Brief Announcement: A Key-Value Map for Massive Real-Time Analytics: 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 publication35th ACM Symposium on Principles of Distributed Computing, PODC 2016
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

Name25-28-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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