This is part four of my five-part series documenting high scalability in Mule 4. If you haven’t read part one, Scale your APIs with Mule 4, part two, Reactive programming in Mule 4, and part three, Thread management and auto-tuning in Mule 4, check them out! This post will cover streaming in Mule 4.
My first two posts in this Mule 4 blog series were on scaling your APIs and reactive programming in our newest version of Mule runtime engine. This blog dives into thread management and auto-tuning.
Mule 4 uses reactive programming to enhance scalability so our developers can deliver smooth experiences for their users.
Mule 4 addresses vertical scalability with a radically different design using reactive programming at its core.
One month into 2019 and we’ve already covered a wide range of content – from JSON logging in Mule 4 to work automation and API strategies. To provide you with the best posts to read if you’ve missed, here are the five most popular blog posts in January 2019.
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!
We’re thrilled to announce the broad release of Mule 4 courses in our award-winning, in-demand, on-demand MuleSoft.U portfolio:
Developers play a crucial role in implementing an API-led approach for integration of applications, data, and devices to drive digital transformation. With the release of Mule 4, our goal is to empower you to cycle through projects with speed and velocity.
By now, you have probably heard a lot about how Mule 4 makes it easier to leverage the power of Mule in your integrations. In fact, many of our customers are already adopting Mule 4––providing great feedback about how they can on-ramp new developers much faster.
In my last article, we saw how we can install Mule 4 on a Raspberry Pi. And while Mule 4 runs perfectly fine on the latest Raspberry Pi’s, you may find yourself in need of a more scalable solution than a bare-metal runtime installation. In this post, we will look at running Mule in a Docker container on Raspberry Pi.
