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<title>5. Main Concepts</title><link rel="stylesheet" type="text/css" href="css/manual-multipage.css"><meta name="generator" content="DocBook XSL Stylesheets V1.79.1"><link rel="home" href="multi_spring-cloud-stream.html" title="Spring Cloud Stream Reference Guide"><link rel="up" href="multi__reference_guide.html" title="Part II. Reference Guide"><link rel="prev" href="multi_spring-cloud-stream-overview-introducing.html" title="4. Introducing Spring Cloud Stream"><link rel="next" href="multi__programming_model.html" title="6. Programming Model"></head><body bgcolor="white" text="black" link="#0000FF" vlink="#840084" alink="#0000FF"><div class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="3" align="center">5. Main Concepts</th></tr><tr><td width="20%" align="left"><a accesskey="p" href="multi_spring-cloud-stream-overview-introducing.html">Prev</a> </td><th width="60%" align="center">Part II. Reference Guide</th><td width="20%" align="right"> <a accesskey="n" href="multi__programming_model.html">Next</a></td></tr></table><hr></div><div class="chapter"><div class="titlepage"><div><div><h2 class="title"><a name="_main_concepts" href="#_main_concepts"></a>5. Main Concepts</h2></div></div></div><p>Spring Cloud Stream provides a number of abstractions and primitives that simplify the writing of message-driven microservice applications.
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<title>5. Main Concepts</title><link rel="stylesheet" type="text/css" href="css/manual-multipage.css"><meta name="generator" content="DocBook XSL Stylesheets V1.79.1"><link rel="home" href="multi_spring-cloud-stream.html" title="Spring Cloud Stream Reference Guide"><link rel="up" href="multi__preface.html" title="Part I. Preface"><link rel="prev" href="multi_spring-cloud-stream-overview-introducing.html" title="4. Introducing Spring Cloud Stream"><link rel="next" href="multi__programming_model.html" title="6. Programming Model"></head><body bgcolor="white" text="black" link="#0000FF" vlink="#840084" alink="#0000FF"><div class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="3" align="center">5. Main Concepts</th></tr><tr><td width="20%" align="left"><a accesskey="p" href="multi_spring-cloud-stream-overview-introducing.html">Prev</a> </td><th width="60%" align="center">Part I. Preface</th><td width="20%" align="right"> <a accesskey="n" href="multi__programming_model.html">Next</a></td></tr></table><hr></div><div class="chapter"><div class="titlepage"><div><div><h2 class="title"><a name="_main_concepts" href="#_main_concepts"></a>5. Main Concepts</h2></div></div></div><p>Spring Cloud Stream provides a number of abstractions and primitives that simplify the writing of message-driven microservice applications.
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This section gives an overview of the following:</p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><a class="link" href="multi__main_concepts.html#spring-cloud-stream-overview-application-model" title="5.1 Application Model">Spring Cloud Stream’s application model</a></li><li class="listitem"><a class="xref" href="multi__main_concepts.html#spring-cloud-stream-overview-binder-abstraction" title="5.2 The Binder Abstraction">Section 5.2, “The Binder Abstraction”</a></li><li class="listitem"><a class="link" href="multi__main_concepts.html#spring-cloud-stream-overview-persistent-publish-subscribe-support" title="5.3 Persistent Publish-Subscribe Support">Persistent publish-subscribe support</a></li><li class="listitem"><a class="link" href="multi__main_concepts.html#consumer-groups" title="5.4 Consumer Groups">Consumer group support</a></li><li class="listitem"><a class="link" href="multi__main_concepts.html#partitioning" title="5.6 Partitioning Support">Partitioning support</a></li><li class="listitem"><a class="link" href="multi_spring-cloud-stream-overview-binders.html#spring-cloud-stream-overview-binder-api" title="7.2 Binder SPI">A pluggable Binder SPI</a></li></ul></div><div class="section"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="spring-cloud-stream-overview-application-model" href="#spring-cloud-stream-overview-application-model"></a>5.1 Application Model</h2></div></div></div><p>A Spring Cloud Stream application consists of a middleware-neutral core.
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The application communicates with the outside world through input and output channels injected into it by Spring Cloud Stream.
