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Innovative astronomical applications with a new-generation relational database

机译:具有新一代关系数据库的创新天文应用

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Database technology has been developing to exploit the next-generation hardware in the era of big data processing. At the same time, astronomical data size has been steadily increasing, and astronomical source catalogs obtained from large-scale surveys with a wide-field camera, such as Subaru/Hyper Suprime-Cam (HSC), are a good test bench for evaluating the new database technology with a large data set. Such archive systems often employ a highly versatile relational database management system (RDBMS), but reducing the time required for data transaction and complex analysis has come to an important challenge. To tackle this difficulty, we aim to develop astronomical applications with a new catalog database using a next-generation RDBMS technology, where the query engine is designed to efficiently use computing infrastructures for processing big data. Demonstrations with science applications are essential to evaluate the new database. We verify query performance with the current HSC source catalog. For application to huge astronomical catalog databases, we are pursuing and verifying the capabilities of new database technologies. It will, in turn, enable fast ad hoc search and efficient detection of a wide range of variable events with the technology. Our pilot tests using typical astronomical queries on a cluster system shows significant improvements in response times with the aid of distributed query engines. We report performance of the test database for typical astronomical queries, and discuss optimizing the schema based on query workloads.
机译:数据库技术一直在开发,以利用大数据处理时代的下一代硬件。与此同时,天文数据规模一直在稳步增加,并且从具有宽野摄像头的大规模调查获得的天文源目录,例如诸如斯巴鲁/超级型凸轮(HSC),是评估的良好测试台具有大数据集的新数据库技术。此类归档系统通常采用高度通用的关系数据库管理系统(RDBMS),但减少了数据交易所需的时间,复杂分析已成为一个重要的挑战。为了解决这种困难,我们的目的是使用新的目录数据库开发天文应用,使用下一代RDBMS技术,其中查询引擎旨在有效地使用计算基础架构来处理大数据。具有科学应用程序的示范对于评估新数据库至关重要。我们使用当前的HSC源目录验证查询性能。对于庞大的天文目录数据库,我们正在追求和验证新数据库技术的功能。反过来,它将启用快速的临时搜索和有效地检测与技术的各种可变事件。我们在集群系统上使用典型天文查询的试验测试显示了借助分布式查询引擎的响应时间的显着改进。我们向典型天文查询报告测试数据库的性能,并讨论基于查询工作负载优化模式。

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