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new 84d6850 update documentation for main branch
84d6850 is described below
commit 84d68507730b72fc7c7289ad485303abeaee5763
Author: GitHub Actions <[email protected]>
AuthorDate: Wed Sep 24 17:44:27 2025 +0000
update documentation for main branch
---
404.html | 237 +++++++-
contributors-guide/index.html | 247 +++++++-
datasets-generators/index.html | 247 +++++++-
index.html | 239 +++++++-
overview-methodology/index.html | 239 +++++++-
queries/index.html | 269 ++++++++-
search/search_index.json | 2 +-
single-node-benchmarks/index.html | 255 +++++++-
.../index.html | 654 ++++++++++++++-------
9 files changed, 2067 insertions(+), 322 deletions(-)
diff --git a/404.html b/404.html
index b4ab0f2..1adadbe 100644
--- a/404.html
+++ b/404.html
@@ -249,6 +249,26 @@
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index af36e39..1a673c6 100644
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diff --git a/datasets-generators/index.html b/datasets-generators/index.html
index 262ff07..58632ab 100644
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+++ b/datasets-generators/index.html
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+
<li class="md-nav__item">
<a href="https://sedona.apache.org"; class="md-nav__link">
@@ -1956,7 +2169,7 @@
<li>Hierarchical Thomas: Multi-level clustering (cities → neighborhoods →
points), useful for mimicking urban settlement patterns.</li>
</ul>
<p>These options let you tailor the spatial skew to your benchmarking needs.
</p>
-<p>See the distributions page to learn more about the supported spatial
distributions, the parameters that control them, and how they impact the
data.</p>
+<p>See the <a href="../spatialbench-distributions/">SpatialBench Data
Distributions</a> page to learn more about the supported spatial distributions,
the parameters that control them, and how they impact the data.</p>
<h2 id="data-generators">Data generators<a class="headerlink"
href="#data-generators" title="Permanent link">¶</a></h2>
<p>You can generate the tables for Scale Factor 1 with the following
command:</p>
<div class="highlight"><pre><span></span><code>spatialbench-cli -s 1
--format=parquet --output-dir sf1-parquet
@@ -1991,7 +2204,7 @@
<span class="md-icon" title="Last update">
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UTC">September 24, 2025 07:55:49</span>
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git-revision-date-localized-plugin-datetime" title="September 24, 2025 17:43:26
UTC">September 24, 2025 17:43:26</span>
</span>
@@ -2044,13 +2257,13 @@
- <a href="../queries/" class="md-footer__link md-footer__link--next"
aria-label="Next: Run the SpatialBench Queries">
+ <a href="../spatialbench-distributions/" class="md-footer__link
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diff --git a/index.html b/index.html
index 833f76c..6ae6357 100644
--- a/index.html
+++ b/index.html
@@ -266,6 +266,26 @@
+ <li class="md-tabs__item">
+ <a href="spatialbench-distributions/" class="md-tabs__link">
+
+
+
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+
+ </a>
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+
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+
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<a href="queries/" class="md-tabs__link">
@@ -1086,7 +1106,7 @@
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+ SpatialBench Data Distributions
</span>
@@ -1097,7 +1117,7 @@
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aria-expanded="false">
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+ <a href="spatialbench-distributions/" class="md-nav__link">
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@@ -1279,7 +1299,7 @@
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+ SpatialBench Queries
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@@ -1290,7 +1310,7 @@
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@@ -1301,12 +1321,12 @@
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+ <a href="queries/" class="md-nav__link">
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+ Run the SpatialBench Queries
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@@ -1483,7 +1503,7 @@
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+ <a href="single-node-benchmarks/" class="md-nav__link">
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@@ -1839,7 +2052,7 @@ with a star schema extended with spatial attributes like
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- <span class="git-revision-date-localized-plugin
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diff --git a/overview-methodology/index.html b/overview-methodology/index.html
index f331293..51e3164 100644
--- a/overview-methodology/index.html
+++ b/overview-methodology/index.html
@@ -268,6 +268,26 @@
+ <li class="md-tabs__item">
+ <a href="../spatialbench-distributions/" class="md-tabs__link">
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@@ -1106,7 +1126,7 @@
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+ SpatialBench Data Distributions
</span>
@@ -1117,7 +1137,7 @@
<nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label"
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+ <a href="../spatialbench-distributions/" class="md-nav__link">
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+ SpatialBench Data Distributions
</span>
@@ -1299,7 +1319,7 @@
<span class="md-ellipsis">
- Single node benchmarks
