This work aims at reducing the main-memory footprint in high performance hybrid OLTP & OLAP databases, while retaining high query performance and transactional throughput. For this purpose, an innovative compressed columnar storage format for cold data, called Data Blocks is introduced. Data Blocks further incorporate a new light-weight index structure called Positional SMA that narrows scan ranges within Data Blocks even if the entire block cannot be ruled out. To achieve highest OLTP performance, the compression schemes of Data Blocks are very light-weight, such that OLTP transactions can still quickly access individual tuples. This sets our storage scheme apart from those used in specialized analytical databases where data must usually be bit-unpacked. Up to now, high-performance analytical systems use either vectorized query execution or just-in-time (JIT) query compilation. The fine-grained adaptivity of Data Blocks necessitates the integration of the best features of each approach by an interpreted vectorized scan subsystem feeding into JIT-compiled query pipelines. Experimental evaluation of HyPer, our full-fledged hybrid OLTP & OLAP database system, shows that Data Blocks accelerate performance on a variety of query workloads while retaining high transaction throughput.

Memory-Resident Database Management Systems (MRDBMS) have to be optimized for two resources: CPU cycles and memory bandwidth. To optimize for bandwidth in mixed OLTP/OLAP scenarios, the hybrid or Partially Decomposed Storage Model (PDSM) has been proposed. However, in current implementations, bandwidth savings achieved by partial decomposition come at increased CPU costs. To achieve the aspired bandwidth savings without sacrificing CPU efficiency, we combine partially decomposed storage with Just-in-Time (JiT) compilation of queries, thus eliminating CPU inefficient function calls. Since existing cost based optimization components are not designed for JiT-compiled query execution, we also develop a novel approach to cost modeling and subsequent storage layout optimization. Our evaluation shows that the JiT-based processor maintains the bandwidth savings of previously presented hybrid query processors but outperforms them by two orders of magnitude due to increased CPU efficiency.

Growing main memory sizes have facilitated database management systems that keep the entire database in main memory. The drastic performance improvements that came along with these in-memory systems have made it possible to reunite the two areas of online transaction processing (OLTP) and online analytical processing (OLAP): An emerging class of hybrid OLTP and OLAP database systems allows to process analytical queries directly on the transactional data. By offering arbitrarily current snapshots of the transactional data for OLAP, these systems enable real-time business intelligence. Despite memory sizes of several Terabytes in a single commodity server, RAM is still a precious resource: Since free memory can be used for intermediate results in query processing, the amount of memory determines query performance to a large extent. Consequently, we propose the compaction of memory-resident databases. Compaction consists of two tasks: First, separating the mutable working set from the immutable "frozen" data. Second, compressing the immutable data and optimizing it for efficient, memory-consumption-friendly snapshotting. Our approach reorganizes and compresses transactional data online and yet hardly affects the mission-critical OLTP throughput. This is achieved by unburdening the OLTP threads from all additional processing and performing these tasks asynchronously.