This can be extremely surprising at first, as you might not realize that by adding the extra transformation behavior you cancel the default one. This implies that if you want to add your transformation logic to an implicit one, you first have to declare the default transformation explicitly. If you declare one or several transformers on a global endpoint and add other transformers to the inbound or outbound endpoint that references the global one, the locally declared transformers will override the globally declared ones.
For example, consider the global endpoint declaration and its reference shown in listing 5. Global endpoint with a transformer Inbound endpoint with a transformer override In this example, the inbound endpoint built from referencing the global one named sysin will have only the Base64Decoder transformer applied to it. The local declaration of this transformer has overridden the declaration of the Base64Encoder on the global endpoint definition. One is named transformer-refs and the other is named responseTransformer-refs.
Why two types of transformers? The answer to this question is given in figure 5. In contrast, response transformers kick in when a message flows back as a response to the incoming request, when a response is expected in a synchronous manner. This synchronous response can be expected from a component in an inbound router or from the remote destination in an outbound router. In the meantime, our examples will focus only on bridge services built on the implicit bridge-component, which honors inbound transformers.
TIP My transformers are ignored! You may need to apply the transformers on the response. Is the service component honoring transformers? BEST Look for implementations of org. They all provide transport-independent transformation features such as compression, encryption, or payload value extraction. Its main intention is to override an implicit active transformer to prevent its action. If you remember the discussion about service overrides in section 3.
Here you can disable the default transformer that extracts the payload of JMS messages at the inbound endpoint level: With this configuration, JMS messages consumed by the endpoint will be passed untransformed to the component. As you can see, far from being prone to inaction, the NoActionTransformer actively resists working, which allows you to disable default transformation behaviors. In these systems, bytes are usually handled as streams or as arrays, with the former suited for large volumes of data and the latter for smaller chunks of information.
In Mule Core, the ByteArrayTo transformers can handle both arrays and streams of bytes as their input. If this transformer receives a byte payload that represents a serialized Java object, the result will be an object deserialized from these bytes.
The following demonstrates this transformer configured to deserialize byte arrays or streams into instances of java. Map only: CHAPTER 5 Transforming data with Mule If this transformer is used in an endpoint that receives anything other than serialized map objects, a TransformerException will be thrown and the processing of the message will be stopped.
In Mule, the encoding used by a transformer is determined by looking first at the encoding of the message. If none is specified, the encoding defined on the endpoint where the transformation happens will be used.
If the endpoint has no encoding set, then the default encoding of the platform will be used. In Mule 2, the default encoding is UTF Your best option is to have the encoding specified in the message.
Some transports do this automatically for you, such as the HTTP transport, which recognizes the Content-Encoding header and sets the value on the message accordingly. The alter ego of this transformer is the object-to-byte-array transformer. As expected, it works the opposite way: it transforms Strings and streams into byte arrays and marshals serializable payloads into bytes using the standard Java serialization mechanism. The following shows the simple declaration of the transformer we use in the publication example detailed in section 5.
This should never be taken lightly. So why would we do something that foolish? When processing an event asynchronously, you have no guarantee that the input stream will stay open for the duration of the message processing. As their names suggest, they transform from and to hexadecimal representations of bytes.
ByteArrayToMuleMessage and org. MuleMessageToByteArray transformers. These are specialized versions of the serializable-byte array transformers that transform not only the payload of the message but the whole MuleMessage object.
You now have a good idea of the transformers you can leverage to transform your data from bytes to other forms. With XML being a common payload in messaging systems nowadays, you can expect drastic reductions of data volume, as XML is a good candidate for compression. So how do we compress data in Mule? The receiving consumer, which listens on this queue, expects you to compress the data before you send it.
Because the endpoint receives a string payload and because string is serializable, the natural behavior of the compressor would be to serialize the string first, and then compress it.
But what we actually want to send to the JMS queue are the bytes that constitute the string in a compressed manner. This is why we use the string-to-byte array transformer first. The reason why we need the object-to-JMS message transformer is also interesting.
