume, you need an ApplicationListener for ListenerContainerIdleEvent instances. +The frequency at which events are published is controlled by the idleEventInterval property. +Since the consumer is not thread-safe, you must call these methods on the calling thread.

+
+
+

The following simple application shows how to pause and resume:

+
+
+
+
@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<?, ?> consumer) {
+		System.out.println(in);
+		consumer.pause(Collections.singleton(new TopicPartition("myTopic", 0)));
+	}
+
+	@Bean
+	public ApplicationListener<ListenerContainerIdleEvent> idleListener() {
+		return event -> {
+			System.out.println(event);
+			if (event.getConsumer().paused().size() > 0) {
+				event.getConsumer().resume(event.getConsumer().paused());
+			}
+		};
+	}
+
+}
+
+
+
+
+
+
+

Transactional Binder

+
+

Enable transactions by setting spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix to a non-empty value, e.g. tx-. +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 spring.cloud.stream.kafka.binder.transaction.producer.* properties; individual binding Kafka producer properties are ignored.

+
+
+

If you wish to use transactions in a source application, or from some arbitrary thread for producer-only transaction (e.g. @Scheduled method), you must get a reference to the transactional producer factory and define a KafkaTransactionManager bean using it.

+
+
+
+
+
+
@Bean
+public PlatformTransactionManager transactionManager(BinderFactory binders) {
+    ProducerFactory<byte[], byte[]> pf = ((KafkaMessageChannelBinder) binders.getBinder(null,
+            MessageChannel.class)).getTransactionalProducerFactory();
+    return new KafkaTransactionManager<>(pf);
+}
+
+
+
+
+
+

Notice that we get a reference to the binder using the BinderFactory; use null 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 ProducerFactory and create a transaction manager.

+
+
+

Then you would use normal Spring transaction support, e.g. TransactionTemplate or @Transactional, for example:

+
+
+
+
+
+
public static class Sender {
+
+    @Transactional
+    public void doInTransaction(MessageChannel output, List<String> stuffToSend) {
+        stuffToSend.forEach(stuff -> output.send(new GenericMessage<>(stuff)));
+    }
+
+}
+
+
+
+
+
+

If you wish to synchronize producer-only transactions with those from some other transaction manager, use a ChainedTransactionManager.

+
+
+
+

Error Channels

+
+

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 [spring-cloud-stream-overview-error-handling] for more information.

+
+
+

The payload of the ErrorMessage for a send failure is a KafkaSendFailureException with properties:

+
+
+
    +
  • +

    failedMessage: The Spring Messaging Message<?> that failed to be sent.

    +
    • +
  • +

    record: The raw ProducerRecord that was created from the failedMessage

    +
    • +
+
+
+

There is no automatic handling of producer exceptions (such as sending to a Dead-Letter queue). +You can consume these exceptions with your own Spring Integration flow.

+
+
+
+

Kafka Metrics

+
+

Kafka binder module exposes the following metrics:

+
+
+

spring.cloud.stream.binder.kafka.offset: This metric indicates how many messages have not been yet consumed from a given binder’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.

+
+
+
+

Tombstone Records (null record values)

+
+

When using compacted topics, a record with a null value (also called a tombstone record) represents the deletion of a key. +To receive such messages in a @StreamListener method, the parameter must be marked as not required to receive a null value argument.

+
+
+
+
+
+
@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
+    ...
+}
+
+
+
+
+
+
+

Using a KafkaRebalanceListener

+
+

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 KafkaRebalanceListener bean in the application context, it will be wired into all Kafka consumer bindings.

+
+
+
+
+
+
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<?, ?> consumer,
+			Collection<TopicPartition> 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<?, ?> consumer, Collection<TopicPartition> 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<?, ?> consumer, Collection<TopicPartition> partitions,
+			boolean initial) {
+
+	}
+
+}
+
+
+
+
+
+

You cannot set the resetOffsets consumer property to true when you provide a rebalance listener.

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/partitions.html b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/partitions.html new file mode 100644 index 00000000..c998781f --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/partitions.html @@ -0,0 +1,241 @@ + + + + + + + +Partitioning with the Kafka Binder + + + + + + + + + + +
+
+

Partitioning with the Kafka Binder

+
+

Apache Kafka supports topic partitioning natively.

+
+
+

Sometimes it is advantageous to send data to specific partitions — for example, when you want to strictly order message processing (all messages for a particular customer should go to the same partition).

+
+
+

The following example shows how to configure the producer and consumer side:

+
+
+
+
@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<?> generate() {
+        String value = data[RANDOM.nextInt(data.length)];
+        System.out.println("Sending: " + value);
+        return MessageBuilder.withPayload(value)
+                .setHeader("partitionKey", value)
+                .build();
+    }
+
+}
+
+
+
+
application.yml
+
+
spring:
+  cloud:
+    stream:
+      bindings:
+        output:
+          destination: partitioned.topic
+          producer:
+            partition-key-expression: headers['partitionKey']
+            partition-count: 12
+
+
+
+ + + + + +
+ + +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 concurrency is 2, 4 if their concurrency is 3, and so on). +It is generally best to “over-provision” the partitions to allow for future increases in consumers or concurrency. +
+
+
+ + + + + +
+ + +The preceding configuration uses the default partitioning (key.hashCode() % partitionCount). +This may or may not provide a suitably balanced algorithm, depending on the key values. +You can override this default by using the partitionSelectorExpression or partitionSelectorClass properties. +
+
+
+

Since partitions are natively handled by Kafka, no special configuration is needed on the consumer side. +Kafka allocates partitions across the instances.

+
+
+

The following Spring Boot application listens to a Kafka stream and prints (to the console) the partition ID to which each message goes:

+
+
+
+
@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);
+    }
+
+}
+
+
+
+
application.yml
+
+
spring:
+  cloud:
+    stream:
+      bindings:
+        input:
+          destination: partitioned.topic
+          group: myGroup
+
+
+
+

You can add instances as needed. +Kafka rebalances the partition allocations. +If the instance count (or instance count * concurrency) exceeds the number of partitions, some consumers are idle.

+
+
+
+ + + + + + + \ No newline at end of file diff --git a/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/spring-cloud-stream-binder-kafka-aggregate.html b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/spring-cloud-stream-binder-kafka-aggregate.html new file mode 100644 index 00000000..66bd4849 --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/spring-cloud-stream-binder-kafka-aggregate.html @@ -0,0 +1,1295 @@ + + + + + + + +Apache Kafka Binder + + + + + + + + + + +
+
+
+ +
+
+
+

Apache Kafka Binder

+
+

Usage

+
+

To use Apache Kafka binder, you need to add spring-cloud-stream-binder-kafka as a dependency to your Spring Cloud Stream application, as shown in the following example for Maven:

+
+
+
+
<dependency>
+  <groupId>org.springframework.cloud</groupId>
+  <artifactId>spring-cloud-stream-binder-kafka</artifactId>
+</dependency>
+
+
+
+

Alternatively, you can also use the Spring Cloud Stream Kafka Starter, as shown inn the following example for Maven:

+
+
+
+
<dependency>
+  <groupId>org.springframework.cloud</groupId>
+  <artifactId>spring-cloud-starter-stream-kafka</artifactId>
+</dependency>
+
+
+
+
+

Overview

+
+

The following image shows a simplified diagram of how the Apache Kafka binder operates:

+
+
+
+kafka binder +
+
Figure 1. Kafka Binder
+
+
+

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.

+
+
+

The binder currently uses the Apache Kafka kafka-clients 1.0.0 jar and is designed to be used with a broker of at least that version. +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 autoAddPartitions property.

+
+
+
+

Configuration Options

+
+

This section contains the configuration options used by the Apache Kafka binder.

+
+
+

For common configuration options and properties pertaining to binder, see the core documentation.

+
+
+
Kafka Binder Properties
+
+
+
spring.cloud.stream.kafka.binder.brokers
+
+

A list of brokers to which the Kafka binder connects.

+
+

Default: localhost.

+
+
+
spring.cloud.stream.kafka.binder.defaultBrokerPort
+
+

brokers allows hosts specified with or without port information (for example, host1,host2:port2). +This sets the default port when no port is configured in the broker list.

+
+

Default: 9092.

+
+
+
spring.cloud.stream.kafka.binder.configuration
+
+

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 — 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.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.consumerProperties
+
+

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 configuration property above.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.headers
+
+

The list of custom headers that are transported by the binder. +Only required when communicating with older applications (⇐ 1.3.x) with a kafka-clients version < 0.11.0.0. Newer versions support headers natively.

+
+

Default: empty.

+
+
+
spring.cloud.stream.kafka.binder.healthTimeout
+
+

The time to wait to get partition information, in seconds. +Health reports as down if this timer expires.

+
+

Default: 10.

+
+
+
spring.cloud.stream.kafka.binder.requiredAcks
+
+

The number of required acks on the broker. +See the Kafka documentation for the producer acks property.

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.minPartitionCount
+
+

Effective only if autoCreateTopics or autoAddPartitions 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 partitionCount setting of the producer or by the value of instanceCount * concurrency settings of the producer (if either is larger).

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.producerProperties
+
+

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 configuration property above.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.replicationFactor
+
+

The replication factor of auto-created topics if autoCreateTopics is active. +Can be overridden on each binding.

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.autoCreateTopics
+
+

If set to true, the binder creates new topics automatically. +If set to false, the binder relies on the topics being already configured. +In the latter case, if the topics do not exist, the binder fails to start.

+
+ + + + + +
+ + +This setting is independent of the auto.create.topics.enable 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. +
+
+
+

Default: true.

+
+
+
spring.cloud.stream.kafka.binder.autoAddPartitions
+
+

If set to true, the binder creates new partitions if required. +If set to false, 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.

+
+

Default: false.

+
+
+
spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix
+
+

Enables transactions in the binder. See transaction.id in the Kafka documentation and Transactions in the spring-kafka documentation. +When transactions are enabled, individual producer properties are ignored and all producers use the spring.cloud.stream.kafka.binder.transaction.producer.* properties.

+
+

Default null (no transactions)

+
+
+
spring.cloud.stream.kafka.binder.transaction.producer.*
+
+

Global producer properties for producers in a transactional binder. +See spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix and Kafka Producer Properties and the general producer properties supported by all binders.

+
+

Default: See individual producer properties.

+
+
+
spring.cloud.stream.kafka.binder.headerMapperBeanName
+
+

The bean name of a KafkaHeaderMapper used for mapping spring-messaging headers to and from Kafka headers. +Use this, for example, if you wish to customize the trusted packages in a DefaultKafkaHeaderMapper that uses JSON deserialization for the headers.

+
+

Default: none.

+
+
+
+
+
+
+
Kafka Consumer Properties
+
+ + + + + +
+ + +To avoid repetition, Spring Cloud Stream supports setting values for all channels, in the format of spring.cloud.stream.default.<property>=<value>. +
+
+
+

The following properties are available for Kafka consumers only and +must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.consumer..

+
+
+
+
admin.configuration
+
+

Since version 2.1.1, this property is deprecated in favor of topic.properties, and support for it will be removed in a future version.

+
+
admin.replicas-assignment
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replicas-assignment, and support for it will be removed in a future version.

+
+
admin.replication-factor
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replication-factor, and support for it will be removed in a future version.

+
+
autoRebalanceEnabled
+
+

When true, topic partitions is automatically rebalanced between the members of a consumer group. +When false, each consumer is assigned a fixed set of partitions based on spring.cloud.stream.instanceCount and spring.cloud.stream.instanceIndex. +This requires both the spring.cloud.stream.instanceCount and spring.cloud.stream.instanceIndex properties to be set appropriately on each launched instance. +The value of the spring.cloud.stream.instanceCount property must typically be greater than 1 in this case.

+
+

Default: true.

+
+
+
ackEachRecord
+
+

When autoCommitOffset is true, 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 consumer.poll() have been processed. +The number of records returned by a poll can be controlled with the max.poll.records Kafka property, which is set through the consumer configuration property. +Setting this to true may cause a degradation in performance, but doing so reduces the likelihood of redelivered records when a failure occurs. +Also, see the binder requiredAcks property, which also affects the performance of committing offsets.

