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 <a href="#groups">Modules</a>  </div>
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 <div class="title">Sketch-based Estimators<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a></div></div>  </div>
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 Modules</h2></td></tr>
 <tr class="memitem:group__grp__countmin"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__countmin.html">CountMin (Cormode-Muthukrishnan)</a></td></tr>
 <tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:group__grp__fmsketch"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__fmsketch.html">FM (Flajolet-Martin)</a></td></tr>
 <tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:group__grp__mfvsketch"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__mfvsketch.html">MFV (Most Frequent Values)</a></td></tr>
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 <a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
 <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>Sketches (sometimes called "synopsis data structures") are small randomized in-memory data structures that capture statistical properties of a large set of values (e.g., a column of a table). Sketches can be formed in a single pass of the data, and used to approximate a variety of descriptive statistics.</p>
 <p>We implement sketches as SQL User-Defined Aggregates (UDAs). Because they are single-pass, small-space and parallelized, a single query can use many sketches to gather summary statistics on many columns of a table efficiently.</p>
 <p>This module currently implements user-defined aggregates based on three main sketch methods:</p>
 <ul>
 <li><em>Count-Min (CM)</em> sketches, which can be used to approximate a number of descriptive statistics including<ul>
 <li><code>COUNT(*)</code> of rows whose column value matches a given value in a set</li>
 <li><code>COUNT(*)</code> of rows whose column value falls in a range (*)</li>
 <li>order statistics including <em>median</em> and <em>centiles</em> (*)</li>
 <li><em>histograms</em>: both <em>equi-width</em> and <em>equi-depth</em> (*)</li>
 </ul>
 </li>
 <li><em>Flajolet-Martin (FM)</em> sketches for approximating <code>COUNT(DISTINCT)</code>.</li>
 <li><em>Most Frequent Value (MFV)</em> sketches, which output the most frequently-occuring values in a column, along with their associated counts.</li>
 </ul>
 <p><em>Note:</em> Features marked with a star (*) only work for discrete types that can be cast to int8.</p>
 <p>The sketch methods consist of a number of SQL UDAs (user defined aggregates) and UDFs (user defined functions), to be used directly in SQL queries. </p>
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	<div class="title">Sketch-based Estimators<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a></div></div> </div>
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	<tr class="memitem:group__grp__countmin"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__countmin.html">CountMin (Cormode-Muthukrishnan)</a></td></tr>
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	<tr class="memitem:group__grp__fmsketch"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__fmsketch.html">FM (Flajolet-Martin)</a></td></tr>
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	<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
	<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>Sketches (sometimes called "synopsis data structures") are small randomized in-memory data structures that capture statistical properties of a large set of values (e.g., a column of a table). Sketches can be formed in a single pass of the data, and used to approximate a variety of descriptive statistics.</p>
	<p>We implement sketches as SQL User-Defined Aggregates (UDAs). Because they are single-pass, small-space and parallelized, a single query can use many sketches to gather summary statistics on many columns of a table efficiently.</p>
	<p>This module currently implements user-defined aggregates based on three main sketch methods:</p>
	<ul>
	<li><em>Count-Min (CM)</em> sketches, which can be used to approximate a number of descriptive statistics including<ul>
	<li><code>COUNT(*)</code> of rows whose column value matches a given value in a set</li>
	<li><code>COUNT()</code> of rows whose column value falls in a range ()</li>
	<li>order statistics including <em>median</em> and <em>centiles</em> (*)</li>
	<li><em>histograms</em>: both <em>equi-width</em> and <em>equi-depth</em> (*)</li>
	</ul>
	</li>
	<li><em>Flajolet-Martin (FM)</em> sketches for approximating <code>COUNT(DISTINCT)</code>.</li>
	<li><em>Most Frequent Value (MFV)</em> sketches, which output the most frequently-occuring values in a column, along with their associated counts.</li>
	</ul>
	<p><em>Note:</em> Features marked with a star (*) only work for discrete types that can be cast to int8.</p>
	<p>The sketch methods consist of a number of SQL UDAs (user defined aggregates) and UDFs (user defined functions), to be used directly in SQL queries. </p>
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