If you remember the discussion of section 5. Properties are named values that establish context so a message can be properly processed or interpreted. Encoding, session, and correlation identifiers are examples of this kind.
You can get a list of these properties by looking at the source code of org. Transport-specific properties—If a transport supports the notion of properties, the connector will store these values with the actual payload in the message context.
The connector will also convert message properties into transport properties when sending messages. This is why you find all the HTTP headers in message properties after sending a message to Mule over this protocol. User properties—Pretty much like protocols allow you to roll your own properties in addition to the specific ones, Mule allows you to add any number of typed properties to a message.
This can be useful for many things, as these properties will be kept and moved around the Mule instance or instances if you have several connected nodes. Consider, for example, a complex routing scenario, where messages are analyzed by a business rules engine, get tagged with some properties, and must then be sorted or dispatched accordingly.
Adding, modifying, or removing properties is therefore an important aspect of dealing with messages in Mule. This is where the message-properties transformer comes in handy. The following sample shows an HTTP inbound endpoint that removes some of the standard browser headers from incoming messages: As we said before, adding properties helps tag messages so they can be routed smartly.
The following demonstrates a transformer that renames the transport property that holds the incoming HTTP method used from http. A wealth of expression evaluators are available, and each supports a different expression syntax: turn to appendix A if you want to know more about this subject. The expression transformer can leverage these evaluators to transform the payload of the message it processes. This transformer can be configured to evaluate one or several expressions.
Depending on this configuration, the resulting message payload will be an object single expression or an array of objects multiple expressions.
Internally, Clood, Inc. They use instances of java. InetAddress as the Using core transformers payload of administrative messages that run around in their Mule instances. Note that we used the bean evaluator and that the expression is a Jaxen XPath bean property name similar to the standard JavaBean one, except for boolean fields.
The expression transformer is so powerful that it can most of the time replace a custom transformer. Therefore, before writing any code, check first whether you can achieve your goal with the expression transformer. As its name suggests, this transformer can apply a desired transformation automatically. How does it do that? It selects the most appropriate transformer based on the return class that you specify on its declaration: The auto transformer can only select globally configured transformers that are discoverable more on this notion later.
It works better with custom objects, as shown in the example, instead of generic ones such as strings or byte arrays. For the latter, there are way too many choices available for the auto transformer to pick up the right one.
When should this transformer be recommended? Mainly when a single endpoint receives a variety of different payloads and needs to transform them to a particular custom object type. Your bag of transformation tricks already allows you to perform all sorts of message manipulations. Before we look at the XSL transformer, a quick remark about the namespace prefix used for the XML module configuration elements.
Why not xml: you might ask? So what does it take to use this transformer? This is pretty common. The XSL transformer uses a file lookup fallback strategy that consists of looking first in the file system and then in the classpath, and finally trying a regular URL lookup.
This transformer goes to great lengths to accept a wide variety of input types bytes, string, W3C, and dom4j elements , so you seldom need to perform any pretransformation prior to calling it.
How do we do this in Mule? This is achieved by passing the appropriate parameter to the DocBook XSL, as shown here: Note how we used the repeatable xm:context-property element. You might wonder if this is the way to inject dynamic values in the XSL transformer. Suppose the alignment parameter you need to pass to the XSL is defined in a property of the current message named foalignment. The best way to pass this value to the XSL is to leverage the expression evaluation framework that we talked about in section 5.
The XSL transformer supports performance optimization parameters that allow you to fine-tune the maximum load and throughput of your transformation operations. Transformer workers to manage the load.
Without erring on the side of premature optimization, always take a little time to consider the expected concurrent load this transformer will have to deal with. Alternative marshalling techniques have been developed, including creating XML representations of objects.
The following declare an XML object marshaller: Nothing spectacular, but a lot of capability behind the scene. As a variant of the previous example, the following declares a marshaller that serializes the full MuleMessage instead of just the payload: This transformer would be well suited for persisting detailed error messages in an exception strategy, as it would marshal the whole MuleMessage in XML, a format an expert user can analyze and from which you can easily extract parts for later reprocessing.