+
+

Default: false.

+
+
+
autoCommitOffset
+
+

Whether to autocommit offsets when a message has been processed. +If set to false, a header with the key kafka_acknowledgment of the type org.springframework.kafka.support.Acknowledgment 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 false, Kafka binder sets the ack mode to org.springframework.kafka.listener.AbstractMessageListenerContainer.AckMode.MANUAL and the application is responsible for acknowledging records. +Also see ackEachRecord.

+
+

Default: true.

+
+
+
autoCommitOnError
+
+

Effective only if autoCommitOffset is set to true. +If set to false, 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 true, it always auto-commits (if auto-commit is enabled). +If not set (the default), it effectively has the same value as enableDlq, auto-committing erroneous messages if they are sent to a DLQ and not committing them otherwise.

+
+

Default: not set.

+
+
+
resetOffsets
+
+

Whether to reset offsets on the consumer to the value provided by startOffset. +Must be false if a KafkaRebalanceListener is provided; see Using a KafkaRebalanceListener.

+
+

Default: false.

+
+
+
startOffset
+
+

The starting offset for new groups. +Allowed values: earliest and latest. +If the consumer group is set explicitly for the consumer 'binding' (through spring.cloud.stream.bindings.<channelName>.group), 'startOffset' is set to earliest. Otherwise, it is set to latest for the anonymous consumer group. +Also see resetOffsets (earlier in this list).

+
+

Default: null (equivalent to earliest).

+
+
+
enableDlq
+
+

When set to true, it enables DLQ behavior for the consumer. +By default, messages that result in errors are forwarded to a topic named error.<destination>.<group>. +The DLQ topic name can be configurable by setting the dlqName 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 Dead-Letter Topic Processing processing for more information. +Starting with version 2.0, messages sent to the DLQ topic are enhanced with the following headers: x-original-topic, x-exception-message, and x-exception-stacktrace as byte[]. +Not allowed when destinationIsPattern is true.

+
+

Default: false.

+
+
+
configuration
+
+

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 spring.cloud.stream.kafka.bindings.input.consumer.configuration.foo=bar.

+
+

Default: Empty map.

+
+
+
dlqName
+
+

The name of the DLQ topic to receive the error messages.

+
+

Default: null (If not specified, messages that result in errors are forwarded to a topic named error.<destination>.<group>).

+
+
+
dlqProducerProperties
+
+

Using this, DLQ-specific producer properties can be set. +All the properties available through kafka producer properties can be set through this property.

+
+

Default: Default Kafka producer properties.

+
+
+
standardHeaders
+
+

Indicates which standard headers are populated by the inbound channel adapter. +Allowed values: none, id, timestamp, or both. +Useful if using native deserialization and the first component to receive a message needs an id (such as an aggregator that is configured to use a JDBC message store).

+
+

Default: none

+
+
+
converterBeanName
+
+

The name of a bean that implements RecordMessageConverter. Used in the inbound channel adapter to replace the default MessagingMessageConverter.

+
+

Default: null

+
+
+
idleEventInterval
+
+

The interval, in milliseconds, between events indicating that no messages have recently been received. +Use an ApplicationListener<ListenerContainerIdleEvent> to receive these events. +See Example: Pausing and Resuming the Consumer for a usage example.

+
+

Default: 30000

+
+
+
destinationIsPattern
+
+

When true, the destination is treated as a regular expression Pattern used to match topic names by the broker. +When true, topics are not provisioned, and enableDlq 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 metadata.max.age.ms, which (at the time of writing) defaults to 300,000ms (5 minutes). +This can be configured using the configuration property above.

+
+

Default: false

+
+
+
topic.properties
+
+

A Map of Kafka topic properties used when provisioning new topics — for example, spring.cloud.stream.kafka.bindings.input.consumer.topic.properties.message.format.version=0.9.0.0

+
+

Default: none.

+
+
+
topic.replicas-assignment
+
+

A Map<Integer, List<Integer>> of replica assignments, with the key being the partition and the value being the assignments. +Used when provisioning new topics. +See the NewTopic Javadocs in the kafka-clients jar.

+
+

Default: none.

+
+
+
topic.replication-factor
+
+

The replication factor to use when provisioning topics. Overrides the binder-wide setting. +Ignored if replicas-assignments is present.

+
+

Default: none (the binder-wide default of 1 is used).

+
+
+
+
+
+
+
Kafka Producer Properties
+
+ + + + + +
+ + +To avoid repetition, Spring Cloud Stream supports setting values for all channels, in the format of spring.cloud.stream.default.<property>=<value>. +
+
+
+

The following properties are available for Kafka producers only and +must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.producer..

+
+
+
+
admin.configuration
+
+

Since version 2.1.1, this property is deprecated in favor of topic.properties, and support for it will be removed in a future version.

+
+
admin.replicas-assignment
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replicas-assignment, and support for it will be removed in a future version.

+
+
admin.replication-factor
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replication-factor, and support for it will be removed in a future version.

+
+
bufferSize
+
+

Upper limit, in bytes, of how much data the Kafka producer attempts to batch before sending.

+
+

Default: 16384.

+
+
+
sync
+
+

Whether the producer is synchronous.

+
+

Default: false.

+
+
+
batchTimeout
+
+

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.

+
+

Default: 0.

+
+
+
messageKeyExpression
+
+

A SpEL expression evaluated against the outgoing message used to populate the key of the produced Kafka message — for example, headers['myKey']. +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 byte[]. +Now, the expression is evaluated before the payload is converted.

+
+

Default: none.

+
+
+
headerPatterns
+
+

A comma-delimited list of simple patterns to match Spring messaging headers to be mapped to the Kafka Headers in the ProducerRecord. +Patterns can begin or end with the wildcard character (asterisk). +Patterns can be negated by prefixing with !. +Matching stops after the first match (positive or negative). +For example !ask,as* will pass ash but not ask. +id and timestamp are never mapped.

+
+

Default: * (all headers - except the id and timestamp)

+
+
+
configuration
+
+

Map with a key/value pair containing generic Kafka producer properties.

+
+

Default: Empty map.

+
+
+
topic.properties
+
+

A Map of Kafka topic properties used when provisioning new topics — for example, spring.cloud.stream.kafka.bindings.output.producer.topic.properties.message.format.version=0.9.0.0

+
+
topic.replicas-assignment
+
+

A Map<Integer, List<Integer>> of replica assignments, with the key being the partition and the value being the assignments. +Used when provisioning new topics. +See the NewTopic Javadocs in the kafka-clients jar.

+
+

Default: none.

+
+
+
topic.replication-factor
+
+

The replication factor to use when provisioning topics. Overrides the binder-wide setting. +Ignored if replicas-assignments is present.

+
+

Default: none (the binder-wide default of 1 is used).

+
+
+
useTopicHeader
+
+

Set to true to override the default binding destination (topic name) with the value of the KafkaHeaders.TOPIC message header in the outbound message. +If the header is not present, the default binding destination is used. +Default: false.

+
+
recordMetadataChannel
+
+

The bean name of a MessageChannel 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 KafkaHeaders.RECORD_METADATA. +The header contains a RecordMetadata object provided by the Kafka client; it includes the partition and offset where the record was written in the topic.

+
+
+
+
+

ResultMetadata meta = sendResultMsg.getHeaders().get(KafkaHeaders.RECORD_METADATA, RecordMetadata.class)

+
+
+

Failed sends go the producer error channel (if configured); see Error Channels. +Default: null

+
+
+

+

+
+
+ + + + + +
+ + +The Kafka binder uses the partitionCount setting of the producer as a hint to create a topic with the given partition count (in conjunction with the minPartitionCount, the maximum of the two being the value being used). +Exercise caution when configuring both minPartitionCount for a binder and partitionCount for an application, as the larger value is used. +If a topic already exists with a smaller partition count and autoAddPartitions is disabled (the default), the binder fails to start. +If a topic already exists with a smaller partition count and autoAddPartitions is enabled, new partitions are added. +If a topic already exists with a larger number of partitions than the maximum of (minPartitionCount or partitionCount), the existing partition count is used. +
+
+
+
+
compression
+
+

Set the compression.type producer property. +Supported values are none, gzip, snappy and lz4. +If you override the kafka-clients jar to 2.1.0 (or later), as discussed in the Spring for Apache Kafka documentation, and wish to use zstd compression, use spring.cloud.stream.kafka.bindings.<binding-name>.producer.configuration.compression.type=zstd.

+
+

Default: none.

+
+
+
+
+
+
+
Usage examples
+
+

In this section, we show the use of the preceding properties for specific scenarios.

+
+
+
Example: Setting autoCommitOffset to false and Relying on Manual Acking
+
+

This example illustrates how one may manually acknowledge offsets in a consumer application.

+
+
+

This example requires that spring.cloud.stream.kafka.bindings.input.consumer.autoCommitOffset be set to false. +Use the corresponding input channel name for your example.

+
+
+
+
@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<?> message) {
+     Acknowledgment acknowledgment = message.getHeaders().get(KafkaHeaders.ACKNOWLEDGMENT, Acknowledgment.class);
+     if (acknowledgment != null) {
+         System.out.println("Acknowledgment provided");
+         acknowledgment.acknowledge();
+     }
+ }
+}
+
+
+
+
+
Example: Security Configuration
+
+

Apache Kafka 0.9 supports secure connections between client and brokers. +To take advantage of this feature, follow the guidelines in the Apache Kafka Documentation as well as the Kafka 0.9 security guidelines from the Confluent documentation. +Use the spring.cloud.stream.kafka.binder.configuration option to set security properties for all clients created by the binder.

+
+
+

For example, to set security.protocol to SASL_SSL, set the following property:

+
+
+
+
spring.cloud.stream.kafka.binder.configuration.security.protocol=SASL_SSL
+
+
+
+

All the other security properties can be set in a similar manner.

+
+
+

When using Kerberos, follow the instructions in the reference documentation for creating and referencing the JAAS configuration.

+
+
+

Spring Cloud Stream supports passing JAAS configuration information to the application by using a JAAS configuration file and using Spring Boot properties.

+
+
+Using JAAS Configuration Files +
+

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:

+
+
+
+
 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
+
+
+
+
+Using Spring Boot Properties +
+

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.

+
+
+

The following properties can be used to configure the login context of the Kafka client:

+
+
+
+
spring.cloud.stream.kafka.binder.jaas.loginModule
+
+

The login module name. Not necessary to be set in normal cases.

+
+

Default: com.sun.security.auth.module.Krb5LoginModule.

+
+
+
spring.cloud.stream.kafka.binder.jaas.controlFlag
+
+

The control flag of the login module.

+
+

Default: required.

+
+
+
spring.cloud.stream.kafka.binder.jaas.options
+
+

Map with a key/value pair containing the login module options.

+
+

Default: Empty map.

+
+
+
+
+
+

The following example shows how to launch a Spring Cloud Stream application with SASL and Kerberos by using Spring Boot configuration properties:

+
+
+
+
 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
+
+
+
+

The preceding example represents the equivalent of the following JAAS file:

+
+
+
+
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";
+};
+
+
+
+

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.

+
+
+ + + + + +
+ + +Do not mix JAAS configuration files and Spring Boot properties in the same application. +If the -Djava.security.auth.login.config system property is already present, Spring Cloud Stream ignores the Spring Boot properties. +
+
+
+ + + + + +
+ + +Be careful when using the autoCreateTopics and autoAddPartitions 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. +
+
+
+
+
+
Example: Pausing and Resuming the Consumer
+
+

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 Consumer as a parameter to your @StreamListener. +To resume, you need an ApplicationListener for ListenerContainerIdleEvent instances. +The frequency at which events are published is controlled by the idleEventInterval property. +Since the consumer is not thread-safe, you must call these methods on the calling thread.

