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new 724a659 update documentation for main branch
724a659 is described below
commit 724a659bfccd1bd3baa0b8440a40561b340337f0
Author: GitHub Actions <[email protected]>
AuthorDate: Wed Sep 24 00:07:43 2025 +0000
update documentation for main branch
---
404.html | 213 +++++++
contributors-guide/index.html | 233 ++++++-
datasets-generators/index.html | 215 ++++++-
image/sf1.png | Bin 0 -> 133443 bytes
image/sf10.png | Bin 0 -> 189589 bytes
index.html | 215 ++++++-
overview-methodology/index.html | 215 ++++++-
queries/index.html | 215 ++++++-
search/search_index.json | 2 +-
.../index.html | 692 +++++++++++++++------
sitemap.xml.gz | Bin 127 -> 127 bytes
11 files changed, 1787 insertions(+), 213 deletions(-)
diff --git a/404.html b/404.html
index 4f530b7..da7923b 100644
--- a/404.html
+++ b/404.html
@@ -287,6 +287,26 @@
+
+
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+
+
+
+ Single node benchmarks
+
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+
+
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<li class="md-tabs__item">
<a href="https://sedona.apache.org"; class="md-tabs__link">
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aria-expanded="false">
+ <label class="md-nav__title" for="__nav_6">
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+ Single node benchmarks
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+ SpatialBench Single Node Benchmarks
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+ <label class="md-nav__link" for="__nav_6_2" id="__nav_6_2_label"
tabindex="0">
+
+
+
+ <span class="md-ellipsis">
+ Get Involved
+
+ </span>
+
+
+ <span class="md-nav__icon md-icon"></span>
+ </label>
+
+ <nav class="md-nav" data-md-level="2"
aria-labelledby="__nav_6_2_label" aria-expanded="false">
+ <label class="md-nav__title" for="__nav_6_2">
+ <span class="md-nav__icon md-icon"></span>
+ Get Involved
+ </label>
+ <ul class="md-nav__list" data-md-scrollfix>
+
+
+
+
+
+
+
+ <li class="md-nav__item">
+ <a href="https://sedona.apache.org/latest/blog/"; class="md-nav__link">
+
+
+
+ <span class="md-ellipsis">
+ Sedona OSS Blog
+
+ </span>
+
+
+ </a>
+ </li>
+
+
+
+
+
+
+
+
+
+
+ <li class="md-nav__item">
+ <a href="https://sedona.apache.org/latest/community/contact/";
class="md-nav__link">
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+ Community
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+ <a href="https://sedona.apache.org/latest/asf/asf/"; class="md-nav__link">
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+
+ <span class="md-ellipsis">
+ Apache Software Foundation
+
+ </span>
+
+
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+ </li>
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+
+ </ul>
+ </nav>
+
+ </li>
+
+
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<li class="md-nav__item">
<a href="https://sedona.apache.org"; class="md-nav__link">
diff --git a/contributors-guide/index.html b/contributors-guide/index.html
index a01905a..0791897 100644
--- a/contributors-guide/index.html
+++ b/contributors-guide/index.html
@@ -11,6 +11,8 @@
<link rel="prev" href="../queries/">
+ <link rel="next" href="../single-node-benchmarks/">
+
<link rel="icon" href="../image/sedona_logo_symbol.png">
<meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.6.20">
@@ -304,6 +306,26 @@
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tabindex="0">
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+ <span class="md-ellipsis">
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+
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+
+ <span class="md-nav__icon md-icon"></span>
+ </label>
+
+ <nav class="md-nav" data-md-level="2"
aria-labelledby="__nav_6_2_label" aria-expanded="false">
+ <label class="md-nav__title" for="__nav_6_2">
+ <span class="md-nav__icon md-icon"></span>
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+ <ul class="md-nav__list" data-md-scrollfix>
+
+
+
+
+
+
+
+ <li class="md-nav__item">
+ <a href="https://sedona.apache.org/latest/blog/"; class="md-nav__link">
+
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+
+ <span class="md-ellipsis">
+ Sedona OSS Blog
+
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+
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+ </li>
