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.

The goal of the activity is to improve the performance of the existing pyFF in regard to memory consumption to enable metadata processing on machines with few resources.

The SAML metadata appliance pyFF(https://pyff.io/) is widely used in the GÉANT community. PyFF - short for python Federation Feeder - is a simple, yet complete SAML metadata aggregator.

The source code is available on GitHub: https://github.com/IdentityPython/pyFF

Although the tool itself is pretty small and most task can be performed with few resources, the process of processing SAML metadata requires a lot of memory. For this reason, the behaviour of pyFF in terms of memory consumption shall be investigated. Perhaps the opportunity exists to improve the XML processing so that the consumption can be reduced. In the best case pyFF can then run on much smaller servers than before. This would, among other things, make it easier to use external servers, as this could drastically reduce costs.
Furthermore, it is to be examined whether the application can be modularized. In this way, different parts of the application could be encapsulated. This provides the capability to run resource intensive tasks on different servers. An outsourcing of the meta data processing to a serverless architecture at low costs is conceivable. This approach can be done with or without the previously described reduction of memory consumption.

Grant benefits to NRENs using pyFF:

• Reduce resources for running pyFF
• Reduce cost for hosted servers
• Enable separating resource extensive parts of processing metadata to cloud services

• It might turn out that it is not possible to reduce memory consumption much

• The activity does not affect data protection or privacy

The activity is done once:

• An investigation of memory consumption is conducted
• Potential memory hot spots are identified
• If there are hot spots, solutions are planned and implemented
• pyFF is split into multiple modules to externalize the metadata processing
• New implementation is committed to the official repository

After the end of the activity, the source code created will become part of the official repository and can then be used by every NREN interested.

tbd