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    <language>en-us</language>    <build-info>
        <version>9.7.1</version>
        <build-number>970001</build-number>
        <build-date>13-04-2023</build-date>
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<item>
            <title>[SERVER-74296] Consider using linear scaling function in t-digest when computing $median</title>
                <link>https://jira.mongodb.org/browse/SERVER-74296</link>
                <project id="10000" key="SERVER">Core Server</project>
                    <description>&lt;p&gt;The linear scaling function doesn&apos;t provide good enough accuracy for extreme percentiles. This task includes tuning the choice of the function to balance runtime/accuracy. There are three commonly used ones (based on sqrt, arcsin, log) but we also should look into optimizing for the median.&lt;/p&gt;</description>
                <environment></environment>
        <key id="2272811">SERVER-74296</key>
            <summary>Consider using linear scaling function in t-digest when computing $median</summary>
                <type id="3" iconUrl="https://jira.mongodb.org/secure/viewavatar?size=xsmall&amp;avatarId=14718&amp;avatarType=issuetype">Task</type>
                                            <priority id="3" iconUrl="https://jira.mongodb.org/images/icons/priorities/major.svg">Major - P3</priority>
                        <status id="6" iconUrl="https://jira.mongodb.org/images/icons/statuses/closed.png" description="The issue is considered finished, the resolution is correct. Issues which are closed can be reopened.">Closed</status>
                    <statusCategory id="3" key="done" colorName="success"/>
                                    <resolution id="9">Done</resolution>
                                        <assignee username="irina.yatsenko@mongodb.com">Irina Yatsenko</assignee>
                                    <reporter username="irina.yatsenko@mongodb.com">Irina Yatsenko</reporter>
                        <labels>
                    </labels>
                <created>Wed, 22 Feb 2023 23:43:49 +0000</created>
                <updated>Sat, 1 Apr 2023 00:34:38 +0000</updated>
                            <resolved>Sat, 1 Apr 2023 00:34:38 +0000</resolved>
                                                                                        <votes>0</votes>
                                    <watches>1</watches>
                                                                                                                <comments>
                            <comment id="5317092" author="JIRAUSER1259274" created="Sat, 1 Apr 2023 00:34:38 +0000"  >&lt;p&gt;Considered and not found benefitial.&lt;/p&gt;</comment>
                            <comment id="5317090" author="JIRAUSER1259274" created="Sat, 1 Apr 2023 00:33:28 +0000"  >&lt;p&gt;Investigated replacing k2 with k0 but not observing any considerable perf impact (&lt;a href=&quot;https://docs.google.com/spreadsheets/d/1uKuiB2ZXon4R2i153Tlvu8jh7v7FOXePXh6PSo1TF8o/edit#gid=932660904&quot; class=&quot;external-link&quot; target=&quot;_blank&quot; rel=&quot;nofollow noopener&quot;&gt;https://docs.google.com/spreadsheets/d/1uKuiB2ZXon4R2i153Tlvu8jh7v7FOXePXh6PSo1TF8o/edit#gid=932660904&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;The accuracy might be slightly better with k0 for non-extreme percentiles but we don&apos;t think it&apos;s worth pursuing.&lt;/p&gt;</comment>
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