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Posted to commits@datasketches.apache.org by le...@apache.org on 2020/02/24 06:44:23 UTC
[incubator-datasketches-website] 01/06: Updated top Nav + lots of
little edits.
This is an automated email from the ASF dual-hosted git repository.
leerho pushed a commit to branch Update
in repository https://gitbox.apache.org/repos/asf/incubator-datasketches-website.git
commit de2309012e609b1f885b5af2d475ea19887ffd95
Author: Lee Rhodes <le...@users.noreply.github.com>
AuthorDate: Sun Feb 23 14:37:11 2020 -0800
Updated top Nav + lots of little edits.
Added bigger logos to TopNav, lots of text wording cleanup. Github nav
now points to explanation page.
---
_includes/page_footer.html | 4 +-
_includes/page_header.html | 19 ++-
_includes/site_head.html | 5 +-
css/header.css | 24 ++--
docs/Architecture/Components.md | 6 +-
docs/Architecture/KeyFeatures.md | 4 +-
docs/Architecture/OrderSensitivity.md | 31 ++---
docs/Architecture/SketchesByComponent.md | 14 +--
docs/Community/Downloads.md | 2 +-
docs/HLL/Hll_vs_CS_Hllpp.md | 2 +-
docs/MajorSketchFamilies.md | 40 +++---
docs/Quantiles/DruidApproxHistogramStudy.md | 2 +-
docs/Quantiles/MomentsSketchStudy.md | 2 +-
docs/SketchElements.md | 3 -
docs/SketchOrigins.md | 4 -
docs/TheChallenge.md | 2 +-
docs/Tuple/TupleEngagementExample.md | 2 +-
img/datasketches-HorizontalColor-1.svg | 52 ++++++++
img/datasketches-HorizontalWhite.svg | 31 +++++
img/datasketches-ManColor-3.svg | 27 ++++
img/datasketches-ManWhite.svg | 12 ++
...calWhite.svg => datasketches-VerticalWhite.svg} | 0
img/feather.svg | 138 +++++++++++++++++++++
index.md | 17 +--
24 files changed, 352 insertions(+), 91 deletions(-)
diff --git a/_includes/page_footer.html b/_includes/page_footer.html
index cbedf23..10290eb 100644
--- a/_includes/page_footer.html
+++ b/_includes/page_footer.html
@@ -1,4 +1,4 @@
-<!-- Start page_footer.html include -->
+<!-- Start _include/page_footer.html -->
<footer class="ds-footer">
<div class="container">
<div class="text-center">
@@ -15,4 +15,4 @@
</div>
</div>
</footer>
-<!-- End page_footer.html include -->
+<!-- End _include/page_footer.html -->
diff --git a/_includes/page_header.html b/_includes/page_header.html
index 6795f5d..ad92d80 100644
--- a/_includes/page_header.html
+++ b/_includes/page_header.html
@@ -1,4 +1,4 @@
-<!-- Start page_header.html include -->
+<!-- Start _include/page_header.html -->
<div class="navbar navbar-inverse navbar-static-top ds-nav">
<div class="container">
<div class="navbar-header">
@@ -8,9 +8,8 @@
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
- <!-- navbar-brand: https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css -->
- <!-- /css/main.css:57:.logo -->
- <a class="navbar-brand logo" href="/">DataSketches</a>
+ <a href="/" style="padding-top: 0px; padding-bottom: 0px;">
+ <span class="ds-small-h-logo"></span></a>
</div>
<div class="navbar-collapse collapse">
<ul class="nav navbar-nav navbar-right">
@@ -23,7 +22,7 @@
<span class="fa fa-download"></span> DOWNLOAD</a>
</li>
<li>
- <a href="https://github.com/apache?utf8=%E2%9C%93&q=datasketches&type=&language=">
+ <a href="/docs/Architecture/Components.html">
<span class="fa fa-github"></span> GITHUB</a>
</li>
<li>
@@ -31,12 +30,12 @@
<span class="fa fa-paper-plane"></span> RESEARCH</a>
</li>
<li>
- <a href="https://lists.apache.org/list.html?users@datasketches.apache.org">
- <span class="fa fa-comment"></span> FORUM</a>
+ <a href="/docs/Community" style="padding-top: 0; padding-bottom: 0;">
+ <img class="ds-small-man" src="/img/datasketches-ManWhite.svg"/>COMMUNITY</a>
</li>
<ul class="nav navbar-nav navbar-right ds-nav">
- <li class="dropdown ds-nav">
- <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false"><img class="apache-logo" src="https://www.apache.org/foundation/press/kit/feather.svg"/>Apache <span class="caret"></span></a>
+ <li class="dropdown ds-nav" >
+ <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false" style="padding-top: 0; padding-bottom: 0;"><img class="apache-logo" src="/img/feather.svg"/>Apache <span class="caret"></span></a>
<ul class="dropdown-menu ds-nav">
<li><a href="https://www.apache.org/" target="_blank">Foundation</a></li>
<li><a href="https://www.apache.org/events/current-event" target="_blank">Events</a></li>
@@ -51,4 +50,4 @@
</div>
</div>
</div>
-<!-- End page_header.html include -->
+<!-- End _include/page_header.html -->
diff --git a/_includes/site_head.html b/_includes/site_head.html
index 46d25fb..2e41c9b 100644
--- a/_includes/site_head.html
+++ b/_includes/site_head.html
@@ -1,4 +1,4 @@
-<!-- Start site_head.html include-->
+<!-- Start _include/site_head.html -->
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="description" content="">
@@ -21,9 +21,8 @@
<link rel="stylesheet" href="/css/docs.css">
-
<script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML-full">
</script>
<script src="https://code.jquery.com/jquery.min.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
-<!-- End site_head.html include-->
+<!-- End _include/site_head.html -->
diff --git a/css/header.css b/css/header.css
index b9c59dd..d6bb837 100644
--- a/css/header.css
+++ b/css/header.css
@@ -59,18 +59,23 @@
background-size: 300px 300px;
background-repeat: no-repeat;
background-position: center;
- background-image: url('/img/datasketches_VerticalWhite.svg');
+ background-image: url('/img/datasketches-VerticalWhite.svg');
}
-.y-bootlogo {
- display: inline-block;
+.ds-small-man {
+ width: 35.99px;
+ height: 50px;
+}
+
+.ds-small-h-logo {
+ display: block;
margin: 0 auto;
- width: 100px;
- height: 35px;
- background-size: 100px 35px;
+ width: 117.51px;
+ height: 50px;
+ background-size: 117.51px 50px;
background-repeat: no-repeat;
- background-position: 0px 6px;
- background-image: url('/img/Yahoo_white_small.png');
+ background-position: center;
+ background-image: url('/img/datasketches-HorizontalWhite.svg');
}
.ds-booticon {
@@ -176,9 +181,10 @@ a:hover, a:focus {
}
.apache-logo {
- height: 20px;
+ height: 50px;
margin-right: 5px;
}
+
.dropdown:hover .dropdown-menu {
display: block;
margin-top: 0; // remove the gap so it doesn't close
diff --git a/docs/Architecture/Components.md b/docs/Architecture/Components.md
index bb6cdbf..8cc585c 100644
--- a/docs/Architecture/Components.md
+++ b/docs/Architecture/Components.md
@@ -12,10 +12,10 @@ If you like what you see give us a **Star** on one of these two sites!