+ SpatialBench Queries
</span>
@@ -1310,7 +1330,7 @@
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aria-expanded="false">
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- Single node benchmarks
+ SpatialBench Queries
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@@ -1321,12 +1341,12 @@
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- <a href="../single-node-benchmarks/" class="md-nav__link">
+ <a href="../queries/" class="md-nav__link">
<span class="md-ellipsis">
- SpatialBench Single Node Benchmarks
+ Run the SpatialBench Queries
</span>
@@ -1492,7 +1512,7 @@
<span class="md-ellipsis">
- Contributors Guide
+ Single node benchmarks
</span>
@@ -1503,7 +1523,7 @@
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aria-expanded="false">
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+ Single node benchmarks
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@@ -1514,12 +1534,12 @@
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- <a href="../contributors-guide/" class="md-nav__link">
+ <a href="../single-node-benchmarks/" class="md-nav__link">
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+ SpatialBench Single Node Benchmarks
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- <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 07:55:49
UTC">September 24, 2025 07:55:49</span>
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diff --git a/queries/index.html b/queries/index.html
index cbe7358..7132bee 100644
--- a/queries/index.html
+++ b/queries/index.html
@@ -8,7 +8,7 @@
- <link rel="prev" href="../datasets-generators/">
+ <link rel="prev" href="../spatialbench-distributions/">
<link rel="next" href="../single-node-benchmarks/">
@@ -263,6 +263,26 @@
+
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tabindex="">
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<svg xmlns="http://www.w3.org/2000/svg"; viewBox="0 0 24
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+ SpatialBench Data Distributions
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+ <li class="md-nav__item">
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@@ -768,20 +875,17 @@
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tabindex="">
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@@ -889,6 +993,8 @@
+
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+
+
+
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md-nav__item--nested">
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checked>
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tabindex="0">
+ <label class="md-nav__link" for="__nav_4" id="__nav_4_label"
tabindex="">
<span class="md-ellipsis">
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+ SpatialBench Data Distributions
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@@ -921,10 +1030,10 @@
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@@ -932,20 +1041,130 @@
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+ SpatialBench Data Distributions
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+
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+ </label>
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+
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+
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+ <a href="#normal" class="md-nav__link">
+ <span class="md-ellipsis">
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+ </span>
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+
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+
+ <li class="md-nav__item">
+ <a href="#diagonal" class="md-nav__link">
+ <span class="md-ellipsis">
+ Diagonal
+ </span>
+ </a>
+
+</li>
+
+ <li class="md-nav__item">
+ <a href="#bit" class="md-nav__link">
+ <span class="md-ellipsis">
+ Bit
+ </span>
+ </a>
+
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+
+ <li class="md-nav__item">
+ <a href="#sierpinski" class="md-nav__link">
+ <span class="md-ellipsis">
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+ </span>
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+
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+
+ <li class="md-nav__item">
+ <a href="#thomas-process" class="md-nav__link">
+ <span class="md-ellipsis">
+ Thomas Process
+ </span>
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+
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+
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+ <a href="#hierarchical-thomas" class="md-nav__link">
+ <span class="md-ellipsis">
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+ </span>
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+ <a href="#references" class="md-nav__link">
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+ </span>
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+
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@@ -964,17 +1183,20 @@
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@@ -1098,10 +1320,10 @@
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@@ -1164,10 +1386,10 @@
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@@ -1704,63 +1926,72 @@
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
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- <a href="#why-spatialbench" class="md-nav__link">
+ <a href="#uniform" class="md-nav__link">
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+
+</li>
+
+ <li class="md-nav__item">
+ <a href="#normal" class="md-nav__link">
<span class="md-ellipsis">
- Why SpatialBench?
