More Mule customers are adopting MuleSoft Runtime Fabric. This mini-series covers several Runtime Fabric topics — including how to install, configure, test, and troubleshoot Runtime Fabric. Part one of the series will cover the Runtime Fabric installation on AWS.
The AWS Runtime Fabric install process is different from the traditional software install. With the traditional software install, users start with provisioning of the servers that will host the software.
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I’m excited to announce our new release of Anypoint Runtime Fabric which adds support for running on Amazon Elastic Kubernetes Service (EKS) and Azure Kubernetes Service (AKS). This release allows your operations teams to take advantage of your cloud provider’s managed Kubernetes services to run Mule APIs and integrations with a lower infrastructure footprint and with greater control.
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Thank you to everyone who participated in the first-ever MuleSoft Hackathon! It was fantastic seeing all of your projects and the creativity you brought to our Community using MuleSoft. After careful deliberation and reviews by internal and external judges, we are happy to announce our top three winners, as well as 10 runner-ups.
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MuleSoft provides two types of load balancers, shared load balancer, and dedicated load balancer to distribute API traffic deployed on the MuleSoft cloud workers. Shared load balancer is multi-tenant and shared with other customers. Dedicated load balancers enable you to deploy and configure one or more custom load balancers within your Anypoint Virtual Private Cloud (Anypoint VPC). The aim of load balancing is to optimize the use of API instances available, maximize throughput,
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Observability is a comprehensive set of capabilities around logging, analytics, monitoring, troubleshooting, and measurement of a system. Here the term system is broadly used to indicate platform, runtimes, and applications. Observability plays a consequential role in providing the real-time pulse of a system. Lacking observability in a system would be similar to flying an aircraft without a fuel indicator.
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One of the most valuable characteristics of DataWeave is that it is a functional programming language. This means it is dynamically able to solve problems with various approaches — one being currying, which is a common feature of functional programming languages like Haskel (from where it derives), and JavaScript.
In this post, I’ll explain what currying is and how to write curried functions in DataWeave.
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For the last few decades, application and operational teams have been preaching loudly about monitoring and alerting capabilities to improve system resiliency. It is only with COVID-19, however, that these capabilities have shifted from “luxury” items to “absolutely necessary” capabilities to ensure business continuity.
As businesses take the next steps to refine their monitoring and alerting capabilities, it is critical that all stakeholders understand and utilize the same frameworks and metrics.
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Anypoint Runtime Fabric is a container service that automates the deployment and orchestration of Mule applications and API gateways. Runtime Fabric runs within a customer-managed infrastructure on Amazon Web Services (AWS), Microsoft Azure, virtual machines (VMs), and bare-metal servers.
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Testing is an essential part of the software development process used to ensure code quality, but the reality is that it can be a daunting and arduous task — especially in larger applications. This can make it difficult for some users to know where to begin when they’re ready to test their Mule applications in MUnit.
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When first released, Mule 4 introduced a brand new reactive execution engine and threading models. Benefits include high scalability, back pressure capabilities, and auto-tuning.
As part of Mule 4.3, we reiterated on these and made further improvements. More specifically, the three thread pools were combined into a single thread pool. Before jumping into the details, let’s quickly summarize how the threading models were set up prior to this release.
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