Multiversion concurrency control (MVCC) and design principles thereof are ingrained in modern database management systems, thus promoting remarkable progress in managing online transaction processing (OLTP) workloads for decades. However, MVCC systems would battle two vital concerns when facing hybrid transactional/analytical processing (HTAP). The first concern is to ensure rapid version searching for analytic queries with less I/O, and the second concern is to reclaim garbage data versions promptly for easing the strain on storage footprint. These are often tightly coupled since many MVCC systems rely on unified version storage that poses a space-time tradeoff in HTAP, giving rise to disappointing performance metrics that may negatively stereotype OLTP-friendly MVCC systems. This paper refutes the stereotype resulting from coupled design concerns and addresses the core problem by proposing Diva (Decoupling Index from Version dAta) that physically separates version index from version data; for decoupled concerns, we devise independent management policies: provisional version indexing and time interval-based version garbage collection. The separation of coupled concerns would render legacy disk-based MVCC systems more HTAP-friendly. We applied Diva to two full-fledged database systems---PostgreSQL and MySQL---and demonstrated that the systems with Diva escaped the space-time tradeoff under hybrid transactional/analytical workloads.

A scan is one of the fundamental operations in databases for retrieving tuples from tables, and research on access methods has been of importance to query optimization. However, our community is aware of the inconvenient truth that its performance may plummet amid steep increases in search costs when acting on MVCC databases since multi-versioning may forfeit all the benefits of using database indexes. An execution plan for a query on multi-versioned data often comprises a series of point lookup operations, of which each internally executes a linear traversal of record versions. Therefore, the generated plan is surprisingly worse than a full table (or version store) scan, mainly due to redundant access to database pages. To address such an all-or-nothing approach, we propose version weaver (vWeaver), a light-weight access method for record versions, that expedites a scan on record versions with each being augmented by just a few pointer fields. vWeaver incrementally constructs a version search structure over even an append-only version store (e.g., undo space) and allows a scan to traverse new version search structures for fast lookup. We applied vWeaver to in-memory and disk-based MVCC databases and demonstrated that the systems with vWeaver generally improved scan performance under various workloads with negligible space overhead.

Many systems use snapshot isolation, or something similar, as defaults, and multi-version concurrency control (MVCC) remains essential to offering such point-in-time consistency. One major issue in MVCC is the timely removal of unnecessary versions of data items, especially in the presence of long-lived transactions (LLTs). We have observed that the latest versions of MySQL and PostgreSQL are still vulnerable to LLTs. Our analysis of existing proposals suggests that new solutions to this matter must provide rigorous rules for completely identifying unnecessary versions, and elaborate designs for version cleaning lest old versions required for LLTs should suspend garbage collection. In this paper, we formalize such rules into our version pruning theorem and version classification, of which all form theoretical foundations for our new version management system, vDriver, that bases its record versioning on a new principle: Single In-row Remaining Off-row (SIRO) versioning. We implemented a prototype of vDriver and integrated it with MySQL-8.0 and PostgreSQL-12.0. The experimental evaluation demonstrated that the engines with Driver continue to perform the reclamation of dead versions in the face of LLTs while retaining transaction throughput with reduced space consumption.

Actions changing the state of databases are all logged with proper ordering being imposed. Database engines obeying this golden rule of logging enforce total ordering on all events, and this poses challenges in addressing the scalability bottlenecks of database logging on multicore hardware. We reexamined the problem of database logging and realized that in any given log history, obtaining an upper bound on the size of a set that preserves the happen-before relation is the essence of the matter. Based on our understanding, we propose Border-Collie, a wait-free and read-optimal algorithm for database logging that finds such an upper bound even with some worker threads often being idle. We show that (1) Border-Collie always finds the largest set of logged events satisfying the condition in a finite number of steps (i.e., wait-free), (2) the number of logged events to be read is also minimal (i.e., read-optimal), and (3) both properties hold even with threads being in intermittent work. Experimental results demonstrated that Border-Collie proves our claims under various workloads; Border-Collie outperforms the state-of-the-art centralized logging techniques (i.e., Eleda and ERMIA) by up to ~2X and exhibits almost the same throughput with much shorter commit latency than the state-of-the-art decentralized logging techniques (i.e., Silo and FOEDUS).