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<h2><a class="anchor" aria-hidden="true" id="cardinality-aggregator"></a><a href="#cardinality-aggregator" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Cardinality aggregator</h2>
<p>Computes the cardinality of a set of Apache Druid dimensions, using HyperLogLog to estimate the cardinality. Please note that this
aggregator will be much slower than indexing a column with the hyperUnique aggregator. This aggregator also runs over a dimension column, which
means the string dimension cannot be removed from the dataset to improve rollup. In general, we strongly recommend using the hyperUnique aggregator
instead of the cardinality aggregator if you do not care about the individual values of a dimension.</p>
<pre><code class="hljs css language-json">{
  "type": "cardinality",
  "name": "&lt;output_name&gt;",
  "fields": [ &lt;dimension1&gt;, &lt;dimension2&gt;, ... ],
  "byRow": &lt;false | true&gt; # (optional, defaults to false),
  "round": &lt;false | true&gt; # (optional, defaults to false)
}
</code></pre>
<p>Each individual element of the &quot;fields&quot; list can be a String or <a href="/docs/0.23.0/querying/dimensionspecs.html">DimensionSpec</a>. A String dimension in the fields list is equivalent to a DefaultDimensionSpec (no transformations).</p>
<p>The HyperLogLog algorithm generates decimal estimates with some error. &quot;round&quot; can be set to true to round off estimated
values to whole numbers. Note that even with rounding, the cardinality is still an estimate. The &quot;round&quot; field only
affects query-time behavior, and is ignored at ingestion-time.</p>
<h3><a class="anchor" aria-hidden="true" id="cardinality-by-value"></a><a href="#cardinality-by-value" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Cardinality by value</h3>
<p>When setting <code>byRow</code> to <code>false</code> (the default) it computes the cardinality of the set composed of the union of all dimension values for all the given dimensions.</p>
<ul>
<li>For a single dimension, this is equivalent to</li>
</ul>
<pre><code class="hljs css language-sql"><span class="hljs-keyword">SELECT</span> <span class="hljs-keyword">COUNT</span>(<span class="hljs-keyword">DISTINCT</span>(<span class="hljs-keyword">dimension</span>)) <span class="hljs-keyword">FROM</span> &lt;datasource&gt;
</code></pre>
<ul>
<li>For multiple dimensions, this is equivalent to something akin to</li>
</ul>
<pre><code class="hljs css language-sql"><span class="hljs-keyword">SELECT</span> <span class="hljs-keyword">COUNT</span>(<span class="hljs-keyword">DISTINCT</span>(<span class="hljs-keyword">value</span>)) <span class="hljs-keyword">FROM</span> (
  <span class="hljs-keyword">SELECT</span> dim_1 <span class="hljs-keyword">as</span> <span class="hljs-keyword">value</span> <span class="hljs-keyword">FROM</span> &lt;datasource&gt;
  <span class="hljs-keyword">UNION</span>
  <span class="hljs-keyword">SELECT</span> dim_2 <span class="hljs-keyword">as</span> <span class="hljs-keyword">value</span> <span class="hljs-keyword">FROM</span> &lt;datasource&gt;
  <span class="hljs-keyword">UNION</span>
  <span class="hljs-keyword">SELECT</span> dim_3 <span class="hljs-keyword">as</span> <span class="hljs-keyword">value</span> <span class="hljs-keyword">FROM</span> &lt;datasource&gt;
)
</code></pre>
<h3><a class="anchor" aria-hidden="true" id="cardinality-by-row"></a><a href="#cardinality-by-row" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Cardinality by row</h3>
<p>When setting <code>byRow</code> to <code>true</code> it computes the cardinality by row, i.e. the cardinality of distinct dimension combinations.
