Vectorwise: Beyond Column Stores

textabstractThis paper tells the story of Vectorwise, a high-performance analytical database system, from multiple perspectives: its history from academic project to commercial product, the evolution of its technical architecture, customer reactions to the product and its future research and development roadmap. One take-away from this story is that the novelty in Vectorwise is much more than just column-storage: it boasts many query processing innovations in its vectorized execution model, and an adaptive mixed row/column data storage model with indexing support tailored to analytical workloads. Another one is that there is a long road from research prototype to commercial product, though database research continues to achieve a strong innovative influence on product development

Multiparticle Bell's inequalities involving many measurement settings

We present a prescription for obtaining Bell's inequalities for N>2 observers involving more than two alternative measurement settings. We give examples of some families of such inequalities. The inequalities are violated by certain classes of states for which all standard Bell's inequalities with two measurement settings per observer are satisfied.Comment: 4 pages, RevTeX

Spectral decomposition of Bell's operators for qubits

The spectral decomposition is given for the N-qubit Bell operators with two observables per qubit. It is found that the eigenstates (when non-degenerate) are N-qubit GHZ states even for those operators that do not allow the maximal violation of the corresponding inequality. We present two applications of this analysis. In particular, we discuss the existence of pure entangled states that do not violate any Mermin-Klyshko inequality for $N\geq 3$.Comment: 12 pages, 1 figure

From Cooperative Scans to Predictive Buffer Management

In analytical applications, database systems often need to sustain workloads with multiple concurrent scans hitting the same table. The Cooperative Scans (CScans) framework, which introduces an Active Buffer Manager (ABM) component into the database architecture, has been the most effective and elaborate response to this problem, and was initially developed in the X100 research prototype. We now report on the the experiences of integrating Cooperative Scans into its industrial-strength successor, the Vectorwise database product. During this implementation we invented a simpler optimization of concurrent scan buffer management, called Predictive Buffer Management (PBM). PBM is based on the observation that in a workload with long-running scans, the buffer manager has quite a bit of information on the workload in the immediate future, such that an approximation of the ideal OPT algorithm becomes feasible. In the evaluation on both synthetic benchmarks as well as a TPC-H throughput run we compare the benefits of naive buffer management (LRU) versus CScans, PBM and OPT; showing that PBM achieves benefits close to Cooperative Scans, while incurring much lower architectural impact.Comment: VLDB201

Improving I/O Bandwidth for Data-Intensive Applications

High disk bandwidth in data-intensive applications is usually achieved with expensive hardware solutions consisting of a large number of disks. In this article we present our current work on software methods for improving disk bandwidth in ColumnBM, a new storage system for MonetDB/X100 query execution engine. Two novel techniques are discussed: superscalar compression for standalone queries and cooperative scans for multi-query optimization

From X100 to Vectorwise: opportunities, challenges and things most researchers do not think about

textabstractIn 2008 a group of researchers behind the X100 database kernel created Vectorwise: a spin-o which together with the Actian corporation (previously Ingres) worked on bringing this technology to the market. Today, Vectorwise is a popular product and one of the examples of conversion of a research prototype into successful commercial software. We describe here some of the interesting aspects of the work performed by the Vectorwise development team in the process, and discuss the op- portunities and challenges resulting from the decision of integrating a prototype-quality kernel with Ingres, an established commercial product. We also discuss how requirements coming from real-life scenarios sometimes clashed with design choices and simplications often found in research projects, and how Vectorwise team addressed some of of them