1.0.0.M3
Copyright © 2013-2015 Pivotal Software, Inc.
Table of Contents
The Spring Cloud Stream project allows a user to develop and run messaging microservices using Spring Integration. Just add @EnableBinding
and run your app as a Spring Boot app (single application context). You just need to connect to the physical broker for the bindings, which is automatic if the relevant binder implementation is available on the classpath. The sample uses Redis.
Here’s a sample source module (output channel only):
@SpringBootApplication @ComponentScan(basePackageClasses=TimerSource.class) public class ModuleApplication { public static void main(String[] args) { SpringApplication.run(ModuleApplication.class, args); } } @Configuration @EnableBinding(Source.class) public class TimerSource { @Value("${format}") private String format; @Bean @InboundChannelAdapter(value = Source.OUTPUT, poller = @Poller(fixedDelay = "${fixedDelay}", maxMessagesPerPoll = "1")) public MessageSource<String> timerMessageSource() { return () -> new GenericMessage<>(new SimpleDateFormat(format).format(new Date())); } }
@EnableBinding
is parameterized by one or more interfaces (in this case a single Source
interface), which declares input and output channels. The interfaces Source
, Sink
and Processor
are provided off the shelf, but you can define others. Here’s the definition of Source
:
public interface Source { @Output("output") MessageChannel output(); }
The @Output
annotation is used to identify output channels (messages leaving the module) and @Input
is used to identify input channels (messages entering the module). It is optionally parameterized by a channel name - if the name is not provided the method name is used instead. An implementation of the interface is created for you and can be used in the application context by autowiring it, e.g. into a test case:
@RunWith(SpringJUnit4ClassRunner.class) @SpringApplicationConfiguration(classes = ModuleApplication.class) @WebAppConfiguration @DirtiesContext public class ModuleApplicationTests { @Autowired private Source source @Test public void contextLoads() { assertNotNull(this.source.output()); } }
Note | |
---|---|
In this case there is only one |
A module can have multiple input or output channels defined as @Input
and @Output
methods in an interface. Instead of just one channel named "input" or "output" you can add multiple MessageChannel
methods annotated @Input
or @Output
and their names will be converted to external channel names on the broker. It is common to specify the channel names at runtime in order to have multiple modules communicate over a well known channel names. Channel names can be specified as properties that consist of the channel names prefixed with spring.cloud.stream.bindings
(e.g. spring.cloud.stream.bindings.input
or spring.cloud.stream.bindings.output
). These properties can be specified though environment variables, the application YAML file or the other mechanism supported by Spring Boot.
Channel names can also have a channel type as a colon-separated prefix, and the semantics of the external bus channel changes accordingly. For example, you can have two MessageChannels
called "output" and "foo" in a module with spring.cloud.stream.bindings.output=bar
and spring.cloud.stream.bindings.foo=topic:foo
, and the result is 2 external channels called "bar" and "topic:foo". The queue prefix for point to point semantics is also supported. Note, that in a future release only topic (pub/sub) semantics will be supported.
While Spring Cloud Stream makes it easy for individual modules to connect to messaging systems, the typical scenario for Spring Cloud Stream is the creation of multi-module pipelines, where modules are sending data to each other. This can be achieved by correlating the input and output destinations of adjacent modules, as in the following example.
Supposing that the design calls for the time-source
module to send data to the log-sink
module, we will use a common destination named foo
for both modules. time-source
will set spring.cloud.stream.bindings.output=foo
and log-sink
will set spring.cloud.stream.bindings.input=foo
.
The input and output channel names are the common properties to set in order to have Spring Cloud Stream applications communicate with each other as the channels are bound to an external message broker automatically. However, there are a number of scenarios when it is required to configure other attributes besides the channel name. This is done using the following naming scheme: spring.cloud.stream.bindings.<channelName>.<attributeName>=<attributeValue>
. The destination
attribute can also be used for configuring the external channel, as follows: spring.cloud.stream.bindings.input.destination=foo
. This is equivalent to spring.cloud.stream.bindings.input=foo
, but the latter can be used only when there are no other attributes to set on the binding. In other words, spring.cloud.stream.bindings.input.destination=foo
,spring.cloud.stream.bindings.input.partitioned=true
is a valid setup, whereas spring.cloud.stream.bindings.input=foo
,spring.cloud.stream.bindings.input.partitioned=true
is not valid.
Spring Cloud Stream provides support for partitioning data between multiple instances of a given application. In a partitioned scenario, one or more producer modules will send data to one or more consumer modules, ensuring that data with common characteristics is processed by the same consumer instance. The physical communication medium (i.e. the broker topic or queue) is viewed as structured into multiple partitions. Regardless whether the broker type is naturally partitioned (e.g. Kafka) or not (e.g. Rabbit or Redis), Spring Cloud Stream provides a common abstraction for implementing partitioned processing use cases in a uniform fashion.
Setting up a partitioned processing scenario requires configuring both the data producing and the data consuming end.
An output channel is configured to send partitioned data, by setting one and only one of its partitionKeyExpression
or partitionKeyExtractorClass
properties, as well as its partitionCount
property. For example seting spring.cloud.stream.bindings.output.partitionKeyExpression=payload.id
,spring.cloud.stream.bindings.output.partitionCount=5
is a valid and typical configuration.