* **[Java](https://github.com/apache/incubator-datasketches-java)** (Versioned, Apache Released) This is the original and the most comprehensive collection of sketch algorithms. It has a dependence on the Memory component and the Java Adaptors have a dependence on this component.
-* **[C++/Python](https://github.com/apache/incubator-datasketches-cpp)** (Versioned, Apache Released) This is newer and provides most of the major algorithms available in Java. Our C++ adaptors have a dependence on this component. The Pybind adaptors for Python are included here for all the C++ sketches.
+* **[C++/Python](https://github.com/apache/incubator-datasketches-cpp)** (Versioned, Apache Released) This is newer and provides most of the major algorithms available in Java. Our C++ adaptors have a dependence on this component. The Pybind adaptors for Python are included for all the C++ sketches.
## Adapters
-Apapters integrate the core components into the aggregation APIs of specific data processing systems. Some of these adapters are available as part of the library, other adapters may be directly integrated into the specific data processing system.
+Apapters integrate the core components into the aggregation APIs of specific data processing systems. Some of these adapters are available as part of the library, other adapters are directly integrated into the target data processing application.
### Java Adaptors
* **[Apache Hive](https://github.com/apache/incubator-datasketches-hive)** (Versioned, Apache Released)
@@ -34,7 +34,7 @@ them available to the PostgreSQL database users. PostgreSQL users should downloa
## Other Components
* **[Memory](https://github.com/apache/incubator-datasketches-memory):** (Versioned, Apache Released) This is a low-level library that enables fast access to off-heap memory for Java.
* **[Characterization](https://github.com/apache/incubator-datasketches-characterization):** This is a collection of Java and C++ code that we use for long-running studies of accuracy and speed performance over many different parameters. Feel free to run these tests to reproduce many of the graphs and charts you see on our website.
-* **[Vector (Experimental)](https://github.com/apache/incubator-datasketches-vector):** This component implements the [Frequent Directions Algorithm](https://datasketches.apache.org/docs/Community/Research.html) [GLP16]. It is still experimental in that the theoretical work has not yet supplied a suitable measure of error for production work. It can be used as is, but it will not go through a formal Apache Release until we can find a way to provide better error properties. It has a dep [...]
+* **[Vector (Experimental)](https://github.com/apache/incubator-datasketches-vector):** This component implements the [Frequent Directions Algorithm](/docs/Community/Research.html) [GLP16]. It is still experimental in that the theoretical work has not yet supplied a suitable measure of error for production work. It can be used as is, but it will not go through a formal Apache Release until we can find a way to provide better error properties. It has a dependence on the Memory component.
* **[Website](https://github.com/apache/incubator-datasketches-website):** This repository is the home of our website and is constantly being updated with new material.
diff --git a/docs/Architecture/KeyFeatures.md b/docs/Architecture/KeyFeatures.md
index 00a0c97..3fe4d3f 100644
--- a/docs/Architecture/KeyFeatures.md
+++ b/docs/Architecture/KeyFeatures.md
@@ -23,7 +23,7 @@ layout: doc_page
<h3>Common Sketch Properties</h3>
- * [Sketch Criteria]({{site.docs_dir}}/Architecture/SketchCriteria.html) for all sketches in the library.
+ * Please refer to the [Sketch Criteria]({{site.docs_dir}}/Architecture/SketchCriteria.html) for all sketches in the library.
* Query results are <b>approximate</b> but within well defined error bounds that are user
configurable by trading off sketch size with accuracy.
* Designed for <a href="{{site.docs_dir}}/LargeScale.html">Large-scale</a> computing environments
@@ -33,7 +33,7 @@ layout: doc_page
<a href="https://hive.apache.org/">Hive</a>,
<a href="https://druid.io">Druid</a>,
<a href="https://spark.apache.org">Spark</a>),
-and are heavily used within Yahoo.
+and are heavily used within Yahoo / Verizon-Media.
* <b>Maven deployable</b> and registered with
<a href="https://search.maven.org/#search|ga|1|DataSketches">The Central Repository</a>.
* Comprehensive <b>unit tests</b> and testing tools are provided.
diff --git a/docs/Architecture/OrderSensitivity.md b/docs/Architecture/OrderSensitivity.md
index 689cca5..3f0c3e4 100644
--- a/docs/Architecture/OrderSensitivity.md
+++ b/docs/Architecture/OrderSensitivity.md
@@ -20,44 +20,47 @@ layout: doc_page
under the License.
-->
## Sketching and Order Sensitivity
+Definitions:
-Sketching by its nature is a stochastic process and in general we cannot guarantee _order insensitivity_.
-All of our sketches (frequency, quantiles, Theta and HLL) should be assumed to be _order sensitive_.
-The only "guarantee" that we offer is that the "true value" (computed using brute-force techniques) should be within our approximate error bounds with the specified confidence.
+* **Absolute Order Insensitivity** Any permutation of the order of a given input stream produces the exact same result.
+* **Bounded Order Insensitivity** Any permutation of the order of a given input stream produces a result that is still within the defined error bounds of the sketch and confidence.
-Having said that there are a few exceptions to this "assume order sensitivity" guideline.
+Sketching by its nature is a stochastic process and in general we cannot guarantee _absolute order insensitivity_. However, some of our sketches, with the correct configuration, can meet this definition, but in general, we do not recommend users depending on this strict definition of order insensitivity.
-### Theta Sketches
+Nonetheless, all of our sketches do qualify as being _bounded order sensitive_.
+In other words, the "true value" (computed using brute-force techniques) should be within our approximate error bounds with the specified confidence.
