The global lockdown in 2020 created long, unsafe lines at essential stores, sparking the need for a solution to manage wait times.
The project's core idea was to use a map interface and user-submitted data to provide real-time estimates of wait times at local stores.
The initial version of the application was built with Python and Flask and hosted on a Raspberry Pi 3B for local testing among friends.
To handle viral traffic growth, the architecture was moved from a single Raspberry Pi to multiple VPS instances on OVH and Digital Ocean.
Redis and its geospatial index commands were critical for efficiently storing, querying, and managing location points and distances on the map.
The entire project was run on a very low budget, costing only about $25 per month for cloud services to support millions of user sessions.
The open-source project successfully served over a million users and highlighted the power of community collaboration in solving real-world problems.
User traffic on the map showed a strong correlation with the geographical spread of the pandemic, starting in Italy and moving to the Americas.
The project could be improved with better code optimization instead of relying heavily on caching, and its technology could be adapted for other use cases like conferences.
50:06Jens Happe
47:16Maxime Beugnet
28:12Josip Stuhli
30:25Arto Liukkonen
32:56Laurie Voss
55:39Germán Álvarez
22:32George Asafev
43:11Thomas Reiter
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Jobs that call for the skills explored in this talk.
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