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<title>MADlib: MFV (Most Frequent Values)</title>
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<div class="title">MFV (Most Frequent Values)<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a> &raquo; <a class="el" href="group__grp__sketches.html">Cardinality Estimators</a></div></div> </div>
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<div class="toc"><b>Contents</b> </p><ul>
<li>
<a href="#syntax">Syntax</a> </li>
<li>
<a href="#examples">Examples</a> </li>
<li>
<a href="#literature">Literature</a> </li>
<li>
<a href="#related">Related Topics</a> </li>
</ul>
</div><dl class="section warning"><dt>Warning</dt><dd><em> This MADlib method is still in early stage development. There may be some issues that will be addressed in a future version. Interface and implementation is subject to change. </em></dd></dl>
<p>MFVSketch: Most Frequent Values variant of CountMin sketch, implemented as a UDA.</p>
<p>Produces an n-bucket histogram for a column where each bucket counts one of the most frequent values in the column. The output is an array of doubles {value, count} in descending order of frequency; counts are approximated via CountMin sketches. Ties are handled arbitrarily.</p>
<p><a class="anchor" id="syntax"></a> </p><pre class="syntax">
mfvsketch_top_histogram( col_name,
n
)
</pre><p>The MFV frequent-value UDA comes in two different versions:</p><ul>
<li>a faithful implementation that preserves the approximation guarantees of Cormode/Muthukrishnan,</li>
<li>and a "quick and dirty" version that can do parallel aggregation in Greenplum at the expense of missing some of the most frequent values.</li>
</ul>
<p>In PostgreSQL the two UDAs are identical. In Greenplum, the quick version should produce good results unless the number of values requested is very small, or the distribution is very flat.</p>
<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
<ol type="1">
<li>Generate some data. <pre class="example">
CREATE TABLE data(class INT, a1 INT);
INSERT INTO data SELECT 1,1 FROM generate_series(1,10000);
INSERT INTO data SELECT 1,2 FROM generate_series(1,15000);
INSERT INTO data SELECT 1,3 FROM generate_series(1,10000);
INSERT INTO data SELECT 2,5 FROM generate_series(1,1000);
INSERT INTO data SELECT 2,6 FROM generate_series(1,1000);
</pre></li>
<li>Produce a histogram of 5 bins and return the most frequent value and associated count in each bin. <pre class="example">
SELECT mfvsketch_top_histogram( a1,
5
)
FROM data;
</pre> Result: <pre class="result">
mfvsketch_top_histogram
&#160;-------------------------------------------------------------
[0:4][0:1]={{2,15000},{1,10000},{3,10000},{5,1000},{6,1000}}
(1 row)
</pre></li>
</ol>
<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd>This method is not usually called an MFV sketch in the literature; it is a natural extension of the CountMin sketch.</dd></dl>
<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
<p>File <a class="el" href="sketch_8sql__in.html" title="SQL functions for sketch-based approximations of descriptive statistics. ">sketch.sql_in</a> documenting the SQL functions.</p>
<p>Module <a class="el" href="group__grp__countmin.html">CountMin (Cormode-Muthukrishnan)</a>. </p>
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