-Only the QuickSelect Sketch (the default) can be order insensitive and ONLY if the final sketch is "trimmed" back to a maximum of _K_ values before an estimate is retrieved. For example:
+
+### Example: Theta Sketches
+
+Only the internal QuickSelect Sketch (the default) can be order insensitive and _iff_ the final sketch is "trimmed" back to a maximum of _K_ values before an estimate is retrieved. For example:
```java
UpdateSketch sk = Sketches.updateSketchBuilder().build();
-for (...) { /* load sketch with > 2K values */ }
-double est = sk.getEstimate(); //this may be order sensitive (but not if sketch is in exact mode)
+for (...) { /* load sketch with > 2 * K values */ }
+double est = sk.getEstimate(); //this may be order sensitive
UpdateSketch sk2 = sk.rebuild(); //trims retained entries back to K
double est2 = sk2.getEstimate(); //this will be order insensitive
```
-If you want a Compact Sketch to be order insensitive, you must _rebuild()_, first than do _compact()_.
+If you want a Compact Sketch to be order insensitive, you must first _rebuild()_, than do _compact()_.
When doing Unions with Theta Sketches, the getResult(...) automatically trims the result back to _K_.
-The impact of the rebuild() is that the error will not be as good as the un-trimmed sketch, but you will get your desired order insensitivity. [For example](https://datasketches.apache.org/docs/Theta/ThetaAccuracyPlots.html).
+The impact of the rebuild() is that the error will not be as good as the un-trimmed sketch, but you will get your desired order insensitivity. [For example](/docs/Theta/ThetaAccuracyPlots.html).
### HLL Sketches
-If you use the _getCompositeEstimate()_, the result should be order insensitive, but is less accurate than the _getEstimate()_, which uses the HIP estimator. Unfortunately, the HIP estimator does not "survive" the union process so the error of the HLL sketches that have gone through a union process generally must fall back on the composite estimator. (This is tracked internally and is rather complex as there are some special cases where the HIP estimator can still be used.)
+HLL sketches used stand-alone, are _bounded order insensitive_. After any merge / union operation the sketch qualifies as
+_absolute order insensitive_, but is less accurate.
-### The HIP Estimator
-The HIP estimator is inherently order sensitive, but provides significantly improved error properties over the composite approach, so it is too important to ignore.
### System Testing and Sketches
There are two primary ways that a "reference" standard is often obtained to use when system testing with sketches:
* Brute Force computation of the correct result. The recommended approach.
-* Assuming some prior test run produced the correct result. I do not recommend this, but many system teams do this anyway. Even if the sketches are working correctly, this can result in double-sided error, so be careful!
+* Assuming some prior test run produced the correct result. This is not recommended, but many system teams do this anyway. Even if the sketches are working correctly, this can result in double-sided error, so be careful!
Given a Brute Force reference, the proper way to establish correctness of the result of a test is to use the upper and lower bounds (or equivalent, depending on the sketch) provided by the sketch. Suppose you use 2-sigma confidence bounds. Then out of 1000 _statistically independent_ trials (runs), ~50 of the results will be outside the 2-sigma bounds.
diff --git a/docs/Architecture/SketchesByComponent.md b/docs/Architecture/SketchesByComponent.md
index b1bc5c8..afa367d 100644
--- a/docs/Architecture/SketchesByComponent.md
+++ b/docs/Architecture/SketchesByComponent.md
@@ -19,8 +19,8 @@ layout: doc_page
specific language governing permissions and limitations
under the License.
-->
-# Sketches by Component
-
+# Sketches by [Component Repository](https://github.com/apache?utf8=%E2%9C%93&q=datasketches)
+
The DataSketches Library is organized into the following repository groups:
## Java
@@ -29,7 +29,7 @@ The DataSketches Library is organized into the following repository groups:
This repository has the core-java sketching classes, which are leveraged by some of the other repositories.
This repository has no external dependencies outside of the DataSketches/memory repository, Java and TestNG for unit tests.
This code is versioned and the latest release can be obtained from
-<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/java">incubator-datasketches-java<a/>.
+<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/java">incubator-datasketches-java</a>.
<b>High-level Repositories Structure</b>
@@ -53,7 +53,7 @@ org.apache.datasketches.tuple.Strings | A Tuple sketch with a Summary of an arra
### incubator-datasketches-memory
This code is versioned and the latest release can be obtained from
-<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/memory">incubator-datasketches-memory<a/>.
+<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/memory">incubator-datasketches-memory</a>.
Memory Packages | Package Description
-------------------------------|---------------------
@@ -65,7 +65,7 @@ This repository contains Hive UDFs and UDAFs for use within Hadoop grid enviornm
This code has dependencies on sketches-core as well as Hadoop and Hive.
Users of this code are advised to use Maven to bring in all the required dependencies.
This code is versioned and the latest release can be obtained from
-<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/hive">incubator-datasketches-hive<a/>.
+<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/hive">incubator-datasketches-hive</a>.
Sketches-hive Packages | Package Description
-------------------------------------|---------------------
@@ -82,7 +82,7 @@ This repository contains Pig User Defined Functions (UDF) for use within Hadoop
This code has dependencies on sketches-core as well as Hadoop and Pig.
Users of this code are advised to use Maven to bring in all the required dependencies.
This code is versioned and the latest release can be obtained from
-<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/pig">incubator-datasketches-pig<a/>.
+<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/pig">incubator-datasketches-pig</a>.
Sketches-pig Packages | Package Description
-----------------------------------|---------------------
@@ -124,7 +124,7 @@ org.apache.datasketches.characterization.uniquecount | Base Profiles for Unique
### incubator-datasketches-vector
-This component implements the [Frequent Directions Algorithm](https://datasketches.apache.org/docs/Community/Research.html) [GLP16]. It is still experimental in that the theoretical work has not yet supplied a suitable measure of error for production work. It can be used as is, but it will not go through a formal Apache Release until we can find a way to provide better error properties. It has a dependence on the Memory component.
+This component implements the [Frequent Directions Algorithm](/docs/Community/Research.html) [GLP16]. It is still experimental in that the theoretical work has not yet supplied a suitable measure of error for production work. It can be used as is, but it will not go through a formal Apache Release until we can find a way to provide better error properties. It has a dependence on the Memory component.
## C++ and Python
diff --git a/docs/Community/Downloads.md b/docs/Community/Downloads.md
index 25bedd7..6f13f7c 100644
--- a/docs/Community/Downloads.md
+++ b/docs/Community/Downloads.md
@@ -22,7 +22,7 @@ layout: doc_page
## Downloads
### Download Zip Files
-Choose the most recent release version from
+Choose the most recent release version from one of these mirrors:
[incubator-datasketches-xxx](https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches).