It can also be useful for scenarios where you want to send a MuleMessage over the wire without resorting to standard Java serialization.
The following demonstrates an XML marshaller that aliases org. So why bother mentioning them? If you remember the discussion from the beginning of section 5. This section will focus on the transformers that come with the JMS transport, as this messaging API is popular and often involved in integration projects.
MessageProducer to send messages to a particular destination queue or topic. Therefore, the transport needs to transform the current Mule message into a JMS message. For this, it uses the object-to-JMS message transformer, which transforms the payload to the specific JMS message whose body data type is best fitted.
How does Mule select the JMS message type? You may remember that JMS 1. Table 5. The jms:object-to-jmsmessagetransformer also takes care of copying all the current message properties into JMS message properties, unless their name is a reserved JMS header name. This propagation of correlation IDs across systems is an essential messaging pattern. Configuring this transformer is trivial, as shown in the following example: Table 5. Mule restricts the types of objects that can go in this list based on the supported types defined in the JMS specification for StreamMessages.
Nothing more is needed, as the transformer is smart enough to just do the right thing with your messages. Consequently, the object-to-JMS message transformer needs access to a valid session to do its work. This is achieved by either retrieving the session from the current transaction if any or by getting one, possibly cached, from the JMS connector itself.
Message object. How does this transformer decide what type of data it will return? This decision is based on the rules shown in table 5. Existing transformers in action Why do we have a sixth type of JMS message?
This is because JMS supports the notion of a bodyless message, which is a message with no payload and only headers and properties. In that case, the payload itself is of no interest, and you should only focus on the headers and properties that the JMS transport message adapter extracted for you.
Like its alter ego, configuring this transformer is straightforward, as shown: This declares a global JMS message—to-object transformer named JmsMessageToObject.
As a bonus, this transformer can do a little more for you. It can convert the extracted string to a byte array or the extracted byte array into a string if you specify the desired return class: This transformer will only return a String if it has extracted a byte array from the JMS message.
Otherwise, the return value will stay unchanged, as it was after extraction from the JMS message. Integrating JMS providers will now be bliss!
When they turned to Clood for advice on how to implement their authoring platform presented in section 1. After all, nothing prevents our clients from using the same tools we do.
Knowing that the XSLT transformer is perfectly able to consume this kind of input, why do we transform the stream into a byte array first? The reason lies in the way the XSLT transformer consumes the input stream, which prevents it from being fully drained before the HTTP connection and the related input stream gets closed.
By using this transformer first, we fully drain the input stream before the HTTP connection gets closed. The client is already disconnected when the XSL transformation happens, which fits well the overall asynchronous design of the solution. This is why we have this third transformer kicking in to finalize the transformation chain.
Why is that? We would then have had to explicitly configure this JMS transformer on the outbound endpoint. By declaring the transformers on the inbound endpoint, we avoided this. Concretely, this amounts to a limited Mule configuration. Making portrait pages on USletter paper 8. The result of the transformation is sent to the target JMS queue named publicationQueue.
This allowed us to deliver a turnkey solution to the publishing company in a short time: a running prototype was ready in a matter of hours and production soon followed. The existing Mule transformers are numerous and support a wide range of transformations. We can only encourage you to explore Mule Core, its modules, and libraries to discover all of them.
Mule supports the notion of custom transformers, a powerful means to plug in your own transformation logic. For this they decided to use Mule, as it offers all the features they need: tapping different sources of data, transforming them into nice human-readable emails, and sending these over the wire to clients.
NOTE 5. In section Transforming payloads The first version of our custom transformer uses the message payload as the Velocity context and applies a template to it in order to generate a string. In other words, how should it behave if the source type is of the same type as the expected return type?
Being able to transform back and forth from one form to another gives greater flexibility in the way the transformer will be used. If a transformation can easily be reverted by an alter-ego transformer, this can allow it to transform back to the original form in case of exception.
