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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>MonetDBLite: An Embedded Analytical Database · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1805.08520/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1805.08520/feed.xml" rel="self" type="application/rss+xml" /><item><title>MonetDBLite: An Embedded Analytical Database — mentioned by mytherin</title><link>https://news.ycombinator.com/item?id=23290682</link><guid isPermaLink="false">urn:hackerpapers:mention:1805.08520:23290682</guid><pubDate>Sun, 24 May 2020 10:56:12 GMT</pubDate><description>&lt;p&gt;Hannes and me developed both MonetDBLite and DuckDB, precisely for the need that you described :) We noticed that there was no easy RDBMS aimed at single-machine analytical workloads, whereas these kind of workloads are very common (e.g. R/Python data science workloads). MonetDBLite was our initial approach, and is essentially an embedded version of MonetDB. We wrote a paper about it (https://arxiv.org/pdf/1805.08520.pdf). While it works, the system was not built with embeddability in mind, and we had to rewrite a lot of code to get it to work. Because of that we ended up with a fork, as the…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1805.08520/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DB</category></item></channel></rss>