### Download Java Jar Files
diff --git a/docs/HLL/Hll_vs_CS_Hllpp.md b/docs/HLL/Hll_vs_CS_Hllpp.md
index 8418288..110a335 100644
--- a/docs/HLL/Hll_vs_CS_Hllpp.md
+++ b/docs/HLL/Hll_vs_CS_Hllpp.md
@@ -226,7 +226,7 @@ Depending on the chosen configuration, the *HllSketch* can be from one to almost
****
* [1] [DataSketches HllSketch GitHub](https://github.com/apache/incubator-datasketches-java/tree/master/src/main/java/org/apache/datasketches/hll)
-* [2] [DataSketches HllSketch JavaDocs](https://datasketches.apache.org/api/java/snapshot/apidocs/index.html)
+* [2] [DataSketches HllSketch JavaDocs](/api/java/snapshot/apidocs/index.html)
* [3] [HyperLogLogPlus GitHub](https://github.com/addthis/stream-lib/blob/master/src/main/java/com/clearspring/analytics/stream/cardinality/HyperLogLogPlus.java)
* [4] [Google: HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/40671.pdf)
* [5] The Root-Mean-Square of the Relative Error (RMS-RE) is sensitive to bias of the mean if there is any. However, if the bias is zero RMS-RE will produce the same results as the theoretical Relative Standard Error (RSE) of the stochastic process.
diff --git a/docs/MajorSketchFamilies.md b/docs/MajorSketchFamilies.md
index 2943044..32a3da2 100644
--- a/docs/MajorSketchFamilies.md
+++ b/docs/MajorSketchFamilies.md
@@ -21,11 +21,13 @@ layout: doc_page
-->
# Sketch Capability Matrix
+Use the following table to compare the capabilities of the different sketch families.
+
<div>
<table>
<tr style="font-weight:bold"><td colspan="2"></td><td colspan="3">Languages</td><td colspan="4">Set Operations</td><td colspan="4">System Integrations</td><td colspan="5">Misc.</td></tr>
-<tr style="font-weight:bold"><td>Type</td><td>Sketch</td><td>Java</td><td>C++</td><td>Python</td><td>Union</td><td>Inter-section</td><td>Difference</td><td>Jaccard</td><td>Hive</td><td>Pig</td><td>Druid<sup>1</sup></td><td>Spark<sup>2</sup></td><td>Con-current</td><td>Compact</td><td>Java Generics</td><td>Off-Heap</td><td>Error Bounds</td></tr>
+<tr style="font-weight:bold"><td>Type</td><td>Sketch</td><td>Java</td><td>C++</td><td>Python</td><td>Union</td><td>Inter-section</td><td>Difference</td><td>Jaccard</td><td>Hive</td><td>Pig</td><td>Druid<sup>1</sup></td><td>Spark<sup>2</sup></td><td>Con-current</td><td>Compact</td><td>Java Generics</td><td>Off Java Heap</td><td>Error Bounds</td></tr>
<tr style="font-weight:bold"><td colspan="18">Major Sketches</td></tr>
<tr><td>Cardinality/FM85</td><td>CpcSketch</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td></td><td></td><td></td><td>Y</td><td>Y</td><td></td><td></td><td></td><td>Y</td><td></td><td></td><td>Y</td></tr>
@@ -39,7 +41,7 @@ layout: doc_page
<tr><td>Frequencies</td><td>ItemsSketch</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td></td><td></td><td></td><td>Y</td><td>Y</td><td></td><td></td><td></td><td></td><td>Y</td><td></td><td>Y</td></tr>
<tr><td>Sampling</td><td>ReservoirLongsSketch</td><td>Y</td><td></td><td></td><td>Y</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Y</td></tr>
<tr><td>Sampling</td><td>ReserviorItemsSketch</td><td>Y</td><td></td><td></td><td>Y</td><td></td><td></td><td></td><td></td><td>Y</td><td></td><td></td><td></td><td></td><td>Y</td><td></td><td>Y</td></tr>
-<tr><td>Sampling</td><td>VarOptItemsSketch</td><td>Y</td><td></td><td></td><td>Y</td><td></td><td></td><td></td><td></td><td>Y</td><td></td><td></td><td></td><td></td><td>Y</td><td></td><td>Y</td></tr>
+<tr><td>Sampling</td><td>VarOptItemsSketch</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td></td><td></td><td></td><td></td><td>Y</td><td></td><td></td><td></td><td></td><td>Y</td><td></td><td>Y</td></tr>
<tr style="font-weight:bold"><td colspan="18">Specialty Sketches</td></tr>
@@ -54,7 +56,7 @@ layout: doc_page
</div>
<sup>1</sup> Integrated into Druid<br>
-<sup>2</sup> Example Code on website
+<sup>2</sup> Example Code on website<br>
<sup>3</sup> Example Code in test/.../tuple/aninteger
----
@@ -65,7 +67,7 @@ layout: doc_page
## Cardinality Sketches
### CPC Sketch: Estimating Stream Cardinalities more efficiently than the famous HLL sketch!
-This sketch was developed by the late Keven Lang, our chief scientist at the time, is an amazing *tour de force* of scientific design and engineering and has substantially better accuracy / per stored size than the famous HLL sketch. The theory and demonstration of its performance is detailed in Lang's paper [Back to the Future: an Even More Nearly Optimal Cardinality Estimation Algorithm](https://arxiv.org/abs/1708.06839). This sketch is available in Java, C++ and Python.
+This sketch was developed by the late Keven J. Lang, our chief scientist at the time. It is an amazing *tour de force* of scientific design and engineering and has substantially better accuracy / per stored size than the famous HLL sketch. The theory and demonstration of its performance is detailed in Lang's paper [Back to the Future: an Even More Nearly Optimal Cardinality Estimation Algorithm](https://arxiv.org/abs/1708.06839).
### [Theta Sketches]({{site.docs_dir}}/Theta/ThetaSketchFramework.html): Estimating Stream Expression Cardinalities
Internet content, search and media companies like Yahoo, Google, Facebook, etc., collect many tens of billions of event records from the many millions of users to their web sites each day. These events can be classified by many different dimensions, such as the page visited and user location and profile information. Each event also contains some unique identifiers associated with the user, specific device (cell phone, tablet, or computer) and the web browser used.
