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commit 5f37073c5ce9b5d987a8d3125d05b2b7d1742b26
Author: GitHub Actions <[email protected]>
AuthorDate: Wed Sep 24 04:39:52 2025 +0000
update documentation for main branch
---
contributors-guide/index.html | 2 +-
datasets-generators/index.html | 2 +-
index.html | 55 +++++++++++++++++++++------------------
overview-methodology/index.html | 2 +-
queries/index.html | 2 +-
search/search_index.json | 2 +-
single-node-benchmarks/index.html | 15 +++++------
stylesheets/extra.css | 48 +++++++++++++++++++++++++---------
8 files changed, 77 insertions(+), 51 deletions(-)
diff --git a/contributors-guide/index.html b/contributors-guide/index.html
index b9d6c95..3e6a806 100644
--- a/contributors-guide/index.html
+++ b/contributors-guide/index.html
@@ -1815,7 +1815,7 @@ under the <code>LICENSE</code> file in the root directory
of this source tree.</
<span class="md-icon" title="Last update">
<svg xmlns="http://www.w3.org/2000/svg"; viewBox="0 0 24 24"><path d="M21
13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6
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- <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 03:50:34
UTC">September 24, 2025 03:50:34</span>
+ <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 04:38:44
UTC">September 24, 2025 04:38:44</span>
</span>
diff --git a/datasets-generators/index.html b/datasets-generators/index.html
index 88191c3..cee360d 100644
--- a/datasets-generators/index.html
+++ b/datasets-generators/index.html
@@ -1991,7 +1991,7 @@
<span class="md-icon" title="Last update">
<svg xmlns="http://www.w3.org/2000/svg"; viewBox="0 0 24 24"><path d="M21
13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6
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- <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 03:50:34
UTC">September 24, 2025 03:50:34</span>
+ <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 04:38:44
UTC">September 24, 2025 04:38:44</span>
</span>
diff --git a/index.html b/index.html
index 55a6338..5f92533 100644
--- a/index.html
+++ b/index.html
@@ -498,9 +498,9 @@
</li>
<li class="md-nav__item">
- <a href="#key-advantages" class="md-nav__link">
+ <a href="#key-features" class="md-nav__link">
<span class="md-ellipsis">
- Key advantages
+ Key Features
</span>
</a>
@@ -1693,9 +1693,9 @@
</li>
<li class="md-nav__item">
- <a href="#key-advantages" class="md-nav__link">
+ <a href="#key-features" class="md-nav__link">
<span class="md-ellipsis">
- Key advantages
+ Key Features
</span>
</a>
@@ -1769,39 +1769,42 @@
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
---><p>SpatialBench is a benchmark for assessing geospatial SQL analytics query
performance across database systems.</p>
-<p>SpatialBench makes it easy to run spatial benchmarks on a realistic dataset
with any query engine.</p>
-<p>The methodology is unbiased and the benchmarks in any environment to
compare relative performance between runtimes.</p>
+--><p>SpatialBench is a benchmark for assessing geospatial SQL analytics query
performance across database systems, making it easy to run tests on a realistic
dataset with any query engine.</p>
+<p>The methodology is unbiased, allowing you to run the benchmarks in any
environment to compare the relative performance between runtimes.</p>
<h2 id="why-spatialbench">Why SpatialBench<a class="headerlink"
href="#why-spatialbench" title="Permanent link">¶</a></h2>
-<p>SpatialBench is a geospatial benchmark for testing and optimizing spatial
analytical query performance in database systems. Inspired by the SSB and NYC
taxi data, it combines realistic urban mobility scenarios with a star schema
extended with spatial attributes like pickup/dropoff points, zones, and
building footprints.</p>
+<p>SpatialBench was created because standard database benchmarks don't
adequately test the unique demands of geospatial queries. SpatialBench provides
an open-source, standardized, and scalable framework designed specifically for
geospatial analytics.</p>
+<p>Inspired by the Star Schema Benchmark (SSB) and NYC taxi data, SpatialBench
combines realistic urban mobility scenarios
+with a star schema extended with spatial attributes like pickup/dropoff
points, zones, and building footprints.</p>
<p>This design enables evaluation of the following geospatial operations:</p>
<ul>
-<li>spatial joins</li>
-<li>distance queries</li>
-<li>aggregations</li>
-<li>point-in-polygon analysis</li>
+<li>Spatial joins</li>
+<li>Distance queries</li>
+<li>Aggregations</li>
+<li>Point-in-polygon analysis</li>
</ul>
-<p>Let’s dive into the advantages of SpatialBench.</p>
-<h2 id="key-advantages">Key advantages<a class="headerlink"
href="#key-advantages" title="Permanent link">¶</a></h2>
+<p>Let's dive into the advantages of SpatialBench.</p>
+<h2 id="key-features">Key Features<a class="headerlink" href="#key-features"
title="Permanent link">¶</a></h2>
+<p>To ensure fair and comprehensive testing, SpatialBench provides the
following advantages:</p>
<ul>
-<li>Uses spatial datasets with geometry columns.</li>
-<li>Includes queries with different spatial predicates.</li>
-<li>Easily reproducible results.</li>
-<li>Includes a dataset generator to so results are reproducible.</li>
-<li>The scale factors of the datasets can be changed so that you can run the
queries locally, in a data warehouse, or on a large cluster in the cloud.</li>
-<li>All the specifications used to run the benchmarks are documented, and the
methodology is unbiased.</li>
-<li>The code is open source, allowing the community to provide feedback and
keep the benchmarks up-to-date and reliable over time.</li>
+<li>Features realistic spatial datasets with native geometry columns.</li>
