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<title>MADlib: CountMin (Cormode-Muthukrishnan)</title>
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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>
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<div class="contents">
<div class="toc"><b>Contents</b> <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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