@@ -77,12 +79,12 @@ These same unique identifiers will appear on every page that the user visits. I
Computing cardinalities with massive data requires lots of computer resources and time.
However, if an approximate answer to these problems is acceptable, [Theta Sketches]({{site.docs_dir}}/Theta/ThetaSketchFramework.html) can provide reasonable estimates, in a single pass, orders of magnitude faster, even fast enough for analysis in near-real time.
-The [theta/Sketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/theta/Sketch.java) can operate both on-heap and off-heap, has powerful Union, Intersection, AnotB and Jaccard operators, has a high-performance concurrent form for multi-threaded environments, has both immutable compact, and updatable representations, and is quite fast. It is available in Java, C++ and Python. Because of its flexibility, it is one of the most popular [...]
+The [theta/Sketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/theta/Sketch.java) can operate both on-heap and off-heap, has powerful Union, Intersection, AnotB and Jaccard operators, has a high-performance concurrent form for multi-threaded environments, has both immutable compact, and updatable representations, and is quite fast. Because of its flexibility, it is one of the most popular sketches in our library.
### [Tuple Sketches]({{site.docs_dir}}/Tuple/TupleOverview.html): Extending Theta Sketches to Perform Associative Analysis
-It is often not enough to perform stream expressions on sets of unique identifiers, it is very valuable to be able to associate additive data with these identifiers, such as impression counts, clicks or timestamps. Tuple Sketches are a natural extension of the Theta sketch and have Java Genric forms, that enable the user do define the sketch with arbitrary "summary" data. The [tuple/Sketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datas [...]
+It is often not enough to perform stream expressions on sets of unique identifiers, it is very valuable to be able to associate additive data with these identifiers, such as impression counts, clicks or timestamps. Tuple Sketches are a natural extension of the Theta sketch and have Java Genric forms that enable the user to define the sketch with arbitrary "summary" data. The [tuple/Sketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datask [...]
-The Tuple sketch is effectively infinitely extendable and there are several common variants of the Tuple Sketch, which also serve as examples on how to extend the base classes, that are also in the library, including:
+The Tuple sketch is effectively infinitely extendable and there are several common variants of the Tuple Sketch, which also serve as examples on how to extend the base classes that are also in the library, including:
- [tuple/adouble/DoubleSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/adouble/DoubleSketch.java) with a single column of *double* values as the *summary*.
- [tuple/aninteger/IntegerSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/aninteger/IntegerSketch.java) with a single column of *int* values as the *summary*.
@@ -91,12 +93,12 @@ The Tuple sketch is effectively infinitely extendable and there are several comm
### [HyperLogLog Sketches]({{site.docs_dir}}/HLL/HLL.html): Estimating Stream Cardinalities
-The HyperLogLog (HLL) is a cardinality sketch similar to the above Theta sketches except they are anywhere from 2 to 16 times smaller in size. The HLL sketches can be Unioned, but set intersection and difference operations are not provided intrinsically, because the resulting error would be quite poor. If your application only requires cardinality estimation and Unioning and space is at a premium, the HLL sketch provided could be your best choice.
+The HyperLogLog (HLL) is a cardinality sketch similar to the above Theta sketches except they are anywhere from 2 to 16 times smaller in size. The HLL sketches can be merged via the Union operator, but set intersection and difference operations are not provided intrinsically, because the resulting error would be quite poor. If your application only requires cardinality estimation and merging and space is at a premium, the HLL or CPC sketches would be your best choice.
-The [hll/HllSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/hll/HllSketch.java) can operate both on-heap and off-heap, provides the Union operators, and has both immutable compact, and updatable representations. It is available in Java, C++ and Python.
+The [hll/HllSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/hll/HllSketch.java) can operate both on-heap and off-heap, provides the Union operators, and has both immutable compact and updatable representations.
### [HyperLogLog Map Sketch]({{site.docs_dir}}/HLL/HllMap.html): Estimating Stream Cardinalities of Key-Value Pairs
-This is a specially designed sketch that addresses the problem of individually tracking value cardinalities of Key-Value (K,V) pairs in real-time, where the number of keys can be very large, such as IP addresses, or Geo identifiers, etc. Assigning individual sketches to each key would create unnecessary overhead. This sketch streamlines the process with much better space management. This [hllmap/UniqueCountMap](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/j [...]
+This is a specially designed sketch that addresses the problem of individually tracking value cardinalities of Key-Value (K,V) pairs in real-time, where the number of keys can be very large, such as IP addresses, or Geo identifiers, etc. Assigning individual sketches to each key would create unnecessary overhead. This sketch streamlines the process with much better space management. This [hllmap/UniqueCountMap](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/j [...]
## Quantiles Sketches
@@ -105,32 +107,31 @@ There are many situations where is valuable to understand the distribution of va
<img class="doc-img-full" src="{{site.docs_img_dir}}/quantiles/TimeSpentHistogram.png" alt="TimeSpentHistogram" />
-There are two different families of quantiles sketches, the original [quantiles/DoublesSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/quantiles/DoublesSketch.java), which can be operated either on-heap or off-heap, and is also available in a Java Generic form for arbitrary comparable objects. It is only available in Java.
+There are two different families of quantiles sketches, the original [quantiles/DoublesSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/quantiles/DoublesSketch.java), which can be operated either on-heap or off-heap, and is also available in a Java Generic form for arbitrary comparable objects.
-Later we developed the [kll/KllFloatsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/kll/KllFloatsSketch.java) (Named after its authors), which is also a quantiles sketch, that achieves near optimal small size for a given accuracy. It is only available on-heap. It is available in Java, C++ and Python.
+Later we developed the [kll/KllFloatsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/kll/KllFloatsSketch.java) (Named after its authors), which is also a quantiles sketch, that achieves near optimal small size for a given accuracy.
## Frequent Items / Heavy Hitters Sketches
### [Frequent Items Sketches]({{site.docs_dir}}/Frequency/FrequentItemsOverview.html): Finding the Heavy Hitter Objects from a Stream
-It is very useful to be able to scan a stream of objects, such as song titles, and be able to quickly identify those items that occur most frequently. The term <i>Heavy Hitter</i> is defined to be an item that occurs more frequently than some fractional share of the overall count of items
-in the stream including duplicates. Suppose you have a stream of 1M song titles, but in that stream there are only 100K song titles that are unique. If any single title consumes more than 10% of the stream elements it is a Heavy Hitter, and the 10% is a threshold parameter we call epsilon.
