TY - GEN
T1 - BatchDB
T2 - 2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017
AU - Makreshanski, Darko
AU - Giceva, Jana
AU - Barthels, Claude
AU - Alonso, Gustavo
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/5/9
Y1 - 2017/5/9
N2 - In this paper we present BatchDB, an in-memory database engine designed for hybrid OLTP and OLAP workloads. BatchDB achieves good performance, provides a high level of data freshness, and minimizes load interaction between the transactional and analytical engines, thus enabling real time analysis over fresh data under tight SLAs for both OLTP and OLAP workloads. BatchDB relies on primary-secondary replication with dedicated replicas, each optimized for a particular workload type (OLTP, OLAP), and a light-weight propagation of transactional updates. The evaluation shows that for standard TPC-C and TPC-H benchmarks, BatchDB can achieve competitive performance to specialized engines for the corresponding transactional and analytical workloads, while providing a level of performance isolation and predictable runtime for hybrid workload mixes (OLTP+OLAP) otherwise unmet by existing solutions.
AB - In this paper we present BatchDB, an in-memory database engine designed for hybrid OLTP and OLAP workloads. BatchDB achieves good performance, provides a high level of data freshness, and minimizes load interaction between the transactional and analytical engines, thus enabling real time analysis over fresh data under tight SLAs for both OLTP and OLAP workloads. BatchDB relies on primary-secondary replication with dedicated replicas, each optimized for a particular workload type (OLTP, OLAP), and a light-weight propagation of transactional updates. The evaluation shows that for standard TPC-C and TPC-H benchmarks, BatchDB can achieve competitive performance to specialized engines for the corresponding transactional and analytical workloads, while providing a level of performance isolation and predictable runtime for hybrid workload mixes (OLTP+OLAP) otherwise unmet by existing solutions.
UR - http://www.scopus.com/inward/record.url?scp=85021222972&partnerID=8YFLogxK
U2 - 10.1145/3035918.3035959
DO - 10.1145/3035918.3035959
M3 - Conference contribution
AN - SCOPUS:85021222972
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 37
EP - 50
BT - SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data
PB - Association for Computing Machinery
Y2 - 14 May 2017 through 19 May 2017
ER -