Hybrid memory systems consisting of DRAM and NVRAM offer a great opportunity for column-oriented data systems to persistently store and to efficiently process columnar data completely in main memory. While vectorization (SIMD) of query operators is state-of-the-art to increase the single-thread performance, it has to be combined with thread-level parallelism (MIMD) to satisfy growing needs for higher performance and scalability. However, it is not well investigated how such a SIMD-MIMD interplay could be leveraged efficiently in hybrid memory systems. On the one hand, we deliver an extensive experimental evaluation of typical workloads on columnar data in this paper. We reveal that the choice of the most performant SIMD version differs greatly for both memory types. Moreover, we show that the throughput of concurrent queries can be boosted (up to 2x) when combining various SIMD flavors in a multi-threaded execution. On the other hand, to enable that optimization, we propose an adaptive SIMD-MIMD cocktail approach incurring only a negligible runtime overhead.

In-memory database systems adopting a columnar storage model play a crucial role with respect to data analytics. While data is completely kept in-memory by these systems for efficiency, data has to be stored on a non-volatile medium for persistence and fault tolerance as well. Traditionally, slow block-level devices like HDDs or SSDs are used which, however, can be replaced by fast byte-addressable NVRAM nowadays. Thus, hybrid memory systems consisting of DRAM and NVRAM offer a great opportunity for column-oriented database systems to persistently store and to efficiently process columnar data exclusively in main-memory. However, possible DRAM and NVRAM failures still necessitate the protection of primary data. While data replication is a suitable means, it increases the NVRAM endurance problem through increased write activities. To tackle that challenge and to reduce the overhead of replication, we propose a novel Polymorphic Compressed Replication (PCR) mechanism representing replicas using lightweight compression algorithms to reduce NVRAM writes, while supporting different compressed formats for the replicas of one column to facilitate different database operations during query processing. To show the feasibility and applicability, we developed an inmemory column-store prototype transparently employing PCR through an abstract user-space library. Based on this prototype, our conducted experiments show the effectiveness of our proposed PCR mechanism.

Lightweight integer compression algorithms play an important role in in-memory database systems to tackle the growing gap between processor speed and main memory bandwidth. Thus, there is a large number of algorithms to choose from, while different algorithms are tailored to different data characteristics. As we show in this paper, with the availability of byte-addressable non-volatile random-access memory (NVRAM), a novel type of main memory with specific characteristics increases the overall complexity in this domain. In particular, we provide a detailed evaluation of state-of-the-art lightweight integer compression schemes and database operations on NVRAM and compare it with DRAM. Furthermore, we reason about possible deployments of middle- and heavyweight approaches for better adaptation to NVRAM characteristics. Finally, we investigate a combined approach where both volatile and non-volatile memories are used in a cooperative fashion that is likely to be the case for hybrid and NVRAM-centric database systems.

In this demo, we present MorphStore, an in-memory column store with a novel compression-aware query processing concept. Basically, compression using lightweight integer compression algorithms already plays an important role in existing in-memory column stores, but mainly for base data. The continuous handling of compression from the base data to the intermediate results during query processing has already been discussed, but not investigated in detail since the computational effort for compression as well as decompression is often assumed to exceed the benefits of a reduced transfer cost between CPU and main memory. However, this argument increasingly loses its validity as we are going to show in our demo. Generally, our novel compression-aware query processing concept is characterized by the fact that we are able to speed up the query execution by morphing compressed intermediate results from one scheme to another scheme to dynamically adapt to the changing data characteristics during query processing. Our morphing decisions are made using a cost-based approach.

Lightweight integer compression algorithms are frequently applied in in-memory database systems to tackle the growing gap between processor speed and main memory bandwidth. In recent years, the vectorization of basic techniques such as delta coding and null suppression has considerably enlarged the corpus of available algorithms. As a result, today there is a large number of algorithms to choose from, while different algorithms are tailored to different data characteristics. However, a comparative evaluation of these algorithms with different data and hardware characteristics has never been sufficiently conducted in the literature. To close this gap, we conducted an exhaustive experimental survey by evaluating several state-of-the-art lightweight integer compression algorithms as well as cascades of basic techniques. We systematically investigated the influence of data as well as hardware properties on the performance and the compression rates. The evaluated algorithms are based on publicly available implementations as well as our own vectorized reimplementations. We summarize our experimental findings leading to several new insights and to the conclusion that there is no single-best algorithm. Moreover, in this article, we also introduce and evaluate a novel cost model for the selection of a suitable lightweight integer compression algorithm for a given dataset.

Modern database systems are very often in the position to store and efficiently process their entire data in main memory. Aside from increased main memory capacities, a further driver for in-memory database systems has been the shift to a column-oriented storage format in combination with lightweight data compression techniques. In recent years, a lot of lightweight data compression algorithms have been developed to efficiently support different data characteristics. Therefore, database systems should include a large number of these algorithms. To enable this, we introduce our novel modularization concept including our model kit implementation for lightweight data compression algorithms.

Nowadays, database systems pursuit a main memory-centric architecture, where the entire business-related data is stored and processed in a compressed form in main memory. In this case, the performance gain is massive because database operations can benefit from its higher bandwidth and lower latency. However, current main memory-centric database systems utilize general-purpose error detection and correction solutions to address the emerging problem of increasing dynamic error rate of main memory. The costs of these generalpurpose methods dramatically increases with increasing error rates. To reduce these costs, we have to exploit context knowledge of database systems for resiliency. Therefore, we introduce our vision of resiliency-aware data compression in this paper, where we want to exploit the benefits of both fields in an integrated approach with low performance and memory overhead. In detail, we present and evaluate a first approach using AN encoding and two different compression schemes to show the potentials and challenges of our vision.

Relational database management systems build on the closed world assumption requiring upfront modeling of a usually stable schema. However, a growing number of today's database applications are characterized by self-descriptive data. The schema of self-descriptive data is very dynamic and prone to frequent changes; a situation which is always troublesome to handle in relational systems. This demo presents the relational database management system FRDM. With flexible relational tables FRDM greatly simplifies the management of self-descriptive data in a relational database system. Self-descriptive data can reside directly next to traditionally modeled data and both can be queried together using SQL. This demo presents the various features of FRDM and provides first-hand experience of the newly gained freedom in relational database systems.