pyFF is widely used in our community to provide Discovery and Metadata Query services. This topic is about some optimizations of pyFF for operations.

When processing the eduGAIN metadata, pyFF memory usage balloons to the gigabytes, hereby inflicting some extra cost when running in procured VM-s like AWS. The startup/restart process speed, and service behavior while being started/restarted may also be improved. In particular, the service should never throw 5xx errors while in a normal startup/shutdown process. 

The goal of this project is to optimize pyFF memory consumption and (re-)start behavior. 

For the memory consumption, the underlying XML processing library may be swapped, or the memory-intensive part of the processing may be done on a short-lived cheap VM and the resulting in-memory representation serialized, transferred to the production instances and de-serialized. 

For the in-(re)start behavior it must be established what is the right way of configuring pyFF so that it won’t take queries while its internal database is still incomplete.