M split
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The document summarizes different types of splitters that can be used to split messages and process them in parallel in Mule flows. It describes collection splitters that split collections, message chunk splitters that split messages into fixed-size chunks, and scatter-gather splitters that send a single message to multiple endpoints concurrently. It provides examples of splitting payloads into lists or byte arrays and aggregating the results after parallel processing. Split messages may be resequenced before aggregation to reconstruct the original order.
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Mule splitters
The document discusses different routing techniques in Mule such as splitters and aggregators that split messages and recombine them, collection and message chunk splitters that split collections and messages into chunks, scatter gather that sends a message to multiple endpoints concurrently and aggregates responses, and filters that route messages based on conditions. It provides examples of using collection and message chunk splitters to split payloads into individual messages or chunks that are processed in parallel and recombined using aggregators.
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The document discusses different types of routers and splitters in Mule flows. It explains that splitters split messages to be processed in parallel, then aggregated. It provides examples of a collection splitter splitting a list object, with each item routed to another flow and resequenced before aggregation. A message chunk splitter chunks a message into fixed-size segments, routing each to another flow before aggregation. Scatter-gather is also described as sending a message to multiple endpoints concurrently and aggregating the responses.
Mule splitters
This document summarizes different routing techniques in Mule including splitters, aggregators, collection splitters, message chunk splitters, scatter gather, and filters. It provides examples of using a collection splitter to split a list object and process each item individually, then resequence and aggregate the results. It also shows an example of using a message chunk splitter to split a message payload into fixed-length chunks, route each chunk individually, and then aggregate the responses. Scatter gather is described as sending a single message to multiple endpoints concurrently and aggregating the responses into one message.
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Creating a custom aggregator in Mule allows aggregating results from scatter-gather routing in a custom way. A class must implement the AggregationContext interface and override the aggregate method. This method accepts an AggregationContext with events from each route and the original event. The implementation should iterate events to access payloads, compose a new payload if needed, and return the original event with the new payload and any added properties. Understanding the AggregationContext parameters of events and original event is key to properly implementing custom aggregation.
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The document summarizes different routing techniques in Mule such as splitters, aggregators, filters, for each scope, main flows, sub flows, and flow references. It explains that splitters split messages into parts and aggregators combine message parts. The for each scope iterates over elements in a collection. Main flows contain message sources while sub flows only contain processors and are called via flow references. Filters filter messages using expressions and can throw exceptions if the expression is not satisfied.
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The document discusses different routing techniques in Mule such as splitters and aggregators that split messages and recombine them, collection and message chunk splitters that split collections and messages into chunks, scatter gather that sends a message to multiple endpoints concurrently and aggregates responses, and filters that route messages based on conditions. It provides examples of using collection and message chunk splitters to split payloads into individual messages or chunks that are processed in parallel and recombined using aggregators.
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This document discusses using Java Message Service (JMS) with MuleSoft's messaging transport to integrate applications. It provides an example of configuring JMS with ActiveMQ to send and receive messages from queues. The key steps are:
1. Import the ActiveMQ JAR library
2. Create an ActiveMQ connector in Mule
3. Use a JMS endpoint to enqueue messages to a queue via an HTTP request
4. Use another JMS endpoint and logger to receive and log messages from the queue
M messaging 1
JMS (Java Message Service) is an API that allows loosely coupled and asynchronous communication between distributed application components using message queues and topics. It supports point-to-point messaging via queues and publish-subscribe messaging via topics. Mule's JMS transport allows sending and receiving messages to any JMS-compliant message broker. Serialization converts objects to bytes for transmission over JMS and deserialization recreates the objects on the receiving end. Examples demonstrate configuring Mule flows to publish messages to a topic and subscribe to receive them, and to serialize Java objects into messages.
M enrichment
The enricher component is used to enrich Mule messages by adding additional data from external systems or transformations. It extracts information from the payload and saves it to variables without modifying the original payload. For example, an enricher could call a database to lookup a state code based on a zip code in the payload and add the state to a new variable for the target system. The enricher supports more complex use cases by allowing parts of the external system response to enrich specific fields rather than the full payload.
M property
Properties files allow configuration data to be stored in key-value pairs that can be accessed by the java.util.Properties class. Property placeholders enable reusing configuration values across environments by loading parameters from an external properties file. In Mule, properties can be configured using placeholders, global properties defined within the configuration, properties files loaded using the spring property-placeholder element, and variables like system properties and environment variables based on their priority order.
M system prop
System properties can be used to store configuration data and are parsed by the java.util.Properties class. They can come from JDK system properties specified on the command line when starting Mule, environment variables set in the run configurations, or properties configured in mule-app.properties. The order of priority is mule-app.properties, global variables, runtime arguments, environment variables, then other property files alphabetically.
M batching
The document discusses batch processing in three phases: input, process records, and on complete. In the input phase, a collection object is prepared with the input messages. The process records phase processes each record in the collection individually and in parallel. The on complete phase provides statistics on successful, failed, and total records. An example transforms a CSV file to XML using batch processing, with the input reading the CSV file, process records transforming and writing to XML files in batches, and on complete logging statistics.
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M split
- 2. Routing • The Routing module reviews the different types of Routers and how Routers are used to control how messages are sent and received by components. The message can be route in different ways. Below are explained in this example. • Splitters and Aggregators • Collection splitter and Collection Aggregator • Message chunk splitter and Message chunk aggregator • Scatter gather • For each • Mule flows, Sub flows and flow reference • Filters
- 3. Splitters • Splitters are used to split the message and process split messages in parallel. After processing completed, those messages get aggregate by aggregator components. Below is the splitters main flow diagram. • Below flow exposes a HTTP service to implement collection splitter and message chunk splitter. This flow expects a query parameter ‘splitter’. • If ‘splitter’ parameter value is ‘collection’ then choice router routes the flow to collection splitter or if the value is ‘chunk’ then it routes to message chunk splitter implementation.
- 5. Collection splitter and Collection Aggregator: • Collection splitter is used to split collection object and process split messages individually. Collection splitter expects a collection object as a payload. A java component has been used to create a List object. Each object of List is routed to another flow via VM queue in one-way mode.
- 6. • In the above flow after logger component (which logs payload) we have two important message processors. 1. Resequencer 2. Collection Aggregator. • While elements of List are processing invidually, the elements may get change their order. Resequencer is used to reorder the elements of List object. Collection Aggregator is used to aggregate the processed invidual message payload.
- 7. Message chunk splitter and Message chunk Aggregator: • We need to provide some message payload to split the message. • Message chunk splitter is used to chunk a message into a number of fixed- length messages based on the Message Size.
- 8. • This splitter first convert the message into byte array then split this array into chunks. Each chunked message is routed to another flow via VM queue in one- way mode. • Message Chunk Aggregator is used to aggregate the chunked messages. Byte Array to String component needs to co
- 9. Scatter Gather: • Scatter Gather is used to send a message to multiple endpoints concurrently. It collects the response of all the routes and aggregate into a single message.
- 11. •Thank You All