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Channels are connected to external brokers through middleware-specific Binder implementations.</p><div class="figure"><a name="d0e737" href="#d0e737"></a><p class="title"><b>Figure 5.1. Spring Cloud Stream Application</b></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="https://raw.githubusercontent.com/spring-cloud/spring-cloud-stream/master/docs/src/main/asciidoc/images/SCSt-with-binder.png" align="middle" alt="SCSt with binder"></div></div></div><br class="figure-break"><div class="section"><div class="titlepage"><div><div><h3 class="title"><a name="_fat_jar" href="#_fat_jar"></a>5.1.1 Fat JAR</h3></div></div></div><p>Spring Cloud Stream applications can be run in stand-alone mode from your IDE for testing.
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Channels are connected to external brokers through middleware-specific Binder implementations.</p><div class="figure"><a name="d0e728" href="#d0e728"></a><p class="title"><b>Figure 5.1. Spring Cloud Stream Application</b></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="https://raw.githubusercontent.com/spring-cloud/spring-cloud-stream/master/docs/src/main/asciidoc/images/SCSt-with-binder.png" align="middle" alt="SCSt with binder"></div></div></div><br class="figure-break"><div class="section"><div class="titlepage"><div><div><h3 class="title"><a name="_fat_jar" href="#_fat_jar"></a>5.1.1 Fat JAR</h3></div></div></div><p>Spring Cloud Stream applications can be run in stand-alone mode from your IDE for testing.
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To run a Spring Cloud Stream application in production, you can create an executable (or <span class="quote">“<span class="quote">fat</span>”</span>) JAR by using the standard Spring Boot tooling provided for Maven or Gradle. See the <a class="link" href="https://docs.spring.io/spring-boot/docs/current/reference/html/howto-build.html#howto-create-an-executable-jar-with-maven" target="_top">Spring Boot Reference Guide</a> for more details.</p></div></div><div class="section"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="spring-cloud-stream-overview-binder-abstraction" href="#spring-cloud-stream-overview-binder-abstraction"></a>5.2 The Binder Abstraction</h2></div></div></div><p>Spring Cloud Stream provides Binder implementations for <a class="link" href="https://github.com/spring-cloud/spring-cloud-stream-binder-kafka" target="_top">Kafka</a> and <a class="link" href="https://github.com/spring-cloud/spring-cloud-stream-binder-rabbit" target="_top">Rabbit MQ</a>.
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Spring Cloud Stream also includes a <a class="link" href="https://github.com/spring-cloud/spring-cloud-stream/blob/master/spring-cloud-stream-test-support/src/main/java/org/springframework/cloud/stream/test/binder/TestSupportBinder.java" target="_top">TestSupportBinder</a>, which leaves a channel unmodified so that tests can interact with channels directly and reliably assert on what is received.
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You can also use the extensible API to write your own Binder.</p><p>Spring Cloud Stream uses Spring Boot for configuration, and the Binder abstraction makes it possible for a Spring Cloud Stream application to be flexible in how it connects to middleware.
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@@ -13,7 +13,7 @@ In the sink example from the <a class="xref" href="multi_spring-cloud-stream-ove
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You can use different types of middleware with the same code.
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To do so, include a different binder at build time.
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For more complex use cases, you can also package multiple binders with your application and have it choose the binder( and even whether to use different binders for different channels) at runtime.</p></div><div class="section"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="spring-cloud-stream-overview-persistent-publish-subscribe-support" href="#spring-cloud-stream-overview-persistent-publish-subscribe-support"></a>5.3 Persistent Publish-Subscribe Support</h2></div></div></div><p>Communication between applications follows a publish-subscribe model, where data is broadcast through shared topics.
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This can be seen in the following figure, which shows a typical deployment for a set of interacting Spring Cloud Stream applications.</p><div class="figure"><a name="d0e800" href="#d0e800"></a><p class="title"><b>Figure 5.2. Spring Cloud Stream Publish-Subscribe</b></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="https://raw.githubusercontent.com/spring-cloud/spring-cloud-stream/master/docs/src/main/asciidoc/images/SCSt-sensors.png" align="middle" alt="SCSt sensors"></div></div></div><br class="figure-break"><p>Data reported by sensors to an HTTP endpoint is sent to a common destination named <code class="literal">raw-sensor-data</code>.
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This can be seen in the following figure, which shows a typical deployment for a set of interacting Spring Cloud Stream applications.</p><div class="figure"><a name="d0e791" href="#d0e791"></a><p class="title"><b>Figure 5.2. Spring Cloud Stream Publish-Subscribe</b></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="https://raw.githubusercontent.com/spring-cloud/spring-cloud-stream/master/docs/src/main/asciidoc/images/SCSt-sensors.png" align="middle" alt="SCSt sensors"></div></div></div><br class="figure-break"><p>Data reported by sensors to an HTTP endpoint is sent to a common destination named <code class="literal">raw-sensor-data</code>.
