By Revenue Platform on
Learn how our Revenue Platform team re-built Twitter’s Ad Platform Architecture for the future.
By Peilin Yang on
Twitter has recently built a streaming logging pipeline that facilitates the Home Timeline ranking model training. The new pipeline not only shortens the latency but also improves the resiliency.
By Daniel Schonberg and Todd Segal on
Twitter’s traffic has had a dramatic increase over the course of 2020. South American traffic peaked at roughly 3x our available capacity. We describe how we survived.
By Hongjian Wang on
We explore state-of-the-art metric learning ML techniques recently launched to production which improve ad relevance, helping Twitter better serve the right ad to the right user at the right time.
By Nico Tonozzi and Dumitru Daniliuc on
The challenges the Search Infrastructure team at Twitter went through in order to reduce the search indexing latency to one second.
By Deepak Dilipkumar and Corbin Betheldo on
Twitter discusses a simple machine learning model that prioritizes ad requests based on how much revenue we expect to make from them.
By Megan Kanne on
Twitter shares tips for deleting data in a microservices architecture using an erasure pipeline.
By Cong Wang and Dan Luu on
This outlines the whole debugging process of a Linux kernel bug we found during an incident and demonstrates how we worked with Linux kernel open source community.
By Bryce Anderson and Ruben Oanta on
As parts of the Twitter application grow, we can scale demands on capacity by adding more instances or replicas to a respective service cluster (i.e., horizontal scaling)
By Matt Gross and Dean Hiller on
A design pattern that can be applied to all service types to increase team productivity.