Message enricher example
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The Mule Message Enricher allows the current message to be enriched by invoking an enrichment resource, which can be any message processor. The enrichment resource runs independently and modifies a copy of the message rather than the original message. The enricher supports enriching flow variables and message headers. It will leave the original message untouched if the enrichment resource does not return a result.
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M split
M splitVasanthii Chowdary 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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More from Ramakrishna kapa (20)
Load balancer in mule
Load balancer in muleRamakrishna kapa This document provides instructions for creating a load balancer and virtual private cloud (VPC) on MuleSoft's Anypoint Platform. It describes how to create a VPC with a specified name, region, and CIDR block. It then explains how to create a load balancer within that VPC, associating it with an SSL certificate and setting firewall rules. It also discusses how to add mapping rules and update an existing load balancer's configuration via the API.
Anypoint connectors
Anypoint connectorsRamakrishna kapa Anypoint Connectors allow Mule to communicate with external systems by receiving or sending messages. There are two types of connectors: endpoint-based connectors follow request-response patterns and are named after protocols like FTP; operation-based connectors perform specific third-party API operations. Connectors can act as message sources, processors, or destinations. Some require global configuration for connection details shared across multiple uses of the same connector type.
Batch processing
Batch processingRamakrishna kapa Mule allows processing of messages in batches through batch jobs. A batch job splits incoming messages into individual records, processes each record using message processors, and reports results. Batch processing is useful for streaming data integration, synchronizing data between applications like Salesforce and Netsuite, ETL processes, and handling large API data. A batch job exists outside flows and contains steps to sequentially process records through variables and MEL expressions.
Msmq connectivity
Msmq connectivityRamakrishna kapa The document provides steps to configure connectivity between Mule and MSMQ (Microsoft Message Queuing) by installing and configuring the MSMQ connector in Mule. It discusses that the MSMQ connector allows sending and receiving data from MSMQ queues via the Anypoint Gateway for Windows. It then outlines the steps to download and install the Windows Gateway Services, .NET 4.0, MSMQ services, and the MSMQ connector in Mule. Finally, it mentions running a sample application which involves setting up a demo queue in MSMQ and configuring a Mule project.
Data weave more operations
Data weave more operationsRamakrishna kapa This document describes several string matching operations in DataWeave including matches, startsWith, endsWith, find, match, and similar. It provides examples of the operations by showing DataWeave code that defines the operation, applies it to sample data, and outputs the results as JSON.
Basic math operations using dataweave
Basic math operations using dataweaveRamakrishna kapa This document demonstrates how to perform basic math operations like addition, subtraction, multiplication, division, finding maximum and minimum values, rounding, and square root functions using DataWeave. It provides code examples for each operation, showing how to transform and output the results in JSON or XML format.
Dataweave types operators
Dataweave types operatorsRamakrishna kapa This document summarizes various data weave operators for coercing data types and transforming data. It describes operators for coercing data to strings, numbers, and dates. It also covers the flatten and split by operators for transforming array and string data.
Operators in mule dataweave
Operators in mule dataweaveRamakrishna kapa This document discusses common operations in Mule Dataweave including map, pluck, filter, and remove. Map applies a function to each element of an array and returns a new array. Pluck maps an object to an array by applying a function to each key-value pair. Filter returns an array or object containing only elements that pass the criteria in the provided function. Remove returns an array with specified indexes removed.
Data weave in mule
Data weave in muleRamakrishna kapa DataWeave is Mule's tool for transforming data between different formats. The Transform Message component uses a DataWeave script to transform the incoming Mule message. DataWeave scripts contain a header section defining directives and an output structure, and a body section describing the transformations using the DataWeave syntax. DataWeave allows mapping between input and output fields visually or by writing code to aggregate, normalize, group, join, partition, pivot and filter data.
Servicenow connector
Servicenow connectorRamakrishna kapa The Mule ESB Servicenow connector allows integration between the ServiceNow IT service management platform and other applications by automating IT services and enabling data exchange. It provides over 30 operations to interact with the ServiceNow API to manage incidents, requests, and users. Developers can use the connector to create and fetch ServiceNow data from anywhere.
Introduction to testing mule
Introduction to testing muleRamakrishna kapa Unit testing validates individual units of code, like Mule flows, to check their correctness in isolation. Integration testing validates how units of code work together by testing entire applications or groups of interconnected flows. The document provides guidance on unit and integration testing for Mule applications, including writing modular, short flows; using environments for integration tests; and parameterizing code with placeholders.
