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update documentation for main branch
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---><h1 id="spatialbench-overview-and-methodology">SpatialBench Overview and
Methodology<a class="headerlink" href="#spatialbench-overview-and-methodology"
title="Permanent link">¶</a></h1>
-<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>
-<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>
-</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. Let’s take a look at some of
the older spatial benchmarks.</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>
+--><h1 id="spatialbench-datasets-and-generators">SpatialBench Datasets and
Generators<a class="headerlink" href="#spatialbench-datasets-and-generators"
title="Permanent link">¶</a></h1>
+<p>This page describes the SpatialBench datasets and shows you how to use the
generators to create the spatial tables.</p>
+<p>SpatialBench is a geospatial benchmark designed for evaluating and
optimizing spatial query performance in data systems. Inspired by the Star
Schema Benchmark (SSB) and the New York City Taxi and Limousine Commission (NYC
TLC) dataset, SpatialBench blends realistic urban mobility scenarios with
standardized benchmarking practices.</p>
+<p>The benchmark adopts the familiar star schema structure from SSB, augmented
with spatial attributes such as pickup and dropoff points, spatial polygon
boundaries for zones, and building footprints. These spatial enhancements allow
SpatialBench to effectively test geospatial operations, including spatial
joins, distance-based queries, spatial aggregations, and point-in-polygon
analyses.</p>
+<p>By combining the systematic approach of SSB with authentic, real-world
scenarios drawn from NYC TLC data, SpatialBench provides meaningful and
practical benchmarks relevant to urban mobility and spatial analytics
workloads.</p>
+<h2 id="data-model">Data model<a class="headerlink" href="#data-model"
title="Permanent link">¶</a></h2>
+<p>SpatialBench tables:</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><strong>Trip (Fact Table)</strong>: Records individual trips, including
spatial attributes (pickup and dropoff points), trip fare, distance, duration,
and timestamps for pickup and dropoff.</li>
+<li><strong>Customer</strong>: Represents customers who book trips.</li>
+<li><strong>Driver</strong>: Represents drivers who fulfill trips.</li>
+<li><strong>Vehicle</strong>: Details about vehicles used for trips.</li>
+<li><strong>Zone</strong>: Polygon boundaries representing city areas or
zones.</li>
+<li><strong>Building</strong>: Polygon footprints representing building
locations, types, and names.</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>
+<table>
+<thead>
+<tr>
+<th><strong>Table</strong></th>
+<th><strong>Type</strong></th>
+<th><strong>Abbr.</strong></th>
+<th><strong>Primary Role</strong></th>
+<th><strong>Spatial Attributes</strong></th>
+<th><strong>Size per Scale Factor (SF)</strong></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Building</td>
+<td>Dimension</td>
+<td>b_</td>
+<td>Polygon footprints representing building locations</td>
+<td>Polygon footprints</td>
+<td>20K × (1 + log₂(SF))</td>
+</tr>
+<tr>
+<td>Customer</td>
+<td>Dimension</td>
+<td>c_</td>
+<td>Represents customers</td>
+<td>None</td>
+<td>30K × SF</td>
+</tr>
+<tr>
+<td>Driver</td>
+<td>Dimension</td>
+<td>s_</td>
+<td>Represents drivers</td>
+<td>None</td>
+<td>500 x SF</td>
+</tr>
+<tr>
+<td>Trip</td>
+<td>Fact Table</td>
+<td>t_</td>
+<td>Records individual trips</td>
+<td>Pickup/Dropoff Points (location)</td>
+<td>6M × SF</td>
+</tr>
+<tr>
+<td>Vehicle</td>
+<td>Dimension</td>
+<td>v_</td>
+<td>Details about vehicles</td>
+<td>None</td>
+<td>100 x SF</td>
+</tr>
+<tr>
+<td>Zone</td>
+<td>Dimension</td>
+<td>z_</td>
+<td>Polygon boundaries for city zones</td>
+<td>Polygon boundaries</td>
+<td>Tiered by SF range (see below)</td>
+</tr>
+</tbody>
+</table>
+<h3 id="zone-table-scaling">Zone Table Scaling<a class="headerlink"
href="#zone-table-scaling" title="Permanent link">¶</a></h3>
+<table>
+<thead>
+<tr>
+<th><strong>Scale Factor (SF)</strong></th>
+<th><strong>Zone Subtypes Included</strong></th>
+<th><strong>Zone Cardinality</strong></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>[0, 10)</td>
+<td>microhood, macrohood, county</td>
+<td>156,095</td>
+</tr>
+<tr>
+<td>[10, 100)</td>
+<td>+ neighborhood</td>
+<td>455,711</td>
+</tr>
+<tr>
+<td>[100, 1000)</td>
+<td>+ localadmin, locality, region, dependency</td>
