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PM aware storage engine for MongoDB

Published: 04 June 2018 Publication History

Abstract

With the maturity of Persistant Memories (PM) such as storage class memory technologies, e.g., STT-MRAM, PCM, ReRAM and 3DXpoint, we expect to see practical implementation of data structures, data stores and databases for use-cases such as IoT, mobile, and cloud. We developed a PM-aware storage engine for MongoDB which leverages PM hardware capabilities such as byte addressability and persistency. With our storage engine we see improved latency, less write amplification, less capacity and simpler implementation due to the fact that some code paths become unnecessary compared to past implementations.

References

[1]
2018. Atomicity and Transactions. MongoDB, Inc. https://docs.mongodb.com/manual/core/write-operations-atomicity/
[2]
2018. DB-Engines Ranking. DB-Engines. https://db-engines.com/en/ranking
[3]
Stephen M. Rumble, Ankita Kejriwal, and John K. Ousterhout. 2014. Log-structured memory for DRAM-based storage. In Proceedings of the 12th USENIX conference on File and Storage Technologies, FAST 2014, Santa Clara, CA, USA, February 17--20, 2014. 1--16.

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cover image ACM Conferences
SYSTOR '18: Proceedings of the 11th ACM International Systems and Storage Conference
June 2018
144 pages
ISBN:9781450358491
DOI:10.1145/3211890
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2018

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Author Tags

  1. Database Architecture
  2. Storage Class Memory
  3. Storage Engine

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SYSTOR '18
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Overall Acceptance Rate 108 of 323 submissions, 33%

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