This post is the third post in a series where I look at the Java extension in SQL Server, i.e. the ability to execute Java code from inside SQL Server. To see what other posts there are in the series, go to SQL Server 2019 Extensibility Framework & Java. In this post we look at something related to the data passing post; how to handle null values. DISCLAIMER: This post contains Java code. I am not a Java guy, in fact, the only Java I have ever written is the code in this post and the previous SQL Server 2019 Java posts.
This is the seventh post in a series about Microsoft SQL Server R Services, and the sixth post that drills down into the internal of how it works. To see other posts (including this) in the series, go to SQL Server R Services. These directories act as storage for files, results, objects, etc., during execution of an external script. When a user executes an external script in SQL Server, that account is being mapped to one of the 20 Windows account created, and it is under that Windows account the external part of the script is executed. Subsequently the files, etc., mentioned above, ends up in a sub-directory of the user account directory.
By the time I publish this blog post MS Ignite is over. During Ignite, Microsoft announced quite a few new things, amongst them SQL Server 2019 with a whole lot of new features and functionality. I touched briefly on some of them in my What is New in SQL Server 2019 Public Preview post. A couple of things that caught my eye were that SQL Server 2019 for Linux now supports In-Database analytics, what we know as SQL Server Machine Learning Services (R and Python), as well as the Java language extension. NOTE: You may ask yourself what the Java language extension is; well, that is the ability to access Java code from T-SQL.
Search engines present fix-length passages from documents ranked by relevance against the query. In this paper, we present and compare novel, language-model based methods for extracting variable length document snippets by real-time processing of documents using the query issued by the user. With this extra level of information, the returned snippets are considerably more informative. Unlike previous work on passage retrieval which relies on searching relevant segments for filtering of preoccupied passages, we focus on query-informed segmentation to extract context-aware relevant snippets with variable length. In particular, we show that, when informed through an appropriate relevance language model, curvature analysis and Hidden Markov model (HMM) based content segmentation techniques can facilitate to extract relevant document snippets.