Achieving both high throughput and low commit latency has long been a difficult challenge for Database Management Systems (DBMSs). As we show in this paper, existing commit processing protocols fail to fully leverage modern NVMe SSDs to deliver both high throughput and low-latency durable commits. We therefore propose autonomous commit , the first commit protocol that fully utilizes modern NVMe SSDs to achieve both objectives. Our approach exploits the high parallelism and low write latency of SSDs, enabling workers to explicitly write logs in smaller batches, thereby minimizing the impact of logging I/O on commit latency. Additionally, by parallelizing the acknowledgment procedure, where the DBMS iterates through a set of transactions to inspect their commit state, we mitigate excessive delays resulting from single-threaded commit operations in high-throughput workloads. Our experimental results show that autonomous commit achieves exceptional scalability and low-latency durable commits across a wide range of 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.

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.

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.