Table of Contents
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.
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>
The following image shows a simplified diagram of how the Apache Kafka binder operates:
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.
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.
A list of brokers to which the Kafka binder connects.
Default: localhost
.
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
.
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.
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.
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.
The time to wait to get partition information, in seconds. Health reports as down if this timer expires.
Default: 10.
The number of required acks on the broker.
See the Kafka documentation for the producer acks
property.
Default: 1
.
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
.
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.
The replication factor of auto-created topics if autoCreateTopics
is active.
Can be overridden on each binding.
Default: 1
.
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.
Note | |
---|---|
This setting is independent of the |
Default: true
.
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
.
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)
Global producer properties for producers in a transactional binder.
See spring.cloud.stream.kafka.binder.transaction.transactionIdPrefix
and Section 3.3, “Kafka Producer Properties” and the general producer properties supported by all binders.
Default: See individual producer properties.
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.
The following properties are available for Kafka consumers only and
must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.consumer.
.
A Map
of Kafka topic properties used when provisioning topics — for example, spring.cloud.stream.kafka.bindings.input.consumer.admin.configuration.message.format.version=0.9.0.0
Default: none.
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.
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).
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
.
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
.
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
.
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.
Whether to reset offsets on the consumer to the value provided by startOffset.
Default: false
.
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
).
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 Chapter 7, 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
.
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.
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>
).
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.
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
The name of a bean that implements RecordMessageConverter
. Used in the inbound channel adapter to replace the default MessagingMessageConverter
.
Default: null
The interval, in milliseconds, between events indicating that no messages have recently been received.
Use an ApplicationListener<ListenerContainerIdleEvent>
to receive these events.
See Section 3.4.3, “Example: Pausing and Resuming the Consumer” for a usage example.
Default: 30000
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
The following properties are available for Kafka producers only and
must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.producer.
.
A Map
of Kafka topic properties used when provisioning new topics — for example, spring.cloud.stream.kafka.bindings.input.consumer.admin.configuration.message.format.version=0.9.0.0
Default: none.
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 NewTopic
javadocs in the kafka-clients
jar.
Default: none.
The replication factor to use when provisioning new topics. Overrides the binder-wide setting.
Ignored if replicas-assignments
is present.
Default: none (the binder-wide default of 1 is used).
Upper limit, in bytes, of how much data the Kafka producer attempts to batch before sending.
Default: 16384
.
Whether the producer is synchronous.
Default: false
.
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
.
A SpEL expression evaluated against the outgoing message used to populate the key of the produced Kafka message — for example, headers['myKey']
.
The payload cannot be used because, by the time this expression is evaluated, the payload is already in the form of a byte[]
.
Default: none
.
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
)
Map with a key/value pair containing generic Kafka producer properties.
Default: Empty map.
Note | |
---|---|
The Kafka binder uses the |
In this section, we show the use of the preceding properties for specific scenarios.
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(); } } }
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.
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
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:
The login module name. Not necessary to be set in normal cases.
Default: com.sun.security.auth.module.Krb5LoginModule
.
The control flag of the login module.
Default: required
.
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="[email protected]"; };
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.
Note | |
---|---|
Do not mix JAAS configuration files and Spring Boot properties in the same application.
If the |
Note | |
---|---|
Be careful when using the |
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()); } }; } }
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 ??? 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 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.
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 ... }
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:
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.output.producer.partitioned=true spring.cloud.stream.bindings.parkingLot.destination=so8400in.parkingLot spring.cloud.stream.bindings.parkingLot.producer.partitioned=true 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(); } }
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: partitioned: true partition-key-expression: headers['partitionKey'] partition-count: 12
Important | |
---|---|
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 |
Note | |
---|---|
The preceding configuration uses the default partitioning ( |
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.
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.
Note | |
---|---|
You can also install Maven (>=3.3.3) yourself and run the |
Note | |
---|---|
Be aware that you might need to increase the amount of memory
available to Maven by setting a |
The projects that require middleware generally include a
docker-compose.yml
, so consider using
Docker Compose to run the middeware servers
in Docker containers.
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.
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.
Note | |
---|---|
Alternatively you can copy the repository settings from |
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.
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.
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.
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..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..java
files (copy from existing files
in the project)@author
to the .java files that you modify substantially (more
than cosmetic changes).Fixes gh-XXXX
at the end of the commit
message (where XXXX is the issue number).