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+
+
+
+
+
+
+
+
+ <li class="md-nav__item">
+ <a href="https://sedona.apache.org/latest/community/contact/";
class="md-nav__link">
+
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+ Community
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+ <a href="https://sedona.apache.org/latest/asf/asf/"; class="md-nav__link">
+
+
+
+ <span class="md-ellipsis">
+ Apache Software Foundation
+
+ </span>
+
+
+ </a>
+ </li>
+
+
+
+
+ </ul>
+ </nav>
+
+ </li>
+
+
+
+
+ </ul>
+ </nav>
+
+ </li>
+
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+
+
+
+
+
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+
<li class="md-nav__item">
<a href="https://sedona.apache.org"; class="md-nav__link">
@@ -1600,7 +1815,7 @@ under the <code>LICENSE</code> file in the root directory
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UTC">September 24, 2025 00:04:33</span>
</span>
@@ -1652,6 +1867,22 @@ under the <code>LICENSE</code> file in the root
directory of this source tree.</
</a>
+
+ <a href="../single-node-benchmarks/" class="md-footer__link
md-footer__link--next" aria-label="Next: SpatialBench Single Node Benchmarks">
+ <div class="md-footer__title">
+ <span class="md-footer__direction">
+ Next
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+ <div class="md-ellipsis">
+ SpatialBench Single Node Benchmarks
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+ </div>
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diff --git a/datasets-generators/index.html b/datasets-generators/index.html
index 87c8ed3..77a5f63 100644
--- a/datasets-generators/index.html
+++ b/datasets-generators/index.html
@@ -306,6 +306,26 @@
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+ Single node benchmarks
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+ </label>
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aria-labelledby="__nav_6_2_label" aria-expanded="false">
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+
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+
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index deeaf93..c436e25 100644
--- a/index.html
+++ b/index.html
@@ -304,6 +304,26 @@
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+
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+
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+ Single node benchmarks
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+ </li>
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index d08dd60..734ce92 100644
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diff --git a/search/search_index.json b/search/search_index.json
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+
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+ Single node benchmarks
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<a href="https://sedona.apache.org"; class="md-tabs__link">
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Methodology
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</a>
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+ Single node benchmarks
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+ </span>
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+
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+ </label>
+
+ <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label"
aria-expanded="true">
+ <label class="md-nav__title" for="__nav_6">
+ <span class="md-nav__icon md-icon"></span>
+ Single node benchmarks
+ </label>
+ <ul class="md-nav__list" data-md-scrollfix>
+
+
+
+
+
+
+
+
+
+ <li class="md-nav__item md-nav__item--active">
+
+ <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+
+
+
+ <label class="md-nav__link md-nav__link--active" for="__toc">
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+
+
+ <span class="md-ellipsis">
+ SpatialBench Single Node Benchmarks
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+ </span>
+
+
+ <span class="md-nav__icon md-icon"></span>
+ </label>
+
+ <a href="./" class="md-nav__link md-nav__link--active">
+
+
+
+ <span class="md-ellipsis">
+ SpatialBench Single Node Benchmarks
+
+ </span>
+
+
+ </a>
+
+
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+
+
+
+
+ <label class="md-nav__title" for="__toc">
+ <span class="md-nav__icon md-icon"></span>
+ Table of contents
