Darko Makreshanski

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As a result of increases in both the query load and the data managed , as well as changes in hardware architecture (multicore), the last years have seen a shift from query-at-a-time approaches towards shared work (SW) systems where queries are executed in groups. Such groups share operators like scans and joins, leading to systems that process hundreds to(More)
The release of hardware transactional memory (HTM) in commodity CPUs has major implications on the design and implementation of main-memory databases, especially on the architecture of high-performance lock-free indexing methods at the core of several of these systems. This paper studies the interplay of HTM and lock-free indexing methods. First, we(More)
This demonstration presents SharedDB, an implementation of a relational database system capable of executing all SQL operators by sharing computation and resources across all running queries. SharedDB sidesteps the traditional query-at-a-time approach and executes queries in batches. Unlike proposed multi-query optimization ideas, in SharedDB queries do not(More)
Database architectures typically process queries one-at-a-time, executing concurrent queries in independent execution contexts. Often , such a design leads to unpredictable performance and poor scalability. One approach to circumvent the problem is to take advantage of sharing opportunities across concurrently running queries. In this paper we propose(More)
Modern workloads are becoming more and more diverse and involve a large volumes of frequently updated data. On such workloads, enterprises often have strict requirements with regards to data freshness and performance guarantees. This demand can be fulfilled in a scal-able way by engineering data processing systems that are inherently distributed and(More)
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