期刊名称：Bulletin of the Technical Committee on Data Engineering
出版年度：2012
卷号：35
期号：01
出版社：IEEE Computer Society
摘要：Traditionally, business applications have separated their data into an OLTP data store for high through-
put transaction processing and a data warehouse for complex query processing. This separation bears
severe maintenance and data consistency disadvantages. Two emerging hardware trends allow the con-
solidation of the two disparate workloads onto the same database state on one system: the increasing
main memory capacities of several terabytes per server and multi-threaded processing based on multi-
core parallelism. The prevalent data representation of hybrid OLTP&OLAP main memory database
systems is columnar in order to achieve best possible query execution performance for OLAP applica-
tions. In order to shield the OLTP transaction processing from long-running queries without costly lock-
ing/latching, all queries are executed on an arbitrarily recent snapshot of the data. The paper contrasts
several snapshotting techniques for columnar data (twin block, versioning) with the hardware-supported
shadow paging we employ in the HyPer system. While OLAP-query processing can rely mostly on colum-
nar scans, high OLTP throughput requirements necessitate index techniques for exact match and small
range queries. Despite the ever growing capacity, main memory is still a scare resource. Therefore, com-
pressing main memory resident databases is beneficial. The paper will devise techniques that achieve
good compression ratios without hurting the mission-critical OLTP throughput by adaptively separating
cold (i.e. immutable) data for aggressive compression from the hot (i.e. mutable) working set data that
remains uncompressed.
