Join Execution Using Fragmented Columnar Indices on GPU and MIC

The paper describes an approach to the parallel natural join execution on computing clusters with GPU and MIC Coprocessors. This approach is based on a decomposition of natural join relational operator using the column indices and domain-interval fragmentation. This decomposition admits parallel executing the resource-intensive relational operators without data transfers. All column index fragments are stored in main memory. To process the join of two relations, each pair of index fragments corresponding to particular domain interval is joined on a separate processor core. Described approach allows efficient parallel query processing for very large databases on modern computing cluster systems with many-core accelerators. A prototype of the DBMS coprocessor system was implemented using this technique. The results of computational experiments for GPU and Xeon Phi are presented. These results confirm the efficiency of proposed approach

Decomposing and re-composing lightweight compression schemes - and why it matters

We argue for a richer view of the space of lightweight compression schemes for columnar DBMSes: We demonstrate how even simple simple schemes used in DBMSes decompose into constituent schemes through a columnar perspective on their decompression. With our concrete examples, we touch briefly on what follows from these and other decompositions: Composition of alternative compression schemes as well as other practical and analytical implications

Системне конструювання та модель розгортання розподіленої системи управління інвестиційним портфелем

В роботі представлена модель розгортання розподіленої системи управління інвестиційним портфелем цінних паперів з використанням розподіленої нереляційної бази даних Cassandra. Проаналізовані переваги використання розподілених баз у порівнянні з реляційними базами даних.In this paper deployment model of distributed investment portfolio management system is presented with application of distributed non-relational database Cassandra. Benefits of distributed databases are analyzed in comparison with relational databases

Business Analytics in (a) Blink

The Blink project’s ambitious goal is to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. Blink is a new DBMS aimed primarily at read-mostly BI query processing that exploits scale-out of commodity multi-core processors and cheap DRAM to retain a (copy of a) data mart completely in main memory. Additionally, it exploits proprietary compression technology and cache-conscious algorithms that reduce memory bandwidth consumption and allow most SQL query processing to be performed on the compressed data. Blink always scans (portions of) the data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorp