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 <p>This is a high performance implementation of Phillipe Flajolet's HLL sketch but with significantly improved error behavior.
  <a href="classdatasketches_1_1HllSketchImpl.html#details">More...</a></p>

 <p>Inherited by CouponList&lt; A &gt;, and HllArray&lt; A &gt;.</p>
 <a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
 <div class="textblock"><h3>template&lt;typename A&gt;<br />
 class datasketches::HllSketchImpl&lt; A &gt;</h3>

 <p>This is a high performance implementation of Phillipe Flajolet's HLL sketch but with significantly improved error behavior. </p>
 <p>If the ONLY use case for sketching is counting uniques and merging, the HLL sketch is a reasonable choice, although the highest performing in terms of accuracy for storage space consumed is CPC (Compressed Probabilistic Counting). For large enough counts, this HLL version (with HLL_4) can be 2 to 16 times smaller than the Theta sketch family for the same accuracy.</p>
 <p>This implementation offers three different types of HLL sketch, each with different trade-offs with accuracy, space and performance. These types are specified with the <a class="el" href="namespacedatasketches.html#a76c1ad9235877691984d49415bb263e4">target_hll_type</a> parameter.</p>
 <p>In terms of accuracy, all three types, for the same <em>lg_config_k</em>, have the same error distribution as a function of <em>n</em>, the number of unique values fed to the sketch. The configuration parameter <em>lg_config_k</em> is the log-base-2 of <em>K</em>, where <em>K</em> is the number of buckets or slots for the sketch.</p>
 <p>During warmup, when the sketch has only received a small number of unique items (up to about 10% of <em>K</em>), this implementation leverages a new class of estimator algorithms with significantly better accuracy.</p>
 <p>This sketch also offers the capability of operating off-heap. Given a WritableMemory object created by the user, the sketch will perform all of its updates and internal phase transitions in that object, which can actually reside either on-heap or off-heap based on how it is configured. In large systems that must update and merge many millions of sketches, having the sketch operate off-heap avoids the serialization and deserialization costs of moving sketches to and from off-heap memory-mapped files, for example, and eliminates big garbage collection delays.</p>
 <p>author Jon Malkin author Lee Rhodes author Kevin Lang </p>
 </div><hr/>The documentation for this class was generated from the following files:<ul>
 <li>hll/include/<a class="el" href="hll_8hpp_source.html">hll.hpp</a></li>
 <li>hll/include/<a class="el" href="HllSketchImpl_8hpp_source.html">HllSketchImpl.hpp</a></li>
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	<div class="title">HllSketchImpl< A > Class Template Reference<span class="mlabels"><span class="mlabel">abstract</span></span></div> </div>
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	<p>This is a high performance implementation of Phillipe Flajolet's HLL sketch but with significantly improved error behavior.
	<a href="classdatasketches_1_1HllSketchImpl.html#details">More...</a></p>

	<p>Inherited by CouponList< A >, and HllArray< A >.</p>
	<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
	<div class="textblock"><h3>template<typename A><br />
	class datasketches::HllSketchImpl< A ></h3>

	<p>This is a high performance implementation of Phillipe Flajolet's HLL sketch but with significantly improved error behavior. </p>
	<p>If the ONLY use case for sketching is counting uniques and merging, the HLL sketch is a reasonable choice, although the highest performing in terms of accuracy for storage space consumed is CPC (Compressed Probabilistic Counting). For large enough counts, this HLL version (with HLL_4) can be 2 to 16 times smaller than the Theta sketch family for the same accuracy.</p>
	<p>This implementation offers three different types of HLL sketch, each with different trade-offs with accuracy, space and performance. These types are specified with the <a class="el" href="namespacedatasketches.html#a76c1ad9235877691984d49415bb263e4">target_hll_type</a> parameter.</p>
	<p>In terms of accuracy, all three types, for the same <em>lg_config_k</em>, have the same error distribution as a function of <em>n</em>, the number of unique values fed to the sketch. The configuration parameter <em>lg_config_k</em> is the log-base-2 of <em>K</em>, where <em>K</em> is the number of buckets or slots for the sketch.</p>
	<p>During warmup, when the sketch has only received a small number of unique items (up to about 10% of <em>K</em>), this implementation leverages a new class of estimator algorithms with significantly better accuracy.</p>
	<p>This sketch also offers the capability of operating off-heap. Given a WritableMemory object created by the user, the sketch will perform all of its updates and internal phase transitions in that object, which can actually reside either on-heap or off-heap based on how it is configured. In large systems that must update and merge many millions of sketches, having the sketch operate off-heap avoids the serialization and deserialization costs of moving sketches to and from off-heap memory-mapped files, for example, and eliminates big garbage collection delays.</p>
	<p>author Jon Malkin author Lee Rhodes author Kevin Lang </p>
	</div><hr/>The documentation for this class was generated from the following files:<ul>
	<li>hll/include/<a class="el" href="hll_8hpp_source.html">hll.hpp</a></li>
	<li>hll/include/<a class="el" href="HllSketchImpl_8hpp_source.html">HllSketchImpl.hpp</a></li>
	<li>hll/include/<a class="el" href="HllSketchImpl-internal_8hpp_source.html">HllSketchImpl-internal.hpp</a></li>
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