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<div class="header">
<h1 class="heading"><a href="../index.html">
<span>Presto 0.123 Documentation</span></a></h1>
<h2 class="heading"><span>5.12. Aggregate Functions</span></h2>
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<div class="section" id="aggregate-functions">
<h1>5.12. Aggregate Functions</h1>
<p>Aggregate functions operate on a set of values to compute a single result.</p>
<p>Except for <a class="reference internal" href="#count" title="count"><tt class="xref py py-func docutils literal"><span class="pre">count()</span></tt></a>, <a class="reference internal" href="#count_if" title="count_if"><tt class="xref py py-func docutils literal"><span class="pre">count_if()</span></tt></a>, <a class="reference internal" href="#max_by" title="max_by"><tt class="xref py py-func docutils literal"><span class="pre">max_by()</span></tt></a>, <a class="reference internal" href="#min_by" title="min_by"><tt class="xref py py-func docutils literal"><span class="pre">min_by()</span></tt></a> and
<a class="reference internal" href="#approx_distinct" title="approx_distinct"><tt class="xref py py-func docutils literal"><span class="pre">approx_distinct()</span></tt></a>, all of these aggregate functions ignore null values
and return null for no input rows or when all values are null. For example,
<a class="reference internal" href="#sum" title="sum"><tt class="xref py py-func docutils literal"><span class="pre">sum()</span></tt></a> returns null rather than zero and <a class="reference internal" href="#avg" title="avg"><tt class="xref py py-func docutils literal"><span class="pre">avg()</span></tt></a> does not include null
values in the count. The <tt class="docutils literal"><span class="pre">coalesce</span></tt> function can be used to convert null into
zero.</p>
<div class="section" id="general-aggregate-functions">
<h2>General Aggregate Functions</h2>
<dl class="function">
<dt id="arbitrary">
<tt class="descname">arbitrary</tt><big>(</big><em>x</em><big>)</big> → [same as input]</dt>
<dd><p>Returns an arbitrary non-null value of <tt class="docutils literal"><span class="pre">x</span></tt>, if one exists.</p>
</dd></dl>
<dl class="function">
<dt id="array_agg">
<tt class="descname">array_agg</tt><big>(</big><em>x</em><big>)</big> → array<[same as input]></dt>
<dd><p>Returns an array created from the input <tt class="docutils literal"><span class="pre">x</span></tt> elements.</p>
</dd></dl>
<dl class="function">
<dt id="avg">
<tt class="descname">avg</tt><big>(</big><em>x</em><big>)</big> → double</dt>
<dd><p>Returns the average (arithmetic mean) of all input values.</p>
</dd></dl>
<dl class="function">
<dt id="bool_and">
<tt class="descname">bool_and</tt><big>(</big><em>boolean</em><big>)</big> → boolean</dt>
<dd><p>Returns <tt class="docutils literal"><span class="pre">TRUE</span></tt> if every input value is <tt class="docutils literal"><span class="pre">TRUE</span></tt>, otherwise <tt class="docutils literal"><span class="pre">FALSE</span></tt>.</p>
</dd></dl>
<dl class="function">
<dt id="bool_or">
<tt class="descname">bool_or</tt><big>(</big><em>boolean</em><big>)</big> → boolean</dt>
<dd><p>Returns <tt class="docutils literal"><span class="pre">TRUE</span></tt> if any input value is <tt class="docutils literal"><span class="pre">TRUE</span></tt>, otherwise <tt class="docutils literal"><span class="pre">FALSE</span></tt>.</p>
</dd></dl>
<dl class="function">
<dt id="checksum">
<tt class="descname">checksum</tt><big>(</big><em>x</em><big>)</big> → varbinary</dt>
<dd><p>Returns an order-insensitive checksum of the given values.</p>
</dd></dl>
<dl class="function">
<dt id="count">
<tt class="descname">count</tt><big>(</big><em>*</em><big>)</big> → bigint</dt>
<dd><p>Returns the number of input rows.</p>
</dd></dl>
<dl class="function">
<dt>
<tt class="descname">count</tt><big>(</big><em>x</em><big>)</big> → bigint</dt>
<dd><p>Returns the number of non-null input values.</p>
</dd></dl>
<dl class="function">
<dt id="count_if">
<tt class="descname">count_if</tt><big>(</big><em>x</em><big>)</big> → bigint</dt>
<dd><p>Returns the number of <tt class="docutils literal"><span class="pre">TRUE</span></tt> input values.