+ Normal
</span>
</a>
</li>
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- <a href="#hardware-and-software" class="md-nav__link">
+ <a href="#diagonal" class="md-nav__link">
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- Hardware and software
+ Diagonal
</span>
</a>
</li>
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- <a href="#accurately-comparing-different-engines" class="md-nav__link">
+ <a href="#bit" class="md-nav__link">
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+ Bit
</span>
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- <a href="#engine-tuning-in-benchmarks" class="md-nav__link">
+ <a href="#sierpinski" class="md-nav__link">
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+ Sierpinski
</span>
</a>
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- <a href="#open-source-benchmarks-vs-vendor-benchmarks" class="md-nav__link">
+ <a href="#thomas-process" class="md-nav__link">
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- Open source benchmarks vs. vendor benchmarks
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</span>
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+ <a href="#hierarchical-thomas" class="md-nav__link">
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+ Hierarchical Thomas
</span>
</a>
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- <a href="#future-work" class="md-nav__link">
+ <a href="#references" class="md-nav__link">
<span class="md-ellipsis">
- Future work
+ References
</span>
</a>
@@ -1782,7 +2013,7 @@
- <a
href="https://github.com/apache/sedona-spatialbench/blob/main/docs/overview-methodology.md";
title="Edit this page" class="md-content__button md-icon" rel="edit">
+ <a
href="https://github.com/apache/sedona-spatialbench/blob/main/docs/spatialbench-distributions.md";
title="Edit this page" class="md-content__button md-icon" rel="edit">
<svg xmlns="http://www.w3.org/2000/svg"; viewBox="0 0 24 24"><path d="M10
20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4zm10.2-7c.1 0
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</a>
@@ -1790,7 +2021,7 @@
- <h1>SpatialBench Methodology</h1>
+ <h1>SpatialBench Data Distributions</h1>
<!---
Licensed to the Apache Software Foundation (ASF) under one
@@ -1807,55 +2038,78 @@
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
---><p>SpatialBench is an open benchmark suite of representative spatial
queries designed to evaluate the performance of different engines at multiple
scale factors.</p>
-<p>The SpatialBench queries are a great way to compare the relative
performance between engines for analytical spatial workloads. You can use a
small scale factor for single-machine queries, and a large scale factor to
benchmark an engine that distributes computations in the cloud.</p>
-<p>Let’s take a deeper look at why SpatialBench is so essential.</p>
-<h2 id="why-spatialbench">Why SpatialBench?<a class="headerlink"
href="#why-spatialbench" title="Permanent link">¶</a></h2>
-<p>Spatial workflows encompass queries such as spatial joins, spatial
filtering, and spatial-specific operations, including KNN joins.</p>
-<p>General-purpose analytics query benchmarks don’t cover spatial queries.
They focus on analytical queries, such as joins and aggregations, on tabular
data. Here are some popular analytical benchmarks:</p>
+--><p>SpatialBench offers a set of spatial distributions to generate synthetic
datasets with different levels of skew and realism. Each distribution has its
own mathematical foundation, parameters, and characteristic spatial patterns.
