Týr: Efficient Transactional Storage for Data-Intensive Applications

Abstract : As the computational power used by large-scale applications increases, the amount of data they need to manipulate tends to increase as well. A wide range of such applications requires robust and flexible storage support for atomic, durable and concurrent transactions. Historically, databases have provided the de facto solution to transactional data management, but they have forced applications to drop control over data layout and access mechanisms, while remaining unable to meet the scale requirements of Big Data. More recently, key-value stores have been introduced to address these issues. However, this solution does not provide transactions, or only restricted transaction support, compelling users to carefully coordinate access to data in order to avoid race conditions, partial writes, overwrites, and other hard problems that cause erratic behaviour. We argue there is a gap between existing storage solutions and application requirements that limits the design of transaction-oriented data-intensive applications. In this paper we introduce Týr, a massively parallel distributed transactional blob storage system. A key feature behind Týr is its novel multi-versioning management designed to keep the metadata overhead as low as possible while still allowing fast queries or updates and preserving transaction semantics. Its share-nothing architecture ensures minimal contention and provides low latency for large numbers of concurrent requests. Týr is the first blob storage system to provide sequential consistency and high throughput, while enabling unforeseen transaction support. Experiments with a real-life application from the CERN LHC show Týr throughput outperforming state-of-the-art solutions by more than 100%.
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Submitted on : Wednesday, February 24, 2016 - 4:49:08 PM
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Pierre Matri, Alexandru Costan, Gabriel Antoniu, Jesús Montes, María Pérez. Týr: Efficient Transactional Storage for Data-Intensive Applications. [Technical Report] RT-0473, Inria Rennes Bretagne Atlantique; Universidad Politécnica de Madrid. 2016, pp.25. ⟨hal-01256563v2⟩

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