F1

Shute, Jeff; Vingralek, Radek; Samwel, Bart; Handy, Ben; Whipkey, Chad; Rollins, Eric; Oancea, Mircea; Littlefield, Kyle; Menestrina, David; Ellner, Stephan; Cieslewicz, John; Rae, Ian; Stancescu, Traian; Apte, Himani

doi:10.14778/2536222.2536232

Cited by 185 publications

(6 citation statements)

References 4 publications

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“…Google's large scale analytics systems such as Spanner [8], F1 [48,46], and Dremel [40] support querying against complex objects. Dremel performs evaluation over a "semiflattened" [2] format in order to avoid the space inefficiencies caused by fully flattening data.…”

Section: Related Workmentioning

confidence: 99%

“…Highlevel scripting languages such as Pig Latin [43] and Scope [13] ease the programming of distributed data processing systems. Apache Hive [6], F1 [48,46], Dremel [40], and Big-Query [47] provide SQL-like languages with extensions for querying nested structured data at scale. Emma [3] and DIQL [27] support for-comprehensions and respectively SQLlike syntax via DSLs deeply-embedded in Scala, and target several distributed processing frameworks.…”

Section: Introductionmentioning

confidence: 99%

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Scalable Querying of Nested Data

Smith¹,

Benedikt²,

Nikolić³

et al. 2020

Preprint

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While large-scale distributed data processing platforms have become an attractive target for query processing, these systems are problematic for applications that deal with nested collections. Programmers are forced either to perform nontrivial translations of collection programs or to employ automated flattening procedures, both of which lead to performance problems. These challenges only worsen for nested collections with skewed cardinalities, where both handcrafted rewriting and automated flattening are unable to enforce load balancing across partitions.In this work, we propose a framework that translates a program manipulating nested collections into a set of semantically equivalent shredded queries that can be efficiently evaluated. The framework employs a combination of query compilation techniques, an efficient data representation for nested collections, and automated skew-handling. We provide an extensive experimental evaluation, demonstrating significant improvements provided by the framework in diverse scenarios for nested collection programs.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scalable Querying of Nested Data

Smith¹,

Benedikt²,

Nikolić³

et al. 2020

Preprint

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“…As an example Google uses lmctfy [19] and Docker to run F1 [20], a distribuited relational database used to store AdWords data. Using containers, distribuited systems can scale up and down according to their load more rapidly than with traditional VMs.…”

Section: Challenges and Issuesmentioning

confidence: 99%

Container-Based Orchestration in Cloud: State of the Art and Challenges

Tosatto

Ruiu

Attanasio

2015

2015 Ninth International Conference on Complex, Intelligent, and Software Intensive Systems

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How to effectively manage increasingly complex enterprise computing environments is one of the hardest challenges that most organizations have to face in the era of cloud computing, big data and IoT. Advanced automation and orchestration systems are the most valuable solutions helping IT staff to handle large-scale cloud data centers. Containers are the new revolution in the cloud computing world, they are more lightweight than VMs, and can radically decrease both the startup time of instances and the processing and storage overhead with respect to traditional VMs. The aim of this paper is to provide a comprehensive description of cloud orchestration approaches with containers, analyzing current research efforts, existing solutions and presenting issues and challenges facing this topic.

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“…However, this does not mean relational databases do not have a role to play in the big data story. 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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F1

Cited by 185 publications

References 4 publications

Scalable Querying of Nested Data

Scalable Querying of Nested Data

Container-Based Orchestration in Cloud: State of the Art and Challenges

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

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