This function is equivalent to <tt class="docutils literal"><span class="pre">count(CASE</span> <span class="pre">WHEN</span> <span class="pre">x</span> <span class="pre">THEN</span> <span class="pre">1</span> <span class="pre">END)</span></tt>.</p>
</dd></dl>
<dl class="function">
<dt id="every">
<tt class="descname">every</tt><big>(</big><em>boolean</em><big>)</big> → boolean</dt>
<dd><p>This is an alias for <a class="reference internal" href="#bool_and" title="bool_and"><tt class="xref py py-func docutils literal"><span class="pre">bool_and()</span></tt></a>.</p>
</dd></dl>
<dl class="function">
<dt id="geometric_mean">
<tt class="descname">geometric_mean</tt><big>(</big><em>x</em><big>)</big> → double</dt>
<dd><p>Returns the geometric mean of all input values.</p>
</dd></dl>
<dl class="function">
<dt id="max_by">
<tt class="descname">max_by</tt><big>(</big><em>x</em>, <em>y</em><big>)</big> → [same as x]</dt>
<dd><p>Returns the value of <tt class="docutils literal"><span class="pre">x</span></tt> associated with the maximum value of <tt class="docutils literal"><span class="pre">y</span></tt> over all input values.</p>
</dd></dl>
<dl class="function">
<dt>
<tt class="descname">max_by</tt><big>(</big><em>x</em>, <em>y</em>, <em>n</em><big>)</big> → array<[same as x]></dt>
<dd><p>Returns <tt class="docutils literal"><span class="pre">n</span></tt> values of <tt class="docutils literal"><span class="pre">x</span></tt> associated with the <tt class="docutils literal"><span class="pre">n</span></tt> largest of all input values of <tt class="docutils literal"><span class="pre">y</span></tt>.</p>
</dd></dl>
<dl class="function">
<dt id="min_by">
<tt class="descname">min_by</tt><big>(</big><em>x</em>, <em>y</em><big>)</big> → [same as x]</dt>
<dd><p>Returns the value of <tt class="docutils literal"><span class="pre">x</span></tt> associated with the minimum value of <tt class="docutils literal"><span class="pre">y</span></tt> over all input values.</p>
</dd></dl>
<dl class="function">
<dt>
<tt class="descname">min_by</tt><big>(</big><em>x</em>, <em>y</em>, <em>n</em><big>)</big> → array<[same as x]></dt>
<dd><p>Returns <tt class="docutils literal"><span class="pre">n</span></tt> values of <tt class="docutils literal"><span class="pre">x</span></tt> associated with the <tt class="docutils literal"><span class="pre">n</span></tt> smallest of all input values of <tt class="docutils literal"><span class="pre">y</span></tt>.</p>
</dd></dl>
<dl class="function">
<dt id="max">
<tt class="descname">max</tt><big>(</big><em>x</em><big>)</big> → [same as input]</dt>
<dd><p>Returns the maximum value of all input values.</p>
</dd></dl>
<dl class="function">
<dt>
<tt class="descname">max</tt><big>(</big><em>x</em>, <em>n</em><big>)</big> → array<[same as x]></dt>
<dd><p>Returns <tt class="docutils literal"><span class="pre">n</span></tt> largest values of all input values of <tt class="docutils literal"><span class="pre">x</span></tt>.</p>
</dd></dl>
<dl class="function">
<dt id="min">
<tt class="descname">min</tt><big>(</big><em>x</em><big>)</big> → [same as input]</dt>
<dd><p>Returns the minimum value of all input values.</p>
</dd></dl>
<dl class="function">
<dt>
<tt class="descname">min</tt><big>(</big><em>x</em>, <em>n</em><big>)</big> → array<[same as x]></dt>
<dd><p>Returns <tt class="docutils literal"><span class="pre">n</span></tt> smallest values of all input values of <tt class="docutils literal"><span class="pre">x</span></tt>.</p>
</dd></dl>
<dl class="function">
<dt id="sum">
<tt class="descname">sum</tt><big>(</big><em>x</em><big>)</big> → [same as input]</dt>
<dd><p>Returns the sum of all input values.</p>
</dd></dl>
</div>
<div class="section" id="map-aggregate-functions">
<h2>Map Aggregate Functions</h2>
<dl class="function">
<dt id="histogram">
<tt class="descname">histogram</tt><big>(</big><em>x</em><big>)</big> → map<K,bigint></dt>
<dd><p>Returns a map containing the count of the number of times each input value occurs.</p>
</dd></dl>
<dl class="function">
<dt id="map_agg">
<tt class="descname">map_agg</tt><big>(</big><em>key</em>, <em>value</em><big>)</big> → map<K,V></dt>
<dd><p>Returns a map created from the input <tt class="docutils literal"><span class="pre">key</span></tt> / <tt class="docutils literal"><span class="pre">value</span></tt> pairs.</p>
</dd></dl>
<dl class="function">
<dt id="multimap_agg">
<tt class="descname">multimap_agg</tt><big>(</big><em>key</em>, <em>value</em><big>)</big> → map<K,array<V>></dt>
<dd><p>Returns a multimap created from the input <tt class="docutils literal"><span class="pre">key</span></tt> / <tt class="docutils literal"><span class="pre">value</span></tt> pairs.
