Incremental elasticity for array databases

Relational databases benefit significantly from elasticity, whereby they execute on a set of changing hardware resources provisioned to match their storage and processing requirements. Such flexibility is especially attractive for scientific databases because their users often have a no-overwrite storage model, in which they delete data only when their available space is exhausted. This results in a database that is regularly growing and expanding its hardware proportionally. Also, scientific databases frequently store their data as multidimensional arrays optimized for spatial querying. This brings about several novel challenges in clustered, skew-aware data placement on an elastic shared-nothing database. In this work, we design and implement elasticity for an array database. We address this challenge on two fronts: determining when to expand a database cluster and how to partition the data within it. In both steps we propose incremental approaches, affecting a minimum set of data and nodes, while maintaining high performance. We introduce an algorithm for gradually augmenting an array database's hardware using a closed-loop control system. After the cluster adds nodes, we optimize data placement for n-dimensional arrays. Many of our elastic partitioners incrementally reorganize an array, redistributing data only to new nodes. By combining these two tools, the scientific database efficiently and seamlessly manages its monotonically increasing hardware resources.Intel Corporation (Science and Technology Center for Big Data

LINVIEW: Incremental View Maintenance for Complex Analytical Queries

Many analytics tasks and machine learning problems can be naturally expressed by iterative linear algebra programs. In this paper, we study the incremental view maintenance problem for such complex analytical queries. We develop a framework, called LINVIEW, for capturing deltas of linear algebra programs and understanding their computational cost. Linear algebra operations tend to cause an avalanche effect where even very local changes to the input matrices spread out and infect all of the intermediate results and the final view, causing incremental view maintenance to lose its performance benefit over re-evaluation. We develop techniques based on matrix factorizations to contain such epidemics of change. As a consequence, our techniques make incremental view maintenance of linear algebra practical and usually substantially cheaper than re-evaluation. We show, both analytically and experimentally, the usefulness of these techniques when applied to standard analytics tasks. Our evaluation demonstrates the efficiency of LINVIEW in generating parallel incremental programs that outperform re-evaluation techniques by more than an order of magnitude.Comment: 14 pages, SIGMO

Scalable data management in distributed information systems

[EN] In the era of cloud computing and huge information systems, distributed applications should manage dynamic workloads; i.e., the amount of client requests per time unit may vary frequently and servers should rapidly adapt their computing efforts to those workloads. Cloud systems provide a solid basis for this kind of applications but most of the traditional relational database systems are unprepared to scale up with this kind of distributed systems. This paper surveys different techniques being used in modern SQL, NoSQL and NewSQL systems in order to increase the scalability and adaptability in the management of persistent data. © 2011 Springer-Verlag.This work has been supported by EU FEDER and Spanish MICINN under research grants TIN2009-14460-C03-01 and TIN2010-17193Pallardó Lozoya, MR.; Esparza Peidro, J.; García Escriva, JR.; Decker, H.; Muñoz Escoí, FD. (2011). Scalable data management in distributed information systems. 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Student See Versus Student Do: A Comparative Study of Two Online Tutorials

This study examines the impact on student performance after interactive and non-interactive tutorials using a 2 × 2 treatment-control design. In an undergraduate management course, a control group watched a video tutorial while the treatment group received the same content using a dynamic tutorial. Both groups received the same quiz questions. Using effect size to determine magnitude of change, it was found that those in the treatment condition performed better than those in the control condition. Students were able to take the quiz up to two times. When examining for change in performance from attempt one to attempt two, the treatment group showed a greater magnitude of change. Students who consistently performed lowest on the quizzes outperformed all students in learning gains

Functional pearl: a SQL to C compiler in 500 lines of code

We present the design and implementation of a SQL query processor that outperforms existing database systems and is written in just about 500 lines of Scala code - a convincing case study that high-level functional programming can handily beat C for systems-level programming where the last drop of performance matters. The key enabler is a shift in perspective towards generative programming. The core of the query engine is an interpreter for relational algebra operations, written in Scala. Using the open-source LMS Framework (Lightweight Modular Staging), we turn this interpreter into a query compiler with very low effort. To do so, we capitalize on an old and widely known result from partial evaluation known as Futamura projections, which state that a program that can specialize an interpreter to any given input program is equivalent to a compiler. In this pearl, we discuss LMS programming patterns such as mixed-stage data structures (e.g. data records with static schema and dynamic field components) and techniques to generate low-level C code, including specialized data structures and data loading primitives

Class of Service in the High Performance Storage System

Quality of service capabilities are commonly deployed in archival mass storage systems as one or more client-specified parameters to influence physical location of data in multi-level device hierarchies for performance or cost reasons. The capabilities of new high-performance storage architectures and the needs of data-intensive applications require better quality of service models for modern storage systems. HPSS, a new distributed, high-performance, scalable, storage system, uses a Class of Service (COS) structure to influence system behavior. The authors summarize the design objectives and functionality of HPSS and describes how COS defines a set of performance, media, and residency attributes assigned to storage objects managed by HPSS servers. COS definitions are used to provide appropriate behavior and service levels as requested (or demanded) by storage system clients. They compare the HPSS COS approach with other quality of service concepts and discuss alignment possibilities

A grid-based infrastructure for distributed retrieval

In large-scale distributed retrieval, challenges of latency, heterogeneity, and dynamicity emphasise the importance of infrastructural support in reducing the development costs of state-of-the-art solutions. We present a service-based infrastructure for distributed retrieval which blends middleware facilities and a design framework to ‘lift’ the resource sharing approach and the computational services of a European Grid platform into the domain of e-Science applications. In this paper, we give an overview of the DILIGENT Search Framework and illustrate its exploitation in the field of Earth Science

Implementation of Multidimensional Databases with Document-Oriented NoSQL

International audienceNoSQL (Not Only SQL) systems are becoming popular due to known advantages such as horizontal scalability and elasticity. In this paper, we study the implementation of data warehouses with document-oriented NoSQL systems. We propose mapping rules that transform the multidimensional data model to logical document-oriented models. We consider three different logical models and we use them to instantiate data warehouses. We focus on data loading, model-to-model conversion and OLAP cuboid computation