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<oembed><version>1.0</version><provider_name>dbi Blog</provider_name><provider_url>https://www.dbi-services.com/blog</provider_url><title>Dynamic Sampling vs. Extended Statistics</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="eqUR0JpKl3"&gt;&lt;a href="https://www.dbi-services.com/blog/dynamic-sampling-vs-extended-statistics/"&gt;Dynamic Sampling vs. Extended Statistics&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.dbi-services.com/blog/dynamic-sampling-vs-extended-statistics/embed/#?secret=eqUR0JpKl3" width="600" height="338" title="&#x201C;Dynamic Sampling vs. Extended Statistics&#x201D; &#x2014; dbi Blog" data-secret="eqUR0JpKl3" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>By Franck Pachot . On datawarehouse databases, I frequently recommend increasing the level of dynamic sampling because: Queries have complex predicates with AND, OR, IN(), ranges and correlated values for which the optimizer cannot estimate the cardinality properly Queries are long anyway (compared to OLTP) and can afford more parse time to get an optimized [&hellip;]</description></oembed>

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