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“Access Path Selection in Main-Memory Optimized Data Systems: Should I Scan or Should I Probe?” suggests that it is not enough to take into consideration only data layout, selectivity, and hardware characteristics. When performing a query on a column that has a secondary index, database systems often use cardinality estimation to determine the selectivity of the given query and make a decision to either perform an index scan (probing) or a sequential scan on the table. Just like asymmetric I/O operations on SSDs made the database community reconsider access patterns, the recent trend of cheaper main memory and CPUs with more cores suggests that now might be a good time to revisit some of the rules used for index selection.
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Research papers on database management system pdf software#
Heuristics that used to work well might change as hardware and underlying software layers evolve. While old systems can try to retrofit existing stores for the same purpose, having a database specifically built for this use case may yield much better results – both in terms of processing performance and clarity of outputs. Such a system can have a wide application for users whose primary interaction mechanism is voice, which is rather popular today in mobile devices and home assistants. For example, a query that requests financial statements of some institutions can be described by giving some baseline and outputting how other results are clustered relative to this baseline. In “A Holistic Approach for Query Evaluation and Result Vocalization in Voice-Based OLAP”, authors describe a system specialized in responding to voice-based queries by both providing a concise query language, a query engine built specifically for this purpose, and a result processor that formats the output as concisely and simply as possible, i.e., by summarizing the results. This means that user inputs have to be interpreted and encoded into the query, and query results have to be presented in a format that best suits the user by the client software.
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Most of the database management systems today focus on processing OLAP queries using some structured query language (not always SQL!) and giving responses in the form of a table. In other words, everything that might be related to query processing. We’ll start with papers that cover what’s going on in database nodes locally: after a request is received from the client, but before it has been processed by the storage engine. I’ve attempted to coarsely group papers into categories where they best fit, but some papers discuss more than just one concept, so they may deserve mention in multiple sections. In this post, I’ll try to cover the highlights of the major 2019 conferences and draw your attention to some publications from your favorite research groups. In the same way that our database management systems have to keep up with the growing sizes of our data sets, as database enthusiasts, we have to keep up with everything that is going on in this already vast yet ever-growing field. He’s interested in CS Theory, algorithms, Distributed Systems, understanding how things work and sharing it with others through blog posts, articles and conference talks. He is also the author of Database Internals: A Deep Dive Into How Distributed Data Systems Work.ĭatabase Research in 2019: The Year in Review
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Alex Petrov is an Infrastructure Engineer, Apache Cassandra Committer, working on building data infrastructure and processing pipelines.