TY - GEN
T1 - Stronger semantics for low-latency geo-replicated storage
AU - Lloyd, Wyatt
AU - Freedman, Michael J.
AU - Kaminsky, Michael
AU - Andersen, David G.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - We present the first scalable, geo-replicated storage system that guarantees low latency, offers a rich data model, and provides “stronger” semantics. Namely, all client requests are satisfied in the local datacenter in which they arise; the system efficiently supports useful data model abstractions such as column families and counter columns; and clients can access data in a causally-consistent fashion with read-only and write-only transactional support, even for keys spread across many servers. The primary contributions of this work are enabling scalable causal consistency for the complex column-family data model, as well as novel, non-blocking algorithms for both read-only and write-only transactions. Our evaluation shows that our system, Eiger, achieves low latency (single-ms), has throughput competitive with eventually-consistent and non-transactional Cassandra (less than 7% overhead for one of Facebook's real-world workloads), and scales out to large clusters almost linearly (averaging 96% increases up to 128 server clusters).
AB - We present the first scalable, geo-replicated storage system that guarantees low latency, offers a rich data model, and provides “stronger” semantics. Namely, all client requests are satisfied in the local datacenter in which they arise; the system efficiently supports useful data model abstractions such as column families and counter columns; and clients can access data in a causally-consistent fashion with read-only and write-only transactional support, even for keys spread across many servers. The primary contributions of this work are enabling scalable causal consistency for the complex column-family data model, as well as novel, non-blocking algorithms for both read-only and write-only transactions. Our evaluation shows that our system, Eiger, achieves low latency (single-ms), has throughput competitive with eventually-consistent and non-transactional Cassandra (less than 7% overhead for one of Facebook's real-world workloads), and scales out to large clusters almost linearly (averaging 96% increases up to 128 server clusters).
UR - http://www.scopus.com/inward/record.url?scp=85076711749&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076711749&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2013
SP - 313
EP - 328
BT - Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2013
PB - USENIX Association
T2 - 10th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2013
Y2 - 2 April 2013 through 5 April 2013
ER -