In Mule 4, DataWeave is everywhere: every listener and processor can be configured with it. Because most Mule users already know Java well, this article will help Java developers to easily use DataWeave by rewriting their lambdas expressions.
I have been asked so many times about DataWeave Performance during my time in the field. This is because developers try to find arguments to not use it when they realize that a new and proprietary programming language is introduced. Most of the time they have the same “natural response” of resolving the problem by going to the known and comfortable zone called “Java.”
When building DataWeave transformations for your Mule application, you will run into situations in which you will need to invoke external logic that may be encapsulated in a Java POJO, Groovy, Python, Ruby script, or really any lookup that uses a CSV file or database table as part of the transformation.
Anypoint Platform is fast. The legacy systems that it often connects to? Not so much.
Therefore, in real world use cases, the requirements often call for limiting the message throughput to protect the endpoint systems from being overwhelmed by traffic. Architectural designs that support message throttling commonly incorporate some elements of message queues to stage and hold messages in-flight, so that the endpoints can process them at a steadier pace.
If you’re an assiduous reader of this blog, then you probably already heard about our vision around APIs, our Anypoint API Manager solution and all our RAML based stories. Those are our recommended way of approaching REST APIs and if you haven’t already, we all highly recommend you to take a look at them. However, we’re about connecting everything, everywhere. Thus we recognize that there are a lot of APIs out there built in plain old Java code and a migration process is not something you can do overnight.
I recently had a customer wanting to build a simple UI to maintain additional filtering data associated to a defined “Contract” contained within API Manager. This code would have to run outside of the MuleSoft eco-system, as a service, within a JAVA Data Layer container environment.
My goal was to develop a very simple JAVA API Manager Client Access Example, whose concept prototype could be used as a basis to construct a necessary Mashup of API Manager Resources and Custom Client oriented resources.
It’s been a long time coming, but we’re glad it’s here. Oracle just announced the arrival of Java 8 – a long-anticipated update which includes the addition of lambda expressions and support for functional programming. Here’s a look at some of the new features and why they are important to engineering at MuleSoft.
Lambda expressions bring functional programming to the Java language.
Here at MuleSoft, we’ve had a lot of exciting things going on lately. With our new lean startup mountain climbing principles, MuleSoft summits wrapping up and the launch of RAML tooling, we’ve been pretty busy; not to mention we’ve been hiring talent like crazy!
We thought it might be a good idea to give our community an idea of what goes on internally – what we’re up to,
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.