Want to be a MuleSoft Champion? Join today!
At MuleSoft, we are very lucky to have a large developer community who have inspired us, driven us forward, and contributed to our growth and success in many ways. The people who make up this community are dedicated to the success and development of its members. One of the most dedicated community champions is Anirban Sen Chowdhary, Analyst Programmer in Hyderabad, India. He has contributed a great deal to help others use and get to know MuleSoft, while applying his own MuleSoft knowledge to a great position at a top global bank.
Learn more about MUnit and learn more about how to test Mule.
MUnit is a Mule application testing framework which allows you to build automated tests for your Mule integrations and API’s. MUnit is very well integrated with Anypoint Studio.
Various features available with Mule MUnit:
- Create and build Mule tests by writing Mule code.
- Create and build Mule tests by writing Java code.
- Verify Message Processor calls.
- Mock Message Processor.
Is it possible to create a conversational bot, fully functional, using Natural Language Processing (NLP), in minutes? Good news, it is.
In this post we will show you how to start from the ground up, giving you everything you need to create your own basic conversational bot, using 100% free accounts and software (yes, all this power for free).
The free accounts and software shown in this post have some limitations and it is designed for you to understand and learn the basics of:
MuleSoft’s lightweight Runtime Engine can be used to expose microservices and APIs on any IoT device. In this post, I will demonstrate how users can use MuleSoft and a Python script to create a simple API that lights up an LED bulb 10 times in a loop. To get started, please refer to the requirements, video tutorial, and steps below.
Raspberry Pi 3, LED Bulbs, resistors, jumper wires, Mule server, and a breadboard.
Mule IoT LED Project
Load balancing across multiple server instances is one of the amazing techniques and ways for optimizing resource utilization, maximizing throughput, and reducing latency to ensure high availability of servers in an environment where some concurrent requests are in millions from users or clients and appear in a very fast and reliable manner.
There is a lot of interest in how Mule supports emerging patterns like CQRS (Command Query Responsibility Segregation), so I wanted to create a series of blog posts discussing an insightful approach. Over the course of the series so far, we described the initial problem at hand and how to solve it using CQRS and API-led Connectivity. Next, we designed and implemented the synchronous Query API application followed by the implementation of the asynchronous Command API application with a composable API architecture.
As you might have read, Mule 3.8 includes a number of improvements regarding TLS. In this post, we will analyze the TLS environment prior to this release and explore all of the new enhancements in detail so that you can start taking advantage of them.
When MuleSoft engineering recently organized a two-day internal hackathon, our team of four:
Welcome to the final post in the three post series about batch improvements on Mule 3.8!
The last new feature we have is a simple one which comes quite handy when you need to read through logs. As you know, batch jobs are just programs processed in batch mode, and each time the job is triggered, a new job instance is created and tracked separately. Each of those instances is unique and therefore has a unique ID.