2017/09/13 by Ankur Sharma, Felix Martin Schuhknecht, Sharma, Ankur +4
Computer Science · #Advanced Data Storage Technologies #Cloud Computing and Resource Management #Databases (cs.DB) #Distributed systems and fault tolerance #FOS: Computer and information sciences #cs.DB
paper · pdf · doi:10.48550/arxiv.1709.04284
arxiv created 2017/09/13 · openalex publication_date 2017/09/13 · arxiv updated 2017/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Efficient transactional management is a delicate task. As systems face transactions of inherently different types, ranging from point updates to long running analytical computations, it is hard to satisfy their individual requirements with a single processing component. Unfortunately, most systems nowadays rely on such a single component that implements its parallelism using multi-version concurrency control (MVCC). While MVCC parallelizes short-running OLTP transactions very well, it struggles in the presence of mixed workloads containing long-running scan-centric OLAP queries, as scans have to work their way through large amounts of versioned data. To overcome this problem, we propose a system, which reintroduces the concept of heterogeneous transaction processing: OLAP transactions are outsourced to run on separate (virtual) snapshots while OLTP transactions run on the most recent representation of the database. Inside both components, MVCC ensures a high degree of concurrency. The biggest challenge of such a heterogeneous approach is to generate the snapshots at a high frequency. Previous approaches heavily suffered from the tremendous cost of snapshot creation. In our system, we overcome the restrictions of the OS by introducing a custom system call vmsnapshot, that is hand-tailored to our precise needs: it allows fine-granular snapshot creation at very high frequencies, rendering the snapshot creation phase orders of magnitudes faster than state-of-the-art approaches. Our experimental evaluation on a heterogeneous workload based on TPC-H transactions and handcrafted OLTP transactions shows that our system enables significantly higher analytical transaction throughputs on mixed workloads than homogeneous approaches. In this sense, we introduce a system that accelerates Analytical processing by introducing custom Kernel functionalities: AnKerDB.