Open Access System for Information Sharing

Login Library

 

Conference
Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Asymmetric-Partition Replication for Highly Scalable Distributed Transaction Processing in Practice

Title
Asymmetric-Partition Replication for Highly Scalable Distributed Transaction Processing in Practice
Authors
HAN, WOOK SHINLEE, JUCHANGLEE, HYEJEONGKO, SEONGYUNKIM, KYU HWANAndrei, MihneaKeller, Fredrich
Date Issued
4-Sep-2020
Publisher
VLDB Endowment
Abstract
Database replication is widely known and used for high availability or load balancing in many practical database systems. In this paper, we show how a replication engine can be used for three important practical cases that have not previously been studied very well. The three practical use cases include: 1) scaling out OLTP/OLAP-mixed workloads with partitioned replicas, 2) efficiently maintaining a distributed secondary index for a partitioned table, and 3) efficiently implementing an online re-partitioning operation. All three use cases are crucial for enabling a high performance shared-nothing distributed database system. To support the three use cases more efficiently, we propose the concept of asymmetric-partition replication, so that replicas of a table can be independently partitioned regardless of whether or how its primary copy is partitioned. In addition, we propose the optimistic synchronous commit protocol which avoids the expensive two-phase commit without sacrificing transactional consistency. The proposed asymmetric-partition replication and its optimized commit protocol are incorporated in the production versions of the SAP HANA in-memory database system. Through extensive experiments, we demonstrate the significant benefits that the proposed replication engine brings to the three use cases.
URI
http://oasis.postech.ac.kr/handle/2014.oak/104131
ISSN
2150-8097
Article Type
Conference
Citation
46th Int'l Conf. on Very Large Data Bases (VLDB) / Proc. the VLDB Endowment (PVLDB), page. 2150 - 8097, 2020-09-04
Files in This Item:
There are no files associated with this item.

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher

한욱신HAN, WOOK SHIN
Grad. School of AI
Read more

Views & Downloads

Browse