Paper Info
Title
Squall: Fine-Grained Live Reconfiguration for Partitioned Main Memory Databases
Abstract
For data-intensive applications with many concurrent users, modern distributed main memory database management systems (DBMS) provide the necessary scale-out support beyond what is possible with single-node systems. These DBMSs are optimized for the short-lived transactions that are common in on-line transaction processing (OLTP) workloads. One way that they achieve this is to partition the database into disjoint subsets and use a single-threaded transaction manager per partition that executes transactions one-at-a-time in serial order. This minimizes the overhead of concurrency control mechanisms, but requires careful partitioning to limit distributed transactions that span multiple partitions. Previous methods used off-line analysis to determine how to partition data, but the dynamic nature of these applications means that they are prone to hotspots. In these situations, the DBMS needs to reconfigure how data is partitioned in real-time to maintain performance objectives. Bringing the system off-line to reorganize the database is unacceptable for on-line applications. To overcome this problem, we introduce the Squall technique for supporting live reconfiguration in partitioned, main memory DBMSs. Squall supports fine-grained repartitioning of databases in the presence of distributed transactions, high throughput client workloads, and replicated data. An evaluation of our approach on a distributed DBMS shows that Squall can reconfigure a database with no downtime and minimal overhead on transaction latency.
Year
DOI
Venue
2015
10.1145/2723372.2723726
ACM SIGMOD Conference
Field
DocType
Citations 
Transaction processing,Concurrency control,Load balancing (computing),Computer science,Online transaction processing,Database transaction,Distributed transaction,Downtime,Control reconfiguration,Database,Distributed computing
Conference
17
PageRank 
References 
Authors
0.60
26
6
Name
Order
Citations
PageRank
Aaron J. Elmore135234.03
Vaibhav Arora2655.55
Rebecca Taft3494.64
Andrew Pavlo41614122.03
Divyakant Agrawal582011674.75
Amr El Abbadi667671569.95