@inproceedings{bfe3072d91e344689d4aa214d8dbe9bc,
title = "Brief announcement: A key-value map for massive real-time analytics",
abstract = "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.",
author = "Dmitry Basin and Edward Bortnikov and Anastasia Braginsky and Gueta, {Guy Golan} and Eshcar Hillel and Idit Keidar and Moshe Sulamy",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 35th ACM Symposium on Principles of Distributed Computing, PODC 2016 ; Conference date: 25-07-2016 Through 28-07-2016",
year = "2016",
month = jul,
day = "25",
doi = "10.1145/2933057.2933061",
language = "الإنجليزيّة",
series = "Proceedings of the Annual ACM Symposium on Principles of Distributed Computing",
publisher = "Association for Computing Machinery",
pages = "487--489",
booktitle = "PODC 2016 - Proceedings of the 2016 ACM Symposium on Principles of Distributed Computing",
address = "الولايات المتّحدة",
}