This also applies to a format transformer, which can revert transformed data into its original form. Even if all transformers implement org. This will shield you from changes in the interface, as the abstract base class will be updated so your custom code will still work.
Moreover, the abstract base class takes care of pesky details such as enforcing the source and return types. The abstract base class to extend for transforming only payloads is org. This is the one that the VelocityPayloadTransformer extends, as shown in listing 5. The source type is Object as defined in B. Most of the time a collection or a Java bean will be processed by this transformer. We pass the message payload as a context entry named payload not src or data simply for consistency reasons, as other transformers, such as the script one discussed in chapter 14 use payload too.
Consistency is good. As Velocity throws an Exception, we have to catch it too. Note how we create a static message when this happens. The Mule internal API, including lifecycle events, will be covered in chapter Now, how do we use this transformer?
Since it relies on Spring for the creation and injection of the Velocity engine, we need to use some Spring elements see section 2. Then we inject the Velocity engine into the transformer alongside the template name.
This approach allows us to minimize the code of the transformer listing 5. This should give you enough guidelines to reuse your existing transformation code inside Mule.
But it should also give you enough guidance to get started with a complete transformer from scratch, should you need one. Just like we said before, this transformer can only use data in the payload. But what if we need to also use values extracted from the properties? Since we wanted clients to see this reference number in the generated emails so they can refer to it when contacting Clood to complain about the outrageous fees , we needed to access the message properties from the Velocity transformer.
How is this possible? Whenever you need to read or modify message properties in a transformer, you need to have access to the org. This time, the abstract base class to extend for transforming messages is org. This is the class that the VelocityMessageTransformer extends, as shown in listing 5. Why do that when message should be enough, since you can get the payload from it?
Again this is a matter of consistency with the existing transformers. Moreover, this makes the second transformer backward-compatible with the first one. You can use the second one as a drop-in replacement for the first one, as existing Velocity templates will find the object they expect bound under the same name.
Configuring the Velocity message transformer is done exactly as for the payload transformer: The only difference is that with this version, we can now access message properties. At this point, you might wonder what it would take to modify the properties of a message. Not much, really. TIP Making your message transformer discoverable Unlike payload transformers, which extend org.
If you want your transformer to be discovered, you must then implement org. The transformer discovery mechanism supports the notion of weighting, which allows you to give precedence to a transformer above ones with similar source and return types.
Mule will look for discoverable transformers when you use the auto transformer that we mentioned earlier, but also when you use payload accessor methods discussed in section Some of them came from the core library, while others from specific modules or transports.
This similarity makes them easy to learn and use. It also allows you to compose them in transformation chains to perform even more advanced or specific transformation operations.
Message transformation is yet another domain where Mule shines by its simplicity and extensibility. We understand that components represent entities that can perform specific operations, with well-defined interfaces and an accent on encapsulation. Unsurprisingly, Mule supports its own component model. This difficulty stems from the extensive capacities of the routing, filtering, and transforming infrastructure that surrounds the components.
So why bother about components? Components are at the core of Mule services: each service hosts a component. As illustrated in figure 6. This can happen if no outbound router is configured or if the component instructs Mule to stop further processing of the message. Component invocations can also be wrapped by interceptors that allow you to share common behavior in a transversal manner. This advanced subject will be discussed in chapter Figure 6.
All the message exchange patterns supported by Mule, which are described in the online user guide,1 are enabled by this behavior. The following sections will demonstrate how to leverage the component response in different scenarios. A component can also take control of the message process flow. Where do components fit into this scheme? Processing a message might require you to communicate with another service.
Components can act as one-stop communication devices and perform operations such as synchronous RPC calls in a more straightforward manner than what you could do with, say, a chaining router and a bunch of transformers. Components can reify a preexisting business interface, defined, for example, with a WSDL in a contract-first approach.
For example, you can pool components only by means of configuration. The rest of the chapter will help you grok components.
If you remember the discussion from the beginning of chapter 5, you should recall that the component will decide whether to take this transformer into account.
