Brief announcement: A key-value map for massive real-time analytics

Dmitry Basin, Edward Bortnikov, Anastasia Braginsky, Guy Golan Gueta, Eshcar Hillel, Idit Keidar, Moshe Sulamy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationPODC 2016 - Proceedings of the 2016 ACM Symposium on Principles of Distributed Computing
Pages487-489
Number of pages3
ISBN (Electronic)9781450339643
DOIs
StatePublished - 25 Jul 2016
Event35th ACM Symposium on Principles of Distributed Computing, PODC 2016 - Chicago, United States
Duration: 25 Jul 201628 Jul 2016

Publication series

NameProceedings of the Annual ACM Symposium on Principles of Distributed Computing
Volume25-28-July-2016

Conference

Conference35th ACM Symposium on Principles of Distributed Computing, PODC 2016
Country/TerritoryUnited States
CityChicago
Period25/07/1628/07/16

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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