+
+
+

The following simple application shows how to pause and resume:

+
+
+
+
@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<?, ?> consumer) {
+		System.out.println(in);
+		consumer.pause(Collections.singleton(new TopicPartition("myTopic", 0)));
+	}
+
+	@Bean
+	public ApplicationListener<ListenerContainerIdleEvent> idleListener() {
+		return event -> {
+			System.out.println(event);
+			if (event.getConsumer().paused().size() > 0) {
+				event.getConsumer().resume(event.getConsumer().paused());
+			}
+		};
+	}
+
+}
+
+
+
+
+
+
+

Transactional Binder

+
+

Enable transactions by setting spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix to a non-empty value, e.g. tx-. +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 spring.cloud.stream.kafka.binder.transaction.producer.* properties; individual binding Kafka producer properties are ignored.

+
+
+

If you wish to use transactions in a source application, or from some arbitrary thread for producer-only transaction (e.g. @Scheduled method), you must get a reference to the transactional producer factory and define a KafkaTransactionManager bean using it.

+
+
+
+
+
+
@Bean
+public PlatformTransactionManager transactionManager(BinderFactory binders) {
+    ProducerFactory<byte[], byte[]> pf = ((KafkaMessageChannelBinder) binders.getBinder(null,
+            MessageChannel.class)).getTransactionalProducerFactory();
+    return new KafkaTransactionManager<>(pf);
+}
+
+
+
+
+
+

Notice that we get a reference to the binder using the BinderFactory; use null 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 ProducerFactory and create a transaction manager.

+
+
+

Then you would use normal Spring transaction support, e.g. TransactionTemplate or @Transactional, for example:

+
+
+
+
+
+
public static class Sender {
+
+    @Transactional
+    public void doInTransaction(MessageChannel output, List<String> stuffToSend) {
+        stuffToSend.forEach(stuff -> output.send(new GenericMessage<>(stuff)));
+    }
+
+}
+
+
+
+
+
+

If you wish to synchronize producer-only transactions with those from some other transaction manager, use a ChainedTransactionManager.

+
+
+
+

Error Channels

+
+

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 [spring-cloud-stream-overview-error-handling] for more information.

+
+
+

The payload of the ErrorMessage for a send failure is a KafkaSendFailureException with properties:

+
+
+
    +
  • +

    failedMessage: The Spring Messaging Message<?> that failed to be sent.

    +
    • +
  • +

    record: The raw ProducerRecord that was created from the failedMessage

    +
    • +
+
+
+

There is no automatic handling of producer exceptions (such as sending to a Dead-Letter queue). +You can consume these exceptions with your own Spring Integration flow.

+
+
+
+

Kafka Metrics

+
+

Kafka binder module exposes the following metrics:

+
+
+

spring.cloud.stream.binder.kafka.offset: This metric indicates how many messages have not been yet consumed from a given binder’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.

+
+
+
+

Tombstone Records (null record values)

+
+

When using compacted topics, a record with a null value (also called a tombstone record) represents the deletion of a key. +To receive such messages in a @StreamListener method, the parameter must be marked as not required to receive a null value argument.

+
+
+
+
+
+
@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
+    ...
+}
+
+
+
+
+
+
+

Using a KafkaRebalanceListener

+
+

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 KafkaRebalanceListener bean in the application context, it will be wired into all Kafka consumer bindings.

+
+
+
+
+
+
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<?, ?> consumer,
+			Collection<TopicPartition> 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<?, ?> consumer, Collection<TopicPartition> 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<?, ?> consumer, Collection<TopicPartition> partitions,
+			boolean initial) {
+
+	}
+
+}
+
+
+
+
+
+

You cannot set the resetOffsets consumer property to true when you provide a rebalance listener.

+
+
+
+

Dead-Letter Topic Processing

+
+

Because you cannot anticipate how users would want to dispose of dead-lettered messages, the framework 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 “parking lot” 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.

+
+
+

The examples assume the original destination is so8400out and the consumer group is so8400.

+
+
+

There are a couple of strategies to consider:

+
+
+
    +
  • +

    Consider running the rerouting only when the main application is not running. +Otherwise, the retries for transient errors are used up very quickly.

    +
    • +
  • +

    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.

    +
    • +
+
+
+

The following code listings show the sample application:

+
+
+
application.properties
+
+
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
+
+
+
+
Application
+
+
@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<?> reRoute(Message<?> 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() < 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();
+
+    }
+
+}
+
+
+
+
+

Partitioning with the Kafka Binder

+
+

Apache Kafka supports topic partitioning natively.

+
+
+

Sometimes it is advantageous to send data to specific partitions — for example, when you want to strictly order message processing (all messages for a particular customer should go to the same partition).

+
+
+

The following example shows how to configure the producer and consumer side:

+
+
+
+
@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<?> generate() {
+        String value = data[RANDOM.nextInt(data.length)];
+        System.out.println("Sending: " + value);
+        return MessageBuilder.withPayload(value)
+                .setHeader("partitionKey", value)
+                .build();
+    }
+
+}
+
+
+
+
application.yml
+
+
spring:
+  cloud:
+    stream:
+      bindings:
+        output:
+          destination: partitioned.topic
+          producer:
+            partition-key-expression: headers['partitionKey']
+            partition-count: 12
+
+
+
+ + + + + +
+ + +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 concurrency is 2, 4 if their concurrency is 3, and so on). +It is generally best to “over-provision” the partitions to allow for future increases in consumers or concurrency. +
+
+
+ + + + + +
+ + +The preceding configuration uses the default partitioning (key.hashCode() % partitionCount). +This may or may not provide a suitably balanced algorithm, depending on the key values. +You can override this default by using the partitionSelectorExpression or partitionSelectorClass properties. +
+
+
+

Since partitions are natively handled by Kafka, no special configuration is needed on the consumer side. +Kafka allocates partitions across the instances.

+
+
+

The following Spring Boot application listens to a Kafka stream and prints (to the console) the partition ID to which each message goes:

+
+
+
+
@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);
+    }
+
+}
+
+
+
+
application.yml
+
+
spring:
+  cloud:
+    stream:
+      bindings:
+        input:
+          destination: partitioned.topic
+          group: myGroup
+
+
+
+

You can add instances as needed. +Kafka rebalances the partition allocations. +If the instance count (or instance count * concurrency) exceeds the number of partitions, some consumers are idle.

+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/spring-cloud-stream-binder-kafka.html b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/spring-cloud-stream-binder-kafka.html new file mode 100644 index 00000000..08f91c60 --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/html/spring-cloud-stream-binder-kafka.html @@ -0,0 +1,2987 @@ + + + + + + + + +Spring Cloud Stream Kafka Binder Reference Guide + + + + + + + + + + +
+
+
+
+

3.0.0.M3

+
+
+
+

Reference Guide

+
+
+
+

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.

+
+
+
+
+

1. Apache Kafka Binder

+
+
+

1.1. Usage

+
+

To use Apache Kafka binder, you need to add spring-cloud-stream-binder-kafka as a dependency to your Spring Cloud Stream application, as shown in the following example for Maven:

+
+
+
+
<dependency>
+  <groupId>org.springframework.cloud</groupId>
+  <artifactId>spring-cloud-stream-binder-kafka</artifactId>
+</dependency>
+
+
+
+

Alternatively, you can also use the Spring Cloud Stream Kafka Starter, as shown inn the following example for Maven:

+
+
+
+
<dependency>
+  <groupId>org.springframework.cloud</groupId>
+  <artifactId>spring-cloud-starter-stream-kafka</artifactId>
+</dependency>
+
+
+
+
+

1.2. Overview

+
+

The following image shows a simplified diagram of how the Apache Kafka binder operates:

+
+
+
+kafka binder +
+
Figure 1. Kafka Binder
+
+
+

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.

+
+
+

The binder currently uses the Apache Kafka kafka-clients 1.0.0 jar and is designed to be used with a broker of at least that version. +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 autoAddPartitions property.

+
+
+
+

1.3. Configuration Options

+
+

This section contains the configuration options used by the Apache Kafka binder.

+
+
+

For common configuration options and properties pertaining to binder, see the core documentation.

+
+
+

1.3.1. Kafka Binder Properties

+
+
+
spring.cloud.stream.kafka.binder.brokers
+
+

A list of brokers to which the Kafka binder connects.

+
+

Default: localhost.

+
+
+
spring.cloud.stream.kafka.binder.defaultBrokerPort
+
+

brokers allows hosts specified with or without port information (for example, host1,host2:port2). +This sets the default port when no port is configured in the broker list.

+
+

Default: 9092.

+
+
+
spring.cloud.stream.kafka.binder.configuration
+
+

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 — 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.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.consumerProperties
+
+

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 configuration property above.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.headers
+
+

The list of custom headers that are transported by the binder. +Only required when communicating with older applications (⇐ 1.3.x) with a kafka-clients version < 0.11.0.0. Newer versions support headers natively.

+
+

Default: empty.

+
+
+
spring.cloud.stream.kafka.binder.healthTimeout
+
+

The time to wait to get partition information, in seconds. +Health reports as down if this timer expires.

+
+

Default: 10.

+
+
+
spring.cloud.stream.kafka.binder.requiredAcks
+
+

The number of required acks on the broker. +See the Kafka documentation for the producer acks property.

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.minPartitionCount
+
+

Effective only if autoCreateTopics or autoAddPartitions 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 partitionCount setting of the producer or by the value of instanceCount * concurrency settings of the producer (if either is larger).

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.producerProperties
+
+

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 configuration property above.

+
+

Default: Empty map.

+
+
+
spring.cloud.stream.kafka.binder.replicationFactor
+
+

The replication factor of auto-created topics if autoCreateTopics is active. +Can be overridden on each binding.

+
+

Default: 1.

+
+
+
spring.cloud.stream.kafka.binder.autoCreateTopics
+
+

If set to true, the binder creates new topics automatically. +If set to false, the binder relies on the topics being already configured. +In the latter case, if the topics do not exist, the binder fails to start.

+
+ + + + + +
+ + +This setting is independent of the auto.create.topics.enable 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. +
+
+
+

Default: true.

+
+
+
spring.cloud.stream.kafka.binder.autoAddPartitions
+
+

If set to true, the binder creates new partitions if required. +If set to false, 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.

+
+

Default: false.

+
+
+
spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix
+
+

Enables transactions in the binder. See transaction.id in the Kafka documentation and Transactions in the spring-kafka documentation. +When transactions are enabled, individual producer properties are ignored and all producers use the spring.cloud.stream.kafka.binder.transaction.producer.* properties.

+
+

Default null (no transactions)

+
+
+
spring.cloud.stream.kafka.binder.transaction.producer.*
+
+

Global producer properties for producers in a transactional binder. +See spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix and Kafka Producer Properties and the general producer properties supported by all binders.

+
+

Default: See individual producer properties.

+
+
+
spring.cloud.stream.kafka.binder.headerMapperBeanName
+
+

The bean name of a KafkaHeaderMapper used for mapping spring-messaging headers to and from Kafka headers. +Use this, for example, if you wish to customize the trusted packages in a DefaultKafkaHeaderMapper that uses JSON deserialization for the headers.

+
+

Default: none.

+
+
+
+
+
+
+

1.3.2. Kafka Consumer Properties

+
+ + + + + +
+ + +To avoid repetition, Spring Cloud Stream supports setting values for all channels, in the format of spring.cloud.stream.default.<property>=<value>. +
+
+
+

The following properties are available for Kafka consumers only and +must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.consumer..

+
+
+
+
admin.configuration
+
+

Since version 2.1.1, this property is deprecated in favor of topic.properties, and support for it will be removed in a future version.

+
+
admin.replicas-assignment
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replicas-assignment, and support for it will be removed in a future version.

+
+
admin.replication-factor
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replication-factor, and support for it will be removed in a future version.

+
+
autoRebalanceEnabled
+
+

When true, topic partitions is automatically rebalanced between the members of a consumer group. +When false, each consumer is assigned a fixed set of partitions based on spring.cloud.stream.instanceCount and spring.cloud.stream.instanceIndex. +This requires both the spring.cloud.stream.instanceCount and spring.cloud.stream.instanceIndex properties to be set appropriately on each launched instance. +The value of the spring.cloud.stream.instanceCount property must typically be greater than 1 in this case.

