<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data-Structures on Umit Unal</title><link>https://umitunal.net/tags/data-structures/</link><description>Recent content in Data-Structures on Umit Unal</description><generator>Hugo</generator><language>en-us</language><copyright>© 2020 Umit Unal.</copyright><lastBuildDate>Sun, 22 Dec 2024 14:09:07 +0300</lastBuildDate><atom:link href="https://umitunal.net/tags/data-structures/index.xml" rel="self" type="application/rss+xml"/><item><title>Using RoaringBitmap in Distributed Data Systems</title><link>https://umitunal.net/2024/12/using-roaringbitmap-in-distributed-data-systems/</link><pubDate>Sun, 22 Dec 2024 14:09:07 +0300</pubDate><guid>https://umitunal.net/2024/12/using-roaringbitmap-in-distributed-data-systems/</guid><description>&lt;p&gt;In modern high-traffic or data-intensive applications, &lt;strong&gt;distributed data systems&lt;/strong&gt; are crucial for handling large volumes of information. But when we deal with &lt;strong&gt;huge sets of integer IDs&lt;/strong&gt;—such as user IDs, IP addresses, or event indices—we need a data structure that is not only &lt;strong&gt;memory-efficient&lt;/strong&gt; but also supports &lt;strong&gt;fast set operations&lt;/strong&gt;. This is where &lt;strong&gt;RoaringBitmap&lt;/strong&gt; comes in.&lt;/p&gt;
&lt;p&gt;&lt;a href="https://roaringbitmap.org/"&gt;&lt;strong&gt;RoaringBitmap&lt;/strong&gt;&lt;/a&gt; stores large sets of integer values in a compressed format and offers rapid operations like intersection, union, and difference. This blog will explore how RoaringBitmap can make distributed data systems more efficient and easier to manage.&lt;/p&gt;</description></item></channel></rss>