Adapting to a crunch: the Mask Match story

I just got back from Strange Loop, and my favorite talk was Tech When the Sky is Falling: Tools for Crisis Response by Emma Ferguson and Colin Schimmelfing. I’m going to use this talk to illustrate one of the ideas in David Woods theory of graceful extensibility. The idea is that a system needs to deploy, mobilize, or generate capacity when it is at risk of saturation.

My silly doodle of the speakers

Back in March 2020, frontline hospital workers dealing with COVID-19 patients were running short on N95 face masks. Hospitals simply didn’t have enough masks to supply their workers. This shortage of masks is a great example of what Woods calls a crunch, where a system runs short on some resource that it needs. When a system is crunched like this, it needs to adapt. It has to make some sort of change in order to get more of that resource so that it can function properly.

Woods lists three methods for getting more of a resource. If you’ve prepared in advance by stockpiling resources, you can deploy those stockpiles. If you don’t have those extra resources on hand, but your larger network has resources to spare, you can mobilize your network to access those resources. Finally, if you can’t tap into your network to get those resources, your only option is to generate the resources you need. In order to generate resources, you need access to raw materials, and then you need to do work to transform those raw materials into the right resources.

In the case of the mask shortages, the hospitals did not have sufficient stockpiles of N95 masks on hand, so deploying wasn’t an option. It turns out that there were many American households that happened to have N95 masks sitting in storage, and many of those households were willing to donate these unused masks to healthcare workers. In theory, hospitals could mobilize this network of volunteers in order to get these masks to the frontline workers.

There was a problem, though: hospital administrators refused to accept donated N95 masks because of liability concerns. So, this wasn’t something the hospitals were going to do.

Workers wanted masks, and people wanted to donate, but hospital admins wouldn’t let them

Fortunately, there was a loophole: frontline workers could bring in their own masks. Now, the problem to be solved was: how do you get masks from donors who had masks to the workers who wanted them?

Emma and Colin needed to generate a new capability: a mechanism for matching up the donors with the healthcare workers. The raw materials that they initially used to generate this capability were Google Sheets and Gmail for coordinating among the volunteers.

And it worked! However, they quickly ran into a new risk of saturation. Google Sheets has a limit of 50 concurrent editors, and Gmail limits an email account to a maximum of 500 emails per day. And so, once again, the team had to generate a new capability that would scale beyond what Google Sheets and Gmail were capable of. They ended up building a system called Mask Match, by writing a Flask app that they deployed on Heroku, and using Mailgun for sending the emails.

My favorite part of this talk was when Emma Ferguson mentioned that they originally just wanted to pay Google in order to get the Google Sheets and Gmail limits increased (their GoFundMe campaign was quite successful, so getting access to money wasn’t a problem for them). However, they couldn’t figure out how to actually pay Google for a limit increase! This is a wonderful example of what Woods calls brittleness, where a system is unable to extend itself when it reaches its limits. Google is great at building robust systems, but their ethos of removing humans from the loop means that it’s more difficult for consumers of Google services to adapt them to unexpected, emergency scenarios.