Discussions in section 6. But, for now, considering that components decide to apply transformers or not is a fairly good approximation. This is why the transformation seems to happen inside the components in these figures. Massaging messages The core library of Mule contains components that can massage your messages in different manners. What can these core components do to your messages? What does this mean? If components are at the core of Mule services, why do so many services not have any components?
In fact, these examples rely on an implicit component that Mule uses when none is specified for a service. This is the bridge component. This said, it does honor any transformer defined on the inbound endpoint, as illustrated in figure 6. In that case, the routers and transformers that kick in before or after the bridge will be enough to support the message-related operations you need.
Transformer to Inbound router Bridge component Outbound router Figure 6. Massaging messages The bridge component returns the input message transformed by the inbound transformers, if any. In listing 6. Listing 6. That said, relying on its implicit presence is the canonical way to define a bridge service in Mule. Hence, you should prefer the implicit bridge approach.
The next component is useful for this. Not only does it echo the incoming message, but it also logs it. Transformer to Echo component Inbound router Outbound router Figure 6.
Log file Like the bridge component, it returns the input message transformed by the inbound transformers. This makes the echo component usable with synchronous inbound endpoints and outbound routers. The only difference is that the inbound messages will be logged before being transformed and returned out of the echo component. This log message is sent to the same logging framework as the one used by Mule itself. You can find more information about logging in chapter 8. Massaging messages Even if functionally equivalent, the echo component is much less efficient than the bridge and should be used only if logging is necessary.
A common approach is to start a configuration with echo components in services of particular interest and to replace these components with bridges when things work as expected. NOTE Log or echo? Prior to Mule 2. The only difference that remains between these two components is the interfaces they implement: the log component implements org.
LogService, whereas the echo component implements both org. LogService and org. This aggregation mechanism is principally used to build a new message out of asynchronous responses. The transformed message is synchronously sent to each endpoint of the outbound router. The payload of each response from each of the outbound endpoints is passed to the best matching method of the original untransformed inbound message.
Transformer to Reflection message builder component Inbound router Outbound router 1 2 1 2 Figure 6. If you recall the discussion in section 5. The UML diagram of this con- class used to generate client emails with the text class1 is shown in figure 6. When we create an instance of com. Velocity transformer statistic. ActivityEmailContext, we only set the client ID on it. This enricher service, named EmailContextBuilder, is illustrated in listing 6.
The response of the ClientLookupChannel, which unsurprisingly returns Client objects, will be passed to the relevant setter of ActivityEmailContext by the reflection message builder component B.
The same will be done with the response of the ActivityReportLookupChannel. Finally, the response of the service will be the original ActivityEmailContext instance whose properties have been set by values coming from the outbound endpoints. At this point, this object will be ready to be transformed into a meaningful email and sent to the relevant client. TIP 1 Under construction A good practice in software development consists of setting your IDE new method templates to contain a statement that throws an unsupported operation.
This creates an unmissable reminder We spared you the usual accessors in this diagram. Mule offers a component that supports a similar semantic: the null component. The null component will throw an exception if it receives a message.
These components act as proxies for remote services, making them the one-stop solution you were looking for. The notable feature of this component is that it performs its remote call within the normal message-processing flow of the message in the local Mule service, without mobilizing its routing infrastructure. Consequently, the routing infrastructure can be leveraged as with any nonremoting component, allowing the response of the SOAP service to be dispatched as needed.
Sending a string that contains an address to the GeoCoderSearch service inbound endpoint will make the wrapper component send a SOAP request. The wrapper component will use the configured address, method, and SOAP style and encoding for the request.
It will then return the response synchronously. The response will be an array of GeocoderAddressResult, which is a value object that contains geolocation and address data.
Calling geocoder. This implies the need to create local proxy classes to map the result of the remote invocation, a practice that creates a sometimes undesirable tight coupling to the remote service. Among other reasons, REST has been designed to alleviate this kind of rigidity. This component, appropriately named REST service component, performs remote HTTP invocations within the normal message process flow that occurs in a Mule service, as illustrated by figure 6.
