Variety in structure of the data and diverse access methodologies is one of the

Variety in structure of the data and diverse access

This preview shows page 192 - 194 out of 211 pages.

Variety in structure of the data and diverse access methodologies is one of the essential elements of the data management ecosystem. This variety in structure and the different accesses, has resulted in heterogeneity at the data infrastructure level. Applications now employ a multitude of data management systems with different underlying representa- tions for their data processing needs. The resultant multi-representation architecture can individually cater to many workloads, but has many challenges. This dissertation tackles some of these challenges and proposes solutions: 1) to provide support for consistent real-time analytics while supporting a high throughput of transactions, and 2) defining consistency semantics in a multi-representation architecture setting. In this dissertation, we propose Janus, a hybrid multi-representation cloud datastore, 176
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Conclusion and Future Work Chapter 8 to efficiently support both transactions and diverse, consistent real-time analytics. Janus uses multiple representations to support a high throughput of transactions, as well as support diverse real-time analytics. Transactions and analytics are executed on different representations, but the novel data-movement pipeline in Janus guarantees that each analytical query observes the same order of transaction execution, and does not observe the partial impact of any transaction. Janus is a cloud datastore and supports large-scale data via partitioning, and allows distributed transactions as well as different partitioning strategies, to support diverse characteristics of representations. Experiments on AWS, a public cloud platform, demonstrate that Janus can support consistent real-time analytics at scale. For better understanding of consistency semantics over heterogeneous data represen- tations, we propose Typhon, which defines a formal consistency model for data split across representations. The consistency model in Typhon uses a notion of entities for relating data items and defines dependencies at the granularity of an entity. These dependencies capture the happens-before relationships for accesses to data across repre- sentations. The model is integrated with the traditional conflict-graph model, and can be used to analyze the consistency guarantees provided by existing protocols. We also design a protocol, Cerberus, which enforces the implicit-casual dependencies introduced in Typhon. Cerberus operates in a single-entity transaction model, which is ideal for social media and other such applications, which require consistency guarantees at the granularity of an entity, but do not want to employ a general purpose transaction model across their entire dataset. We perform extensive experimental evaluation to demon- strate that Cerberus is scalable, and offers a practical solution for providing consistency in a multi-representation setting. It can prevent consistency violations by only adding a small performance overhead, as compared to a solution providing no consistency guaran- tee across multiple representations.
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