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AuthorDate: Fri Oct 25 16:56:12 2024 +0000
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---
output/docs/QuantilesAll/QuantilesOverview.html | 103 ++++++++++++++++--------
1 file changed, 69 insertions(+), 34 deletions(-)
diff --git a/output/docs/QuantilesAll/QuantilesOverview.html
b/output/docs/QuantilesAll/QuantilesOverview.html
index 5bf984f9..c34e7285 100644
--- a/output/docs/QuantilesAll/QuantilesOverview.html
+++ b/output/docs/QuantilesAll/QuantilesOverview.html
@@ -323,7 +323,7 @@
-->
<h1 id="introduction-to-the-quantile-sketches">Introduction to the Quantile
Sketches</h1>
-<p>This is a quick overview of the quantiles sketches in the library. Each of
these quantile types may have one or more specific implementaions and some
variation in API depending on the language. Three of the quantile sketches have
mathematically provable error bounds while the fourth is an empirical
algorithm.</p>
+<p>This is an overview of the quantiles sketches in the library. Each of these
quantile types may have one or more specific implementaions and some variation
in API depending on the language. Three of the quantile sketches have
mathematically provable error bounds while the fourth is an empirical
algorithm.</p>
<p>The three sketches with mathematically provable error bounds are:</p>
@@ -341,7 +341,7 @@
<p>For the Classic and KLL sketches, the difference between the rank upper
bound and the rank lower bound is a 99% confidence interval and is an additive
constant for all normalized ranks between 0.0 and 1.0. The specific error is a
function of the parameter <i>K</i> of the sketch and can be derived from the
sketch. For example, if the rank error for a given K is 1%, then the error of
a result rank of .01 is +/- .01 with a 99% confidence; the error of a result
rank of .99 is +/- .01 wit [...]
-<p>The REQ sketch is special in that it’s error is also relative to the actual
result rank (thus its name: Relative Error Quantiles). It was designed to
proved very high rank accuacy for either the high end of the range of ranks
(close to 1.0) or, based on the user’s choice, the low end of ranks (close to
0.0). Please refer to the spcific documentation about the REQ sketch.</p>
+<p>The REQ sketch is special in that its error is also relative to the actual
result rank (thus its name: Relative Error Quantiles). It was designed to
provide very high rank accuacy for either the high end of the range of ranks
(close to 1.0) or, based on the user’s choice, the low end of ranks (close to
0.0). Please refer to the spcific documentation about the REQ sketch.</p>
<p>Although upper and lower quantile bounds can be approximately computed from
the upper and lower rank bounds, and the difference between the quantile bounds
is also an approximate confidence interval, the size of the quantile confidence
interval may not be meaningful and is not constrained by the defined rank error
of the sketch.</p>
@@ -349,114 +349,149 @@
<p>These sketches have many parallel methods. Please refer to the sketch API
documentation by language for more information.</p>
-<h3 id="the-classic-quantiles-sketch">The Classic Quantiles Sketch</h3>
-
+<h3 id="classic-quantiles-sketch">Classic Quantiles Sketch</h3>
<ul>
<li>Java
<ul>
<li>Repo: <a
href="https://github.com/apache/datasketches-java";>https://github.com/apache/datasketches-java</a></li>
- <li>Package: org.apache.datasketches.quantiles</li>
+ <li>Package: <a
href="https://github.com/apache/datasketches-java/tree/master/src/main/java/org/apache/datasketches/quantiles";>org.apache.datasketches.quantiles</a></li>
+ <li>Dedicated <em>double</em> and generic <em>item</em> implentations
for arbitrary comparable types.
