Friday, June 08, 2018 - 10:00 am–10:55 am
Sai Kiran Kanuri, LinkedIn
Abstract:
A system is called scalable if it manages to take additional users and requests without losing any noticeable performance. Scaling a data system involves significant movement and replication of data within a cluster. This can put considerable load on a system that is already running hot, affecting the service experience.
Some of the topics that I would touch upon include:
- Replication
- Data distribution
- Load balancing
- Cluster management
- Client Data
- Capacity Management
- Tuning
Sai Kiran Kanuri, LinkedIn
Data Chef at LinkedIn
Open Access Media
USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.
BibTeX
@conference {214977,
author = {Sai Kiran Kanuri},
title = {Scaling a Distributed Stateful System: A {LinkedIn} Case Study},
year = {2018},
publisher = {USENIX Association},
month = jun
}
author = {Sai Kiran Kanuri},
title = {Scaling a Distributed Stateful System: A {LinkedIn} Case Study},
year = {2018},
publisher = {USENIX Association},
month = jun
}