+
+

Default: true.

+
+
+
ackEachRecord
+
+

When autoCommitOffset is true, 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 consumer.poll() have been processed. +The number of records returned by a poll can be controlled with the max.poll.records Kafka property, which is set through the consumer configuration property. +Setting this to true may cause a degradation in performance, but doing so reduces the likelihood of redelivered records when a failure occurs. +Also, see the binder requiredAcks property, which also affects the performance of committing offsets.

+
+

Default: false.

+
+
+
autoCommitOffset
+
+

Whether to autocommit offsets when a message has been processed. +If set to false, a header with the key kafka_acknowledgment of the type org.springframework.kafka.support.Acknowledgment 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 false, Kafka binder sets the ack mode to org.springframework.kafka.listener.AbstractMessageListenerContainer.AckMode.MANUAL and the application is responsible for acknowledging records. +Also see ackEachRecord.

+
+

Default: true.

+
+
+
autoCommitOnError
+
+

Effective only if autoCommitOffset is set to true. +If set to false, 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 true, it always auto-commits (if auto-commit is enabled). +If not set (the default), it effectively has the same value as enableDlq, auto-committing erroneous messages if they are sent to a DLQ and not committing them otherwise.

+
+

Default: not set.

+
+
+
resetOffsets
+
+

Whether to reset offsets on the consumer to the value provided by startOffset. +Must be false if a KafkaRebalanceListener is provided; see Using a KafkaRebalanceListener.

+
+

Default: false.

+
+
+
startOffset
+
+

The starting offset for new groups. +Allowed values: earliest and latest. +If the consumer group is set explicitly for the consumer 'binding' (through spring.cloud.stream.bindings.<channelName>.group), 'startOffset' is set to earliest. Otherwise, it is set to latest for the anonymous consumer group. +Also see resetOffsets (earlier in this list).

+
+

Default: null (equivalent to earliest).

+
+
+
enableDlq
+
+

When set to true, it enables DLQ behavior for the consumer. +By default, messages that result in errors are forwarded to a topic named error.<destination>.<group>. +The DLQ topic name can be configurable by setting the dlqName 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 Dead-Letter Topic Processing processing for more information. +Starting with version 2.0, messages sent to the DLQ topic are enhanced with the following headers: x-original-topic, x-exception-message, and x-exception-stacktrace as byte[]. +Not allowed when destinationIsPattern is true.

+
+

Default: false.

+
+
+
configuration
+
+

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 spring.cloud.stream.kafka.bindings.input.consumer.configuration.foo=bar.

+
+

Default: Empty map.

+
+
+
dlqName
+
+

The name of the DLQ topic to receive the error messages.

+
+

Default: null (If not specified, messages that result in errors are forwarded to a topic named error.<destination>.<group>).

+
+
+
dlqProducerProperties
+
+

Using this, DLQ-specific producer properties can be set. +All the properties available through kafka producer properties can be set through this property.

+
+

Default: Default Kafka producer properties.

+
+
+
standardHeaders
+
+

Indicates which standard headers are populated by the inbound channel adapter. +Allowed values: none, id, timestamp, or both. +Useful if using native deserialization and the first component to receive a message needs an id (such as an aggregator that is configured to use a JDBC message store).

+
+

Default: none

+
+
+
converterBeanName
+
+

The name of a bean that implements RecordMessageConverter. Used in the inbound channel adapter to replace the default MessagingMessageConverter.

+
+

Default: null

+
+
+
idleEventInterval
+
+

The interval, in milliseconds, between events indicating that no messages have recently been received. +Use an ApplicationListener<ListenerContainerIdleEvent> to receive these events. +See Example: Pausing and Resuming the Consumer for a usage example.

+
+

Default: 30000

+
+
+
destinationIsPattern
+
+

When true, the destination is treated as a regular expression Pattern used to match topic names by the broker. +When true, topics are not provisioned, and enableDlq 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 metadata.max.age.ms, which (at the time of writing) defaults to 300,000ms (5 minutes). +This can be configured using the configuration property above.

+
+

Default: false

+
+
+
topic.properties
+
+

A Map of Kafka topic properties used when provisioning new topics — for example, spring.cloud.stream.kafka.bindings.input.consumer.topic.properties.message.format.version=0.9.0.0

+
+

Default: none.

+
+
+
topic.replicas-assignment
+
+

A Map<Integer, List<Integer>> of replica assignments, with the key being the partition and the value being the assignments. +Used when provisioning new topics. +See the NewTopic Javadocs in the kafka-clients jar.

+
+

Default: none.

+
+
+
topic.replication-factor
+
+

The replication factor to use when provisioning topics. Overrides the binder-wide setting. +Ignored if replicas-assignments is present.

+
+

Default: none (the binder-wide default of 1 is used).

+
+
+
+
+
+
+

1.3.3. Kafka Producer Properties

+
+ + + + + +
+ + +To avoid repetition, Spring Cloud Stream supports setting values for all channels, in the format of spring.cloud.stream.default.<property>=<value>. +
+
+
+

The following properties are available for Kafka producers only and +must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.producer..

+
+
+
+
admin.configuration
+
+

Since version 2.1.1, this property is deprecated in favor of topic.properties, and support for it will be removed in a future version.

+
+
admin.replicas-assignment
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replicas-assignment, and support for it will be removed in a future version.

+
+
admin.replication-factor
+
+

Since version 2.1.1, this property is deprecated in favor of topic.replication-factor, and support for it will be removed in a future version.

+
+
bufferSize
+
+

Upper limit, in bytes, of how much data the Kafka producer attempts to batch before sending.

+
+

Default: 16384.

+
+
+
sync
+
+

Whether the producer is synchronous.

+
+

Default: false.

+
+
+
batchTimeout
+
+

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.

+
+

Default: 0.

+
+
+
messageKeyExpression
+
+

A SpEL expression evaluated against the outgoing message used to populate the key of the produced Kafka message — for example, headers['myKey']. +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 byte[]. +Now, the expression is evaluated before the payload is converted.

+
+

Default: none.

+
+
+
headerPatterns
+
+

A comma-delimited list of simple patterns to match Spring messaging headers to be mapped to the Kafka Headers in the ProducerRecord. +Patterns can begin or end with the wildcard character (asterisk). +Patterns can be negated by prefixing with !. +Matching stops after the first match (positive or negative). +For example !ask,as* will pass ash but not ask. +id and timestamp are never mapped.

+
+

Default: * (all headers - except the id and timestamp)

+
+
+
configuration
+
+

Map with a key/value pair containing generic Kafka producer properties.

+
+

Default: Empty map.

+
+
+
topic.properties
+
+

A Map of Kafka topic properties used when provisioning new topics — for example, spring.cloud.stream.kafka.bindings.output.producer.topic.properties.message.format.version=0.9.0.0

+
+
topic.replicas-assignment
+
+

A Map<Integer, List<Integer>> of replica assignments, with the key being the partition and the value being the assignments. +Used when provisioning new topics. +See the NewTopic Javadocs in the kafka-clients jar.

+
+

Default: none.

+
+
+
topic.replication-factor
+
+

The replication factor to use when provisioning topics. Overrides the binder-wide setting. +Ignored if replicas-assignments is present.

+
+

Default: none (the binder-wide default of 1 is used).

+
+
+
useTopicHeader
+
+

Set to true to override the default binding destination (topic name) with the value of the KafkaHeaders.TOPIC message header in the outbound message. +If the header is not present, the default binding destination is used. +Default: false.

+
+
recordMetadataChannel
+
+

The bean name of a MessageChannel 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 KafkaHeaders.RECORD_METADATA. +The header contains a RecordMetadata object provided by the Kafka client; it includes the partition and offset where the record was written in the topic.

+
+
+
+
+

ResultMetadata meta = sendResultMsg.getHeaders().get(KafkaHeaders.RECORD_METADATA, RecordMetadata.class)

+
+
+

Failed sends go the producer error channel (if configured); see Error Channels. +Default: null

+
+
+

+

+
+
+ + + + + +
+ + +The Kafka binder uses the partitionCount setting of the producer as a hint to create a topic with the given partition count (in conjunction with the minPartitionCount, the maximum of the two being the value being used). +Exercise caution when configuring both minPartitionCount for a binder and partitionCount for an application, as the larger value is used. +If a topic already exists with a smaller partition count and autoAddPartitions is disabled (the default), the binder fails to start. +If a topic already exists with a smaller partition count and autoAddPartitions is enabled, new partitions are added. +If a topic already exists with a larger number of partitions than the maximum of (minPartitionCount or partitionCount), the existing partition count is used. +
+
+
+
+
compression
+
+

Set the compression.type producer property. +Supported values are none, gzip, snappy and lz4. +If you override the kafka-clients jar to 2.1.0 (or later), as discussed in the Spring for Apache Kafka documentation, and wish to use zstd compression, use spring.cloud.stream.kafka.bindings.<binding-name>.producer.configuration.compression.type=zstd.

+
+

Default: none.

+
+
+
+
+
+
+

1.3.4. Usage examples

+
+

In this section, we show the use of the preceding properties for specific scenarios.

+
+
+
Example: Setting autoCommitOffset to false and Relying on Manual Acking
+
+

This example illustrates how one may manually acknowledge offsets in a consumer application.

+
+
+

This example requires that spring.cloud.stream.kafka.bindings.input.consumer.autoCommitOffset be set to false. +Use the corresponding input channel name for your example.

+
+
+
+
@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<?> message) {
+     Acknowledgment acknowledgment = message.getHeaders().get(KafkaHeaders.ACKNOWLEDGMENT, Acknowledgment.class);
+     if (acknowledgment != null) {
+         System.out.println("Acknowledgment provided");
+         acknowledgment.acknowledge();
+     }
+ }
+}
+
+
+
+
+
Example: Security Configuration
+
+

Apache Kafka 0.9 supports secure connections between client and brokers. +To take advantage of this feature, follow the guidelines in the Apache Kafka Documentation as well as the Kafka 0.9 security guidelines from the Confluent documentation. +Use the spring.cloud.stream.kafka.binder.configuration option to set security properties for all clients created by the binder.

+
+
+

For example, to set security.protocol to SASL_SSL, set the following property:

+
+
+
+
spring.cloud.stream.kafka.binder.configuration.security.protocol=SASL_SSL
+
+
+
+

All the other security properties can be set in a similar manner.

+
+
+

When using Kerberos, follow the instructions in the reference documentation for creating and referencing the JAAS configuration.

+
+
+

Spring Cloud Stream supports passing JAAS configuration information to the application by using a JAAS configuration file and using Spring Boot properties.

+
+
+
Using JAAS Configuration Files
+
+

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:

+
+
+
+
 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
+
+
+
+
+
Using Spring Boot Properties
+
+

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.

+
+
+

The following properties can be used to configure the login context of the Kafka client:

+
+
+
+
spring.cloud.stream.kafka.binder.jaas.loginModule
+
+

The login module name. Not necessary to be set in normal cases.

+
+

Default: com.sun.security.auth.module.Krb5LoginModule.

+
+
+
spring.cloud.stream.kafka.binder.jaas.controlFlag
+
+

The control flag of the login module.

+
+

Default: required.

+
+
+
spring.cloud.stream.kafka.binder.jaas.options
+
+

Map with a key/value pair containing the login module options.

+
+

Default: Empty map.

+
+
+
+
+
+

The following example shows how to launch a Spring Cloud Stream application with SASL and Kerberos by using Spring Boot configuration properties:

+
+
+
+
 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
+
+
+
+

The preceding example represents the equivalent of the following JAAS file:

+
+
+
+
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";
+};
+
+
+
+

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.

+
+
+ + + + + +
+ + +Do not mix JAAS configuration files and Spring Boot properties in the same application. +If the -Djava.security.auth.login.config system property is already present, Spring Cloud Stream ignores the Spring Boot properties. +
+
+
+ + + + + +
+ + +Be careful when using the autoCreateTopics and autoAddPartitions 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. +
+
+
+
+
+
Example: Pausing and Resuming the Consumer
+
+

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 Consumer as a parameter to your @StreamListener. +To resume, you need an ApplicationListener for ListenerContainerIdleEvent instances. +The frequency at which events are published is controlled by the idleEventInterval property. +Since the consumer is not thread-safe, you must call these methods on the calling thread.

