This is an automated email from the ASF dual-hosted git repository.
jiayu pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/sedona-spatialbench.git
The following commit(s) were added to refs/heads/main by this push:
new 26a5353 docs: add single node benchmark results (#34)
26a5353 is described below
commit 26a535393406ddee00dbbcc1df2c07144041d355
Author: Matthew Powers <[email protected]>
AuthorDate: Tue Sep 23 20:04:33 2025 -0400
docs: add single node benchmark results (#34)
* docs: add single node benchmark results
* Apply suggestion from @Copilot
Co-authored-by: Copilot <[email protected]>
* Apply suggestion from @Copilot
Co-authored-by: Copilot <[email protected]>
---------
Co-authored-by: Jia Yu <[email protected]>
Co-authored-by: Copilot <[email protected]>
---
docs/image/sf1.png | Bin 0 -> 133443 bytes
docs/image/sf10.png | Bin 0 -> 189589 bytes
docs/single-node-benchmarks.md | 103 +++++++++++++++++++++++++++++++++++++++++
mkdocs.yml | 8 ++--
4 files changed, 107 insertions(+), 4 deletions(-)
diff --git a/docs/image/sf1.png b/docs/image/sf1.png
new file mode 100644
index 0000000..2218a2d
Binary files /dev/null and b/docs/image/sf1.png differ
diff --git a/docs/image/sf10.png b/docs/image/sf10.png
new file mode 100644
index 0000000..9c932ea
Binary files /dev/null and b/docs/image/sf10.png differ
diff --git a/docs/single-node-benchmarks.md b/docs/single-node-benchmarks.md
new file mode 100644
index 0000000..08f83c8
--- /dev/null
+++ b/docs/single-node-benchmarks.md
@@ -0,0 +1,103 @@
+---
+title: SpatialBench Single Node Benchmarks
+---
+
+<!---
+ Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+ http://www.apache.org/licenses/LICENSE-2.0
+ Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+-->
+
+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).
+
+Here are the results for v0.1 of the SpatialBench queries for scale factor 1
(SF1):
+
+
+
+Here are the results for SF10:
+
+
+
+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.
+
+## Hardware
+
+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.
+
+
+## Benchmark settings
+
+Here are the software versions used in this benchmark:
+
+* GeoPandas: 1.1.1
+* Shapely: 2.1.1
+* NumPy: 2.3.3
+* DuckDB: 1.4.0
+* SedonaDB: 0.1
+
+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.
+
+The code execution runtime includes the entire query runtime for all engines.
The query timeout is set to 1200 seconds.
+
+## GeoPandas query methodology
+
+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.
+
+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.
+
+## Result analysis
+
+### Spatial filters (Q1–Q3, Q6)
+
+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.
+
+### Aggregation with spatial joins (Q4, Q10, Q11)
+
+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.
+
+### Geometric computations (Q5, Q7, Q9)
+
+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.
+
+### Nearest-neighbor joins (Q12)
+
+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.
+
+### Overall
+
+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.
+
+## Benchmark code
+
+You can access and run the benchmark code in the [sedona-spatialbench
GitHub](https://github.com/apache/sedona-spatialbench) repository.
+
+It’s easy to generate the datasets locally or in the cloud. You can also run
the benchmarks locally or in the cloud.
+
+The repository has an issue tracker where you can file bug reports or suggest
code improvements.
+
+## Future work
+
+It would be great to include other engines and databases in the future:
+
+* dask-geopandas for single-node parallelism across cores
+* PostGIS (Postgres SQL extension)
+* An R geospatial engine
+
+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.
+
+Note that compute engines designed for multi-node environments are
intentionally excluded from these single-node results for clarity and
simplicity.
+
+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.
+
+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.
diff --git a/mkdocs.yml b/mkdocs.yml
index d8d6840..5c9a90b 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -39,17 +39,14 @@ nav:
- Home: index.md
# Use the alias (*) to insert the Get Involved links here
- <<: *community_links
-
- Methodology:
- overview-methodology.md
# Use the alias (*) to insert the Get Involved links here
- <<: *community_links
-
- Data Generation:
- datasets-generators.md
# Use the alias (*) to insert the Get Involved links here
- <<: *community_links
-
- SpatialBench Queries:
- queries.md
# Use the alias (*) to insert the Get Involved links here
@@ -58,8 +55,11 @@ nav:
- contributors-guide.md
# Use the alias (*) to insert the Get Involved links here
- <<: *community_links
+ - Single node benchmarks:
+ - single-node-benchmarks.md
+ # Use the alias (*) to insert the Get Involved links here
+ - <<: *community_links
- Sedona Homepage: "https://sedona.apache.org";
-
repo_url: https://github.com/apache/sedona-spatialbench/
edit_uri: https://github.com/apache/sedona-spatialbench/blob/main/docs/
repo_name: apache/sedona-spatialbench