+ </label>
+ <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+
+ <li class="md-nav__item">
+ <a href="#hardware" class="md-nav__link">
+ <span class="md-ellipsis">
+ Hardware
+ </span>
+ </a>
+
+</li>
+
+ <li class="md-nav__item">
+ <a href="#benchmark-settings" class="md-nav__link">
+ <span class="md-ellipsis">
+ Benchmark settings
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+ <a href="#geopandas-query-methodology" class="md-nav__link">
+ <span class="md-ellipsis">
+ GeoPandas query methodology
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+ Result analysis
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+ <ul class="md-nav__list">
+
+ <li class="md-nav__item">
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+ <li class="md-nav__item">
+ <a href="#aggregation-with-spatial-joins-q4-q10-q11" class="md-nav__link">
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+ <a href="#geometric-computations-q5-q7-q9" class="md-nav__link">
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+ <a href="#nearest-neighbor-joins-q12" class="md-nav__link">
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+ Nearest-neighbor joins (Q12)
+ </span>
+ </a>
+
+</li>
+
+ <li class="md-nav__item">
+ <a href="#overall" class="md-nav__link">
+ <span class="md-ellipsis">
+ Overall
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+ </a>
+
+</li>
+
+ </ul>
+ </nav>
+
+</li>
+
+ <li class="md-nav__item">
+ <a href="#benchmark-code" class="md-nav__link">
+ <span class="md-ellipsis">
+ Benchmark code
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+
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+
+ <li class="md-nav__item">
+ <a href="#future-work" class="md-nav__link">
+ <span class="md-ellipsis">
+ Future work
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+</nav>
+
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+ <input class="md-nav__toggle md-toggle " type="checkbox"
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@@ -1491,54 +1744,96 @@
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
<li class="md-nav__item">
- <a href="#why-spatialbench" class="md-nav__link">
+ <a href="#hardware" class="md-nav__link">
<span class="md-ellipsis">
- Why SpatialBench?
+ Hardware
</span>
</a>
</li>
<li class="md-nav__item">
- <a href="#hardware-and-software" class="md-nav__link">
+ <a href="#benchmark-settings" class="md-nav__link">
<span class="md-ellipsis">
- Hardware and software
+ Benchmark settings
</span>
</a>
</li>
<li class="md-nav__item">
- <a href="#accurately-comparing-different-engines" class="md-nav__link">
+ <a href="#geopandas-query-methodology" class="md-nav__link">
<span class="md-ellipsis">
- Accurately comparing different engines
+ GeoPandas query methodology
</span>
</a>
</li>
<li class="md-nav__item">
- <a href="#engine-tuning-in-benchmarks" class="md-nav__link">
+ <a href="#result-analysis" class="md-nav__link">
+ <span class="md-ellipsis">
+ Result analysis
+ </span>
+ </a>
+
+ <nav class="md-nav" aria-label="Result analysis">
+ <ul class="md-nav__list">
+
+ <li class="md-nav__item">
+ <a href="#spatial-filters-q1q3-q6" class="md-nav__link">
<span class="md-ellipsis">
- Engine tuning in benchmarks
+ Spatial filters (Q1–Q3, Q6)
</span>
</a>
</li>
-
- <li class="md-nav__item">
- <a href="#open-source-benchmarks-vs-vendor-benchmarks" class="md-nav__link">
+
+ <li class="md-nav__item">
+ <a href="#aggregation-with-spatial-joins-q4-q10-q11" class="md-nav__link">
+ <span class="md-ellipsis">
+ Aggregation with spatial joins (Q4, Q10, Q11)
+ </span>
+ </a>
+
+</li>
+
+ <li class="md-nav__item">
+ <a href="#geometric-computations-q5-q7-q9" class="md-nav__link">
+ <span class="md-ellipsis">
+ Geometric computations (Q5, Q7, Q9)
+ </span>
+ </a>
+
+</li>
+
+ <li class="md-nav__item">
+ <a href="#nearest-neighbor-joins-q12" class="md-nav__link">
<span class="md-ellipsis">
- Open source benchmarks vs. vendor benchmarks
+ Nearest-neighbor joins (Q12)
</span>
</a>
+</li>
+
+ <li class="md-nav__item">
+ <a href="#overall" class="md-nav__link">
+ <span class="md-ellipsis">
+ Overall
+ </span>
+ </a>
+
+</li>
+
+ </ul>
+ </nav>
+
</li>
<li class="md-nav__item">
- <a href="#how-to-contribute" class="md-nav__link">
+ <a href="#benchmark-code" class="md-nav__link">
