Understanding Mule flows

Mule is on the basis of the idea of Event-Driven Architecture, by which messages are initiated by external resources such as for instance events (from HTTP, JMS, File, Scheduler and more). These events are processed as messages by Mule applications through message processors formed together in a flow. Mule still offers the ability to process large or streaming messages as a record using the batch job approach. Learning the fundamental flow architecture and batch job structure is an important thing to understanding Mule. In an extract Mule flow contains a chain of message processors that accepts, then process messages. Usually, Mule applications are combinations of linked flows and/or batch jobs, by which to perform the integration needed for the use case.

Flows

A flow may be the construct within which you link together several individual elements to deal with the receipt, processing, and eventual routing of a message. You are able to connect many flows together to build a complete application which you can then deploy on-premise, on Mule or another application server, or in the cloud.

Flows are sequences of message-processing events. A message that enters a movement may pass by way of a wide variety of processors. In the below example, the message by way of a request-response inbound endpoint received by the Mule and transforms this content as into a new format and processes the company logic in an element before returning a reply via the message source.

Batch Jobs

A group job is really a top-level component in Mule exists outside all Mule flows and provides record I/O for Mule message processing. Batch jobs split large messages into records which Mule processes asynchronously; in the same way, flows process messages, batch jobs process records.

A group job consists of one or extra batch steps which therefore contain a number of message processors that act upon records because they move through the batch job. During batch processing, record-level variables (recordVars) and MEL expressions can be used to enrich and route or else act upon the records.

A batch job executes when triggered by an order executor in a Mule flow or even a message source in a batch-accepting input; when triggered, Mule creates a brand new batch job instance. Once every record has passed through all batch steps, the batch job instance ends and the batch job result maybe summarized in a report to indicate which records succeeded and which failed.

Message Sources

Mule processes messages, also known as events, which can be transmitted from resources external to Mule. Like, an email may be initiated by an event like a consumer request from a portable device, or even a change to data in a database, or the creation of a brand new customer ID in a SaaS application.

The initial building block of all flows or batch jobs is really a receiver which receives new messages and places them in the queue for processing. This message source, an inbound HTTP endpoint – receives messages from one or more external sources, thus triggering the carrying out of a movement or batch job.

Message Processors

In Mule, message processors are grouped by category.

Mule transformers are the important things to exchanging data between nodes because they allow Mule to convert message payload data to a structure that another application can understand. Mule also enables content enhancement of messages which lets you recover additional data during processing and add it to the message.

Mule uses components to conduct backend processes for specific business logic such as for instance checking customer and inventory databases. Components route messages to the proper application, such as for instance an order fulfillment system. Mule uses Staged Event-Driven Architecture (SEDA) for core nonsynchronous message processing inflows. Prominently, components are not mandatory to have any Mule-specific code; they are able to simply be POJOs, Spring beans, Java beans, Groovy scripts, or web services carrying the business logic for processing data. Components can even be developed in other languages such as for instance Python, JavaScript, Ruby, and PHP. Mule's catalog of foundations supports the most commonly used Enterprise Integration Patterns.

Flows and batch jobs can also contain filters, scopes, and routers. Like, a filter can be used to whitelist IP addresses from your application accept messages; you can use a scope to "wrap" around numerous message processors and cache the consequence of the processing they perform; you can use a switch to send messages down different paths in your application with respect to the content of the message payload. Mule includes many different filters, scopes, and routers to customize what sort of flow or batch job processes messages.

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