PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 1, 2017Proceedings of the VLDB Endowment188 citations

An empirical evaluation of in-memory multi-version concurrency control

View Full Paper
YWYingjun WuJAJoy ArulrajJLJiexi Lin

Key Points

  • This research aims to evaluate the performance of multi-version concurrency control (MVCC) in modern database systems.
  • Conducted an empirical study of MVCC design decisions including concurrency control protocol and version storage.
  • Implemented state-of-the-art variants of MVCC in an in-memory DBMS.
  • Evaluated the system's performance under OLTP workloads.
  • Identified various fundamental bottlenecks in the MVCC design choices for in-memory settings.
  • Found that synchronization overhead can negate the advantages of using multiple versions in a multi-core environment.

Abstract

Multi-version concurrency control (MVCC) is currently the most popular transaction management scheme in modern database management systems (DBMSs). Although MVCC was discovered in the late 1970s, it is used in almost every major relational DBMS released in the last decade. Maintaining multiple versions of data potentially increases parallelism without sacrificing serializability when processing transactions. But scaling MVCC in a multi-core and in-memory setting is non-trivial: when there are a large number of threads running in parallel, the synchronization overhead can outweigh the benefits of multi-versioning. To understand how MVCC perform when processing transactions in modern hardware settings, we conduct an extensive study of the scheme's four key design decisions: concurrency control protocol, version storage, garbage collection, and index management. We implemented state-of-the-art variants of all of these in an in-memory DBMS and evaluated them using OLTP workloads. Our analysis identifies the fundamental bottlenecks of each design choice.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Wu et al. (2017) studied this question.

synapsesocial.com/papers/69df2406d85e58e37b7a162dhttps://doi.org/10.14778/3067421.3067427
Ask AI
Helpful
Bookmark
Share
View Full Paper