+ <ul>
+ <li>The <em>double</em> implementation can be configured for
off-heap operation.</li>
+ </ul>
+ </li>
</ul>
</li>
<li>C++
<ul>
<li>Repo: <a
href="https://github.com/apache/datasketches-cpp";>https://github.com/apache/datasketches-cpp</a></li>
+ <li>Directory: <a
href="https://github.com/apache/datasketches-cpp/tree/master/quantiles";>quantiles</a></li>
+ <li>Template implementation for arbitrary comparable types.</li>
</ul>
</li>
<li>Python
<ul>
<li>Repo: <a
href="https://github.com/apache/datasketches-python";>https://github.com/apache/datasketches-python</a></li>
+ <li>Dedicated <em>float</em>, <em>double</em>, <em>integer</em> and
arbitrary Python object implementations</li>
</ul>
</li>
- <li>Key Features (both Java & C++)
+ <li>Key Features (Java, C++ and Python)
<ul>
+ <li>Accuracy %: a function of <em>K</em> and independent of rank.</li>
<li>User selectable search criteria QuantileSearchCriteria:
<ul>
- <li>Exclusive, which is compatible with the KLL and older Quantiles
Sketch</li>
- <li>Inclusive, a common definition in some of the theoretical
literature.</li>
+ <li>Inclusive, a common definition in some of the theoretical
literature (default).</li>
+ <li>Exclusive, which is compatible with older Quantiles Sketch.</li>
</ul>
</li>
- <li>Accuracy %: a function of <em>K</em> and independent of rank.</li>
- <li>Dedicated <em>double</em> and generic <em>item</em> implentations
for arbitrary comparable types.</li>
- <li>The <em>double</em> implementation can be configured for off-heap
operation.</li>
</ul>
</li>
</ul>
-<h3 id="the-kll-sketch">The KLL Sketch</h3>
-
+<h3 id="kll-sketch">KLL Sketch</h3>
<ul>
<li>Java
<ul>
- <li>Repo: <a
href="https://github.com/apache/datasketches-java";>https://github.com/apache/datasketches-java</a>:</li>
- <li>Package: org.apache.datasketches.kll</li>
+ <li>Repo: <a
href="https://github.com/apache/datasketches-java";>https://github.com/apache/datasketches-java</a></li>
+ <li>Package: <a
href="https://github.com/apache/datasketches-java/tree/master/src/main/java/org/apache/datasketches/kll";>org.apache.datasketches.kll</a></li>
+ <li>Dedicated <em>float</em>, <em>double</em>, <em>long</em>, and
generic <em>item</em> implementations.
+ <ul>
+ <li>The <em>float</em>, <em>double</em>, and <em>long</em>
implementations can be configured for off-heap operation.</li>
+ </ul>
+ </li>
</ul>
</li>
<li>C++
<ul>
<li>Repo: <a
href="https://github.com/apache/datasketches-cpp";>https://github.com/apache/datasketches-cpp</a></li>
- <li>Directory: kll</li>
+ <li>Directory: <a
href="https://github.com/apache/datasketches-cpp/tree/master/kll";>kll</a></li>
+ <li>Template implementation for arbitrary comparable types.</li>
</ul>
</li>
<li>Python
<ul>
<li>Repo: <a
href="https://github.com/apache/datasketches-python";>https://github.com/apache/datasketches-python</a></li>
+ <li>Dedicated <em>float</em>, <em>double</em>, <em>integer</em> and
arbitrary Python object implementations</li>
</ul>
</li>
- <li>Key Features (both Java & C++)
+ <li>Key Features (Java, C++ and Python)
<ul>
+ <li>Accuracy %: a function of <em>K</em> and independent of rank.</li>
<li>User selectable comparison QuantileSearchCriteria:
<ul>
- <li>Exclusive, which is compatible with the KLL and older Quantiles
Sketch</li>
- <li>Inclusive, a common definition in some of the theoretical
literature.</li>
+ <li>Inclusive, a common definition in some of the theoretical
literature (default).</li>
+ <li>Exclusive, which is compatible with older Quantiles Sketch.</li>
</ul>
</li>
- <li>Accuracy %: a function of <em>K</em> and independent of rank.</li>
<li>Near optimal accuracy per sketch size compared to other constant
accuracy quantiles sketches.</li>
- <li>Java: Dedicated <em>float</em>, <em>double</em>, <em>long</em>, and
