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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> » <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> |
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| <a href="#related">Related Topics</a> </li> |
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| </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 |
|  ------------------------------------------------------------- |
| [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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