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Suppose you need to implement a Jira Cloud app with a backend written in Java that should be capable of executing both long-running calculation-heavy issue processing jobs and lightweight requests from different Jira users in parallel. And suppose that this backend needs a data persistence layer with the following properties:
Sure enough you came across some of these requirements in your projects but what if you need to fulfill all of them at the same time? In Boosting Jira Cloud app development with Apache Ignite we show how Apache Ignite distributed database combined with Spring Boot framework can be used to build services with outlined properties, including our own service Alliedium AIssistant Cloud app built as Jira Cloud app that leverages machine learning (ML) to make Jira issue processing easier. This choice of technologies has proven to be a practically viable solution that runs equally well on both AWS EKS as our production environment and our local Kubernetes cluster deployed via Kubespray used as our development environment. Moreover, we use Apache Ignite for both data persistence and fast in-memory issue processing via distributed jobs:
Besides having all the properties listed above, Apache Ignite:
In Boosting Jira Cloud app development with Apache Ignite paper we have demonstrated how Apache Ignite and Atlassian Connect Spring Boot can be integrated to serve as a base for a scalable distributed backend. However, many interesting challenges when building distributed services and the ways to overcome them we left out of the scope of this paper on purpose as unrelated directly to Jira and Jira apps. Thus we still have a lot to share in a few subsequent papers.
Head of Modelling And Analytics