+It is very useful to be able to scan a stream of objects, such as song titles, and be able to quickly identify those titles that occur most frequently. The term <i>Heavy Hitter</i> is defined to be an item that occurs more frequently than its fair share of occurrences. This "fair share" is simply the total count of all occurrences of all items divided by the number of distinct items. Suppose you have a stream of 1M song titles, but in that stream there are only 100K song titles that ar [...]
-The accuracy of a Frequent Items Sketch is proportional to the configured size of the sketch, the larger the sketch, the smaller is the epsilon threshold that can detect Heavy Hitters. This sketch is available in two forms, as the [frequencies/LongsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/frequencies/LongsSketch.java) used for processing a stream of tuples {*long*, weight}, and the [frequencies/ItemsSketch](https://gi [...]
+The accuracy of a Frequent Items Sketch is proportional to the configured size of the sketch, the larger the sketch, the smaller is the epsilon threshold that can detect Heavy Hitters. This sketch is available in two forms, as the [frequencies/LongsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/frequencies/LongsSketch.java) used for processing a stream of tuples {*long*, weight}, and the [frequencies/ItemsSketch](https://gi [...]
### [Frequent Distinct Tuples Sketch]({{site.docs_dir}}/Frequency/FrequentDistinctTuplesSketch.html): Finding the Heavy Hitter tuples from a Stream.
Suppose our data is a stream of pairs {IP address, User ID} and we want to identify the IP addresses that have the most distinct User IDs. Or conversely, we would like to identify the User IDs that have the most distinct IP addresses. This is a common challenge in the analysis of big data and the FDT sketch helps solve this problem using probabilistic techniques.
More generally, given a multiset of tuples with *N* dimensions *{d1,d2, d3, …, dN}*, and a primary subset of dimensions *M < N*, our task is to identify the combinations of *M* subset dimensions that have the most frequent number of distinct combinations of the *N - M* non-primary dimensions.
-The [fdt/FdtSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/fdt/FdtSketch.java) is currently only available in Java, but because it is an extension of the Tuple Sketch family, it inherits many of the same properties: it can operate both on-heap and off-heap, includes both Union and Intersection operators, has both immutable compact, and updatable representations.
+The [fdt/FdtSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/fdt/FdtSketch.java) is currently only available in Java, but because it is an extension of the Tuple Sketch family, it inherits many of the same properties: it can operate both on-heap and off-heap, it includes both Union and Intersection operators, and it has both immutable compact and updatable representations.
### Frequent Directions: Distributed, mergeable Singular Value Decomposition
-Part of a new separate sketches-vector package, Frequent Directions is in many ways a generalization of the Frequent Items sketch to handle vector data. This sketch computes an approximate singular value decomposition (SVD) of a matrix, providing a projection matrix that can be used for dimensionality reduction. SVD is a key technique in many recommender systems, providing shopping suggestions based on a customer's past purchases compared with other similar customers. This sketch is stil [...]
+Part of a new separate datasketches-vector component, Frequent Directions is in many ways a generalization of the Frequent Items sketch in order to handle vector data. This sketch computes an approximate singular value decomposition (SVD) of a matrix, providing a projection matrix that can be used for dimensionality reduction. SVD is a key technique in many recommender systems, such as providing shopping suggestions based on a customer's past purchases compared with other similar custome [...]
## Sampling Sketches
-### [Sampling Sketches]({{site.docs_dir}}/Sampling/ReservoirSampling.html): Uniform Sampling of a Stream into a fixed size space
-This family of sketches implements an enhanced version of the famous Reservoir sampling algorithm and extends it with the capabilities that large-scale distributed systems really need: mergability (even with different sized sketches), uses Java Generics so that the base classes can be trivially extended for any input type (even polymorphic types), and an extensible means of performing serialization and deserialization.
+### [Sampling Sketches]({{site.docs_dir}}/Sampling/ReservoirSampling.html): Uniform and Weighted Sampling of a Stream into a fixed size space
+This family of sketches implements an enhanced version of the famous Reservoir sampling algorithm and extends it with the capabilities that large-scale distributed systems really need: mergability (even with different sized sketches). The Java implementaion uses Java Generics so that the base classes can be trivially extended for any input type (even polymorphic types), and also enables an extensible means of performing serialization and deserialization.
The [sampling/ReservoirLongsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/sampling/ReservoirLongsSketch.java) accepts a stream of *long* values as identifiers with a weight of one, and produces a result Reservoir of a pre-determined size that represents a uniform random sample of the stream.
@@ -138,6 +139,5 @@ The [sampling/ReservoirItemsSketch](https://github.com/apache/incubator-datasket
The [sampling/VarOptItemsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/sampling/VarOptItemsSketch.java) extends the Reservoir family to weighted sampling, additionally providing subset sum estimates from the sample with provably optimal variance.
-This family is currently only available in Java.
diff --git a/docs/Quantiles/DruidApproxHistogramStudy.md b/docs/Quantiles/DruidApproxHistogramStudy.md
index 5506c51..e0b95f9 100644
--- a/docs/Quantiles/DruidApproxHistogramStudy.md
+++ b/docs/Quantiles/DruidApproxHistogramStudy.md
@@ -25,7 +25,7 @@ The goal of this article is to compare the accuracy performance of the Druid bui
Please get familiar with the [Definitions]({{site.docs_dir}}/Quantiles/Definitions.html) for quantiles.
-Compare this study with the DataSketches [Quantiles StreamA Study](https://datasketches.apache.org/docs/Quantiles/QuantilesStreamAStudy.html) with the same input data.
+Compare this study with the DataSketches [Quantiles StreamA Study](/docs/Quantiles/QuantilesStreamAStudy.html) with the same input data.
## Versions
diff --git a/docs/Quantiles/MomentsSketchStudy.md b/docs/Quantiles/MomentsSketchStudy.md
index 4ffb504..96d636b 100644
--- a/docs/Quantiles/MomentsSketchStudy.md
+++ b/docs/Quantiles/MomentsSketchStudy.md
@@ -25,7 +25,7 @@ The goal of this article is to compare the accuracy performance of the Moments S
Please get familiar with the [Definitions]({{site.docs_dir}}/Quantiles/Definitions.html) for quantiles.
-Compare this study with the DataSketches [Quantiles StreamA Study](https://datasketches.apache.org/docs/Quantiles/QuantilesStreamAStudy.html) with the same input data.
+Compare this study with the DataSketches [Quantiles StreamA Study](/docs/Quantiles/QuantilesStreamAStudy.html) with the same input data.