+
+
+

The following simple application shows how to pause and resume:

+
+
+
+
@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<?, ?> consumer) {
+		System.out.println(in);
+		consumer.pause(Collections.singleton(new TopicPartition("myTopic", 0)));
+	}
+
+	@Bean
+	public ApplicationListener<ListenerContainerIdleEvent> idleListener() {
+		return event -> {
+			System.out.println(event);
+			if (event.getConsumer().paused().size() > 0) {
+				event.getConsumer().resume(event.getConsumer().paused());
+			}
+		};
+	}
+
+}
+
+
+
+
+
+
+

1.4. Transactional Binder

+
+

Enable transactions by setting spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix to a non-empty value, e.g. tx-. +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 spring.cloud.stream.kafka.binder.transaction.producer.* properties; individual binding Kafka producer properties are ignored.

+
+
+

If you wish to use transactions in a source application, or from some arbitrary thread for producer-only transaction (e.g. @Scheduled method), you must get a reference to the transactional producer factory and define a KafkaTransactionManager bean using it.

+
+
+
+
+
+
@Bean
+public PlatformTransactionManager transactionManager(BinderFactory binders) {
+    ProducerFactory<byte[], byte[]> pf = ((KafkaMessageChannelBinder) binders.getBinder(null,
+            MessageChannel.class)).getTransactionalProducerFactory();
+    return new KafkaTransactionManager<>(pf);
+}
+
+
+
+
+
+

Notice that we get a reference to the binder using the BinderFactory; use null 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 ProducerFactory and create a transaction manager.

+
+
+

Then you would use normal Spring transaction support, e.g. TransactionTemplate or @Transactional, for example:

+
+
+
+
+
+
public static class Sender {
+
+    @Transactional
+    public void doInTransaction(MessageChannel output, List<String> stuffToSend) {
+        stuffToSend.forEach(stuff -> output.send(new GenericMessage<>(stuff)));
+    }
+
+}
+
+
+
+
+
+

If you wish to synchronize producer-only transactions with those from some other transaction manager, use a ChainedTransactionManager.

+
+
+
+

1.5. Error Channels

+
+

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 [spring-cloud-stream-overview-error-handling] for more information.

+
+
+

The payload of the ErrorMessage for a send failure is a KafkaSendFailureException with properties:

+
+
+
    +
  • +

    failedMessage: The Spring Messaging Message<?> that failed to be sent.

    +
    • +
  • +

    record: The raw ProducerRecord that was created from the failedMessage

    +
    • +
+
+
+

There is no automatic handling of producer exceptions (such as sending to a Dead-Letter queue). +You can consume these exceptions with your own Spring Integration flow.

+
+
+
+

1.6. Kafka Metrics

+
+

Kafka binder module exposes the following metrics:

+
+
+

spring.cloud.stream.binder.kafka.offset: This metric indicates how many messages have not been yet consumed from a given binder’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.

+
+
+
+

1.7. Tombstone Records (null record values)

+
+

When using compacted topics, a record with a null value (also called a tombstone record) represents the deletion of a key. +To receive such messages in a @StreamListener method, the parameter must be marked as not required to receive a null value argument.

+
+
+
+
+
+
@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
+    ...
+}
+
+
+
+
+
+
+

1.8. Using a KafkaRebalanceListener

+
+

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 KafkaRebalanceListener bean in the application context, it will be wired into all Kafka consumer bindings.

+
+
+
+
+
+
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<?, ?> consumer,
+			Collection<TopicPartition> 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<?, ?> consumer, Collection<TopicPartition> 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<?, ?> consumer, Collection<TopicPartition> partitions,
+			boolean initial) {
+
+	}
+
+}
+
+
+
+
+
+

You cannot set the resetOffsets consumer property to true when you provide a rebalance listener.

+
+
+
+

1.9. Dead-Letter Topic Processing

+
+

Because you cannot anticipate how users would want to dispose of dead-lettered messages, the framework 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 “parking lot” 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.

+
+
+

The examples assume the original destination is so8400out and the consumer group is so8400.

+
+
+

There are a couple of strategies to consider:

+
+
+
    +
  • +

    Consider running the rerouting only when the main application is not running. +Otherwise, the retries for transient errors are used up very quickly.

    +
    • +
  • +

    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.

    +
    • +
+
+
+

The following code listings show the sample application:

+
+
+
application.properties
+
+
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
+
+
+
+
Application
+
+
@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<?> reRoute(Message<?> 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() < 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();
+
+    }
+
+}
+
+
+
+
+

1.10. Partitioning with the Kafka Binder

+
+

Apache Kafka supports topic partitioning natively.

+
+
+

Sometimes it is advantageous to send data to specific partitions — for example, when you want to strictly order message processing (all messages for a particular customer should go to the same partition).

+
+
+

The following example shows how to configure the producer and consumer side:

+
+
+
+
@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<?> generate() {
+        String value = data[RANDOM.nextInt(data.length)];
+        System.out.println("Sending: " + value);
+        return MessageBuilder.withPayload(value)
+                .setHeader("partitionKey", value)
+                .build();
+    }
+
+}
+
+
+
+
application.yml
+
+
spring:
+  cloud:
+    stream:
+      bindings:
+        output:
+          destination: partitioned.topic
+          producer:
+            partition-key-expression: headers['partitionKey']
+            partition-count: 12
+
+
+
+ + + + + +
+ + +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 concurrency is 2, 4 if their concurrency is 3, and so on). +It is generally best to “over-provision” the partitions to allow for future increases in consumers or concurrency. +
+
+
+ + + + + +
+ + +The preceding configuration uses the default partitioning (key.hashCode() % partitionCount). +This may or may not provide a suitably balanced algorithm, depending on the key values. +You can override this default by using the partitionSelectorExpression or partitionSelectorClass properties. +
+
+
+

Since partitions are natively handled by Kafka, no special configuration is needed on the consumer side. +Kafka allocates partitions across the instances.

+
+
+

The following Spring Boot application listens to a Kafka stream and prints (to the console) the partition ID to which each message goes:

+
+
+
+
@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);
+    }
+
+}
+
+
+
+
application.yml
+
+
spring:
+  cloud:
+    stream:
+      bindings:
+        input:
+          destination: partitioned.topic
+          group: myGroup
+
+
+
+

You can add instances as needed. +Kafka rebalances the partition allocations. +If the instance count (or instance count * concurrency) exceeds the number of partitions, some consumers are idle.

+
+
+
+
+
+

2. Kafka Streams Binder

+
+
+

2.1. Usage

+
+

For using the Kafka Streams binder, you just need to add it to your Spring Cloud Stream application, using the following +Maven coordinates:

+
+
+
+
<dependency>
+  <groupId>org.springframework.cloud</groupId>
+  <artifactId>spring-cloud-stream-binder-kafka-streams</artifactId>
+</dependency>
+
+
+
+

A quick way to bootstrap a new project for Kafka Streams binder is to use Spring Initializr and then select "Cloud Streams" and "Spring for Kafka Streams" as shown below

+
+
+
+spring initializr kafka streams +
+
+
+
+

2.2. Overview

+
+

Spring Cloud Stream includes a binder implementation designed explicitly for Apache Kafka Streams binding. +With this native integration, a Spring Cloud Stream "processor" application can directly use the +Apache Kafka Streams APIs in the core business logic.

+
+
+

Kafka Streams binder implementation builds on the foundations provided by the Spring for Apache Kafka project.

+
+
+

Kafka Streams binder provides binding capabilities for the three major types in Kafka Streams - KStream, KTable and GlobalKTable.

+
+
+

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.

+
+
+

In the following sections, we are going to look at the details of Spring Cloud Stream’s integration with Kafka Streams.

+
+
+
+

2.3. Programming Model

+
+

When using the programming model provided by Kafka Streams binder, both the high-level Streams DSL and the lower level Processor-API can be used as options.

+
+
+

2.3.1. Functional Style

+
+

Starting with Spring Cloud Stream 3.0, 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 java.util.function.Function or java.util.function.Consumer.

+
+
+

Let’s take a very basic example.

+
+
+
+
@SpringBootApplication
+public class SimpleConsumerApplication {
+
+    @Bean
+    public java.util.function.Consumer<KStream<Object, String>> process() {
+
+        return input ->
+                input.foreach((key, value) -> {
+                    System.out.println("Key: " + key + " Value: " + value);
+                });
+    }
+}
+
+
+
+

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 transformation as standard output. +The application contains the SpringBootApplication annotation and a method that is marked as Bean. +The bean method is of type java.util.function.Consumer which is parameterized with KStream. +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.

+
+
+

In this application, there is a single input binding that is of type KStream. +The binder creates this binding for the application with a name process_in, i.e. the name of the function bean name followed by an underscore and the literal in. +You use this binding name to set other properties such as destination. +For example, spring.cloud.stream.bindings.process_in.destinaion=my-topic.

+
+
+

Once built as a uber-jar (e.g., kstream-consumer-app.jar), you can run the above example like the following.

+
+
+
+
java -jar `kstream-consumer-app.jar --spring.cloud.stream.bindings.process_in.destinaion=my-topic --spring.cloud.stream.bindings.output.destination=count
+
+
+
+

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.

+
+
+
+
@SpringBootApplication
+public class WordCountProcessorApplication {
+
+  @Bean
+  public Function<KStream<Object, String>, KStream<?, WordCount>> process() {
+
+    return input -> input
+        .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
+        .map((key, value) -> new KeyValue<>(value, value))
+        .groupByKey(Serialized.with(Serdes.String(), Serdes.String()))
+        .windowedBy(TimeWindows.of(5000))
+        .count(Materialized.as("word-counts-state-store"))
+        .toStream()
+        .map((key, value) -> new KeyValue<>(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);
+	}
+}
+
+
+
+

Here again, this is a complete Spring Boot application. The difference here from the first application, though, the bean method is of type java.util.function.Function. +The first parameterized type for the Function is for the input KStream and the second one is for the output. +In the method body, a lambda expression is provided that is of type Function and as implementation, the actual business logic is given. +Similar to the previously discussed Consumer based application, the input binding here is named as process_in by default. For the output, the binding name is automatically also set to process_out.

+
+
+

Once built as a uber-jar (e.g., wordcount-processor.jar), you can run the above example like the following.

+
+
+
+
java -jar wordcount-processor.jar  --spring.cloud.stream.bindings.process_in.destination=words --spring.cloud.stream.bindings.process_out.destination=counts
+
+
+
+

This application will consume messages from the Kafka topic words and the computed results are published to an output +topic counts.

+
+
+

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 the Streams DSL specific configuration required by the Kafka Streams infrastructure +is automatically handled by the framework.

+
+
+

The two examples we saw above have a single KStream input binding. In both cases, the bindings received the records from a single topic. +If you want to multiplex multiple topics into a single KStream binding, you can provide comma separated Kafka topics as destinations below.

+
+
+

spring.cloud.stream.bindings.process_in.destination=topic-1,topic-2,topic-3

+
+
+
Multiple Input Bindings
+
+

Any non-trivial Kafka Streams applications often consume data from more than one topic through multiple bindings. +For instance, one topic is consumed as Kstream and another as KTable or GlobalKTable. +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 KTable or GlobalKTable, then Kafka Streams binder will properly bind the destination to a KTable or GlobalKTable 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.

+
+
+
BiFunction in Kafka Streams Binder
+
+

Here is an example where we have two inputs and an output. In this case, the application can leverage on java.util.function.BiFunction.

