Most database management systems cache pages from storage in a main memory buffer pool. To do this, they either rely on a hash table that translates page identifiers into pointers, or on pointer swizzling which avoids this translation. In this work, we propose vmcache, a buffer manager design that instead uses hardware-supported virtual memory to translate page identifiers to virtual memory addresses. In contrast to existing mmap-based approaches, the DBMS retains control over page faulting and eviction. Our design is portable across modern operating systems, supports arbitrary graph data, enables variable-sized pages, and is easy to implement. One downside of relying on virtual memory is that with fast storage devices the existing operating system primitives for manipulating the page table can become a performance bottleneck. As a second contribution, we therefore propose exmap, which implements scalable page table manipulation on Linux. Together, vmcache and exmap provide flexible, efficient, and scalable buffer management on multi-core CPUs and fast storage devices.

The number of commuters has been increasing for many years and the negative effects on wellbeing are therefore affecting more and more people. Following a user centered design process that focuses on known wellbeing determinants, such as relatedness and empathy, we developed the Honeypot socializing app. The app allows commuters to find other travelers to chat with and meet in person to enhance their wellbeing through fostering meaningful and contextual social interactions. First, we describe the development of the idea and the design of the app. Then, we report on a field study with 16 participants, which we carried out on trains. The study results show that the app helps to get in contact with fellow travelers and that it has the potential to promote the wellbeing of commuters in the long term.

MVCC-based snapshot isolation promises that read queries can proceed without interfering with concurrent writes. However, as we show experimentally, in existing implementations a single long-running query can easily cause transactional throughput to collapse. Moreover, existing out-of-memory commit protocols fail to meet the scalability needs of modern multi-core systems. In this paper, we present three complementary techniques for robust and scalable snapshot isolation in out-of-memory systems. First, we propose a commit protocol that minimizes cross-thread communication for better scalability, avoids touching the write set on commit, and enables efficient fine-granular garbage collection. Second, we introduce the Graveyard Index, an auxiliary data structure that moves logically-deleted tuples out of the way of operational transactions. Third, we present an adaptive version storage scheme that enables fast garbage collection and improves scan performance of frequently-modified tuples. All techniques are engineered to scale well on multi-core processors, and together enable robust performance for complex hybrid workloads.

Analytics is moving to the cloud and data is moving into data lakes. These reside on object storage services like S3 and enable seamless data sharing and system interoperability. To support this, many systems build on open storage formats like Apache Parquet. However, these formats are not optimized for remotely-accessed data lakes and today's high-throughput networks. Inefficient decompression makes scans CPU-bound and thus increases query time and cost. With this work we present BtrBlocks, an open columnar storage format designed for data lakes. BtrBlocks uses a set of lightweight encoding schemes, achieving fast and efficient decompression and high compression ratios.