## Versions
diff --git a/docs/SketchElements.md b/docs/SketchElements.md
index 2bdec1c..e6388e1 100644
--- a/docs/SketchElements.md
+++ b/docs/SketchElements.md
@@ -60,6 +60,3 @@ The sketch only needs to see each item in the stream once.
be merged without losing accuracy.
* Approximate. As an example, for unique count sketches the relative error bounds
are a function of the configured size of the sketch.
-
-With this background, let's examine some of the
-<a href="{{site.docs_dir}}/Architecture/KeyFeatures.html">Key Features</a> of the DataSketches library.
diff --git a/docs/SketchOrigins.md b/docs/SketchOrigins.md
index f58f4a6..760d43f 100644
--- a/docs/SketchOrigins.md
+++ b/docs/SketchOrigins.md
@@ -48,10 +48,6 @@ Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches</a> by
<a href="https://www2.warwick.ac.uk/fac/sci/dcs/people/graham_cormode/">Graham Cormode</a>, et al,
is an excellent review of this field.
-At this point it is useful to describe the
-<a href="/docs/SketchElements.html">sketch elements</a> of a common sub-class of sketching
-algorithms used for solving the
-<a href="https://en.wikipedia.org/wiki/Count-distinct_problem">count-distinct</a> problem.
________
<sup>1</sup><small>Also known as "Approximate Query Processing", see
diff --git a/docs/TheChallenge.md b/docs/TheChallenge.md
index 7ed2e17..93f313a 100644
--- a/docs/TheChallenge.md
+++ b/docs/TheChallenge.md
@@ -116,7 +116,7 @@ Processing the continuous real-time stream from the edge web servers is possible
### Big Win #7: Resource Utilization and Cost
-It has been our experience at Yahoo, that a good implementation of these large analysis systems using sketches reduces the overall cost of the system considerably. It is difficult to quote exact numbers as your mileage will vary as it is system and data dependent.
+It has been our experience at Yahoo/VM, that a good implementation of these large analysis systems using sketches reduces the overall cost of the system considerably. It is difficult to quote exact numbers as your mileage will vary as it is system and data dependent.
diff --git a/docs/Tuple/TupleEngagementExample.md b/docs/Tuple/TupleEngagementExample.md
index bc0df9e..8e56f5d 100644
--- a/docs/Tuple/TupleEngagementExample.md
+++ b/docs/Tuple/TupleEngagementExample.md
@@ -65,7 +65,7 @@ Once we have our 30 day sketches, we merge all 30 sketches together into one fin
To help us code our example we will leverage the [IntegerSketch Package](https://github.com/apache/incubator-datasketches-java/tree/master/src/main/java/org/apache/datasketches/tuple/aninteger) from the library. This package consists of 5 classes, the _IntegerSketch_ and 4 helper classes, all of which extend generic classes of the parent _tuple_ package. Normally, the user/developer would develop these 5 classes to solve a particular analysis problem. These 5 classes can serve as an exa [...]
-Please refer to the [Tuple Overview](https://datasketches.apache.org/docs/Tuple/TupleOverview.html) section on this website for a quick review of how the Tuple Sketch works.
+Please refer to the [Tuple Overview](/docs/Tuple/TupleOverview.html) section on this website for a quick review of how the Tuple Sketch works.
### IntegerSketch class
```java
diff --git a/img/datasketches-HorizontalColor-1.svg b/img/datasketches-HorizontalColor-1.svg
new file mode 100755
index 0000000..66fc1dd
--- /dev/null
+++ b/img/datasketches-HorizontalColor-1.svg
@@ -0,0 +1,52 @@
+<svg id="Layer_1" data-name="Layer 1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="839" height="357" viewBox="0 0 839 357">
+ <defs>
+ <linearGradient id="linear-gradient" x1="13.66" y1="106.04" x2="210.12" y2="269.11" gradientUnits="userSpaceOnUse">
+ <stop offset="0" stop-color="#c70000"/>
+ <stop offset="0" stop-color="#ffa017"/>
+ <stop offset="1" stop-color="#c70000"/>
+ </linearGradient>
+ <linearGradient id="linear-gradient-2" x1="42.07" y1="71.81" x2="238.53" y2="234.89" gradientUnits="userSpaceOnUse">
+ <stop offset="0" stop-color="#c70000"/>
+ <stop offset="0" stop-color="#ffa017"/>
+ <stop offset="0.71" stop-color="#c90501"/>
+ <stop offset="1" stop-color="#4d0000"/>
+ </linearGradient>
+ <linearGradient id="linear-gradient-3" x1="388.26" y1="238.77" x2="659.2" y2="152.74" xlink:href="#linear-gradient-2"/>
+ <linearGradient id="linear-gradient-4" x1="393.86" y1="256.4" x2="664.8" y2="170.37" xlink:href="#linear-gradient-2"/>
+ <linearGradient id="linear-gradient-5" x1="401.37" y1="280.05" x2="672.31" y2="194.02" xlink:href="#linear-gradient-2"/>
+ <linearGradient id="linear-gradient-6" x1="403.2" y1="285.82" x2="674.14" y2="199.79" xlink:href="#linear-gradient-2"/>
+ <linearGradient id="linear-gradient-7" x1="409.51" y1="305.69" x2="680.45" y2="219.66" xlink:href="#linear-gradient-2"/>
+ <linearGradient id="linear-gradient-8" x1="411.46" y1="311.83" x2="682.4" y2="225.8" xlink:href="#linear-gradient-2"/>
+ <linearGradient id="linear-gradient-9" x1="419.57" y1="337.36" x2="690.51" y2="251.33" xlink:href="#linear-gradient-2"/>
+ <linearGradient id="linear-gradient-10" x1="424.42" y1="352.63" x2="695.36" y2="266.6" xlink:href="#linear-gradient-2"/>
+ </defs>
+ <title>ApacheDataSketch_ColorHorizontal</title>
+ <g>
+ <g>
+ <circle cx="93.7" cy="172.4" r="24.7" fill="url(#linear-gradient)"/>
+ <path d="M199.1,81.5c16.7-22.5,45-60.4,52.8-69.6,0-.2-18.2,5.4-18.2,5.4s-29.3,41.4-48.5,74.6-34.8,60.5-73,138.1c-10.4,21-22.2,16.3-38.3,1.8S19.2,180.7,19.2,156c0-29.4,21.9-36.5,37.6-39.6,8.8-1.8,12.1,1.2,12.1,3.5s-9,5.2-16.3,3.2c-2.1-.6,2,5.1,11.3,5.1,5.9,0,17.6-2.8,17.6-8.6,0-3.5-3.3-7.8-16-7.8-9.2,0-26.2,5-35,10.3s-19.2,14.1-19.2,27.2c0,40,79.6,103.5,88.5,109.1,0-.2-13.4,38.3-13.4,59.4,0,15.3,2.4,26.2,2.9,27.3s27.7-20.6,51.6-27.8,46.8-14.1,92.8-13.9c0,0-47.4-7.3-85-23.9-40.8-18.1 [...]