+
+
+
+
@Bean
+public BiFunction<KStream<String, Long>, KTable<String, String>, KStream<String, Long>> process() {
+    return (userClicksStream, userRegionsTable) -> (userClicksStream
+            .leftJoin(userRegionsTable, (clicks, region) -> new RegionWithClicks(region == null ?
+                            "UNKNOWN" : region, clicks),
+                    Joined.with(Serdes.String(), Serdes.Long(), null))
+            .map((user, regionWithClicks) -> new KeyValue<>(regionWithClicks.getRegion(),
+                    regionWithClicks.getClicks()))
+            .groupByKey(Serialized.with(Serdes.String(), Serdes.Long()))
+            .reduce(Long::sum)
+            .toStream());
+}
+
+
+
+

Here again, the basic theme is the same as previous examples, the difference, though, you have two inputs and the Java’s BiFunction support is used to bind the inputs to the desired destinations. +The default binding names generated by the binder for the inputs are process_in_0 and process_in_1 respectively. The default output binding remains to be process_out. +In this example, the first parameter of BiFunction is bound as a KStream for the first input and the second parameter is bound as a KTable.

+
+
+
+
BiConsumer in Kafka Streams Binder
+
+

If there are two inputs, but no outputs, in that case we can use java.util.funcion.BiConsumer. +Here is a blueprint for such an application.

+
+
+
+
@Bean
+public BiConsumer<KStream<String, Long>, KTable<String, String>> process() {
+    return (userClicksStream, userRegionsTable) -> {}
+}
+
+
+
+

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. +The Kafka Streams binder can make use of this feature to enable multiple input bindings.

+
+
+

Let’s see an example.

+
+
+
+
@Bean
+public Function<KStream<Long, Order>,
+        Function<GlobalKTable<Long, Customer>,
+                Function<GlobalKTable<Long, Product>, KStream<Long, EnrichedOrder>>>> process() {
+
+    return orders -> (
+              customers -> (
+                    products -> (
+                        orders.join(customers,
+                            (orderId, order) -> order.getCustomerId(),
+                                (order, customer) -> new CustomerOrder(customer, order))
+                                .join(products,
+                                        (orderId, customerOrder) -> customerOrder
+                                                .productId(),
+                                        (customerOrder, product) -> {
+                                            EnrichedOrder enrichedOrder = new EnrichedOrder();
+                                            enrichedOrder.setProduct(product);
+                                            enrichedOrder.setCustomer(customerOrder.customer);
+                                            enrichedOrder.setOrder(customerOrder.order);
+                                            return enrichedOrder;
+                                        })
+                        )
+                )
+    );
+}
+
+
+
+

In this model, we have 3 partial functions as inputs. The first function has the first input binding of the application (Order) and its output is another function. +This output function’s input is the second input binding for the application (Customer) and its output is another function. +This output function’s input is the third input for the application (Product) and its output is a KStream which is final output binding for the application. +The input from the three partial functions which are KStream, GlobalKTable, GlobalKTable respectively are available for you in the method body for implementing the business logic as part of the lambda expression.

+
+
+

Input bindings are named as process_in_0, process_in_1 and process_in_2 respectively. Output binding is named as process_out.

+
+
+

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.

+
+
+
+
+
Multiple Output Bindings
+
+

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 (KStream[]) as the outbound return type.

+
+
+

Here is an example:

+
+
+
+
@Bean
+public Function<KStream<Object, String>, KStream<?, WordCount>[]> process() {
+
+    Predicate<Object, WordCount> isEnglish = (k, v) -> v.word.equals("english");
+    Predicate<Object, WordCount> isFrench = (k, v) -> v.word.equals("french");
+    Predicate<Object, WordCount> isSpanish = (k, v) -> v.word.equals("spanish");
+
+    return input -> input
+            .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
+            .groupBy((key, value) -> value)
+            .windowedBy(TimeWindows.of(5000))
+            .count(Materialized.as("WordCounts-branch"))
+            .toStream()
+            .map((key, value) -> new KeyValue<>(null, new WordCount(key.key(), value,
+                    new Date(key.window().start()), new Date(key.window().end()))))
+            .branch(isEnglish, isFrench, isSpanish);
+}
+
+
+
+

The programming model remains the same, however the outbound parameterized type is KStream[]. +The default output binding names are process_out_0, process_out_1, process_out_2 respectively.

+
+
+
+
Function based Programming Styles for Kafka Streams
+
+

In summary, the following table shows the various options that can be used in the functional paradigm.

+
+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Number of InputsNumber of OutputsComponent to use

1

0

java.util.function.Consumer

2

0

java.util.function.BiConsumer

1

1..n

java.util.function.Function

2

1..n

java.util.function.BiFunction

>= 3

0..n

Use curried functions

+
+
    +
  • +

    In the case of more than one output in this table, the type simply becomes KStream[].

    +
    • +
+
+
+
+
+

2.3.2. Imperative programming model.

+
+

Although the functional programming model outlined above is the preferred approach, you can still use the classic StreamListener based approach if you prefer.

+
+
+

Here are some examples.

+
+
+

Following is the equivalent of the Word count example using StreamListener.

+
+
+
+
@SpringBootApplication
+@EnableBinding(KafkaStreamsProcessor.class)
+public class WordCountProcessorApplication {
+
+    @StreamListener("input")
+    @SendTo("output")
+    public KStream<?, WordCount> process(KStream<?, String> input) {
+        return input
+                .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
+                .groupBy((key, value) -> value)
+                .windowedBy(TimeWindows.of(5000))
+                .count(Materialized.as("WordCounts-multi"))
+                .toStream()
+                .map((key, value) -> new KeyValue<>(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);
+    }
+
+
+
+

As you can see, this is a bit more verbose since you need to provide EnableBinding and the other extra annotations like StreamListener and SendTo to make it a complete application. +EnableBinding is where you specify your binding interface that contains your bindings. +In this case, we are using the stock KafkaStreamsProcessor binding interface that has the following contracts.

+
+
+
+
public interface KafkaStreamsProcessor {
+
+	@Input("input")
+	KStream<?, ?> input();
+
+	@Output("output")
+	KStream<?, ?> output();
+
+}
+
+
+
+

Binder will create bindings for the input KStream and output KStream since you are using a binding interface that contains those declarations.

+
+
+

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 KafkaStreamsProcessor, the binding names are input and output. +Binding properties need to use those names. For instance spring.cloud.stream.bindings.input.destination, spring.cloud.stream.bindings.output.destination 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 EnableBinding.

+
+
+

Here is another example of a sink where we have two inputs.

+
+
+
+
@EnableBinding(KStreamKTableBinding.class)
+.....
+.....
+@StreamListener
+public void process(@Input("inputStream") KStream<String, PlayEvent> playEvents,
+                    @Input("inputTable") KTable<Long, Song> songTable) {
+                    ....
+                    ....
+}
+
+interface KStreamKTableBinding {
+
+    @Input("inputStream")
+    KStream<?, ?> inputStream();
+
+    @Input("inputTable")
+    KTable<?, ?> inputTable();
+}
+
+
+
+

Following is the StreamListener equivalent of the same BiFunction based processor that we saw above.

+
+
+
+
@EnableBinding(KStreamKTableBinding.class)
+....
+....
+
+@StreamListener
+@SendTo("output")
+public KStream<String, Long> process(@Input("input") KStream<String, Long> userClicksStream,
+                                     @Input("inputTable") KTable<String, String> userRegionsTable) {
+....
+....
+}
+
+interface KStreamKTableBinding extends KafkaStreamsProcessor {
+
+    @Input("inputX")
+    KTable<?, ?> inputTable();
+}
+
+
+
+

Finally, here is the StreamListener equivalent of the application with three inputs and curried functions.

+
+
+
+
@EnableBinding(CustomGlobalKTableProcessor.class)
+...
+...
+    @StreamListener
+		@SendTo("output")
+		public KStream<Long, EnrichedOrder> process(
+				@Input("input-1") KStream<Long, Order> ordersStream,
+				@Input("input-"2) GlobalKTable<Long, Customer> customers,
+				@Input("input-3") GlobalKTable<Long, Product> products) {
+
+			KStream<Long, CustomerOrder> customerOrdersStream = ordersStream.join(
+					customers, (orderId, order) -> order.getCustomerId(),
+					(order, customer) -> new CustomerOrder(customer, order));
+
+			return customerOrdersStream.join(products,
+					(orderId, customerOrder) -> customerOrder.productId(),
+					(customerOrder, product) -> {
+						EnrichedOrder enrichedOrder = new EnrichedOrder();
+						enrichedOrder.setProduct(product);
+						enrichedOrder.setCustomer(customerOrder.customer);
+						enrichedOrder.setOrder(customerOrder.order);
+						return enrichedOrder;
+					});
+		}
+
+    interface CustomGlobalKTableProcessor {
+
+            @Input("input-1")
+            KStream<?, ?> input1();
+
+            @Input("input-2")
+            GlobalKTable<?, ?> input2();
+
+            @Input("input-3")
+            GlobalKTable<?, ?> input3();
+
+            @Output("output")
+            KStream<?, ?> output();
+    }
+
+
+
+

You might notice that the above two examples are even more verbose since in addition to provide EnableBinding, you also need to write your own custom binding interface as well. +Using the functional model, you can avoid all those ceremonial details.

+
+
+

Before we move on looking at the general programming model offered by Kafka Streams binder, here is the StreamListener version of multiple output bindings.

+
+
+
+
EnableBinding(KStreamProcessorWithBranches.class)
+public static class WordCountProcessorApplication {
+
+    @Autowired
+    private TimeWindows timeWindows;
+
+    @StreamListener("input")
+    @SendTo({"output1","output2","output3})
+    public KStream<?, WordCount>[] process(KStream<Object, String> input) {
+
+			Predicate<Object, WordCount> isEnglish = (k, v) -> v.word.equals("english");
+			Predicate<Object, WordCount> isFrench =  (k, v) -> v.word.equals("french");
+			Predicate<Object, WordCount> isSpanish = (k, v) -> v.word.equals("spanish");
+
+			return input
+					.flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
+					.groupBy((key, value) -> value)
+					.windowedBy(timeWindows)
+					.count(Materialized.as("WordCounts-1"))
+					.toStream()
+					.map((key, value) -> new KeyValue<>(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<?, ?> input();
+
+    		@Output("output1")
+    		KStream<?, ?> output1();
+
+    		@Output("output2")
+    		KStream<?, ?> output2();
+
+    		@Output("output3")
+    		KStream<?, ?> output3();
+    	}
+}
+
+
+
+

To recap, we have reviewed the various programming model choices when using the Kafka Streams binder.

+
+
+

The binder provides binding capabilities for KStream, KTable and GlobalKTable on the input. +KTable and GlobalKTable bindings are only available on the input. +Binder supports both input and output bindings for KStream.

+
+
+

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 StreamListener based imperative approach.

+
+
+
+
+

2.4. Ancillary to the programming model

+
+

2.4.1. Kafka Streams Application ID

+
+

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.

+
+
+

If you only have one single processor in the application, then you can set this at the binder level using the following property:

+
+
+

spring.cloud.stream.kafka.streams.binder.applicationId.

+
+
+

As a convenience, if you only have a single processor, you can also use spring.application.name as the property to delegate the application id.

+
+
+

If you have multiple Kafka Streams processors in the application, then you need to set the application id per processor. +You can set this on the input binding on each processor.

+
+
+

For e.g. imagine that you have to two following functions.

+
+
+
+
@Bean
+public java.util.function.Consumer<KStream<Object, String>> process() {
+   ...
+}
+
+
+
+

and

+
+
+
+
@Bean
+public java.util.function.Consumer<KStream<Object, String>> anotherProcess() {
+  ...
+}
+
+
+
+

Then you must set the application id for each, using the following binding properties.

+
+
+

spring.cloud.stream.kafka.streams.bindings.process_in.applicationId

+
+
+

and

+
+
+

spring.cloud.stream.kafka.streams.bindings.anotherProcess_in.applicationId

+
+
+

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.

+
+
+

If the application does not provide an application ID, then in that case the binder will auto generate a random application ID for you. +This is convenient in development scenarios as it avoids the need for explicitly providing the application ID. +Please keep in mind that when you rely on this, each time you start the application, it starts with a brand new application id. +In the case of functional model, the generated application ID will be the function bean name followed by a UUID which is then postfixed with the literal applicationID. +In the case of StreamListener, instead of using the function bean name, the generated application ID will be use the containing class name followed by the method name.

