![]() Using the Google Cloud Tasks API, you can submit HTTP requests to a queue, and Google Cloud Tasks will make sure these requests get executed eventually. Google however provides integration with another product called Google Cloud Tasks. Google App Engine by itself doesn’t allow processes to execute in the background. The fourth constraint was the lack of support for background executions. The state of each ticket is stored as a file in that storage. ![]() For simplicity’s sake, I picked Google Cloud Storage. To achieve the same result with Google App Engine, I needed to pick a persistence mechanism. In a typical servlet deployment, there are many options to achieve this: the service can use the Java Heap to store the ticket information or use a database. The REST API of a computing service has to give a “ticket” number back to the client when a process starts, and provide a way for this client to observe the progress of that ticket, all the way to the completion. The computation might take minutes, even hours. To effectively support computing services, a REST API cannot make an HTTP client “wait” for the completion of this computing. Google App Engine doesn’t provide a way to “remember” information between two HTTP calls. The third constraint was the lack of persistence. To support Google App Engine, I had to make the discovery of these configuration files pluggable so that they can be read from the WEB-INF directory of the servlet instead of a directory external to that servlet. The Admin Backend exposes REST services for the validation of Bearer tokens. To authorize users, the services developed with the IVAAP Data Backend SDK need to know how to validate Bearer tokens, and this validation requires the knowledge of the host name of the IVAAP Admin Backend. The IVAAP Data Backend SDK typically doesn’t make much use of the local file system, except at startup when it reads its service configuration. Google App Engine doesn’t let developers access the local files of the virtual machine where an application is deployed. The second constraint was the lack of a local file system. The main change was to modify the concrete implementation ofĬom.Īnd wait on a instead of calling (). Supporting the synchronous requirements of Google App Engine didn’t take much time. Google App Engine doesn’t support asynchronous servlets and the IVAAP servlet shipped with the SDK is strictly asynchronous. The first constraint was the lack of support for asynchronous servlets. This “infinite” scalability comes with quite a few constraints, and I was curious to find out whether services developed with the IVAAP Data Backend SDK could live within these constraints (spoiler alert: it can). It was the first product that allowed servlet deployments that scale automatically, without having to worry about the underlying infrastructure of virtual machines. Google App Engine is a bit of a pioneer of cloud-based deployments. One of these options is Google App Engine. ![]() Our customers are rapidly moving to the cloud, and while VMs are often a practical choice, other options are sometimes available. When we tested this service, we didn’t deploy it to IVAAP’s cluster it was deployed as a standalone application, as a servlet, on a virtual machine (VM).ĭeploying Apache Tomcat on a virtual machine is “old school”. In a nutshell, this service computes a grid surface based upon the positions of a top across the wells of a project. For example, as part of IVAAP 2.8, we worked on a gridding service. While the Data Backend SDK is traditionally used to serve data, it is also a good candidate when it comes to developing non-data-related services. Practically, it means that a REST service developed on your PC using your favorite IDE and deployed locally to Apache Tomcat will run without changes on IVAAP’s Play cluster. One of the productivity features of the IVAAP Data Backend SDK is that the services developed with this SDK are container-agnostic.
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