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 <div class="header">
   <div class="headertitle">
 <div class="title">CountMin (Cormode-Muthukrishnan)<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>
 </div><!--header-->
 <div class="contents">
 <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>This module implements Cormode-Muthukrishnan <em>CountMin</em> sketches on integer values, implemented as a user-defined aggregate. It also provides scalar functions over the sketches to produce approximate counts, order statistics, and histograms.</p>
 <p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Syntax</dt><dd><ul>
 <li>Get a sketch of a selected column specified by <em>col_name</em>. <pre class="syntax">
 cmsketch( col_name )
 </pre></li>
 <li>Get the number of rows where <em>col_name = p</em>, computed from the sketch obtained from <code>cmsketch</code>. <pre class="syntax">
 cmsketch_count( cmsketch,
                 p
               )
 </pre></li>
 <li>Get the number of rows where <em>col_name</em> is between <em>m</em> and <em>n</em> inclusive. <pre class="syntax">
 cmsketch_rangecount( cmsketch,
                      m,
                      n
                    )
 </pre></li>
 <li>Get the <em>k</em>th percentile of <em>col_name</em> where <em>count</em> specifies number of rows. <em>k</em> should be an integer between 1 to 99. <pre class="syntax">
 cmsketch_centile( cmsketch,
                   k,
                   count
                 )
 </pre></li>
 <li>Get the median of col_name where <em>count</em> specifies number of rows. This is equivalent to <code><a class="el" href="sketch_8sql__in.html#a2f2ab2fe3244515f5f73d49690e73b39">cmsketch_centile</a>(<em>cmsketch</em>,50,<em>count</em>)</code>. <pre class="syntax">
 cmsketch_median( cmsketch,
                  count
                )
 </pre></li>
 <li>Get an n-bucket histogram for values between min and max for the column where each bucket has approximately the same width. The output is a text string containing triples {lo, hi, count} representing the buckets; counts are approximate. <pre class="syntax">
 cmsketch_width_histogram( cmsketch,
                           min,
                           max,
                           n
                         )
 </pre></li>
 <li>Get an n-bucket histogram for the column where each bucket has approximately the same count. The output is a text string containing triples {lo, hi, count} representing the buckets; counts are approximate. Note that an equi-depth histogram is equivalent to a spanning set of equi-spaced centiles. <pre class="syntax">
 cmsketch_depth_histogram( cmsketch,
                           n
                         )
 </pre></li>
 </ul>
 </dd></dl>
 <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>Count number of rows where a1 = 2 in each class. <pre class="example">
 SELECT class,
        cmsketch_count(
                        cmsketch( a1 ),
                        2
                       )
 FROM data GROUP BY data.class;
 </pre> Result: <pre class="result">
  class | cmsketch_count
 &#160;------+----------------
      2 |              0
      1 |          15000
 (2 rows)
 </pre></li>
 <li>Count number of rows where a1 is between 3 and 6. <pre class="example">
 SELECT class,
        cmsketch_rangecount(
                             cmsketch(a1),
                             3,
                             6
                           )
 FROM data GROUP BY data.class;
 </pre> Result: <pre class="result">
  class | cmsketch_rangecount
 &#160;------+---------------------
      2 |                2000
      1 |               10000
 (2 rows)
 </pre></li>
 <li>Compute the 90th percentile of all of a1. <pre class="example">
 SELECT cmsketch_centile(
                          cmsketch( a1 ),
                          90,
                          count(*)
                        )
 FROM data;
 </pre> Result: <pre class="result">
  cmsketch_centile
 &#160;-----------------
                 3
 (1 row)
 </pre></li>
 <li>Produce an equi-width histogram with 2 bins between 0 and 10. <pre class="example">
 SELECT cmsketch_width_histogram(
                                  cmsketch( a1 ),
                                  0,
                                  10,
                                  2
                                )
 FROM data;
 </pre> Result: <pre class="result">
       cmsketch_width_histogram
 &#160;-----------------------------------
  [[0L, 4L, 35000], [5L, 10L, 2000]]
 (1 row)
 </pre></li>
 <li>Produce an equi-depth histogram of a1 with 2 bins of approximately equal depth. <pre class="example">
 SELECT cmsketch_depth_histogram(
                                  cmsketch( a1 ),
                                  2
                                )
 FROM data;
 </pre> Result: <pre class="result">
                        cmsketch_depth_histogram
 &#160;----------------------------------------------------------------------
  [[-9223372036854775807L, 1, 10000], [2, 9223372036854775807L, 27000]]
 (1 row)
 </pre></li>
 </ol>
 <p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
 <p>[1] G. Cormode and S. Muthukrishnan. An improved data stream summary: The count-min sketch and its applications. LATIN 2004, J. Algorithm 55(1): 58-75 (2005) . <a href="http://dimacs.rutgers.edu/~graham/pubs/html/CormodeMuthukrishnan04CMLatin.html">http://dimacs.rutgers.edu/~graham/pubs/html/CormodeMuthukrishnan04CMLatin.html</a></p>