Each key can be associated with multiple values.</p>
</dd></dl>
</div>
<div class="section" id="approximate-aggregate-functions">
<h2>Approximate Aggregate Functions</h2>
<dl class="function">
<dt id="approx_distinct">
<tt class="descname">approx_distinct</tt><big>(</big><em>x</em><big>)</big> → bigint</dt>
<dd><p>Returns the approximate number of distinct input values.
This function provides an approximation of <tt class="docutils literal"><span class="pre">count(DISTINCT</span> <span class="pre">x)</span></tt>.
Zero is returned if all input values are null.</p>
<p>This function should produce a standard error of 2.3%, which is the
standard deviation of the (approximately normal) error distribution over
all possible sets. It does not guarantee an upper bound on the error for
any specific input set.</p>
</dd></dl>
<dl class="function">
<dt>
<tt class="descname">approx_distinct</tt><big>(</big><em>x</em>, <em>e</em><big>)</big> → bigint</dt>
<dd><p>Returns the approximate number of distinct input values.
This function provides an approximation of <tt class="docutils literal"><span class="pre">count(DISTINCT</span> <span class="pre">x)</span></tt>.
Zero is returned if all input values are null.</p>
<p>This function should produce a standard error of no more than <tt class="docutils literal"><span class="pre">e</span></tt>, which
is the standard deviation of the (approximately normal) error distribution
over all possible sets. It does not guarantee an upper bound on the error
for any specific input set. The current implementation of this function
requires that <tt class="docutils literal"><span class="pre">e</span></tt> be in the range: [0.01150, 0.26000].</p>
</dd></dl>
<dl class="function">
<dt id="approx_percentile">
<tt class="descname">approx_percentile</tt><big>(</big><em>x</em>, <em>p</em><big>)</big> → [same as input]</dt>
<dd><p>Returns the approximate percentile for all input values of <tt class="docutils literal"><span class="pre">x</span></tt> at the
percentage <tt class="docutils literal"><span class="pre">p</span></tt>. The value of <tt class="docutils literal"><span class="pre">p</span></tt> must be between zero and one and
must be constant for all input rows.</p>
</dd></dl>
<dl class="function">
<dt>
<tt class="descname">approx_percentile</tt><big>(</big><em>x</em>, <em>w</em>, <em>p</em><big>)</big> → [same as input]</dt>
<dd><p>Returns the approximate weighed percentile for all input values of <tt class="docutils literal"><span class="pre">x</span></tt>
using the per-item weight <tt class="docutils literal"><span class="pre">w</span></tt> at the percentage <tt class="docutils literal"><span class="pre">p</span></tt>. The weight must be
an integer value of at least one. It is effectively a replication count for
the value <tt class="docutils literal"><span class="pre">x</span></tt> in the percentile set. The value of <tt class="docutils literal"><span class="pre">p</span></tt> must be between
zero and one and must be constant for all input rows.</p>
</dd></dl>
<dl class="function">
<dt id="numeric_histogram">
<tt class="descname">numeric_histogram</tt><big>(</big><em>buckets</em>, <em>value</em>, <em>weight</em><big>)</big> → map<double, double></dt>
<dd><p>Computes an approximate histogram with up to <tt class="docutils literal"><span class="pre">buckets</span></tt> number of buckets
for all <tt class="docutils literal"><span class="pre">value</span></tt>s with a per-item weight of <tt class="docutils literal"><span class="pre">weight</span></tt>. The algorithm
is based loosely on:</p>
<div class="highlight-none"><div class="highlight"><pre>Yael Ben-Haim and Elad Tom-Tov, "A streaming parallel decision tree algorithm",
