What happens when an application designed for a small user base needs to be scaled up and moved to the cloud?
It needs to live in a distributed environment: responding to an appropriate number of concurrent user requests per second and ensuring users find the application reliable.
Though Jakarta EE and Eclipse MicroProfile can help with reliable clustering, there is no standard API in Jakarta EE that defines how clustering should work currently. This might change in the future, but in the meantime, this gap must be filled by DevOps engineers.
In this blog, we will cover 10 technical strategies to deal with clustering challenges when developing Jakarta EE and MicroProfile for cloud environments.
At Payara Services, we have long been advocates of the benefits of using DevOps practices not only in the development of our products (like Payara Server & Payara Micro), but also in the core of our expert advice to our user base with our blog containing arguments for using DevOps practices, details of DevOps tools and new developments that benefit it.
As part of the Payara Enterprise Support services that we deliver to customers on a daily basis, giving expert advice and clarifying how the internals of the products of the Payara Platform work is one of the most common scenarios we encounter. Here's' a story about the advice we gave to one of our customers regarding the behavior of JDBC Connection Pools in Payara Server.
One of the biggest challenges when developing applications for the web is to understand how they need to be fine-tuned when releasing them into a production environment. This is no exception for Java Enterprise applications deployed on a Payara Server installation.
Running a Payara Server setup is simple: download the current distribution suited for your needs (full, web); head to the /bin folder and start the default domain (domain1)! However, keep in mind that this default domain is tailored for development purposes (a trait inherited from GlassFish Server Open Source). When developing a web application, it’s better to quickly code features, deploy them quickly, test it, un-deploy (or redeploy) it and continue with the next set of features until a stable state is reached.