This is the second in a series about JSON logging. If you want to customize the output data structure you can check part 1 of the blog post. In this post, we are going to explore the new capabilities added in version 2 that were implemented based mostly on user feedback.
This is a sequel to my previous blog post about JSON logging for Mule 3. In this blogpost, I’ll touch upon the re-architected version of the JSON logger for our awesome Mule 4 release while leveraging the (just as awesome) SDK!
This is a guest blog from a member of our developer community. Dr. Roger Butenuth is a Senior Java Consultant at codecentric, he has been using Anypoint Platform for five years, with projects ranging from building simple SOAP routing/transformation to introducing the API-led approach to a Fortune 500 company.
Building Mule applications differs from coding in Java. Instead of typing all your code (with a lot of CTRL+space completion),
Logging is arguably one of the most neglected tasks on any given project. It’s not uncommon for teams to consider logging as a second-class citizen and it is usually taken for granted; until, of course, the team goes live and try to troubleshoot anything.
Streaming in Mule 4 is now as easy as drinking beer!
There are incredible improvements in the way that Mule 4 enables you to process, access, transform, and stream data. For streaming specifically, Mule 4 enables multiple parallel data reads without side effects and without the user caching that data in memory first.
Today you’ll meet the newest member of our Training Talks series, Mark Nguyen. Mark joined the training team in November of 2016 as a Curriculum Developer, and will be a familiar face from now on! And yes, we have Mark’s fun fact too…are you ready?
Mark was part of the original team that launched Taco Bell’s Doritos Locos Tacos and, from what I heard, if you mention his name when ordering one, you’ll get an extra taco for free.
In this article, we will see how Mule can intercept messages on the TCP/IP socket for real-time communication. You will first receive messages on the TCP/IP socket and then transform the messages from byte to object, then from object to XML, and then, finally, from XML to JSON––all using out-of-the-box Mule transformers.
The TCP transport allows users to send or receive messages over TCP connections. TCP is a layer above the IP.
We all know how powerful Dataweave Transform Message component is. This is such a powerful template engine that allows us to transform data to and from any format (XML, CSV, JSON, Pojos, Maps, etc. basically ).
So if we need to transform we need a Dataweave component in our flow. But wait! Dataweave also provides us a function called Dataweave function that helps us to execute Dataweave language outside a Dataweave transform component.
MuleSoft provides the most widely used integration platform for connecting any application, data source or API, whether in the cloud or on-premises. With Anypoint Platform®, MuleSoft delivers a complete integration experience built on proven open source technology, eliminating the pain and cost of point-to-point integration. Anypoint Platform includes CloudHub™ iPaaS, Mule ESB™, and a unified solution for API management™, design and publishing.