+
+
+
Summary of setting Application ID
+
+
    +
  • +

    Auto generated by the binder per processor in the application. This can be overridden by setting at the binding level such as spring.cloud.stream.kafka.streams.bindings.process_in.applicationId. +When you have more than one processor, then you have to choose one of these options - either fall back to the defaults or override per input binding.

    +
    • +
  • +

    If you have a single processor, then you can use spring.kafka.streams.applicationId, spring.application.name or spring.cloud.stream.binder.kafka.streams.applicationId.

    +
    • +
+
+
+
+
+

2.4.2. Custom bindings in the functional style

+
+

By default, the binder uses the strategy discussed out above to generate the binding name when using the functional style, i.e. <function-bean-name>_<in>|<out>_[0..n], for e.g. process_in, process_in_0 etc. +If you want to override those binding names, you can do that by specifying the following properties.

+
+
+

spring.cloud.stream.function.inputBindings.<function-bean-name>.

+
+
+

For e.g. lets say, you have this function.

+
+
+
+
@Bean
+public BiFunction<KStream<String, Long>, KTable<String, String>, KStream<String, Long>> process() {
+...
+}
+
+
+
+

Binder will generate bindings with names, process_in_0, process_in_1 and process_out. +Now, if you want to change them to something else completely, maybe more domain specific binding names. You can do so, as below.

+
+
+

springc.cloud.stream.function.inputBindings.process=users,regions

+
+
+

and

+
+
+

spring.cloud.stream.function.outputBindings.process=clicks

+
+
+

After that, you must set all the binding level properties on these new binding names.

+
+
+

Please keep in mind that with the functional programming model described above, sticking with 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.

+
+
+
+

2.4.3. Setting up bootstrap server configuration

+
+

When running Kafka Streams applications, you must provide the Kafka broker server information. +If you don’t provide this information, the binder expects that you are running the broker at the default localhost:9092. +If that is not the case, then you need to override that. There are a couple of ways to do that.

+
+
+
    +
  • +

    Using the boot property - spring.kafka.bootstrapServers

    +
    • +
  • +

    Binder level property - spring.cloud.stream.kafka.streams.binder.brokers

    +
    • +
+
+
+

When it comes to the binder level property, it doesn’t matter if you use the broker property provided through the regular Kafka binder - spring.cloud.stream.kafka.binder.brokers. +Kafka Streams binder will first check if Kafka Streams binder specific broker property is set (spring.cloud.stream.kafka.streams.binder.brokers) and if nothing found, it looks for spring.cloud.stream.kafka.binder.brokers.

+
+
+
+
+

2.5. Record serialization and deserialization

+
+

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.

+
+
+

2.5.1. Inbound deserialization

+
+

Keys are always deserialized by native Serdes.

+
+
+

By default, for values, 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 in which case the deserialization was done by the framework.

+
+
+

Kafka Streams binder will try to infer matching Serde types by looking at the type signature of java.util.function.Function|Consumer or StreamListener. +Here is the order that it matches Serdes.

+
+
+
    +
  • +

    First 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.

    +
    +
    +
    Integer, Long, Short, Double, Float, byte[] and String.
    +
    +
    +
    • +
  • +

    If none of the Serdes provided by Kafka Streams don’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 json Serde objects. Otherwise, you have to configure Serde and target types on them individually.

    +
    • +
+
+
+

If none of the above strategies worked, then the applications must provide the Serdes through configuration. +This can be configured in two ways - binding or default.

+
+
+

First the binder will look if a Serde is provided at the binding level. +For e.g. if you have the following processor,

+
+
+
+
@Bean
+public BiFunction<KStream<CustomKey, AvroIn1>, KTable<CustomKey, AvroIn2>, KStream<CustomKey, AvroOutput>> process() {...}
+
+
+
+

then, you can provide a binding level Serde using the following:

+
+
+
+
spring.cloud.stream.kafka.streams.bindings.process_in_0.keySerde=CustomKeySerde
+spring.cloud.stream.kafka.streams.bindings.process_in_0.valueSerde=io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde
+
+spring.cloud.stream.kafka.streams.bindings.process_in_1.keySerde=CustomKeySerde
+spring.cloud.stream.kafka.streams.bindings.process_in_1.valueSerde=io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde
+
+
+
+

If you want the default key/value Serdes to be used for inbound deserialization, you can do so at the binder level.

+
+
+
+
spring.cloud.stream.kafka.streams.binder.configuration.default.key.serde
+spring.cloud.stream.kafka.streams.binder.configuration.default.value.serde
+
+
+
+

If you don’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 deserialze the inbound value object, you need to explicitly disable native decoding.

+
+
+

For e.g. if you have the same BiFunction processor as above, then spring.cloud.stream.bindings.process_in_0.nativeDecoding: false +You need to disable native decoding for all the inputs individually. Otherwise, native decoding will still be applied for those you don’t disable.

+
+
+

By default, Spring Cloud Stream will use application/json as the content type and use an appropriate json message converter. +You can use custom message converters by using the following property.

+
+
+
+
spring.cloud.stream.bindings.process_in_0.contentType
+
+
+
+
+

2.5.2. Outbound serialization

+
+

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.

+
+
+

Keys on the outbound are always serialized by Kafka using a matching Serde that is inferred by the binder. +If it can’t infer the type of the key, then that needs to be specified using configuration.

+
+
+

Value serdes are inferred using the same rules used for inbound deserialization. +First it matches to see if the outbound type is of a Serde exposed by Kafka such as - Long, Short, Double, Float, byte[] and String. +If that doesnt’t work, then fall back to JsonSerde provided by the Spring Kafka project. +Keep in mind that all these happen transparently to the application. +If none of these work, then the user has to provide the Serde to use by configuration.

+
+
+

Lets say you are using the same BiFunction processor as above. Then you can configure outbound key/value Serdes as following.

+
+
+
+
spring.cloud.stream.kafka.streams.bindings.process_out.keySerde=CustomKeySerde
+spring.cloud.stream.kafka.streams.bindings.process_out.valueSerde=io.confluent.kafka.streams.serdes.avro.SpecificAvroSerde
+
+
+
+

However, falling back to default Serdes for both input deserialization and output serialization is the last resort. +This may or may not work. Therefore, you need to ensure that you have a path forward for the application to correctly retrive the Serde.

+
+
+

If Serde inference fails, no binding level Serdes are provided, then the binder falls back to the default Serdes.

+
+
+

spring.cloud.stream.kafka.streams.binder.configuration.default.key.serde +spring.cloud.stream.kafka.streams.binder.configuration.default.value.serde

+
+
+

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 Serde types, you don’t need to do this configuration.

+
+
+

If you don’t want the native encoding provided by Kafka, but want to use the framework provided message conversion, then you need to explicitly disable native decoding since since native decoding is the default. +For e.g. if you have the same BiFunction processor as above, then spring.cloud.stream.bindings.process_out.nativeEncoding: false +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’t disable.

+
+
+

By default, Spring Cloud Stream will use application/json as the content type and use an appropriate json message converter. +You can use custom message converters by using the following property.

+
+
+
+
spring.cloud.stream.bindings.process_output.contentType
+
+
+
+

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.

+
+
+
+
+

2.6. Configuration Options

+
+

This section contains the configuration options used by the Kafka Streams binder.

+
+
+

For common configuration options and properties pertaining to binder, refer to the core documentation.

+
+
+

2.6.1. Kafka Streams Properties

+
+

The following properties are available at the binder level and must be prefixed with spring.cloud.stream.kafka.streams.binder.

+
+
+
+
configuration
+
+

Map with a key/value pair containing properties pertaining to Apache Kafka Streams API. + This property must be prefixed with spring.cloud.stream.kafka.streams.binder.. +Following are some examples of using this property.

+
+
+
+
+
+
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
+
+
+
+

For more information about all the properties that may go into streams configuration, see StreamsConfig JavaDocs in +Apache Kafka Streams docs.

+
+
+
+
brokers
+
+

Broker URL

+
+

Default: localhost

+
+
+
zkNodes
+
+

Zookeeper URL

+
+

Default: localhost

+
+
+
serdeError
+
+

Deserialization error handler type. +Possible values are - logAndContinue, logAndFail or sendToDlq

+
+

Default: logAndFail

+
+
+
applicationId
+
+

Convenient way to set the application.id for the Kafka Streams application globally at the binder level. +If the application contains multiple functions or StreamListener methods, then the application id should be set at the binding level per input binding. +See above where setting the application id is discussed in detail.

+
+

Default: none

+
+
+
+
+
+

The following properties are only available for Kafka Streams producers and must be prefixed with spring.cloud.stream.kafka.streams.bindings.<binding name>.producer. +For convenience, if there multiple output bindings and they all require a common value, that can be configured by using the prefix spring.cloud.stream.kafka.streams.default.producer..

+
+
+
+
keySerde
+
+

key serde to use

+
+

Default: See the above discussion on message de/serialization

+
+
+
valueSerde
+
+

value serde to use

+
+

Default: See the above discussion on message de/serialization

+
+
+
useNativeEncoding
+
+

flag to enable/disable native encoding

+
+

Default: true.

+
+
+
+
+
+

The following properties are available for Kafka Streams consumers and must be prefixed with spring.cloud.stream.kafka.streams.bindings.<binding-name>.consumer. +For convenience, if there are multiple input bindings and they all require a common value, that can be configured by using the prefix spring.cloud.stream.kafka.streams.default.consumer..

+
+
+
+
applicationId
+
+

Setting application.id per input binding.

+
+

Default: none

+
+
+
keySerde
+
+

key serde to use

+
+

Default: See the above discussion on message de/serialization

+
+
+
valueSerde
+
+

value serde to use

+
+

Default: See the above discussion on message de/serialization

+
+
+
materializedAs
+
+

state store to materialize when using incoming KTable types

+
+

Default: none.

+
+
+
useNativeDecoding
+
+

flag to enable/disable native decoding

+
+

Default: true.

+
+
+
dlqName
+
+

DLQ topic name.

+
+

Default: none.

+
+
+
startOffset
+
+

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 earliest as the default strategy and +the binder uses the same default. This can be overridden to latest using this property.

+
+

Default: earliest.

+
+
+
+
+
+

Note: Using resetOffsets 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.

+
+
+
+
+

2.7. Materializing KTable as a State Store.

+
+

Lets say you have the following function.

+
+
+
+
@Bean
+public BiFunction<KStream<String, Long>, KTable<String, String>, KStream<String, Long>> process() {
+   ...
+}
+
+
+
+

In the case of incoming KTable, if you want to materialize the computations to a state store, you have to express it +through the following property.

+
+
+
+
spring.cloud.stream.kafka.streams.bindings.process_in_1.consumer.materializedAs: incoming-store
+
+
+
+
+

2.8. Error Handling

+
+

Apache Kafka Streams provide the capability for natively handling exceptions from deserialization errors. +For details on this support, please see this +Out of the box, Apache Kafka Streams provide two kinds of deserialization exception handlers - logAndContinue and logAndFail. +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. LogAndFail is the default deserialization exception handler.

+
+
+
+

2.9. Handling Deserialization Exceptions

+
+

Kafka Streams binder supports a selection of exception handlers through the following properties.

+
+
+
+
spring.cloud.stream.kafka.streams.binder.serdeError: logAndContinue
+
+
+
+

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 topic. Here is how you enable this DLQ exception handler.

+
+
+
+
spring.cloud.stream.kafka.streams.binder.serdeError: sendToDlq
+
+
+
+

When the above property is set, all the deserialization error records are automatically sent to the DLQ topic.

+
+
+
+
spring.cloud.stream.kafka.streams.bindings.input.consumer.dlqName: foo-dlq
+
+
+
+

If this is set, then the error records are sent to the topic foo-dlq. If this is not set, then it will create a DLQ +topic with the name error.<input-topic-name>.<group-name>.

+
+
+

A couple of things to keep in mind when using the exception handling feature in Kafka Streams binder.