<span class="md-ellipsis">
- How to contribute
+ Benchmark code
</span>
</a>
@@ -1569,7 +1864,7 @@
- <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/single-node-benchmarks.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
.3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1
23H12v-2.1l6.1-6.1z"></path></svg>
</a>
@@ -1577,7 +1872,7 @@
- <h1>SpatialBench Methodology</h1>
+ <h1>SpatialBench Single Node Benchmarks</h1>
<!---
Licensed to the Apache Software Foundation (ASF) under one
@@ -1594,55 +1889,54 @@
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>
-<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. 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>
-<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>
-</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 page presents the SpatialBench single-node benchmark results for
SedonaDB, DuckDB, and GeoPandas. The benchmark was conducted on September 22,
2025, using SpatialBench v0.1.0 pre-release (commit 9094be8 on the main
branch).</p>
+<p>Here are the results for v0.1 of the SpatialBench queries for scale factor
1 (SF1):</p>
+<p><a class="glightbox" data-type="image" data-width="100%" data-height="auto"
href="../image/sf1.png" data-desc-position="bottom"><img alt="Scale Factor 1
benchmark results" src="../image/sf1.png"></a></p>
+<p>Here are the results for SF10:</p>
+<p><a class="glightbox" data-type="image" data-width="100%" data-height="auto"
href="../image/sf10.png" data-desc-position="bottom"><img alt="Scale Factor 10
benchmark results" src="../image/sf10.png"></a></p>
+<p>The remainder of this document summarizes the hardware and software
versions, query methodologies for specific engines, and provides information on
where to find the benchmark code.</p>
+<h2 id="hardware">Hardware<a class="headerlink" href="#hardware"
title="Permanent link">¶</a></h2>
+<p>This benchmark was run on an AWS EC2 m7i.2xlarge instance, which has 8 CPUs
and 32 GB of RAM. We encourage you to try running SpatialBench on different
hardware configurations and share your results with the community.</p>
+<h2 id="benchmark-settings">Benchmark settings<a class="headerlink"
href="#benchmark-settings" title="Permanent link">¶</a></h2>
+<p>Here are the software versions used in this benchmark:</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>GeoPandas: 1.1.1</li>
+<li>Shapely: 2.1.1</li>
+<li>NumPy: 2.3.3</li>
+<li>DuckDB: 1.4.0</li>
+<li>SedonaDB: 0.1</li>
</ul>
-<p>Here is how you can communicate with the team:</p>
+<p>This benchmark report lists software versions, so it’s easy to track how
engine performance improves over time. We use the default settings of all
software unless otherwise noted. For DuckDB, we explicitly set
enable_external_file_cache to false to focus on the cold start queries runtime,
consistent with the other engines.</p>
+<p>The code execution runtime includes the entire query runtime for all
engines. The query timeout is set to 1200 seconds.</p>
+<h2 id="geopandas-query-methodology">GeoPandas query methodology<a
class="headerlink" href="#geopandas-query-methodology" title="Permanent
link">¶</a></h2>
+<p>The GeoPandas queries are written in Python, since GeoPandas does not
support SQL. GeoPandas executes queries by loading data fully into memory and
then processing it directly.</p>
+<p>Since GeoPandas runs in a single thread and lacks a query optimizer, any
parallelization or optimization must be implemented manually. This benchmark
implemented a straightforward implementation that mirrors the SQL queries used
for other engines. If you’re a GeoPandas expert, we’d be glad to collaborate on
a more optimized and/or parallelized version.</p>
+<h2 id="result-analysis">Result analysis<a class="headerlink"
href="#result-analysis" title="Permanent link">¶</a></h2>
+<h3 id="spatial-filters-q1q3-q6">Spatial filters (Q1–Q3, Q6)<a
class="headerlink" href="#spatial-filters-q1q3-q6" title="Permanent
link">¶</a></h3>
+<p>DuckDB and SedonaDB achieve similar low-latency performance at both SF 1
and SF 10, while GeoPandas struggles to keep up at larger scales. The main
reasons are the lack of a query optimizer to choose efficient execution
strategies and the absence of multi-core parallelism. By contrast, DuckDB and
SedonaDB leverage columnar data layouts, vectorized execution, multi-core
