Exploring SAP Enterprise Messaging/Event Mesh (SAP EM), and listed down my understanding and scenarios tried using SCP Trial account. Basic Concepts & Scenario Tested Sender can send messages to both Queue and Topic. Queues hold messages, while Topic don’t. Message
Groovy – Json Mapping with Functional Programming Example
Below is my attempt to learn and apply functional programming using json mapping as example. Will see how far I can go and accomplish when using functional programming, compare to procedural programming that I familiar with. Transform Flat Json to
Split Single File to Multiple File using For Each
This example take json file from source folder, split it based on json array’s elements to multiple payload, renamed then saved to multiple files in target folder. See below gif animation: Input file:Orders.txt Output file: 2501.txt Output file: 2502.txt Output
DataWeave – Merge 2 Payloads using map and filter
This is a transformation using concept of Content Enricher Pattern: Below example take 2 payloads that stored in 2 variables (input1, input2).input1 is payload send from source system.input2 is payload externally get from external system via API call. input1 contain
Import External Jars File and Run Groovy Script in Mule 4
DataWeave language is the primary expression language in Mule 4, and is encourage to use DataWeave whenever possible. However for practicality or for whatever reason, there is always some scenario that one wish to use other general purpose programming language,
DataWeave – Transform Flat Structure to Nested using groupBy, map, mapObject and orderBy
In previous post, transformed nested structure to flat. In this post example will do the reverse, transform flat structure back to original nested format, using nested groupBy, map, mapObject and orderBy. Flat Json to Nested Json Requirements:1) nested group by
DataWeave – Transform Nested Structure to Flat using map and flatten
Nested Json to Flat Json This example show usage of DataWeave to transform nested json structure to flat json structure. i break them into step 1 and 2 to better illustrate the transformation. Input Json: After Step 1: After Step
Calling Odata using HTTP Listener, HTTP Request with uriParams and queryParams
This example call from postman to mule app, then call to northwind api with dynamic entity type (Customer, Orders, etc) and dynamic query string. The dynamic part is achieve using url placeholder and mulesoft attributes. We will see mulesoft debugger
Hello MuleSoft using HTTP Listener, Logger and Set Payload
This is simple MuleSoft app to expect http call, then return plain ‘hello world’ kind of response. Test run in postman tool. Main flow: HTTP Listener set with path ‘/hello’. HTTP Listener config using localhost and port 8081. Logger simply
Groovy Scripted Mapping using StreamingMarkupBuilder
Recent project required to use groovy scripted mapping, using StreamingMarkupBuilder to produce output XML, then post to Odata services.Below is the skeleton content in groovy script. With this script, can act as alternative to graphical mapping, and use groovy/java language