Name
Abstract | ||
|---|---|---|
Over the last two releases SQL Server has integrated two specialized engines into the core system: the Apollo column store engine for analytical workloads and the Hekaton in-memory engine for high-performance OLTP workloads. There is an increasing demand for real-time analytics, that is, for running analytical queries and reporting on the same system as transaction processing so as to have access to the freshest data. SQL Server 2016 will include enhancements to column store indexes and in-memory tables that significantly improve performance on such hybrid workloads. This paper describes four such enhancements: column store indexes on in-memory tables, making secondary column store indexes on disk-based tables updatable, allowing B-tree indexes on primary column store indexes, and further speeding up the column store scan oper ator. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.14778/2824032.2824071 | Proceedings of The Vldb Endowment |
Keywords | Field | DocType |
In-memory OLTP,column store,OLAP,operational analytics,real-time analytics,hybrid transactional and analytical processing | Transaction processing,Sql server,Real time analytics,Computer science,Online transaction processing,Analytics,Online analytical processing,Operating system,Database | Journal |
Volume | Issue | ISSN |
8 | 12 | 2150-8097 |
Citations | PageRank | References |
12 | 0.50 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Per-Åke Larson | 1 | 12 | 0.50 |
| Adrian Birka | 2 | 18 | 0.93 |
| Eric N. Hanson | 3 | 917 | 376.11 |
| Weiyun Huang | 4 | 12 | 0.84 |
| Michal Nowakiewicz | 5 | 43 | 2.05 |
| Vassilis Papadimos | 6 | 405 | 17.65 |