PWL June 2021 Meetup: "Facebook's Tao"
Distributed Data Store for the Social Graph
Links for the Session Please use the links below to join the session. Zoom - https://zoom.us/j/93573252736?pwd=bEwyaVdjSXp6Yk11bEM1OGNHS2thQT09 Youtube - https://www.youtube.com/w… more
We will discuss Facebook’s seminal paper on TAO - their distributed data store for their social graph. TAO is a geographically distributed data store that provides efficient and timely access to the social graph for Facebook’s demanding workload using a fixed set of queries. It is deployed at Facebook, replacing memcache for many data types that fit its model. The system runs on thousands of machines, is widely distributed, and provides access to many petabytes of data. TAO can process a billion reads and millions of writes each second.
Paper Link: https://github.com/papers-we-love/papers-we-love/blob/master/datastores/tao-facebook-distributed-datastore.pdf
Presenter: Rohit Raveendran (https://www.linkedin.com/in/rohit-raveendran-1529b1131)
Rohit is a Principal Architect at Capillary Technologies, and works across platform engineering, systems design, and infrastructure engineering. Having been introduced to computers by the local LUG, he has transitioned from an enthusiast and a computational fluid dynamics practitioner to a software developer with a keen interest in Infrastructure Engineering. Cycling helps him keep away from the laptop every now and then.
Format: We will have about 45-60 mins for the paper walkthrough, and keep rest of the time open for Q&A. The participants are requested to read the paper before the session to keep the discussion interactive, and engaging.
Participation: Zoom Link - https://zoom.us/j/93573252736?pwd=bEwyaVdjSXp6Yk11bEM1OGNHS2thQT09
YouTube URL - https://www.youtube.com/watch?v=vG1XHzryh9A