The choice of distribution directly determines whether your data looks like
evenly spaced dots on a map, concentrated hotspots, or layered urban
clusters.</p>
+<h2 id="uniform">Uniform<a class="headerlink" href="#uniform" title="Permanent
link">¶</a></h2>
+<p>The simplest case: every point is drawn independently from a uniform
distribution in the unit square [0,1]^2.</p>
+<div class="arithmatex">
+<div class="MathJax_Preview">
+X \sim U(0,1), \quad Y \sim U(0,1)
+</div>
+<script type="math/tex; mode=display">
+X \sim U(0,1), \quad Y \sim U(0,1)
+</script>
+</div>
+<p>There are no parameters to adjust here. The result is an even, flat
distribution — useful as a baseline, but one that rarely resembles any
real-world spatial dataset. If your goal is to test systems without the
confounding factor of skew, this is the place to start.</p>
+<h2 id="normal">Normal<a class="headerlink" href="#normal" title="Permanent
link">¶</a></h2>
+<p>The normal distribution introduces clustering. Both coordinates are drawn
from a Gaussian with configurable mean and standard deviation:</p>
+<div class="arithmatex">
+<div class="MathJax_Preview">
+X, Y \sim \mathcal{N}(\mu, \sigma^2), \quad \text{clamped to } [0,1]
+</div>
+<script type="math/tex; mode=display">
+X, Y \sim \mathcal{N}(\mu, \sigma^2), \quad \text{clamped to } [0,1]
+</script>
+</div>
+<p>Here, <code>mu</code> determines where the hotspot sits in the square,
while <code>sigma</code> sets the spread - a small <code>sigma</code> produces
a sharp, dense cluster, while a larger sigma spreads points more thinly across
space. This is appropriate if you want to mimic a single dense center of
activity, like one city in an otherwise empty region. The tradeoff is that it’s
too simplistic for modeling multiple hotspots or urban complexity.</p>
+<h2 id="diagonal">Diagonal<a class="headerlink" href="#diagonal"
title="Permanent link">¶</a></h2>
+<p>The diagonal distribution forces correlation between x and y. With
probability percentage, a point is placed exactly on the line y=x. Otherwise,
it is perturbed by Gaussian noise with width controlled by buffer. The result
is a band of points hugging the diagonal.</p>
+<p>This pattern is not realistic geographically, but it is useful for
experiments that need a known correlation structure — for example, seeing how
indexing or filtering behaves when coordinates are not independent.</p>
+<h2 id="bit">Bit<a class="headerlink" href="#bit" title="Permanent
link">¶</a></h2>
+<p>Bit distributions use recursive binary subdivision of the square. Each bit
position is toggled with probability <code>probability</code>, and the depth of
recursion is determined by <code>digits</code>. This produces coordinates that
fall into a deterministic grid structure, with cells that may or may not be
occupied depending on the randomness of the bits.</p>
+<p>The result looks like a lattice of points at varying resolutions.
Increasing digits refines the grid; lowering probability sparsifies it. This
distribution is intentionally synthetic, good for stress-testing systems
against very regular data.</p>
+<h2 id="sierpinski">Sierpinski<a class="headerlink" href="#sierpinski"
title="Permanent link">¶</a></h2>
+<p>Sierpinski patterns come from iterating the “chaos game” toward the
vertices of a triangle. After many steps, the points fall into the classic
self-similar fractal: a carpet of nested triangular holes. There are no
parameters to tune here.</p>
+<p>While this is not meant to reflect any natural process, it does generate
extreme skew — dense regions interspersed with large gaps — making it a good
way to see how systems handle pathological clustering.</p>
+<h2 id="thomas-process">Thomas Process<a class="headerlink"
href="#thomas-process" title="Permanent link">¶</a></h2>
+<p>The Thomas (Gaussian Neyman–Scott) process generates hotspots by layering
parent and offspring points. Parent centers are placed deterministically using
a Halton sequence. Each parent is assigned a weight drawn from a Pareto
distribution, then spawns offspring distributed around it with Gaussian noise
of standard deviation sigma.</p>
+<p>Key parameters:</p>
<ul>
-<li><a href="https://www.tpc.org/tpch/";>TPC-H</a></li>
-<li><a href="https://www.tpc.org/tpcds/";>TPC-DS</a></li>
-<li><a href="https://benchmark.clickhouse.com/";>ClickBench</a></li>
-<li><a href="https://github.com/brianfrankcooper/YCSB";>YCSB</a></li>
-<li><a href="https://duckdblabs.github.io/db-benchmark/";>db-benchmark</a></li>
