Author :
Auradkar, Aditya ; Botev, Chavdar ; Das, Shirshanka ; De Maagd, D. ; Feinberg, Alex ; Ganti, Phanindra ; Gao, Lei ; Ghosh, Bhaskar ; Gopalakrishna, Kishore ; Harris, Brendan ; Koshy, Joel ; Krawez, Kevin ; Kreps, Jay ; Lu, Shi ; Nagaraj, Sunil ; Narkhede,
Abstract :
Linked In is among the largest social networking sites in the world. As the company has grown, our core data sets and request processing requirements have grown as well. In this paper, we describe a few selected data infrastructure projects at Linked In that have helped us accommodate this increasing scale. Most of those projects build on existing open source projects and are themselves available as open source. The projects covered in this paper include: (1) Voldemort: a scalable and fault tolerant key-value store, (2) Data bus: a framework for delivering database changes to downstream applications, (3) Espresso: a distributed data store that supports flexible schemas and secondary indexing, (4) Kafka: a scalable and efficient messaging system for collecting various user activity events and log data.
Keywords :
database indexing; distributed databases; public domain software; social networking (online); software fault tolerance; storage management; Data bus; Espresso; Kafka; LinkedIn; Voldemort; activity events; core data sets; data infrastructure projects; database change delivery; distributed data store; fault tolerant key-value store; flexible schemas; log data; messaging system; open source projects; request processing requirements; secondary indexing; social networking sites; Companies; Indexes; LinkedIn; Pipelines; Routing; Servers;
