TY - GEN
T1 - Brief Announcement: A Key-Value Map for Massive Real-Time Analytics
T2 - 35th ACM Symposium on Principles of Distributed Computing, PODC 2016
AU - Basin, Dmitry
AU - Bortnikov, Edward
AU - Braginsky, Anastasia
AU - Gueta, Guy Golan
AU - Hillel, Eshcar
AU - Keidar, Idit
AU - Sulamy, Moshe
N1 - Publisher Copyright: © 2016 ACM.
PY - 2016/7/25
Y1 - 2016/7/25
N2 - Modern big data processing platforms employ huge in-memory key-value (KV-) maps. Their applications simultaneously drive high-rate data ingestion and large-scale analytics. These two scenarios expect KV-map implementations that scale well with both real-time updates and massive atomic scans triggered by range queries. However, today's state-of-the art concurrent KV-maps fall short of satisfying these re- quirements - they either provide only limited or non-atomic scans, or severely hamper updates when scans are ongoing. We present KiWi, the first atomic KV-map to efficiently support simultaneous massive data retrieval and real-time access. The key to achieving this is treating scans as first class citizens, whereas most existing concurrent KV-maps do not provide atomic scans, and some others add them to existing maps without rethinking the design anew.
AB - Modern big data processing platforms employ huge in-memory key-value (KV-) maps. Their applications simultaneously drive high-rate data ingestion and large-scale analytics. These two scenarios expect KV-map implementations that scale well with both real-time updates and massive atomic scans triggered by range queries. However, today's state-of-the art concurrent KV-maps fall short of satisfying these re- quirements - they either provide only limited or non-atomic scans, or severely hamper updates when scans are ongoing. We present KiWi, the first atomic KV-map to efficiently support simultaneous massive data retrieval and real-time access. The key to achieving this is treating scans as first class citizens, whereas most existing concurrent KV-maps do not provide atomic scans, and some others add them to existing maps without rethinking the design anew.
UR - http://www.scopus.com/inward/record.url?scp=84984689232&partnerID=8YFLogxK
U2 - 10.1145/2933057.2933061
DO - 10.1145/2933057.2933061
M3 - منشور من مؤتمر
T3 - 25-28-July-2016
SP - 487
EP - 489
BT - 35th ACM Symposium on Principles of Distributed Computing, PODC 2016
Y2 - 25 July 2016 through 28 July 2016
ER -