Today's Hybrid Transactional and Analytical Processing (HTAP) systems, tackle the ever-growing data in combination with a mixture of transactional and analytical workloads. While optimizing for aspects such as data freshness and performance isolation, they build on the traditional data-to-code principle and may trigger massive cold data transfers that impair the overall performance and scalability. Firstly, in this paper we show that Near-Data Processing (NDP) naturally fits in the HTAP design space. Secondly, we propose an NDP database architecture, allowing transactionally consistent in-situ executions of analytical operations in HTAP settings. We evaluate the proposed architecture in state-of-the-art key/value-stores and multi-versioned DBMS. In contrast to traditional setups, our approach yields robust, resource- and cost-efficient performance.

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The architecture and algorithms of database systems have been built around the properties of existing hardware technologies. Many such elementary design assumptions are 20-30 years old. Over the last five years we witness multiple new I/O technologies (e.g. Flash SSDs, NV-Memories) that have the potential of changing these assumptions. Some of the key technological differences to traditional spinning disk storage are: (i) asymmetric read/write performance; (ii) low latencies; (iii) fast random reads; (iv) endurance issues.Cost functions used by traditional database query optimizers are directly influenced by these properties. Most cost functions estimate the cost of algorithms based on metrics such as sequential and random I/O costs besides CPU and memory consumption. These do not account for asymmetry or high random read and inferior random write performance, which represents a significant mismatch.In the present paper we show a new asymmetry-aware cost model for Flash SSDs with adapted cost functions for algorithms such as external sort, hash-join, sequential scan, index scan, etc. It has been implemented in PostgreSQL and tested with TPC-H. Additionally we describe a tool that automatically finds good settings for the base coefficients of cost models. After tuning the configuration of both the original and the asymmetry-aware cost model with that tool, the optimizer with the asymmetry-aware cost model selects faster execution plans for 14 out of the 22 TPC-H queries (the rest being the same or negligibly worse). We achieve an overall performance improvement of 48% on SSD.

Abstract. Today's production processes are characterized by global supply chains, short lifecycles, and an increasing personalization of goods. To satisfy the demands for agility we must integrate the production with the logistics processes and knowledge about the underlying transportation services and infrastructure. This requires continuous monitoring and reacting to events. Service-oriented architectures have provided a platform for structuring services within and across enterprises. However, for an effective monitoring and timely reaction to emerging situations it is crucial to integrate event processing and service orientation. In this position paper we show how event processing and service orientation can be combined into an effective delivery platform for an integrated coordination of the flow of goods. We show how simple events, e.g. RFID tag detections or simple sensor readings, can be integrated into abstract events that are meaningful to invoke logistics services and improve the celerity of responses. We propose filtering, aggregating, and on-the-fly analysis of the continuous flow of events and make events persistent in an event warehouse for auditability and input to future planning processes.

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