Choice flow control
Choice flow controlRamakrishna kapa The choice flow control routes messages based on evaluating expressions against message properties or payload. It provides conditional logic similar to if/then/else statements, directing messages to the first routing option whose configuration evaluates to true, or to a default option if none match. Configuring it requires determining the evaluation criteria, routing options, processing for each, and default routing.
Mule exception strategies
Mule exception strategiesRamakrishna kapa Mule applications have two types of exceptions: system exceptions and messaging exceptions. System exceptions occur at the application level while no message is involved. Messaging exceptions occur when processing a message through a Mule flow. There are several exception strategies in Mule including catch, rollback, reference, and choice strategies. Catch strategies consume messages while rollback strategies roll messages back for reprocessing. Choice strategies handle exceptions based on message content.
Anypoint connector basics
Anypoint connector basicsRamakrishna kapa The Anypoint Connector DevKit is a tool that makes building reusable connectors easier. It uses annotations and code generation to handle boilerplate code needed to integrate connectors with Mule runtime and Anypoint Studio. Connectors allow communication between external resources and Mule applications, transforming data between the two. DevKit abstracts this communication and generates user interfaces, simplifying connector usage. Well-built connectors make app development simpler by handling tasks like pagination and session handling.
Mule global elements
Mule global elementsRamakrishna kapa Global elements in Mule allow you to configure elements once and then reference them multiple times from flows, rather than repeatedly writing the same code. They provide flexibility by allowing elements to use a global element's configuration exactly or with modifications, and they are reusable by configuring an element once and sharing it. Global elements also offer efficiency by changing a configuration in one place and having it apply to all elements that reference it.
Mule message structure and varibles scopes
Mule message structure and varibles scopesRamakrishna kapa The document discusses Mule message structure and variable scopes. A Mule message contains a header and payload. It is embedded in a Mule message object that can include variables, attachments, and exceptions. Properties have inbound and outbound scopes, with inbound being immutable metadata from the source and outbound being mutable metadata set during a flow. Variables have flow, session, and record scopes and are temporary pieces of information about a message that are not passed through the entire flow. Session variables apply across all flows in an application.
How to create an api in mule
How to create an api in muleRamakrishna kapa To create an API in Mule, first create a Mule project called "Sample API". Then create a RAML definition file called "test.raml" to define the API. Write the RAML and generate flows from it in Studio. This will create the flows automatically. Run the project as a Mule application. The API can then be accessed at http://localhost:8081/api/console/ and the GET method will return a response.
Log4j is a reliable, fast and flexible
Log4j is a reliable, fast and flexibleRamakrishna kapa Log4j is a popular Java logging framework that provides reliable and fast logging. It follows a layered architecture with core objects like loggers, appenders, and layouts. Loggers capture log information, appenders publish it to destinations like files or databases, and layouts format the information. The log4j.properties configuration file defines the logging level, appenders, and their layout patterns. For example, it can set the root logger level to DEBUG and direct DEBUG logs to a file appender that writes to a log file using a %m%n pattern.
Jsf 2
Jsf 2Ramakrishna kapa This document describes creating a simple JavaServer Faces (JSF) application using Maven that takes user input via a text field and button, and displays the input on another page. It discusses JSF and the MVC architecture, shows the code for the two JSF pages with the form and output, and the managed bean to handle the input data.
Vi editor
Vi editorRamakrishna kapa The vi editor is a powerful text editor installed by default in most Unix systems. It has two modes: command mode for taking actions and insert mode for inserting text. Common commands in vi include i to enter insert mode, Esc to exit insert mode, hjkl to navigate, dd to delete lines, p to paste, and :wq to save and quit. Vi is case sensitive and requires using keyboard commands rather than a mouse to edit files.
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- 1. Message Enricher Example The Mule Message Enricher allows the current message to be reflexive using data from a separate resource which we call it as Enrichment Resource. This Enrichment Resource can be any Message Processor and should always return a result, if it doesn’t then the Enricher will simply leave the current message untouched.
- 2. • The Message Processor that implements the Enrichment Resource is invoked with a copy of the current message along with any flow or session variables that are present. Any modification to the current message and it’s properties and attachments or flow or session variables will not be reflected in the flow where the Enricher is configured.
- 3. • Mule currently supports enrichment of flow variables and message headers only. • In simple language, we can say a message enricher enrich the current payload with some additional message and this is done without disturbing the actual payload..
- 4. • Message Enricher will allow only one message processor inside it if you want multiple message processors needs to be configured inside enricher, then use the Processor-chain inside message enricher and inside processor- chain use multiple message processors.
- 8. After deployed successfully click on the below url:http://localhost:8081/enrich