+<li>Includes a suite of queries that test various operations such as spatial
predicates and joins.</li>
+<li>Provides a built-in synthetic data generator for creating consistent test
data.</li>
+<li>Offers a configurable scale factor to benchmark performance across various
+ environments, from a single local machine to a large-scale cloud
cluster.</li>
+<li>Ensures consistent and reproducible benchmark results across all
environments.</li>
+<li>Utilizes a fully documented and unbiased methodology to facilitate fair
comparisons.</li>
+<li>Open-source and community-driven to foster transparency and continuous
improvement.</li>
</ul>
<h2 id="generate-synthetic-data">Generate synthetic data<a class="headerlink"
href="#generate-synthetic-data" title="Permanent link">¶</a></h2>
-<p>Here’s how you can install the synthetic data generator:</p>
+<p>Here's how you can install the synthetic data generator:</p>
<div class="highlight"><pre><span></span><code>cargo install --path
./spatialbench-cli
</code></pre></div>
-<p>Here’s how you can generate the synthetic dataset:</p>
+<p>Here's how you can generate the synthetic dataset:</p>
<div class="highlight"><pre><span></span><code>spatialbench-cli -s 1
--format=parquet
</code></pre></div>
<p>See the project repository <a
href="https://github.com/apache/sedona-spatialbench";>README</a> for the
complete set of straightforward data generation instructions.</p>
<h2 id="example-query">Example query<a class="headerlink"
href="#example-query" title="Permanent link">¶</a></h2>
-<p>Here’s an example query that counts the number of trips that start within
500 meters of each building:</p>
+<p>Here's an example query that counts the number of trips that start within
500 meters of each building:</p>
<div class="highlight"><pre><span></span><code><span class="k">SELECT</span>
<span class="w"> </span><span class="n">b</span><span
class="p">.</span><span class="n">b_buildingkey</span><span class="p">,</span>
<span class="w"> </span><span class="n">b</span><span
class="p">.</span><span class="n">b_name</span><span class="p">,</span>
@@ -1812,7 +1815,7 @@
<span class="k">GROUP</span><span class="w"> </span><span
class="k">BY</span><span class="w"> </span><span class="n">b</span><span
class="p">.</span><span class="n">b_buildingkey</span><span
class="p">,</span><span class="w"> </span><span class="n">b</span><span
class="p">.</span><span class="n">b_name</span>
<span class="k">ORDER</span><span class="w"> </span><span
class="k">BY</span><span class="w"> </span><span
class="n">nearby_pickup_count</span><span class="w"> </span><span
class="k">DESC</span><span class="p">;</span>
</code></pre></div>
-<p>This query performs a distance join, followed by an aggregation. It’s a
great example of a query that’s useful for performance benchmarking a spatial
engine that can process vector geometries.</p>
+<p>This query performs a distance join, followed by an aggregation. It's a
great example of a query that's useful for performance benchmarking a spatial
engine that can process vector geometries.</p>
<h2 id="join-the-community">Join the community<a class="headerlink"
href="#join-the-community" title="Permanent link">¶</a></h2>
<p>Feel free to start a <a
href="https://github.com/apache/sedona/discussions";>GitHub Discussion</a> or
join the <a href="https://discord.gg/9A3k5dEBsY";>Discord community</a> to ask
the developers any questions you may have.</p>
<p>We look forward to collaborating with you on these benchmarks!</p>
@@ -1836,7 +1839,7 @@
<span class="md-icon" title="Last update">
<svg xmlns="http://www.w3.org/2000/svg"; viewBox="0 0 24 24"><path d="M21
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</span>
- <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 03:50:34
UTC">September 24, 2025 03:50:34</span>
+ <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 04:38:44
UTC">September 24, 2025 04:38:44</span>
</span>
diff --git a/overview-methodology/index.html b/overview-methodology/index.html
index 15687a1..6068cd8 100644
--- a/overview-methodology/index.html
+++ b/overview-methodology/index.html
@@ -1876,7 +1876,7 @@
<span class="md-icon" title="Last update">
<svg xmlns="http://www.w3.org/2000/svg"; viewBox="0 0 24 24"><path d="M21
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9.3-.3-.1-.6-.2-1-.2s-.7.1-1 .2C19.6 7.2 16.2 4 12 4c-4.4 0-8 3.6-8 8 0 4.1 3.1
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</span>
- <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 03:50:34
UTC">September 24, 2025 03:50:34</span>
+ <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 04:38:44
UTC">September 24, 2025 04:38:44</span>
</span>
diff --git a/queries/index.html b/queries/index.html
index 3388106..609289b 100644
--- a/queries/index.html
+++ b/queries/index.html
@@ -2444,7 +2444,7 @@ spatialbench-cli -s 1 --format=parquet --output-dir
sf1-parquet
<span class="md-icon" title="Last update">
<svg xmlns="http://www.w3.org/2000/svg"; viewBox="0 0 24 24"><path d="M21
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- <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 03:50:34
UTC">September 24, 2025 03:50:34</span>
+ <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 04:38:44
UTC">September 24, 2025 04:38:44</span>
</span>
diff --git a/search/search_index.json b/search/search_index.json
index 2541f78..0d3e02a 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"SpatialBench","text":"<p>SpatialBench
is a benchmark for assessing geospatial SQL analytics query performance across
database systems.</p> <p>SpatialBench makes it easy to run spatial benchmarks
on a realistic dataset with any query engine.</p> <p>The methodology is
unbiased and the benchmarks in any environment to compare relative performance
between runtimes.</p>"},{"location [...]
