Spring autowire with qualifiers

Introduction

Autowired is great annotation, which by default inject beans by type to annotated element (constructor, setter or field). But how to use it, when there is more than one bean of requested type.

Autowired with one bean

Suppose we will work with small interface:

interface IHeaderPrinter {
String printHeader(String header)
}

When we have only one bean implementing IHeaderPrinter:

@Component
class HtmlHeaderPrinter implements IHeaderPrinter{
@Override
String printHeader(String header) {
return "<h1>$header</h1>"
}
}

then everything works great and test passes.

@Autowired
IHeaderPrinter headerPrinter

@Test
void shouldPrintHtmlHeader() {
assert headerPrinter.printHeader('myTitle') == '<h1>myTitle</h1>'
}

Two implementations

But what will happen, if we add another implementation of IHeaderPrinter, e. g. MarkdownHeaderPrinter?

@Component
class MarkdownHeaderPrinter implements IHeaderPrinter {
@Override
String printHeader(String header) {
return "# $header"
}
}

Now out test with fail with exception:

Error creating bean with name 'com.blogspot.przybyszd.spring.autowire.SpringAutowireWithQualifiersApplicationTests': Injection of autowired dependencies failed; nested exception is org.springframework.beans.factory.BeanCreationException: Could not autowire field: private com.blogspot.przybyszd.spring.autowire.IHeaderPrinter com.blogspot.przybyszd.spring.autowire.SpringAutowireWithQualifiersApplicationTests.headerPrinter; nested exception is org.springframework.beans.factory.NoUniqueBeanDefinitionException: No qualifying bean of type [com.blogspot.przybyszd.spring.autowire.IHeaderPrinter] is defined: expected single matching bean but found 2: markdownHeaderPrinter,htmlHeaderPrinter

We have to decide which implementation we want to use in our test, so …

Two implementations with Qualifier

Each bean is registered with name equal its class. For example HtmlHeaderPrinter is named htmlHeaderPrinter. The name is also its qualifier. We have to tell Autowired, that it should inject htmlHeaderPrinter:

@Autowired
@Qualifier('htmlHeaderPrinter')
IHeaderPrinter headerPrinter

Now our test passes again.

Two implementations qualified by field name

If field is names like implementing class (for example htmlHeaderPrinter), then this class implementation will be injected:

@Autowired
IHeaderPrinter htmlHeaderPrinter

And test passes:

@Test
void shouldPrintHtmlHeader() {
assert htmlHeaderPrinter.printHeader('myTitle') == '<h1>myTitle</h1>'
}

Thanks to @marcinjasion.

Two implementation with Primary

We often have one implementation which we almost always want to inject, so do we still have to put Qualifier with its name wherever we want to use it? No.

We could mark one implementation as Primary and this bean will be wired by default (unless we explicit give another Qualifier to use injection point):

@Component
@Primary
class HtmlHeaderPrinter implements IHeaderPrinter{
// ...
}
@Autowired
IHeaderPrinter headerPrinter

Summary

Autowired annotation allows us to inject dependencies to beans. It works great without additional configuration, when each bean could be uniquely find by type. When we have more than one bean, that could be injected, we have to use Qualifier or Primary annotation to help it find desired implementation.

Source code is available here.

You May Also Like

Recently at storm-users

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()
  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()
  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.