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From the destination, it is independently processed by a microservice application that computes time-windowed averages and by another microservice application that ingests the raw data into HDFS (Hadoop Distributed File System).
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In order to process the data, both applications declare the topic as their input at runtime.</p><p>The publish-subscribe communication model reduces the complexity of both the producer and the consumer and lets new applications be added to the topology without disruption of the existing flow.
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For example, downstream from the average-calculating application, you can add an application that calculates the highest temperature values for display and monitoring.
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When doing so, different instances of an application are placed in a competing consumer relationship, where only one of the instances is expected to handle a given message.</p><p>Spring Cloud Stream models this behavior through the concept of a consumer group.
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(Spring Cloud Stream consumer groups are similar to and inspired by Kafka consumer groups.)
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Each consumer binding can use the <code class="literal">spring.cloud.stream.bindings.<channelName>.group</code> property to specify a group name.
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For the consumers shown in the following figure, this property would be set as <code class="literal">spring.cloud.stream.bindings.<channelName>.group=hdfsWrite</code> or <code class="literal">spring.cloud.stream.bindings.<channelName>.group=average</code>.</p><div class="figure"><a name="d0e834" href="#d0e834"></a><p class="title"><b>Figure 5.3. Spring Cloud Stream Consumer Groups</b></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="https://raw.githubusercontent.com/spring-cloud/spring-cloud-stream/master/docs/src/main/asciidoc/images/SCSt-groups.png" align="middle" alt="SCSt groups"></div></div></div><br class="figure-break"><p>All groups that subscribe to a given destination receive a copy of published data, but only one member of each group receives a given message from that destination.
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For the consumers shown in the following figure, this property would be set as <code class="literal">spring.cloud.stream.bindings.<channelName>.group=hdfsWrite</code> or <code class="literal">spring.cloud.stream.bindings.<channelName>.group=average</code>.</p><div class="figure"><a name="d0e825" href="#d0e825"></a><p class="title"><b>Figure 5.3. Spring Cloud Stream Consumer Groups</b></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="https://raw.githubusercontent.com/spring-cloud/spring-cloud-stream/master/docs/src/main/asciidoc/images/SCSt-groups.png" align="middle" alt="SCSt groups"></div></div></div><br class="figure-break"><p>All groups that subscribe to a given destination receive a copy of published data, but only one member of each group receives a given message from that destination.
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By default, when a group is not specified, Spring Cloud Stream assigns the application to an anonymous and independent single-member consumer group that is in a publish-subscribe relationship with all other consumer groups.</p></div><div class="section"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="consumer-types" href="#consumer-types"></a>5.5 Consumer Types</h2></div></div></div><p>Two types of consumer are supported:</p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem">Message-driven (sometimes referred to as Asynchronous)</li><li class="listitem">Polled (sometimes referred to as Synchronous)</li></ul></div><p>Prior to version 2.0, only asynchronous consumers were supported. A message is delivered as soon as it is available and a thread is available to process it.</p><p>When you wish to control the rate at which messages are processed, you might want to use a synchronous consumer.</p><div class="section"><div class="titlepage"><div><div><h3 class="title"><a name="durability" href="#durability"></a>5.5.1 Durability</h3></div></div></div><p>Consistent with the opinionated application model of Spring Cloud Stream, consumer group subscriptions are durable.
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That is, a binder implementation ensures that group subscriptions are persistent and that, once at least one subscription for a group has been created, the group receives messages, even if they are sent while all applications in the group are stopped.</p><div class="note" style="margin-left: 0.5in; margin-right: 0.5in;"><table border="0" summary="Note"><tr><td rowspan="2" align="center" valign="top" width="25"><img alt="[Note]" src="images/note.png"></td><th align="left">Note</th></tr><tr><td align="left" valign="top"><p>Anonymous subscriptions are non-durable by nature.
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For some binder implementations (such as RabbitMQ), it is possible to have non-durable group subscriptions.</p></td></tr></table></div><p>In general, it is preferable to always specify a consumer group when binding an application to a given destination.
