Accelerating Analytical Processing in MVCC using Fine-Granular High-Frequency Virtual Snapshotting

TL;DR

This paper presents AnKerDB, a system that accelerates analytical processing in MVCC databases by using high-frequency, fine-granular virtual snapshotting, enabling better handling of mixed OLTP and OLAP workloads.

Contribution

Introducing a custom kernel system call, vm_snapshot, for fast snapshot creation, facilitating heterogeneous transaction processing with high-frequency snapshots.

Findings

Significantly higher analytical throughput on mixed workloads.

Snapshot creation is orders of magnitude faster than previous methods.

Effective separation of OLTP and OLAP transactions improves overall system performance.

Abstract

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 vm_snapshot, 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.