Spanner

Corbett, James C.; Dean, J. Michael; Epstein, Michael; Fikes, Andrew; Frost, Christopher; Furman, J. J.; Ghemawat, Sanjay; Gubarev, Andrey; Heiser, Christopher; Hochschild, Peter; Hsieh, Wilson C.; Kanthak, Sebastian; Kogan, Eugene; Li, Hongyi; Lloyd, Alexander; Melnik, Sergey; Mwaura, David; Nagle, David F.; Quinlan, Sean; Rao, Rajesh; Rolig, Lindsay; Saitō, Yasushi; Szymaniak, Michal; Taylor, Calvin W.; Wang, Ruth; Woodford, Dale

doi:10.1145/2491245

Cited by 365 publications

(48 citation statements)

References 25 publications

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“…In its turn, Google published several cloud-based systems [32] like BigTable [33], Megastore [34], Spanner [35], Cloud SQL [36], and Cloud datastore [37].…”

Section: Google Propositionsmentioning

confidence: 99%

Consistency in Cloud-based database systems

Mahfoud

Nouali-Taboudjemat

2019

IJCAI

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Cloud computing covers the large spectrum of services available on the internet. Cloud services use replication to ensure high availability. Within database replication, various copies of the same data item are stored in different sites, this situation requires managing the consistency of the multiple copies. In fact, the requirement for consistency level can be different according to application natures and other metrics; a delay of some minutes in visualizing latest posts in social networks can be tolerated, while some seconds can make a loss of a bid in an auction system. Wide variety of database management systems are used actually by cloud services, they support different levels of consistency to meet the diversity of needs. This paper draws a presentation of the main characteristics of cloud computing and data management systems and describes different consistency models. Then it discusses the most famous cloud-based database management systems from the point of view of their data and consistency models. Povzetek: Prispevek analizira podatkovna skladišča v oblakih predvsem s stališča konsistentnosti.

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“…In its turn, Google published several cloud-based systems [32] like BigTable [33], Megastore [34], Spanner [35], Cloud SQL [36], and Cloud datastore [37].…”

Section: Google Propositionsmentioning

confidence: 99%

Consistency in Cloud-based database systems

Mahfoud

Nouali-Taboudjemat

2019

IJCAI

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“…Internet-scale distributed systems often replicate data at multiple geographic locations to provide low latency and high availability, despite node and network failures. Some systems [25,48,28,41,75,52] adopt strong consistency models, where the execution of an operation needs to involve coordination of a quorum of replicas. Although it is possible to improve the throughput of these systems (e.g., through batching), the required coordination leads to high latency for executing one operation, that depends on the round-trip time among replicas and the protocol used.…”

Section: Introductionmentioning

confidence: 99%

Conflict-Free Replicated Data Types CRDTs

Preguiça

Baquero

Shapiro

2018

Encyclopedia of Big Data Technologies

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“…3 An example is Google's F1 hybrid database, 4 a scalable distributed Structured Query Language (SQL) database built on top of their globally distributed and synchronously replicated database, Spanner. 5 Google uses this database to support their AdWords business, an ecosystem with hundreds of concurrent applications and thousands of users sharing the database, over 100TB in size in 2013.…”

Section: Introductionmentioning

confidence: 99%

Data transformation as a means towards dynamic data storage and polyglot persistence

et al. 2017

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SummaryLegacy applications have been built around the concept of storing their data in one relational data store. However, with the current differentiation in data store technologies as a consequence of the NoSQL paradigm, new and possibly more performant storage solutions are available to all applications. The concept of dynamic storage makes sure that application data are always stored in the most optimal data store at a given time to increase application performance. Additionally, polyglot persistence aims to push this performance even further by storing each different data type of an application in the data store technology best suited for it. To get legacy applications into dynamic storage and polyglot persistence, schema and data transformations between data store technologies are needed. This usually infers application redesigns as well to support the new data stores. This paper proposes such a transformation approach through a canonical model. It is based on the Lambda architecture to ensure no application downtime is needed during the transformation process, and after the transformation, the application can continue to query in the original query language, thus requiring no application code changes.

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Spanner

Cited by 365 publications

References 25 publications

Consistency in Cloud-based database systems

Consistency in Cloud-based database systems

Conflict-Free Replicated Data Types CRDTs

Data transformation as a means towards dynamic data storage and polyglot persistence

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