We elaborate on the challenges involved in conducting software deployments for our distributed database and share our approach to solving them.

“Adaptive quality factor” is a technique used to make the ad server resilient and scalable, and at the same time achieve revenue optimality.

We explore lessons we learned while adding strong consistency to Manhattan and describe several problems that had to be solved along the way (implementing TTLs in a strongly consistent manner, doing distributed log truncations).

The design, architecture and implementation of Twitter’s failure testing framework.

At Twitter, we strive to prepare for sustained traffic as well as spikes - some of which we can plan for, some of which comes at unexpected times or in unexpected ways. To help us prepare for these varied types of traffic, we continuously run tests against our infrastructure to ensure it remains a scalable and highly available system.

Twitter is a global real-time communications platform that processes many billions of events every day. Aggregating these events in real time presents a massive challenge of scale. Classic time-series applications include site traffic, service health, and user engagement monitoring; these are increasingly complemented by a range of analytics products and features such as Tweet activity, Followers, and Twitter Cards that surface aggregated time-series data directly to end users, publishers, and advertisers.

Twitter has unique and intensive storage requirements. We built a database, called Manhattan, that meets those requirements and allows self-service access for our engineers.