parallelism, and query optimization to achieve strong performance.</p>
+<h3 id="aggregation-with-spatial-joins-q4-q10-q11">Aggregation with spatial
joins (Q4, Q10, Q11)<a class="headerlink"
href="#aggregation-with-spatial-joins-q4-q10-q11" title="Permanent
link">¶</a></h3>
+<p>SedonaDB consistently delivers strong results on heavier joins,
particularly Q10 and Q11, aided by its adaptive spatial join strategy that
picks the best algorithm per partition based on spatial statistics. DuckDB
handles some join queries well but encounters scaling issues in certain cases,
while GeoPandas completes SF 1 but not SF 10.</p>
+<h3 id="geometric-computations-q5-q7-q9">Geometric computations (Q5, Q7, Q9)<a
class="headerlink" href="#geometric-computations-q5-q7-q9" title="Permanent
link">¶</a></h3>
+<p>SedonaDB is especially effective on intersection/IoU (Q9), showing
substantial efficiency improvements, while Q5 (convex hull aggregation)
highlights areas where DuckDB currently performs faster. SedonaDB’s overhead in
geometry copying in spatial aggregation is a known bottleneck and is planned
for improvement.</p>
+<h3 id="nearest-neighbor-joins-q12">Nearest-neighbor joins (Q12)<a
class="headerlink" href="#nearest-neighbor-joins-q12" title="Permanent
link">¶</a></h3>
+<p>SedonaDB completes KNN joins at both SF 1 and SF 10, thanks to its native
operator and optimized algorithm. In contrast, DuckDB and GeoPandas currently
lack built-in KNN join support. For these engines, we had to implement
additional code manually, which proved less efficient. Adding native KNN
capabilities in the future would likely help both engines close this gap.</p>
+<h3 id="overall">Overall<a class="headerlink" href="#overall" title="Permanent
link">¶</a></h3>
+<p>SedonaDB demonstrates balanced strengths across all categories and
successfully scales to SF 10 on an AWS m7i.2xlarge instance. DuckDB delivers
solid performance on simpler filters and certain geometric computations, but
has room to improve on complex joins and KNN queries. GeoPandas, while not
scaling as effectively in this benchmark, remains a widely used tool in the
Python ecosystem; however, it currently requires manual optimization and
parallelization to be deployed at scale.</p>
+<h2 id="benchmark-code">Benchmark code<a class="headerlink"
href="#benchmark-code" title="Permanent link">¶</a></h2>
+<p>You can access and run the benchmark code in the <a
href="https://github.com/apache/sedona-spatialbench";>sedona-spatialbench
GitHub</a> repository.</p>
+<p>It’s easy to generate the datasets locally or in the cloud. You can also
run the benchmarks locally or in the cloud.</p>
+<p>The repository has an issue tracker where you can file bug reports or
suggest code improvements.</p>
+<h2 id="future-work">Future work<a class="headerlink" href="#future-work"
title="Permanent link">¶</a></h2>
+<p>It would be great to include other engines and databases in the future:</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>dask-geopandas for single-node parallelism across cores</li>
+<li>PostGIS (Postgres SQL extension)</li>
+<li>An R geospatial engine</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>If you’re an expert in any of these technologies, we welcome you to take on
this project or reach out to us about collaborating.</p>
+<p>Note that compute engines designed for multi-node environments are
intentionally excluded from these single-node results for clarity and
simplicity.</p>
+<p>Similarly, transactional databases such as PostGIS execute queries in
fundamentally different ways than pure Python engines, like GeoPandas, or
analytical engines, like SedonaDB and DuckDB. Since SpatialBench is primarily
focused on analytical workloads, these systems are not yet included in this
discussion.</p>
+<p>The overarching goal of the SpatialBench initiative is to provide the
spatial community with a reliable set of benchmarks and to help accelerate the
development of better tooling for users.</p>
@@ -1663,7 +1957,7 @@
<span class="md-icon" title="Last update">
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- <span class="git-revision-date-localized-plugin
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@@ -1699,7 +1993,7 @@
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