+<li><code>parents</code> sets how many hotspots exist overall.</li>
+<li><code>mean_offspring</code> scales the global density.</li>
+<li><code>sigma</code> controls how spread out each cluster is.</li>
+<li><code>pareto_alpha</code> and <code>pareto_xm</code> shape the skew in
cluster sizes: small alpha values mean a few parents dominate with very large
clusters, while most parents remain small.</li>
</ul>
-<p>The analytical benchmarks help analyze analytical performance, but that
doesn’t necessarily translate to spatial queries. An engine can be blazing
fast for a large tabular aggregation and terrible for spatial joins.</p>
-<p>SpatialBench is tailored for spatial queries. It’s the best modern option
to assess the spatial performance of an engine. Here are some suggestions for
how to use it for the most accurate and fairest results.</p>
-<h2 id="hardware-and-software">Hardware and software<a class="headerlink"
href="#hardware-and-software" title="Permanent link">¶</a></h2>
-<p>SpatialBench runs benchmarks on commodity hardware, with software versions
fully disclosed for each release.</p>
-<p>When comparing different runtimes, developers should make a good-faith
effort to use similar hardware and software versions. It’s not helpful to
compare one runtime with another runtime that has a lot less computational
power.</p>
-<p>SpatialBench benchmarks should always be presented with associated
hardware/software specifications so readers can assess the reliability of the
comparison.</p>
-<h2 id="accurately-comparing-different-engines">Accurately comparing different
engines<a class="headerlink" href="#accurately-comparing-different-engines"
title="Permanent link">¶</a></h2>
-<p>It is challenging to compare fundamentally different engines, such as
PostGIS (an OLTP database), DuckDB (an OLAP database), and GeoPandas (a Python
engine).</p>
-<p>For example, let’s compare how two engines execute a query differently:</p>
+<p>The result is a landscape of uneven hotspots - some bustling, others barely
populated. This makes it much closer to real-world trip or building
distributions than uniform or normal alone.</p>
+<h2 id="hierarchical-thomas">Hierarchical Thomas<a class="headerlink"
href="#hierarchical-thomas" title="Permanent link">¶</a></h2>
+<p>The Hierarchical (or Nested) Thomas process extends the idea by introducing
two levels. First, a “city” is selected, with city weights drawn from a Pareto
distribution. Within the chosen city, the number of subclusters (neighborhoods)
is itself random — normally distributed around a mean with given variance and
bounded by min/max limits. Finally, a subcluster is picked (again
Pareto-weighted), and the final point is drawn from a Gaussian around that
subcluster.</p>
+<p>The parameters mirror this structure:</p>
<ul>
-<li>PostGIS: create tables, load data into the tables, build an index (can be
expensive), run the query</li>
-<li>GeoPandas: read data into memory and run a query</li>
+<li><code>cities</code> controls the number of top-level hubs.</li>
+<li><code>sub_mean</code>, <code>sub_sd</code>, <code>sub_min</code>,
<code>sub_max</code> govern how many neighborhoods each city has.</li>
+<li><code>sigma_city</code> spreads neighborhoods around the city center;
<code>sigma_sub</code> spreads points around a neighborhood.</li>
+<li>The <code>pareto_alpha</code>/<code>pareto_xm</code> pairs separately skew
city sizes and neighborhood sizes.</li>
</ul>
-<p>PostGIS and GeoPandas execute queries differently, so you need to present
the query runtime with caution. For example, you can’t just ignore the time it
takes to build the PostGIS index because that can be the slowest part of the
query. That’s a critical detail for users running ad hoc queries.</p>
-<p>The SpatialBench results strive to present runtimes for all relevant
portions of the query so users are best informed about how to interpret the
results.</p>
-<h2 id="engine-tuning-in-benchmarks">Engine tuning in benchmarks<a
class="headerlink" href="#engine-tuning-in-benchmarks" title="Permanent
link">¶</a></h2>
-<p>Engines can be tuned by configuring settings or optimizing code. For
example, you can optimize Spark code by tuning the JVM. You can optimize
GeoPandas code by adding indexes. Benchmarks that tune one engine and don’t
tune any of the other engines aren’t reliable.</p>
-<p>All performance tuning is fully disclosed in the SpatialBench results.
