The end of an architectural era: it's time for a complete rewrite

Madden, Samuel; Abadi, Daniel J.; Harizopoulos, Stavros; Hachem, Nabil; Helland, Pat

doi:10.1145/3226595.3226637

Cited by 86 publications

(127 citation statements)

References 84 publications

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“…In a famous paper, Stonebraker et al advocate for designing database systems targeted for specific domains, because doing so can dramatically improve performance over one-size-fits-all solutions [91]. This approach has worked well for several domains: data warehousing, stream processing, text, scientific, online transaction processing, etc.…”

Section: Why a New Enginementioning

confidence: 99%

Hillview

et al. 2019

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Hillview is a distributed spreadsheet for browsing very large datasets that cannot be handled by a single machine. As a spreadsheet, Hillview provides a high degree of interactivity that permits data analysts to explore information quickly along many dimensions while switching visualizations on a whim. To provide the required responsiveness, Hillview introduces visualization sketches, or vizketches, as a simple idea to produce compact data visualizations. Vizketches combine algorithmic techniques for data summarization with computer graphics principles for efficient rendering. While simple, vizketches are effective at scaling the spreadsheet by parallelizing computation, reducing communication, providing progressive visualizations, and offering precise accuracy guarantees. Using Hillview running on eight servers, we can navigate and visualize datasets of tens of billions of rows and trillions of cells, much beyond the published capabilities of competing systems. PVLDB Reference Format:

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Section: Why a New Enginementioning

confidence: 99%

Hillview

et al. 2019

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“…Synchronization overheads are kept to a minimum by executing all transactions within the context of a single thread. This approach reduces the need for locking, and was shown to improve performance compared to conventional databases in typical OLTP workloads [31], [13], [16].…”

Section: Granola: Independent Transactionsmentioning

confidence: 99%

“…We frame our work in a DTM environment because: (i) similar environments were shown to support much higher transaction throughputs than traditional OTLP workloads [31], and (ii) our choice presents us with some interesting problems that allow us to innovate.…”

Section: Introductionmentioning

confidence: 99%

Automated Data Partitioning for Highly Scalable and Strongly Consistent Transactions

Turcu

Palmieri

Ravindran

et al. 2016

IEEE Trans. Parallel Distrib. Syst.

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Modern transactional processing systems need to be fast and scalable, but this means many such systems settled for weak consistency models. It is however possible to achieve all of strong consistency, high scalability and high performance, by using fine-grained partitions and light-weight concurrency control that avoids superfluous synchronization and other overheads such as lock management. Independent transactions are one such mechanism, that rely on good partitions and appropriately defined transactions. On the downside, it is not usually straightforward to determine optimal partitioning schemes, especially when dealing with non-trivial amounts of data. Our work attempts to solve this problem by automating the partitioning process, choosing the correct transactional primitive, and routing transactions appropriately.

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“…Since neither serializability nor strong consistency are achievable when network partitions may occur [13] RDBMSs usually choose ACID over high availability [14], [15]. Brewer's CAP theorem states that of the three properties availability (A), consistency (C) and partition tolerance (P), any distributed database system can achieve at most two [16].…”

Section: A Foundationsmentioning

confidence: 99%

Scalable data management: NoSQL data stores in research and practice

Gessert

Ritter

2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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The unprecedented scale at which data is consumed and generated today has shown a large demand for scalable data management and given rise to non-relational, distributed "NoSQL" database systems. Two central problems triggered this process: 1) vast amounts of user-generated content in modern applications and the resulting requests loads and data volumes 2) the desire of the developer community to employ problemspecific data models for storage and querying. To address these needs, various data stores have been developed by both industry and research, arguing that the era of one-size-fits-all database systems is over. The heterogeneity and sheer amount of these systems -now commonly referred to as NoSQL data storesmake it increasingly difficult to select the most appropriate system for a given application. Therefore, these systems are frequently combined in polyglot persistence architectures to leverage each system in its respective sweet spot. This tutorial gives an in-depth survey of the most relevant NoSQL databases to provide comparative classification and highlight open challenges. To this end, we analyze the approach of each system to derive its scalability, availability, consistency, data modeling and querying characteristics. We present how each system's design is governed by a central set of trade-offs over irreconcilable system properties. We then cover recent research results in distributed data management to illustrate that some shortcomings of NoSQL systems could already be solved in practice, whereas other NoSQL data management problems pose interesting and unsolved research challenges.

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The end of an architectural era: it's time for a complete rewrite

Cited by 86 publications

References 84 publications

Hillview

Hillview

Automated Data Partitioning for Highly Scalable and Strongly Consistent Transactions

Scalable data management: NoSQL data stores in research and practice

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