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Doing so prevents the application’s instances from receiving duplicate messages (unless that behavior is desired, which is unusual).</p></div></div><div class="section"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="partitioning" href="#partitioning"></a>5.6 Partitioning Support</h2></div></div></div><p>Spring Cloud Stream provides support for partitioning data between multiple instances of a given application.
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In a partitioned scenario, the physical communication medium (such as the broker topic) is viewed as being structured into multiple partitions.
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One or more producer application instances send data to multiple consumer application instances and ensure that data identified by common characteristics are processed by the same consumer instance.</p><p>Spring Cloud Stream provides a common abstraction for implementing partitioned processing use cases in a uniform fashion.
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Partitioning can thus be used whether the broker itself is naturally partitioned (for example, Kafka) or not (for example, RabbitMQ).</p><div class="figure"><a name="d0e878" href="#d0e878"></a><p class="title"><b>Figure 5.4. Spring Cloud Stream Partitioning</b></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="https://raw.githubusercontent.com/spring-cloud/spring-cloud-stream/master/docs/src/main/asciidoc/images/SCSt-partitioning.png" align="middle" alt="SCSt partitioning"></div></div></div><br class="figure-break"><p>Partitioning is a critical concept in stateful processing, where it is critical (for either performance or consistency reasons) to ensure that all related data is processed together.
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For example, in the time-windowed average calculation example, it is important that all measurements from any given sensor are processed by the same application instance.</p><div class="note" style="margin-left: 0.5in; margin-right: 0.5in;"><table border="0" summary="Note"><tr><td rowspan="2" align="center" valign="top" width="25"><img alt="[Note]" src="images/note.png"></td><th align="left">Note</th></tr><tr><td align="left" valign="top"><p>To set up a partitioned processing scenario, you must configure both the data-producing and the data-consuming ends.</p></td></tr></table></div></div></div><div class="navfooter"><hr><table width="100%" summary="Navigation footer"><tr><td width="40%" align="left"><a accesskey="p" href="multi_spring-cloud-stream-overview-introducing.html">Prev</a> </td><td width="20%" align="center"><a accesskey="u" href="multi__reference_guide.html">Up</a></td><td width="40%" align="right"> <a accesskey="n" href="multi__programming_model.html">Next</a></td></tr><tr><td width="40%" align="left" valign="top">4. Introducing Spring Cloud Stream </td><td width="20%" align="center"><a accesskey="h" href="multi_spring-cloud-stream.html">Home</a></td><td width="40%" align="right" valign="top"> 6. Programming Model</td></tr></table></div></body></html>
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Partitioning can thus be used whether the broker itself is naturally partitioned (for example, Kafka) or not (for example, RabbitMQ).</p><div class="figure"><a name="d0e869" href="#d0e869"></a><p class="title"><b>Figure 5.4. Spring Cloud Stream Partitioning</b></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="https://raw.githubusercontent.com/spring-cloud/spring-cloud-stream/master/docs/src/main/asciidoc/images/SCSt-partitioning.png" align="middle" alt="SCSt partitioning"></div></div></div><br class="figure-break"><p>Partitioning is a critical concept in stateful processing, where it is critical (for either performance or consistency reasons) to ensure that all related data is processed together.
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For example, in the time-windowed average calculation example, it is important that all measurements from any given sensor are processed by the same application instance.</p><div class="note" style="margin-left: 0.5in; margin-right: 0.5in;"><table border="0" summary="Note"><tr><td rowspan="2" align="center" valign="top" width="25"><img alt="[Note]" src="images/note.png"></td><th align="left">Note</th></tr><tr><td align="left" valign="top"><p>To set up a partitioned processing scenario, you must configure both the data-producing and the data-consuming ends.</p></td></tr></table></div></div></div><div class="navfooter"><hr><table width="100%" summary="Navigation footer"><tr><td width="40%" align="left"><a accesskey="p" href="multi_spring-cloud-stream-overview-introducing.html">Prev</a> </td><td width="20%" align="center"><a accesskey="u" href="multi__preface.html">Up</a></td><td width="40%" align="right"> <a accesskey="n" href="multi__programming_model.html">Next</a></td></tr><tr><td width="40%" align="left" valign="top">4. Introducing Spring Cloud Stream </td><td width="20%" align="center"><a accesskey="h" href="multi_spring-cloud-stream.html">Home</a></td><td width="40%" align="right" valign="top"> 6. Programming Model</td></tr></table></div></body></html>
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