Diva: Making MVCC Systems HTAP-Friendly

Kim, Jongbin; Yu, Jaeseon; Ahn, Jaechan; Kang, Sooyong; Jung, Hyungsoo

doi:10.1145/3514221.3526135

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…Briefly, the first stream uses a monolithic data format with an optimized data structure to run transactions and queries simultaneously (e.g., PTree [158] and Diva [90]). Typical designs allow OLAP queries to observe transaction updates by sharing the same data copy.…”

Section: A Brief History Of Htapmentioning

confidence: 99%

“…In general, unified architecture has strengths in reducing data redundancy and eliminating the burden of managing consistency [90]. However, it has some performance issues:…”

Section: Common Issuesmentioning

confidence: 99%

“…Diva [90] suggests a new provisional version indexing for HTAP based on the observation that data versions are continuous and visible only for a sliding time window. The idea is to co-locate a record and its first old version in the main index and store the rest of the versions in separate version space (i.e., provisional version indexing).…”

Section: Mvcc Indexmentioning

confidence: 99%

See 2 more Smart Citations

A survey on hybrid transactional and analytical processing

Song,

Zhou,

Cui

et al. 2024

The VLDB Journal

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To provide applications with the ability to analyze fresh data and eliminate the time-consuming ETL workflow, hybrid transactional and analytical (HTAP) systems have been developed to serve online transaction processing and online analytical processing workloads in a single system. In recent years, HTAP systems have attracted considerable interest from both academia and industry. Several new architectures and technologies have been proposed. This paper provides a comprehensive overview of these HTAP systems. We review recently published papers and technical reports in this field and broadly classify existing HTAP systems into two categories based on their data formats: monolithic and hybrid HTAP. We further classify hybrid HTAP into four sub-categories based on their storage architecture: row-oriented, column-oriented, separated, and hybrid. Based on such a taxonomy, we outline each stream’s design challenges and performance issues (e.g., the contradictory format demand for monolithic HTAP). We then discuss potential solutions and their trade-offs by reviewing noteworthy research findings. Finally, we summarize emerging HTAP applications, benchmarks, future trends, and open problems.

show abstract

Section: A Brief History Of Htapmentioning

confidence: 99%

“…In general, unified architecture has strengths in reducing data redundancy and eliminating the burden of managing consistency [90]. However, it has some performance issues:…”

Section: Common Issuesmentioning

confidence: 99%

See 1 more Smart Citation

A survey on hybrid transactional and analytical processing

Song,

Zhou,

Cui

et al. 2024

The VLDB Journal

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Polaris: Enabling Transaction Priority in Optimistic Concurrency Control

Hwang

Chen

et al. 2023

Proc. ACM Manag. Data

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Transaction priority is a critical feature for real-world database systems. Under high contention, certain classes of transactions should be given a higher chance to commit than others. Such a prioritization mechanism is commonly implemented in locking-based concurrency control protocols as some lock scheduling mechanisms, but it is rarely supported in the world of optimistic concurrency control. We present Polaris, an optimistic concurrency control protocol that supports multiple priority levels. To enforce priority, Polaris introduces a minimal amount of pessimism through a lightweight reservation mechanism. The protocol is fully optimistic among transactions within the same priority level and preserves the high throughput advantage of optimistic protocols. Our evaluation with YCSB workload shows that Polaris can make the p999 tail latency of high-priority transactions 13x lower than that of low-priority ones. With an abort-aware priority assignment policy, Polaris can deliver 1.9x higher throughput and 17x lower tail latency compared to Silo for high-contention workloads.

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Deploying Computational Storage for HTAP DBMSs Takes More Than Just Computation Offloading

Lee

Ahn

et al. 2023

Proc. VLDB Endow.

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Hybrid transactional/analytical processing (HTAP) would overload database systems. To alleviate performance interference between transactions and analytics, recent research pursues the potential of in-storage processing (ISP) using commodity computational storage devices (CSDs). However, in-storage query processing faces technical challenges in HTAP environments. Continuously updated data versions pose two hurdles: (1) data items keep changing, and (2) finding visible data versions incurs excessive data access in CSDs. Such access patterns dominate the cost of query processing, which may hinder the active deployment of CSDs. This paper addresses the core issues by proposing an a nalyt i c offloa d e ngine (AIDE) that transforms engine-specific query execution logic into vendor-neutral computation through a canonical interface. At the core of AIDE are the canonical representation of vendor-specific data and the separate management of data locators. It enables any CSD to execute vendor-neutral operations on canonical tuples with separate indexes, regardless of host databases. To eliminate excessive data access, we prescreen the indexes before offloading; thus, host-side prescreening can obviate the need for running costly version searching in CSDs and boost analytics. We implemented our prototype for PostgreSQL and MyRocks, demonstrating that AIDE supports efficient ISP for two databases using the same FPGA logic. Evaluation results show that AIDE improves query latency up to 42× on PostgreSQL and 34× on MyRocks.

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Diva: Making MVCC Systems HTAP-Friendly

Cited by 5 publications

References 42 publications

A survey on hybrid transactional and analytical processing

A survey on hybrid transactional and analytical processing

Polaris: Enabling Transaction Priority in Optimistic Concurrency Control

Deploying Computational Storage for HTAP DBMSs Takes More Than Just Computation Offloading

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