J. Machine Learning Research 11 (2010), pp. 849--872.
</pre></div>
</div>
<p><tt class="docutils literal"><span class="pre">buckets</span></tt> must be a <tt class="docutils literal"><span class="pre">bigint</span></tt>. <tt class="docutils literal"><span class="pre">value</span></tt> and <tt class="docutils literal"><span class="pre">weight</span></tt> must be numeric.</p>
</dd></dl>
<dl class="function">
<dt>
<tt class="descname">numeric_histogram</tt><big>(</big><em>buckets</em>, <em>value</em><big>)</big> → map<double, double></dt>
<dd><p>Computes an approximate histogram with up to <tt class="docutils literal"><span class="pre">buckets</span></tt> number of buckets
for all <tt class="docutils literal"><span class="pre">value</span></tt>s. This function is equivalent to the variant of
<a class="reference internal" href="#numeric_histogram" title="numeric_histogram"><tt class="xref py py-func docutils literal"><span class="pre">numeric_histogram()</span></tt></a> that takes a <tt class="docutils literal"><span class="pre">weight</span></tt>, with a per-item weight of <tt class="docutils literal"><span class="pre">1</span></tt>.</p>
</dd></dl>
</div>
<div class="section" id="statistical-aggregate-functions">
<h2>Statistical Aggregate Functions</h2>
<dl class="function">
<dt id="corr">
<tt class="descname">corr</tt><big>(</big><em>y</em>, <em>x</em><big>)</big> → double</dt>
<dd><p>Returns correlation coefficient of input values.</p>
</dd></dl>
<dl class="function">
<dt id="covar_pop">
<tt class="descname">covar_pop</tt><big>(</big><em>y</em>, <em>x</em><big>)</big> → double</dt>
<dd><p>Returns the population covariance of input values.</p>
</dd></dl>
<dl class="function">
<dt id="covar_samp">
<tt class="descname">covar_samp</tt><big>(</big><em>y</em>, <em>x</em><big>)</big> → double</dt>
<dd><p>Returns the sample covariance of input values.</p>
</dd></dl>
<dl class="function">
<dt id="regr_intercept">
<tt class="descname">regr_intercept</tt><big>(</big><em>y</em>, <em>x</em><big>)</big> → double</dt>
<dd><p>Returns linear regression intercept of input values. <tt class="docutils literal"><span class="pre">y</span></tt> is the dependent
value. <tt class="docutils literal"><span class="pre">x</span></tt> is the independent value.</p>
</dd></dl>
<dl class="function">
<dt id="regr_slope">
<tt class="descname">regr_slope</tt><big>(</big><em>y</em>, <em>x</em><big>)</big> → double</dt>
<dd><p>Returns linear regression slope of input values. <tt class="docutils literal"><span class="pre">y</span></tt> is the dependent
value. <tt class="docutils literal"><span class="pre">x</span></tt> is the independent value.</p>
</dd></dl>
<dl class="function">
<dt id="stddev">
<tt class="descname">stddev</tt><big>(</big><em>x</em><big>)</big> → double</dt>
<dd><p>This is an alias for <a class="reference internal" href="#stddev_samp" title="stddev_samp"><tt class="xref py py-func docutils literal"><span class="pre">stddev_samp()</span></tt></a>.</p>
</dd></dl>
<dl class="function">
<dt id="stddev_pop">
<tt class="descname">stddev_pop</tt><big>(</big><em>x</em><big>)</big> → double</dt>
<dd><p>Returns the population standard deviation of all input values.</p>
</dd></dl>
<dl class="function">
<dt id="stddev_samp">
<tt class="descname">stddev_samp</tt><big>(</big><em>x</em><big>)</big> → double</dt>
<dd><p>Returns the sample standard deviation of all input values.</p>
</dd></dl>
<dl class="function">
<dt id="variance">
<tt class="descname">variance</tt><big>(</big><em>x</em><big>)</big> → double</dt>
<dd><p>This is an alias for <a class="reference internal" href="#var_samp" title="var_samp"><tt class="xref py py-func docutils literal"><span class="pre">var_samp()</span></tt></a>.</p>
</dd></dl>
<dl class="function">
<dt id="var_pop">
<tt class="descname">var_pop</tt><big>(</big><em>x</em><big>)</big> → double</dt>
<dd><p>Returns the population variance of all input values.</p>
</dd></dl>
<dl class="function">
<dt id="var_samp">
<tt class="descname">var_samp</tt><big>(</big><em>x</em><big>)</big> → double</dt>
<dd><p>Returns the sample variance of all input values.</p>
</dd></dl>
</div>
</div>
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