+ </g>
+ <g>
+ <path d="M205.6,185.1h26.7c21.6,0,32.3,14.7,32.3,36.4s-11.3,35.9-33,35.9h-26Zm25.6,63.6c16.4,0,23.1-10.6,23.1-27.2s-6-27.8-22.5-27.8H215.6v55Z" fill="#4d0000"/>
+ <path d="M272.4,243.6c0-12.2,8.9-14.8,22.4-16.5,7.7-.8,12.6-2.1,12.6-7.4s-3.9-9.2-11.1-9.2-12.3,3.9-12.6,10.1H275c.4-8.8,7.1-16.7,21.2-16.7,4.9,0,9.3.9,12.6,2.8s7,7.2,7,14.2v24.4c0,4.2,1,6.4,5.5,5.5h.4v6.3a14.4,14.4,0,0,1-4.8.8c-5.6,0-8.3-2.7-9.3-8.2h-.1c-3.3,4.6-8.6,8.7-17.5,8.7S272.4,253.6,272.4,243.6Zm35.1-5.5v-8.5c-2.6,1.6-7.1,2.9-11.4,3.7-8.6,1.4-14.2,3-14.2,10.2s5.1,8.3,10.4,8.3C302.7,251.8,307.5,244.2,307.5,238.1Z" fill="#4d0000"/>
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diff --git a/img/datasketches-HorizontalWhite.svg b/img/datasketches-HorizontalWhite.svg
new file mode 100755
index 0000000..69a0e4a
--- /dev/null
+++ b/img/datasketches-HorizontalWhite.svg
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diff --git a/img/datasketches-ManColor-3.svg b/img/datasketches-ManColor-3.svg
new file mode 100644
index 0000000..3ff3621
--- /dev/null
+++ b/img/datasketches-ManColor-3.svg
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+<?xml version="1.0" encoding="UTF-8"?>
+<svg width="239px" height="332px" viewBox="0 0 239 332" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+ <!-- Generator: Sketch 52.2 (67145) - http://www.bohemiancoding.com/sketch -->
+ <title>ManColor</title>
+ <desc>Created with Sketch.</desc>
+ <defs>
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diff --git a/index.md b/index.md
index 5d5574f..c1c6b37 100644
--- a/index.md
+++ b/index.md
@@ -4,6 +4,7 @@ title: DataSketches
layout: html_page
id: home
---
+<!-- Start /index.md -->
<!--
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
@@ -23,7 +24,6 @@ id: home
under the License.
-->
-<!-- Start index.md -->
<link rel="stylesheet" type="text/css" href="css/index.css">
<link rel="stylesheet" type="text/css" href="css/header.css">
@@ -37,13 +37,15 @@ id: home
<a href="https://en.wikipedia.org/wiki/Stochastic" style="color: #EDE379"><i>stochastic</i></a>
<a href="https://en.wikipedia.org/wiki/Streaming_algorithm" style="color: #EDE379"><i>streaming algorithms</i></a></p>
<!--<p class="lead" style="font-size: 16px; line-height: 1.0; margin-bottom: 15px"><i>"Excellence in theoretically informed algorithm engineering" -- Graham Cormode</i></p> -->
+ <!--
<p>
<a class="btn btn-lg btn-outline-inverse" href="overview.html"><span class="fa fa-info-circle"></span> Overview</a>
<a class="btn btn-lg btn-outline-inverse" href="/docs/Community/Downloads.html"><span class="fa fa-download"></span> Download</a>
<a class="btn btn-lg btn-outline-inverse" href="https://github.com/apache?utf8=%E2%9C%93&q=datasketches"><span class="fa fa-github"></span> GitHub</a>
<a class="btn btn-lg btn-outline-inverse" href="/docs/Community/Research.html"><span class="fa fa-paper-plane"></span> Research</a>
- <a class="btn btn-lg btn-outline-inverse" href="https://lists.apache.org/list.html?users@datasketches.apache.org"><span class="fa fa-comment"></span> Contact Us</a>
+ <a class="btn btn-lg btn-outline-inverse" href="/docs/Community/index.html" style="padding-top: 5px; padding-bottom: 0px; padding-left: 11.64px; padding-right: 12px;"><img class="ds-small-man" src="/img/datasketches-ManWhite.svg"/>Community</a>
</p>
+ -->
</div>
</div>
</div>
@@ -57,7 +59,7 @@ id: home
<p>If approximate results are acceptable, there is a class of specialized algorithms, called streaming algorithms, or <a href="/docs/SketchOrigins.html">sketches</a> that can produce results orders-of magnitude faster and with mathematically proven error bounds. For interactive queries there may not be other viable alternatives, and in the case of real-time analysis, sketches are the only known solution.</p>
-<p>For any system that needs to extract useful information from big data these sketches are a required toolkit that should be tightly integrated into their analysis capabilities. This technology has helped Yahoo successfully reduce data processing times from days to hours or minutes on a number of its internal platforms.</p>
+<p>For any system that needs to extract useful information from big data these sketches are a required toolkit that should be tightly integrated into their analysis capabilities. This technology has helped Yahoo (Verizon Media) successfully reduce data processing times from days to hours or minutes on a number of its internal platforms.</p>
<p>This site is dedicated to providing key sketch algorithms of production quality. Contributions are welcome from those in the big data community interested in further development of this science and art.</p>
</div>
@@ -82,8 +84,9 @@ id: home
</a>
<p class="text-justify">This library has been specifically designed for big data systems.
Included are adaptors for Hadoop Pig and Hive, which also can be used as examples for other systems,
- and many other capabilities typically required in big data analysis systems.
- For example, a Memory package for managing large off-heap memory data structures.</p>
+ and many other capabilities typically required in big data analysis systems, such as compatible
+ binary representations across languages (Java, C++, Python) and platforms.
+ </p>
</div>
<div class="col-md-4">
@@ -100,6 +103,4 @@ id: home
</div>
</div>
</div>
-
-
-<!-- End index.md -->
+<!-- End /index.md -->
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