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"SpatialBench","text":"<p>SpatialBench
is a benchmark for assessing geospatial SQL analytics query performance across
database systems, making it easy to run tests on a realistic dataset with any
query engine.</p> <p>The methodology is unbiased, allowing you to run the
benchmarks in any environment to compare the relative performance between
runtimes.</p>"},{"location":"#why-spa [...]
\ No newline at end of file
diff --git a/single-node-benchmarks/index.html
b/single-node-benchmarks/index.html
index ba66783..9916f4b 100644
--- a/single-node-benchmarks/index.html
+++ b/single-node-benchmarks/index.html
@@ -1905,34 +1905,33 @@
<li>DuckDB: 1.4.0</li>
<li>SedonaDB: 0.1</li>
</ul>
-<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>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>
+<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>
+<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>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>dask-geopandas for single-node parallelism across cores</li>
-<li>PostGIS (Postgres SQL extension)</li>
+<li><code>dask-geopandas</code> for single-node parallelism across cores</li>
<li>An R geospatial engine</li>
</ul>
-<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>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>
@@ -1956,7 +1955,7 @@
<span class="md-icon" title="Last update">
<svg xmlns="http://www.w3.org/2000/svg"; viewBox="0 0 24 24"><path d="M21
13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6
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</span>
- <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 03:50:34
UTC">September 24, 2025 03:50:34</span>
+ <span class="git-revision-date-localized-plugin
git-revision-date-localized-plugin-datetime" title="September 24, 2025 04:38:44
UTC">September 24, 2025 04:38:44</span>
</span>
diff --git a/stylesheets/extra.css b/stylesheets/extra.css
index 1b5a89f..be4ae4f 100644
--- a/stylesheets/extra.css
+++ b/stylesheets/extra.css
@@ -57,18 +57,6 @@
font-size: 0.65rem; /* NEW: Adjust font size */
}
-/* ==========================================================================
- Mobile Navigation Styles
- ==========================================================================
*/
-
-/* This targets the main container of the slide-out navigation on mobile */
-.md-nav--primary .md-nav__title,
-.md-nav__source {
- background-color: var(--color-red); /* Use your red color */
- box-shadow: none; /* Optional: removes the shadow */
-}
-
-
/* ==========================================================================
Logo Size Adjustment
==========================================================================
*/
@@ -82,3 +70,39 @@
height: 42px; /* This should match the Apache Sedona logo size */
width: auto; /* Ensures the width scales proportionally */
}
+
+/* ==========================================================================
+ Mobile Navigation Styles
+ ==========================================================================
*/
+
+/*
+ Force the entire mobile navigation header to be black, overriding theme
defaults.
+*/
+.md-nav--primary .md-nav__title,
+.md-nav--primary .md-nav__source {
+ background-color: var(--color-dark) !important;
+}
+
+/* ==========================================================================
+ Swap Logo ONLY in Mobile Navigation
+ ==========================================================================
*/
+
+/*
+ Target the logo link (<a> tag) directly and apply the new logo
+ as a background image, overriding the theme's default icon.
+*/
+.md-sidebar--primary .md-nav__title a.md-nav__button.md-logo {
+ background-image: url('/docs-overrides/.icons/sedona_logo_symbol_white.svg');
+ background-size: contain;
+ background-repeat: no-repeat;
+ background-position: center;
+
+ /* Hide the theme's default icon which is applied via a mask */
+ -webkit-mask: none;
+ mask: none;
+}
+
+/* Change the mobile nav header label text to white */
+label.md-nav__title {
+ color: #FFFFFF !important;
+}
\ No newline at end of file