This is equivalent to something akin to</p>
<pre><code class="hljs css language-sql"><span class="hljs-keyword">SELECT</span> <span class="hljs-keyword">COUNT</span>(*) <span class="hljs-keyword">FROM</span> ( <span class="hljs-keyword">SELECT</span> DIM1, DIM2, DIM3 <span class="hljs-keyword">FROM</span> &lt;datasource&gt; <span class="hljs-keyword">GROUP</span> <span class="hljs-keyword">BY</span> DIM1, DIM2, DIM3 )
</code></pre>
<p><strong>Example</strong></p>
<p>Determine the number of distinct countries people are living in or have come from.</p>
<pre><code class="hljs css language-json">{
  <span class="hljs-attr">"type"</span>: <span class="hljs-string">"cardinality"</span>,
  <span class="hljs-attr">"name"</span>: <span class="hljs-string">"distinct_countries"</span>,
  <span class="hljs-attr">"fields"</span>: [ <span class="hljs-string">"country_of_origin"</span>, <span class="hljs-string">"country_of_residence"</span> ]
}
</code></pre>
<p>Determine the number of distinct people (i.e. combinations of first and last name).</p>
<pre><code class="hljs css language-json">{
  <span class="hljs-attr">"type"</span>: <span class="hljs-string">"cardinality"</span>,
  <span class="hljs-attr">"name"</span>: <span class="hljs-string">"distinct_people"</span>,
  <span class="hljs-attr">"fields"</span>: [ <span class="hljs-string">"first_name"</span>, <span class="hljs-string">"last_name"</span> ],
  <span class="hljs-attr">"byRow"</span> : <span class="hljs-literal">true</span>
}
</code></pre>
<p>Determine the number of distinct starting characters of last names</p>
<pre><code class="hljs css language-json">{
  <span class="hljs-attr">"type"</span>: <span class="hljs-string">"cardinality"</span>,
  <span class="hljs-attr">"name"</span>: <span class="hljs-string">"distinct_last_name_first_char"</span>,
  <span class="hljs-attr">"fields"</span>: [
    {
     <span class="hljs-attr">"type"</span> : <span class="hljs-string">"extraction"</span>,
     <span class="hljs-attr">"dimension"</span> : <span class="hljs-string">"last_name"</span>,
     <span class="hljs-attr">"outputName"</span> :  <span class="hljs-string">"last_name_first_char"</span>,
     <span class="hljs-attr">"extractionFn"</span> : { <span class="hljs-attr">"type"</span> : <span class="hljs-string">"substring"</span>, <span class="hljs-attr">"index"</span> : <span class="hljs-number">0</span>, <span class="hljs-attr">"length"</span> : <span class="hljs-number">1</span> }
    }
  ],
  <span class="hljs-attr">"byRow"</span> : <span class="hljs-literal">true</span>
}
</code></pre>
<h2><a class="anchor" aria-hidden="true" id="hyperunique-aggregator"></a><a href="#hyperunique-aggregator" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>HyperUnique aggregator</h2>
<p>Uses <a href="http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf">HyperLogLog</a> to compute the estimated cardinality of a dimension that has been aggregated as a &quot;hyperUnique&quot; metric at indexing time.</p>
<pre><code class="hljs css language-json">{
  <span class="hljs-attr">"type"</span> : <span class="hljs-string">"hyperUnique"</span>,
  <span class="hljs-attr">"name"</span> : &lt;output_name&gt;,
  <span class="hljs-attr">"fieldName"</span> : &lt;metric_name&gt;,
  <span class="hljs-attr">"isInputHyperUnique"</span> : <span class="hljs-literal">false</span>,
  <span class="hljs-attr">"round"</span> : <span class="hljs-literal">false</span>
}
</code></pre>
<p>&quot;isInputHyperUnique&quot; can be set to true to index precomputed HLL (Base64 encoded output from druid-hll is expected).
The &quot;isInputHyperUnique&quot; field only affects ingestion-time behavior, and is ignored at query-time.</p>
<p>The HyperLogLog algorithm generates decimal estimates with some error. &quot;round&quot; can be set to true to round off estimated
values to whole numbers. Note that even with rounding, the cardinality is still an estimate. The &quot;round&quot; field only
affects query-time behavior, and is ignored at ingestion-time.</p>
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