Accelerating Hybrid Transactional/Analytical Processing Using Consistent Dual-Snapshot

Li, Liang; Wu, Gang; Wang, Guoren; Yuan, Ye

doi:10.1007/978-3-030-18576-3_4

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…DBMS query workloads are segmented into two broad modes [20], [21]. Online transactional processing (OLTP) workloads consist of WRITE queries that modify small amounts of data, and READ queries that process a few records and project the majority of the attributes available [22].…”

Section: B Benchmark Workloadsmentioning

confidence: 99%

Thespis: Causally-consistent OLTP

Camilleri

Vella

Nezval

2021

Annals of Computer Science and Information Systems

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Data Consistency defines the validity of a data set according to some set of rules, and different levels of data consistency have been proposed. Causal consistency is the strongest type of consistency possible when data is stored in multiple locations, and fault tolerance is desired. Thespis is a middleware that leverages the Actor model to implement causal consistency over a DBMS, whilst abstracting complexities for application developers behind a REST interface. ThespisTRX is an extension that provides read-only transaction capabilities, whilst ThespisDIIP is another extension that handles distributed integrity invariant preservation. Here, we analyse standard transactional workloads on the relational data model, which is richer than the key-value data model supported by the Thespis interface. We show the applicability of the Thespis approach for this data model by designing new operations for the Thespis interface, which ensure correct execution of such workloads in a convergent, causallyconsistent distributed environment.

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Section: B Benchmark Workloadsmentioning

confidence: 99%

Thespis: Causally-consistent OLTP

Camilleri

Vella

Nezval

2021

Annals of Computer Science and Information Systems

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“…MVCC creates a new version on each write operation, to avoid blocking concurrent readers: Read operations can then proceed in parallel with a write operation, as long as there exists a version that does not violate the consistency of the database. However, there is no free lunch: storing several versions of the database increases the memory footprint and the associated maintenance cost [11,16,22].…”

Section: Introductionmentioning

confidence: 99%

One-shot Garbage Collection for In-memory OLTP through Temporality-aware Version Storage

Raza

Chrysogelos²,

Anadiotis³

et al. 2023

Proc. ACM Manag. Data

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Most modern in-memory online transaction processing (OLTP) engines rely on multi-version concurrency control (MVCC) to provide data consistency guarantees in the presence of conflicting data accesses. MVCC improves concurrency by generating a new version of a record on every write, thus increasing the storage requirements. Existing approaches rely on garbage collection and chain consolidation to reduce the length of version chains and reclaim space by freeing unreachable versions. However, finding unreachable versions requires the traversal of long version chains, which incurs random accesses right into the critical path of transaction execution, hence limiting scalability. This paper introduces OneShotGC, a new multi-version storage design that eliminates version traversal during garbage collection, with minimal discovery and memory management overheads. OneShotGC leverages the temporal correlations across versions to opportunistically cluster them into contiguous memory blocks that can be released in one shot. We implement OneShotGC in Proteus and use YCSB and TPC-C to experimentally evaluate its performance with respect to the state-of-the-art, where we observe an improvement of up to 2x in transactional throughput.

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“…In database management systems (DBMS), query workloads are segmented into two broad modes (Elnaffar et al, 2002;Li et al, 2019). Online transactional processing (OLTP) workloads typically consist of write queries that modify small amounts of data, and queries that read a few records whilst projecting the majority of the attributes available (Bach & Werner, 2016).…”

Section: Introductionmentioning

confidence: 99%

HTAP With Reactive Streaming ETL

Camilleri

Vella

Nezval

2021

Journal of Cases on Information Technology

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In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or online analytical processing (OLAP). These often run within separate DBMSs. In hybrid transactional and analytical processing (HTAP), both workloads may execute within the same DBMS. This article shows that it is possible to run separate OLTP and OLAP DBMSs, and still support timely business decisions from analytical queries running off fresh transactional data. Several setups to manage OLTP and OLAP workloads are analysed. Then, benchmarks on two industry standard DBMSs empirically show that, under an OLTP workload, a row-store DBMS sustains a 1000 times higher throughput than a columnar DBMS, whilst OLAP queries are more than 4 times faster on a columnar DBMS. Finally, a reactive streaming ETL pipeline is implemented which connects these two DBMSs. Separate benchmarks show that OLTP events can be streamed to an OLAP database within a few seconds.

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Accelerating Hybrid Transactional/Analytical Processing Using Consistent Dual-Snapshot

Cited by 5 publications

References 26 publications

Thespis: Causally-consistent OLTP

Thespis: Causally-consistent OLTP

One-shot Garbage Collection for In-memory OLTP through Temporality-aware Version Storage

HTAP With Reactive Streaming ETL

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