 <p>[2] G. Cormode. Encyclopedia entry on 'Count-Min Sketch'. In L. Liu and M. T. Ozsu, editors, Encyclopedia of Database Systems, pages 511-516. Springer, 2009. <a href="http://dimacs.rutgers.edu/~graham/pubs/html/Cormode09b.html">http://dimacs.rutgers.edu/~graham/pubs/html/Cormode09b.html</a></p>
 <p><a class="anchor" id="related"></a>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 grp_quantile for a different implementation of quantile function. </p>
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	<div class="header">
	<div class="headertitle">
	<div class="title">CountMin (Cormode-Muthukrishnan)<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>
	</div><!--header-->
	<div class="contents">
	<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>This module implements Cormode-Muthukrishnan <em>CountMin</em> sketches on integer values, implemented as a user-defined aggregate. It also provides scalar functions over the sketches to produce approximate counts, order statistics, and histograms.</p>
	<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Syntax</dt><dd><ul>
	<li>Get a sketch of a selected column specified by <em>col_name</em>. <pre class="syntax">
	cmsketch( col_name )
	</pre></li>
	<li>Get the number of rows where <em>col_name = p</em>, computed from the sketch obtained from <code>cmsketch</code>. <pre class="syntax">
	cmsketch_count( cmsketch,
	p
	)
	</pre></li>
	<li>Get the number of rows where <em>col_name</em> is between <em>m</em> and <em>n</em> inclusive. <pre class="syntax">
	cmsketch_rangecount( cmsketch,
	m,
	n
	)
	</pre></li>
	<li>Get the <em>k</em>th percentile of <em>col_name</em> where <em>count</em> specifies number of rows. <em>k</em> should be an integer between 1 to 99. <pre class="syntax">
	cmsketch_centile( cmsketch,
	k,
	count
	)
	</pre></li>
	<li>Get the median of col_name where <em>count</em> specifies number of rows. This is equivalent to <code><a class="el" href="sketch_8sql__in.html#a2f2ab2fe3244515f5f73d49690e73b39">cmsketch_centile</a>(<em>cmsketch</em>,50,<em>count</em>)</code>. <pre class="syntax">
	cmsketch_median( cmsketch,
	count
	)
	</pre></li>
	<li>Get an n-bucket histogram for values between min and max for the column where each bucket has approximately the same width. The output is a text string containing triples {lo, hi, count} representing the buckets; counts are approximate. <pre class="syntax">
	cmsketch_width_histogram( cmsketch,
	min,
	max,
	n
	)
	</pre></li>
	<li>Get an n-bucket histogram for the column where each bucket has approximately the same count. The output is a text string containing triples {lo, hi, count} representing the buckets; counts are approximate. Note that an equi-depth histogram is equivalent to a spanning set of equi-spaced centiles. <pre class="syntax">
	cmsketch_depth_histogram( cmsketch,
	n
	)
	</pre></li>
	</ul>
	</dd></dl>
	<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>Count number of rows where a1 = 2 in each class. <pre class="example">
	SELECT class,
	cmsketch_count(
	cmsketch( a1 ),
	2
	)
	FROM data GROUP BY data.class;
	</pre> Result: <pre class="result">
	class \| cmsketch_count
	------+----------------
	2 \| 0
	1 \| 15000
	(2 rows)
	</pre></li>
	<li>Count number of rows where a1 is between 3 and 6. <pre class="example">
	SELECT class,
	cmsketch_rangecount(
	cmsketch(a1),
	3,
	6
	)
	FROM data GROUP BY data.class;
	</pre> Result: <pre class="result">
	class \| cmsketch_rangecount
	------+---------------------
	2 \| 2000
	1 \| 10000
	(2 rows)
	</pre></li>
	<li>Compute the 90th percentile of all of a1. <pre class="example">
	SELECT cmsketch_centile(
	cmsketch( a1 ),
	90,
	count(*)
	)
	FROM data;
	</pre> Result: <pre class="result">
	cmsketch_centile
	-----------------
	3
	(1 row)
	</pre></li>
	<li>Produce an equi-width histogram with 2 bins between 0 and 10. <pre class="example">
	SELECT cmsketch_width_histogram(
	cmsketch( a1 ),
	0,
	10,
	2
	)
	FROM data;
	</pre> Result: <pre class="result">
	cmsketch_width_histogram
	-----------------------------------
	[[0L, 4L, 35000], [5L, 10L, 2000]]
	(1 row)
	</pre></li>
	<li>Produce an equi-depth histogram of a1 with 2 bins of approximately equal depth. <pre class="example">
	SELECT cmsketch_depth_histogram(
	cmsketch( a1 ),
	2
	)
	FROM data;
	</pre> Result: <pre class="result">
	cmsketch_depth_histogram
	----------------------------------------------------------------------
	[[-9223372036854775807L, 1, 10000], [2, 9223372036854775807L, 27000]]
	(1 row)
	</pre></li>
	</ol>
	<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
	<p>[1] G. Cormode and S. Muthukrishnan. An improved data stream summary: The count-min sketch and its applications. LATIN 2004, J. Algorithm 55(1): 58-75 (2005) . <a href="http://dimacs.rutgers.edu/~graham/pubs/html/CormodeMuthukrishnan04CMLatin.html">http://dimacs.rutgers.edu/~graham/pubs/html/CormodeMuthukrishnan04CMLatin.html</a></p>
	<p>[2] G. Cormode. Encyclopedia entry on 'Count-Min Sketch'. In L. Liu and M. T. Ozsu, editors, Encyclopedia of Database Systems, pages 511-516. Springer, 2009. <a href="http://dimacs.rutgers.edu/~graham/pubs/html/Cormode09b.html">http://dimacs.rutgers.edu/~graham/pubs/html/Cormode09b.html</a></p>
	<p><a class="anchor" id="related"></a>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 grp_quantile for a different implementation of quantile function. </p>
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