+
+
+
    +
  • +

    The property spring.cloud.stream.kafka.streams.binder.serdeError is applicable for the entire application. This implies +that if there are multiple StreamListener methods in the same application, this property is applied to all of them.

    +
    • +
  • +

    The exception handling for deserialization works consistently with native deserialization and framework provided message +conversion.

    +
    • +
+
+
+

2.9.1. Handling Non-Deserialization Exceptions

+
+

For general error handling in Kafka Streams binder, it is up to the end user applications to handle application level errors. +As a side effect of providing a DLQ for deserialization exception handlers, Kafka Streams binder provides a way to get +access to the DLQ sending bean directly from your application. +Once you get access to that bean, you can programmatically send any exception records from your application to the DLQ.

+
+
+

It continues to remain hard to robust error handling using the high-level DSL; Kafka Streams doesn’t natively support error +handling yet.

+
+
+

However, when you use the low-level Processor API in your application, there are options to control this behavior. See +below.

+
+
+
+
@Autowired
+private SendToDlqAndContinue dlqHandler;
+
+@StreamListener("input")
+@SendTo("output")
+public KStream<?, WordCount> process(KStream<Object, String> input) {
+
+    input.process(() -> new Processor() {
+    			ProcessorContext context;
+
+    			@Override
+    			public void init(ProcessorContext context) {
+    				this.context = context;
+    			}
+
+    			@Override
+    			public void process(Object o, Object o2) {
+
+    			    try {
+    			        .....
+    			        .....
+    			    }
+    			    catch(Exception e) {
+    			        //explicitly provide the kafka topic corresponding to the input binding as the first argument.
+                        //DLQ handler will correctly map to the dlq topic from the actual incoming destination.
+                        dlqHandler.sendToDlq("topic-name", (byte[]) o1, (byte[]) o2, context.partition());
+    			    }
+    			}
+
+    			.....
+    			.....
+    });
+}
+
+
+
+
+
+

2.10. State Store

+
+

State store is created automatically by Kafka Streams when the DSL is used. +When processor API is used, you need to register a state store manually. In order to do so, you can use KafkaStreamsStateStore annotation. +You can specify the name and type of the store, flags to control log and disabling cache, etc. +Once the store is created by the binder during the bootstrapping phase, you can access this state store through the processor API. +Below are some primitives for doing this.

+
+
+

Creating a state store:

+
+
+
+
@KafkaStreamsStateStore(name="mystate", type= KafkaStreamsStateStoreProperties.StoreType.WINDOW, lengthMs=300000)
+public void process(KStream<Object, Product> input) {
+    ...
+}
+
+
+
+

Accessing the state store:

+
+
+
+
Processor<Object, Product>() {
+
+    WindowStore<Object, String> state;
+
+    @Override
+    public void init(ProcessorContext processorContext) {
+        state = (WindowStore)processorContext.getStateStore("mystate");
+    }
+    ...
+}
+
+
+
+
+

2.11. Interactive Queries

+
+

As part of the public Kafka Streams binder API, we expose a class called InteractiveQueryService. +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 "autowire" the bean.

+
+
+
+
@Autowired
+private InteractiveQueryService interactiveQueryService;
+
+
+
+

Once you gain access to this bean, then you can query for the particular state-store that you are interested. See below.

+
+
+
+
ReadOnlyKeyValueStore<Object, Object> keyValueStore =
+						interactiveQueryService.getQueryableStoreType("my-store", QueryableStoreTypes.keyValueStore());
+
+
+
+

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 key. +InteractiveQueryService API provides methods for identifying the host information.

+
+
+

In order for this to work, you must configure the property application.server as below:

+
+
+
+
spring.cloud.stream.kafka.streams.binder.configuration.application.server: <server>:<port>
+
+
+
+

Here are some code snippets:

+
+
+
+
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
+}
+
+
+
+
+

2.12. Accessing the underlying KafkaStreams object

+
+

StreamBuilderFactoryBean from spring-kafka that is responsible for constructing the KafkaStreams object can be accessed programmatically. +Each StreamBuilderFactoryBean is registered as stream-builder and appended with the StreamListener method name. +If your StreamListener method is named as process for example, the stream builder bean is named as stream-builder-process. +Since this is a factory bean, it should be accessed by prepending an ampersand (&) when accessing it programmatically. +Following is an example and it assumes the StreamListener method is named as process

+
+
+
+
StreamsBuilderFactoryBean streamsBuilderFactoryBean = context.getBean("&stream-builder-process", StreamsBuilderFactoryBean.class);
+			KafkaStreams kafkaStreams = streamsBuilderFactoryBean.getKafkaStreams();
+
+
+
+
+

2.13. State Cleanup

+
+

By default, the Kafkastreams.cleanup() method is called when the binding is stopped. +See the Spring Kafka documentation. +To modify this behavior simply add a single CleanupConfig @Bean (configured to clean up on start, stop, or neither) to the application context; the bean will be detected and wired into the factory bean.

+
+
+
+

2.14. Health Indicator

+
+

The health indicator requires the dependency spring-boot-starter-actuator. For maven use:

+
+
+
+
<dependency>
+  <groupId>org.springframework.boot</groupId>
+  <artifactId>spring-boot-starter-actuator</artifactId>
+</dependency>
+
+
+
+

Spring Cloud Stream Binder Kafka Streams provides a health indicator to check the state of the underlying Kafka threads. +Spring Cloud Stream defines a property management.health.binders.enabled to enable the health indicator. See the +Spring Cloud Stream documentation.

+
+
+

The health indicator provides the following details for each Kafka threads:

+
+
+
    +
  • +

    Thread name

    +
    • +
  • +

    Thread state: CREATED, RUNNING, PARTITIONS_REVOKED, PARTITIONS_ASSIGNED, PENDING_SHUTDOWN or DEAD

    +
    • +
  • +

    Active tasks: task ID and partitions

    +
    • +
  • +

    Standby tasks: task ID and partitions

    +
    • +
+
+
+

By default, only the global status is visible (UP or DOWN). To show the details, the property management.endpoint.health.show-details must be set to ALWAYS or WHEN_AUTHORIZED. +For more details about the health information, see the +Spring Boot Actuator documentation.

+
+
+ + + + + +
+ + +The status of the health indicator is UP if all the Kafka threads registered are in the RUNNING state. +
+
+
+

2.14.1. Using custom state stores in functional applications

+
+

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. +Note that, for regular StreamListener based processors, you still need to use the KafkaStreamsStateStore annotation for custom state stores. +Here is an example of using custom state stores with functional style described in this section.

+
+
+
+
@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());
+		}
+
+
+
+

These state stores can be then accessed by the applications directly.

+
+
+
+
+
+

Appendices

+
+

Appendix A: Building

+
+
+

A.1. Basic Compile and Test

+
+

To build the source you will need to install JDK 1.7.

+
+
+

The build uses the Maven wrapper so you don’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.

+
+
+

The main build command is

+
+
+
+
$ ./mvnw clean install
+
+
+
+

You can also add '-DskipTests' if you like, to avoid running the tests.

+
+
+ + + + + +
+ + +You can also install Maven (>=3.3.3) yourself and run the mvn command +in place of ./mvnw in the examples below. If you do that you also +might need to add -P spring if your local Maven settings do not +contain repository declarations for spring pre-release artifacts. +
+
+
+ + + + + +
+ + +Be aware that you might need to increase the amount of memory +available to Maven by setting a MAVEN_OPTS environment variable with +a value like -Xmx512m -XX:MaxPermSize=128m. We try to cover this in +the .mvn 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. +
+
+
+

The projects that require middleware generally include a +docker-compose.yml, so consider using +Docker Compose to run the middeware servers +in Docker containers.

+
+
+
+

A.2. Documentation

+
+

There is a "full" profile that will generate documentation.

+
+
+
+

A.3. Working with the code

+
+

If you don’t have an IDE preference we would recommend that you use +Spring Tools Suite or +Eclipse when working with the code. We use the +m2eclipe eclipse plugin for maven support. Other IDEs and tools +should also work without issue.

+
+
+

A.3.1. Importing into eclipse with m2eclipse

+
+

We recommend the m2eclipe eclipse plugin when working with +eclipse. If you don’t already have m2eclipse installed it is available from the "eclipse +marketplace".

+
+
+

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 .settings.xml 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 .settings.xml file in that project. Click Apply and +then OK to save the preference changes.

+
+
+ + + + + +
+ + +Alternatively you can copy the repository settings from .settings.xml into your own ~/.m2/settings.xml. +
+
+
+
+

A.3.2. Importing into eclipse without m2eclipse

+
+

If you prefer not to use m2eclipse you can generate eclipse project metadata using the +following command:

+
+
+
+
$ ./mvnw eclipse:eclipse
+
+
+
+

The generated eclipse projects can be imported by selecting import existing projects +from the file menu.

+
+
+

[[contributing] +== Contributing

+
+
+

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.

+
+
+
+
+

A.4. Sign the Contributor License Agreement

+
+

Before we accept a non-trivial patch or pull request we will need you to sign the +contributor’s agreement. +Signing the contributor’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.

+
+
+
+

A.5. Code Conventions and Housekeeping

+
+

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.

+
+
+
    +
  • +

    Use the Spring Framework code format conventions. If you use Eclipse +you can import formatter settings using the +eclipse-code-formatter.xml file from the +Spring +Cloud Build project. If using IntelliJ, you can use the +Eclipse Code Formatter +Plugin to import the same file.

    +
    • +
  • +

    Make sure all new .java files to have a simple Javadoc class comment with at least an +@author tag identifying you, and preferably at least a paragraph on what the class is +for.

    +
    • +
  • +

    Add the ASF license header comment to all new .java files (copy from existing files +in the project)

    +
    • +
  • +

    Add yourself as an @author to the .java files that you modify substantially (more +than cosmetic changes).

    +
    • +
  • +

    Add some Javadocs and, if you change the namespace, some XSD doc elements.

    +
    • +
  • +

    A few unit tests would help a lot as well — someone has to do it.

    +
    • +
  • +

    If no-one else is using your branch, please rebase it against the current master (or +other target branch in the main project).

    +
    • +
  • +

    When writing a commit message please follow these conventions, +if you are fixing an existing issue please add Fixes gh-XXXX at the end of the commit +message (where XXXX is the issue number).

    +
    • +
+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/htmlsingle/Guardfile b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/htmlsingle/Guardfile new file mode 100644 index 00000000..bdd4d729 --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/htmlsingle/Guardfile @@ -0,0 +1,20 @@ +require 'asciidoctor' +require 'erb' + +guard 'shell' do + watch(/.*\.adoc$/) {|m| + Asciidoctor.render_file('index.adoc', \ + :in_place => true, \ + :safe => Asciidoctor::SafeMode::UNSAFE, \ + :attributes=> { \ + 'source-highlighter' => 'prettify', \ + 'icons' => 'font', \ + 'linkcss'=> 'true', \ + 'copycss' => 'true', \ + 'doctype' => 'book'}) + } +end + +guard 'livereload' do + watch(%r{^.+\.(css|js|html)$}) +end diff --git a/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/htmlsingle/css/spring.css b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/htmlsingle/css/spring.css new file mode 100644 index 00000000..40821db3 --- /dev/null +++ b/spring-cloud-stream-binder-kafka/3.0.0.M3/reference/htmlsingle/css/spring.css @@ -0,0 +1 @@ +@import url("https://fonts.googleapis.com/css?family=Karla:400,700|Montserrat:400,700");/*! normalize.css v2.1.2 | MIT License | git.io/normalize */article,aside,details,figcaption,figure,footer,header,hgroup,main,nav,section,summary{display:block}audio,canvas,video{display:inline-block}audio:not([controls]){display:none;height:0}[hidden],template{display:none}script{display:none !important}html,body{font-size:100%}html{font-family:Karla, sans-serif;-ms-text-size-adjust:100%;-webkit-text-size-adjust:100%}*,*:before,*:after{-moz-box-sizing:border-box;-webkit-box-sizing:border-box;box-sizing:border-box}body{background:white;color:#000;padding:0;margin:0;font-size:16px;font-family:Karla, 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