Some results are presented both naively and fully tuned to give a better
picture of out-of-the-box performance and what’s possible for expert users.</p>
-<h2 id="open-source-benchmarks-vs-vendor-benchmarks">Open source benchmarks
vs. vendor benchmarks<a class="headerlink"
href="#open-source-benchmarks-vs-vendor-benchmarks" title="Permanent
link">¶</a></h2>
-<p>The SpatialBench benchmarks report results for some open source spatial
engines/databases.</p>
-<p>The SpatialBench repository does not report results for any proprietary
engines or vendor runtimes. Vendors are free to use the SpatialBench data
generators and run the benchmarks on their own. We ask vendors to credit
SpatialBench when they run the benchmarks and fully disclose the results so
that other practitioners can reproduce the results.</p>
-<h2 id="how-to-contribute">How to contribute<a class="headerlink"
href="#how-to-contribute" title="Permanent link">¶</a></h2>
-<p>There are a variety of ways to contribute to the SpatialBench project:</p>
+<p>This distribution produces realistic multi-scale patterns: large cities
with many dense neighborhoods, small towns with just a few sparse clusters. It
captures the layered heterogeneity of real settlement data in a way no
single-level process can.</p>
+<h2 id="references">References<a class="headerlink" href="#references"
title="Permanent link">¶</a></h2>
<ul>
-<li>Submit <a href="https://github.com/apache/sedona-spatialbench/pulls";>pull
requests</a> to add features</li>
-<li>Create <a
href="https://github.com/apache/sedona-spatialbench/issues";>issues</a> for bug
reports</li>
-<li>Reproduce results or help add new spatial engines</li>
-<li>Publish vendor benchmarks</li>
+<li><strong>Spider distributions (Uniform, Normal, Bit, Sierpinski,
Diagonal):</strong><ul>
+<li>Puloma Katiyar, Tin Vu, Sara Migliorini, Alberto Belussi, Ahmed Eldawy.
<em>SpiderWeb: A Spatial Data Generator on the Web</em>. <a
href="https://dl.acm.org/doi/10.1145/3397536.3422351";>ACM SIGSPATIAL 2020</a>,
Seattle, WA.</li>
</ul>
-<p>Here is how you can communicate with the team:</p>
-<ul>
-<li>Chat with us on the <a href="https://discord.gg/9A3k5dEBsY";>Apache Sedona
Discord</a></li>
-<li>Create <a href="https://github.com/apache/sedona/discussions";>GitHub
Discussions</a></li>
+</li>
+<li><strong>Thomas / Neyman–Scott cluster processes:</strong><ul>
+<li>Thomas, M. (1949). <em>A Generalization of Poisson’s Binomial Limit For
use in Ecology</em>. <a
href="https://doi.org/10.2307/2332526";><em>Biometrika</em>, <em>36</em>(½)</a>,
18–25.</li>
+</ul>
+</li>
+<li>Jerzy Neyman, Elizabeth L. Scott, <em>Statistical Approach to Problems of
Cosmology</em>, <a
href="https://doi.org/10.1111/j.2517-6161.1958.tb00272.x";><em>Journal of the
Royal Statistical Society: Series B (Methodological)</em>, Volume 20, Issue 1,
January 1958</a>, Pages 1–29 </li>
+<li><strong>Point process theory:</strong><ul>
+<li>Illian, J., Penttinen, A., Stoyan, H., & Stoyan, D. (2008).
<em>Statistical Analysis and Modelling of Spatial Point Patterns</em>.
Wiley.</li>
+</ul>
+</li>
+<li><strong>Fractal generation (Sierpinski):</strong><ul>
+<li>Barnsley, M. F., & Demko, S. (1985). <em>Iterated function systems and
the global construction of fractals</em>. <a
href="https://doi.org/10.1098/rspa.1985.0057";>Proceedings of the Royal Society
of London. Series A, 399(1817)</a>, 243–275.</li>
+</ul>
+</li>
</ul>
-<h2 id="future-work">Future work<a class="headerlink" href="#future-work"
title="Permanent link">¶</a></h2>
-<p>In the next release, we will add raster datasets and raster queries. These
will stress test an engine’s ability to analyze raster data. They will also
show performance when joining vector and raster datasets.</p>
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