Based on this configuration, the data will be sent to the target partition using the following logic. A partition key’s value is calculated for each message sent to a partitioned output channel based on the partitionKeyExpression
. The partitionKeyExpression
is a SpEL expression that is evaluated against the outbound message for extracting the partitioning key. If a SpEL expression is not sufficent for your needs, you can instead calculate the partition key value by setting the the property partitionKeyExtractorClass
. This class must implement the interface org.springframework.cloud.stream.binder.PartitionKeyExtractorStrategy
. While, in general, the SpEL expression is enough, more complex cases may use the custom implementation strategy.
Once the message key is calculated, the partition selection process will determine the target partition as a value between 0
and partitionCount
. The default calculation, applicable in most scenarios is based on the formula key.hashCode() % partitionCount
. This can be customized on the binding, either by setting a SpEL expression to be evaluated against the key via the partitionSelectorExpression
property, or by setting a org.springframework.cloud.stream.binder.PartitionSelectorStrategy
implementation via the partitionSelectorClass
property.
Additional properties can be configured for more advanced scenarios, as described in the following section.
An input channel is configured to receive partitioned data by setting its partitioned
binding property, as well as the instance index and instance count properties on the module, as follows: spring.cloud.stream.bindings.input.partitioned=true
,spring.cloud.stream.instanceIndex=3
,spring.cloud.stream.instanceCount=5
. The instance count value represents the total number of similar modules between which the data needs to be partitioned, whereas instance index must be value unique across the multiple instances between 0
and instanceCount - 1
. The instance index helps each module to identify the unique partition (or in the case of Kafka, the partition set) that they receive data from. It is important that both values are set correctly in order to ensure that all the data is consumed, as well as that the modules receive mutually exclusive datasets.
While setting up multiple instances for partitioned data processing may be complex in the standalone case, Spring Cloud Data Flow can simplify the process significantly, by populating both the input and output values correctly, as well as relying on the runtime infrastructure to provide information about the instance index and instance count.
Spring Cloud Stream relies on implementations of the Binder SPI to perform the task of connecting channels to message brokers. Each binder implementation typically connects to one type of messaging system. Spring Cloud Stream provides out of the box binders for Redis, Rabbit and Kafka.
By default, Spring Cloud Stream relies on Spring Boot’s auto-configuration configure the binding process. If a single binder implementation is found on the classpath, Spring Cloud Stream will use it automatically. So, for example, a Spring Cloud Stream project that aims to connect to Rabbit MQ can simply add the following dependency to their application:
<dependency> <groupId>org.springframework.cloud</groupId> <artifactId>spring-cloud-stream-binder-rabbit</artifactId> </dependency>
When multiple binders are present on the classpath, the application must indicate what binder has to be used for the channel. Each binder configuration contains a META-INF/spring.binders
, which is in fact a property file:
rabbit:\ org.springframework.cloud.stream.binder.rabbit.config.RabbitServiceAutoConfiguration
Similar files exist for the other binder implementations (i.e. Kafka and Redis), and it is expected that custom binder implementations will provide them, too. The key represents an identifying name for the binder implementation, whereas the value is a comma-separated list of configuration classes that contain one and only one bean definition of the type org.springframework.cloud.stream.binder.Binder
.
Selecting the binder can be done globally by either using the spring.cloud.stream.defaultBinder
property, e.g. spring.cloud.stream.defaultBinder=redis
, or by individually configuring them on each channel.
For instance, a processor module that reads from Rabbit and writes to Redis can specify the following configuration: spring.cloud.stream.bindings.input.binder=rabbit
,spring.cloud.stream.bindings.output.binder=redis
.
By default, binders share the Spring Boot autoconfiguration of the application module and create one instance of each binder found on the classpath. In scenarios where a module should connect to more than one broker of the same type, Spring Cloud Stream allows you to specify multiple binder configurations, with different environment settings. Please note that turning on explicit binder configuration will disable the default binder configuration process altogether, so all the binders in use must be included in the configuration.
For example, this is the typical configuration for a processor that connects to two rabbit instances:
spring: cloud: stream: bindings: input: destination: foo binder: rabbit1 output: destination: bar binder: rabbit2 binders: rabbit1: type: rabbit environment: spring: rabbit: host: <host1> rabbit2: type: rabbit environment: spring: rabbit: host: <host2>
Code using the Spring Cloud Stream library can be deployed as a standalone application or be used as a Spring Cloud Data Flow module. In standalone mode your application will run happily as a service or in any PaaS (Cloud Foundry, Lattice, Heroku, Azure, etc.). Spring Cloud Data Flow helps orchestrating the communication between instances, so the aspects of module configuration that deal with module interconnection will be configured transparently.
There are several samples, all running on the redis transport (so you need redis running locally to test them).
source
is a Java config version of the classic "timer" module from Spring XD. It has a "fixedDelay" option (in milliseconds) for the period between emitting messages.sink
is a Java config version of the classic "log" module from Spring XD. It has no options (but some could easily be added), and just logs incoming messages at INFO level.transform
is a simple pass through logging transformer (just logs the incoming message and passes it on).double
is a combination of 2 modules defined locally (a source and a sink, so the whole app is self contained).extended
is a multi-module mashup of source | transform | transform | sink
, where the modules are defined in the other samples and referred to in this app just as dependencies.If you run the source and the sink and point them at the same redis instance (e.g. do nothing to get the one on localhost, or the one they are both bound to as a service on Cloud Foundry) then they will form a "stream" and start talking to each other. All the samples have friendly JMX and Actuator endpoints for inspecting what is going on in the system.
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 for Redis, Rabbit, and Kafka bindings you should have those servers 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. See the README in the
scripts demo
repository for specific instructions about the common cases of mongo,
rabbit and redis.
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).