+<td>1,035,371</td>
+</tr>
+<tr>
+<td>[1000+)</td>
+<td>+ country</td>
+<td>1,035,749</td>
+</tr>
+</tbody>
+</table>
+<p><a class="glightbox" data-type="image" data-width="100%" data-height="auto"
href="../image/datasets-schema.png" data-desc-position="bottom"><img
alt="schema" src="../image/datasets-schema.png"></a></p>
+<h3 id="geographic-coverage"><strong>Geographic Coverage</strong><a
class="headerlink" href="#geographic-coverage" title="Permanent link">¶</a></h3>
+<p>Spatial Bench's data generator uses continent-bounded affines. Each
continent is defined by a bounding polygon, ensuring generation mostly covers
land areas and introducing the natural skew of real geographies.</p>
+<p>Bounding polygons:</p>
+<table>
+<thead>
+<tr>
+<th>Region</th>
+<th>Bounding Polygon</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Africa</td>
+<td><code>POLYGON ((-20.062752 -40.044425, 64.131567 -40.044425, 64.131567
37.579421, -20.062752 37.579421, -20.062752 -40.044425))</code></td>
+</tr>
+<tr>
+<td>Europe</td>
+<td><code>POLYGON ((-11.964479 37.926872, 64.144374 37.926872, 64.144374
71.82884, -11.964479 71.82884, -11.964479 37.926872))</code></td>
+</tr>
+<tr>
+<td>South Asia</td>
+<td><code>POLYGON ((64.58354 -9.709049, 145.526096 -9.709049, 145.526096
51.672557, 64.58354 51.672557, 64.58354 -9.709049))</code></td>
+</tr>
+<tr>
+<td>North Asia</td>
+<td><code>POLYGON ((64.495655 51.944267, 178.834704 51.944267, 178.834704
77.897255, 64.495655 77.897255, 64.495655 51.944267))</code></td>
+</tr>
+<tr>
+<td>Oceania</td>
+<td><code>POLYGON ((112.481901 -48.980212, 180.768942 -48.980212, 180.768942
-10.228433, 112.481901 -10.228433, 112.481901 -48.980212))</code></td>
+</tr>
+<tr>
+<td>South America</td>
+<td><code>POLYGON ((-83.833822 -56.170016, -33.904338 -56.170016, -33.904338
12.211188, -83.833822 12.211188, -83.833822 -56.170016))</code></td>
+</tr>
+<tr>
+<td>South North America</td>
+<td><code>POLYGON ((-124.890724 12.382931, -69.511192 12.382931, -69.511192
42.55308, -124.890724 42.55308, -124.890724 12.382931))</code></td>
+</tr>
+<tr>
+<td>North North America</td>
+<td><code>POLYGON ((-166.478008 42.681087, -52.053245 42.681087, -52.053245
72.659041, -166.478008 72.659041, -166.478008 42.681087))</code></td>
+</tr>
+</tbody>
+</table>
+<p><a class="glightbox" data-type="image" data-width="100%" data-height="auto"
href="../image/datasets-continents.png" data-desc-position="bottom"><img
alt="continents" src="../image/datasets-continents.png"></a></p>
+<h3 id="distribution-options">Distribution Options<a class="headerlink"
href="#distribution-options" title="Permanent link">¶</a></h3>
+<p>By default, SpatialBench generates points using continent-bounded affines
with a Hierarchical Thomas distribution for the trip and building tables. </p>
+<p>For more realism, you can choose from a variety of spatial distributions
when generating tables:</p>
<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>Uniform: Evenly spread points in the unit square. </li>
+<li>Normal: Gaussian spread around a mean with configurable variance. </li>
+<li>Diagonal: Points concentrated along the y=x diagonal with configurable
buffer. </li>
+<li>Bit: Recursive grid-like pattern controlled by probability and bit depth.
</li>
+<li>Sierpinski: Self-similar fractal pattern for highly skewed coverage. </li>
+<li>Thomas: Clustered distribution with realistic hotspots and heavy-tailed
skew. </li>
+<li>Hierarchical Thomas: Multi-level clustering (cities → neighborhoods →
points), useful for mimicking urban settlement patterns.</li>
</ul>
-<p>Here is how you can communicate with the team:</p>
+<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>
+<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
+</code></pre></div>
+<p>Here are the contents of the <code>sf1-parquet</code> directory:</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><code>building.parquet</code></li>
+<li><code>customer.parquet</code></li>
+<li><code>driver.parquet</code></li>
+<li><code>trip.parquet</code></li>
+<li><code>vehicle.parquet</code></li>
+<li><code>zone.parquet</code></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>
+<p>See <a href="https://github.com/apache/sedona-spatialbench";>the README</a>
for a full description of how to use the SpatialBench data generators.</p>
@@ -798,7 +970,7 @@
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diff --git a/search/search_index.json b/search/search_index.json
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