Sending a search term as a string to the inbound endpoint of this service will trigger a call to the Google search API. Thanks to a chain of transformers, the JSON response of Google search will be transformed into a java. List of web page titles from the hit list.
Note also that we refer to RefererHeader, which is a Spring-configured map not shown in the listing. If you compare this configuration to the one from the SOAP example, you might find it overly complex.
Bear in mind that the SOAP transport performs a lot behind the scenes and requires supporting stub classes. In contrast, when using the REST service component, all the nitty-gritty is visible and under your control. Moreover, your configuration mileage will vary depending on the requirements of the remote REST service you call, its response data format, and the data format your service will need to return.
You can now easily invoke remote business logic from your Mule services. But what if you want this logic to run within Mule itself? Executing business logic 6. What would it take to use these classes within Mule? The answer is: not much. Once-bitten-twice-shy developers have become leery of platforms that force them to depend on proprietary APIs. Aware of that fact, Mule goes to great lengths to allow you to use any existing class as a custom service component.
Mule also allows you to use or reuse beans that are defined in Spring application contexts as custom components. One is when your component needs to be aware of the Mule context. By default, a component processes the payload of a Mule message. This payload is pure data and is independent of Mule. This interface defines only one method: onCall eventContext. Another reason to get coupled with the Mule API is to receive by dependency injection references to configured moving parts such as connectors, transformers, or endpoint builders.
Unless you decide to pool these objects, which is discussed in section 6. This is demonstrated later, in listing 6. This is done by using the prototype-object element or the short syntax where a class name is defined on the component element itself. Let Spring take care of object instantiation.
Granting that creating custom components mostly amounts to creating standard business logic classes, there are technical aspects to consider when it comes to configuring these custom components. What are the possible ways to configure these components? Why and how should you pool custom components?
How do you compose service components? What are the benefits of an internal canonical data model? Component interface. Is this an interface you need to implement if you create a custom component?
Not really. This flexibility raises the following question: how does Mule manage to determine what method to call on the component when a message reaches it? Entry point resolver sets can be defined at model level or at component level, with the latter overriding the former.
Mule offers a variety of entry point resolvers that you can leverage to ensure that messages reach the intended method on your components. Table 6. Each resolver supports different attributes and child elements, depending on its own configuration needs. This attribute tells the resolvers to accept void methods. Entry point resolver name Behavior array-entry-point-resolver Selects methods that accept an array as a single argument.
The inbound message payload must be an array. This resolver is always in the default set that Mule defines. The message property name to look for is defined on the resolver itself. If the message payload is an array, its members will be used as method arguments if you really want to receive an array as an argument, you must use the array-entry-point-resolver discussed previously.
If the message has a null payload, the resolver will look for a method without argument. For this, try subclassing org. AbstractEntryPointResolver rather than implementing org. EntryPointResolver from scratch. In this case, you must call one of the transformMessage methods of the eventContent argument if you want to work with the transformed message in your callable custom component. Caution: as soon as you use one exclude-entrypoint element, you disable the intrinsic exclusion of the Java object methods.
If you specify several names, the resolver will use the first method with a matching name. At this point, you might feel overwhelmed by the versatility of the entry point resolution mechanism. The best hint we can give you at this point is not to overengineer your configuration with armies of finely tuned entry point resolvers. The default entry point resolver set is equivalent to the legacy-entry-pointresolver-set, which does all these contortions in order to be compatible with the behavior of Mule 1.
The response to any message sent to its inbound router will be the value returned by a call on the nextInt method of the random object. We use a noarguments-entry-point resolver configured to precisely pick up the nextInt method. By pretransforming an incoming payload into a type that fits the argument of the desired method, you can finely guide the entry point resolver to hit the right target.
In section 6. ActivityEmailContext and what these services actually expect a long representing the client ID. Ask yourself: Should I live with no regrets, or learn from my mistakes?
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