generic <em>item</em> implementations.</li>
- <li>The <em>float</em>, <em>double</em>, and <em>long</em>
implementations can be configured for off-heap operation.</li>
- <li>C++: Template implementation for arbitrary comparible types.</li>
- <li>Python: Dedicated <em>float</em> and <em>integer</em>
implementations</li>
</ul>
</li>
</ul>
-<h3 id="the-req-sketch">The REQ Sketch</h3>
-
+<h3 id="req-sketch">REQ Sketch</h3>
<ul>
<li>Java
<ul>
<li>Repo: <a
href="https://github.com/apache/datasketches-java";>https://github.com/apache/datasketches-java</a></li>
- <li>Package: org.apache.datasketches.req</li>
+ <li>Package: <a
href="https://github.com/apache/datasketches-java/tree/master/src/main/java/org/apache/datasketches/req";>org.apache.datasketches.req</a></li>
+ <li>Dedicated <em>float</em> implementation.</li>
</ul>
</li>
<li>C++
<ul>
<li>Repo: <a
href="https://github.com/apache/datasketches-cpp";>https://github.com/apache/datasketches-cpp</a></li>
- <li>Directory: req</li>
+ <li>Directory: <a
href="https://github.com/apache/datasketches-cpp/tree/master/req";>req</a></li>
+ <li>Template implementation for arbitrary comparable types.</li>
</ul>
</li>
<li>Python
<ul>
<li>Repo: <a
href="https://github.com/apache/datasketches-python";>https://github.com/apache/datasketches-python</a></li>
+ <li>Dedicated <em>float</em>, <em>integer</em> and arbitrary Python
object implementations</li>
</ul>
</li>
- <li>Key Features (both Java & C++)
+ <li>Key Features (Java, C++ and Python)
<ul>
<li>Accuracy %: a function of <em>K</em> and <strong>relative</strong>
with respect to rank. The user can select either High Rank Accuracy (HRA) or
Low Rank Accuracy (LRA). This enables extremely high accuracy for the ends of
the rank domain. E.g., 99.999%ile quantiles.</li>
<li>User selectable comparison QuantileSearchCriteria:
<ul>
- <li>Exclusive, which is compatible with the KLL and older Quantiles
Sketch</li>
- <li>Inclusive, a common definition in some of the theoretical
literature.</li>
+ <li>Inclusive, a common definition in some of the theoretical
literature (default).</li>
+ <li>Exclusive, which is compatible with older Quantiles Sketch.</li>
</ul>
</li>
- <li>Java: Dedicated <em>float</em> implementation.</li>
- <li>C++: Template implementation for arbitrary comparible types.</li>
- <li>Python: Dedicated <em>float</em> and <em>integer</em>
implementations.</li>
</ul>
</li>
</ul>
+<h3 id="t-digest">T-Digest</h3>
+<ul>
+ <li>Java
+ <ul>
+ <li>Repo: <a
href="https://github.com/apache/datasketches-java";>https://github.com/apache/datasketches-java</a></li>
+ <li>Package: <a
href="https://github.com/apache/datasketches-java/tree/master/src/main/java/org/apache/datasketches/tdigest";>org.apache.datasketches.req</a></li>
+ <li>Dedicated <em>double</em> implementation.</li>
+ </ul>
+ </li>
+ <li>C++
+ <ul>
+ <li>Repo: <a
href="https://github.com/apache/datasketches-cpp";>https://github.com/apache/datasketches-cpp</a></li>
+ <li>Directory: <a
href="https://github.com/apache/datasketches-cpp/tree/master/tdigest";>req</a></li>
+ <li>Template implementation for <em>float</em> and <em>double</em>
types.</li>
+ </ul>
+ </li>
+ <li>Python
+ <ul>
+ <li>Repo: <a
href="https://github.com/apache/datasketches-python";>https://github.com/apache/datasketches-python</a></li>
+ <li>Dedicated <em>float</em> and <em>double</em> implementations</li>
+ </ul>
+ </li>
+ <li>Key Features (Java, C++ and Python)
+ <ul>
+ <li>Works on numeric (floating point) types only.</li>
+ <li>Accuracy: a function of <em>K</em> and <strong>relative</strong>
with respect to rank. Prioritizes both high rank accuracy and low rank accuracy
with lower accuracy in the middle.</li>
+ </ul>
+ </li>
+</ul>
</div> <!-- End content -->
</div> <!-- End row -->
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