Natto: Providing Distributed Transaction Prioritization for High-Contention Workloads

6Citations
Citations of this article
8Readers
Mendeley users who have this article in their library.
Get full text

Abstract

This paper introduces Natto, a geo-distributed database system that supports transaction prioritization. Instead of having each shard process transactions in their arrival order, Natto leverages network measurements to estimate the transaction arrival time at each shard, and assigns a timestamp to the transaction based on its arrival time to the furthest shard. These timestamps establish a global ordering of transactions, and introduces opportunities to selectively abort pending low-priority transactions that conflict with a high-priority transaction, or even preempt transactions that are already partially prepared. Our experiments on both Microsoft Azure and a local cluster show that Natto's tail latency for high-priority transactions are significantly lower than the tail latencies of Carousel and TAPIR, which are the current state-of-the-art in geo-distributed transaction processing systems.

References Powered by Scopus

Benchmarking cloud serving systems with YCSB

2958Citations
N/AReaders
Get full text

Priority Inheritance Protocols: An Approach to Real-Time Synchronization

1412Citations
N/AReaders
Get full text

Calvin: Fast distributed transactions for partitioned database systems

428Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Fine-Grained Re-Execution for Efficient Batched Commit of Distributed Transactions

3Citations
N/AReaders
Get full text

Cloud Actor-Oriented Database Transactions in Orleans

1Citations
N/AReaders
Get full text

Better Clients, Less Conflicts: Hyperledger Fabric Conflict Avoidance

1Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Yang, L., Yan, X., & Wong, B. (2022). Natto: Providing Distributed Transaction Prioritization for High-Contention Workloads. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 715–729). Association for Computing Machinery. https://doi.org/10.1145/3514221.3526161

Readers' Seniority

Tooltip

Lecturer / Post doc 2

67%

Researcher 1

33%

Readers' Discipline

Tooltip

Computer Science 3

75%

Engineering 1

25%

Save time finding and organizing research with Mendeley

Sign up for free