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new a1cba00 docs: add landing page (#9)
a1cba00 is described below
commit a1cba009154cbd814da38b1574f6e1ca977bbc5f
Author: Matthew Powers <[email protected]>
AuthorDate: Wed Sep 17 14:15:34 2025 -0400
docs: add landing page (#9)
* docs: add landing page
* pr comments
---
docs/get-started/data-model.md | 1 -
docs/get-started/overview.md | 11 -------
docs/get-started/performance.md | 1 -
docs/index.md | 69 +++++++++++++++++++++++++++++++++++++++--
docs/overview-methodology.md | 3 ++
mkdocs.yml | 8 ++---
6 files changed, 72 insertions(+), 21 deletions(-)
diff --git a/docs/get-started/data-model.md b/docs/get-started/data-model.md
deleted file mode 100644
index 008c096..0000000
--- a/docs/get-started/data-model.md
+++ /dev/null
@@ -1 +0,0 @@
-# Data Model
diff --git a/docs/get-started/overview.md b/docs/get-started/overview.md
deleted file mode 100644
index 97b0dc1..0000000
--- a/docs/get-started/overview.md
+++ /dev/null
@@ -1,11 +0,0 @@
-SpatialBench is a high-performance geospatial benchmark for generating
synthetic spatial data at scale. Inspired by the Star Schema Benchmark (SSB)
and real-world mobility data like the NYC TLC dataset, SpatialBench is designed
to evaluate spatial query performance in modern data platforms.
-
-Built in Rust and powered by Apache Arrow, SpatialBench brings fast, scalable,
and streaming-friendly data generation for spatial workloads—minimal
dependencies, blazing speed.
-
-SpatialBench provides a reproducible and scalable way to evaluate the
performance of spatial data engines using realistic synthetic workloads.
-
-Goals:
-
-Establish a fair and extensible benchmark suite for spatial data processing.
-Help users compare engines and frameworks across different data scales.
-Support open standards and foster collaboration in the spatial computing
community.
\ No newline at end of file
diff --git a/docs/get-started/performance.md b/docs/get-started/performance.md
deleted file mode 100644
index 6babb31..0000000
--- a/docs/get-started/performance.md
+++ /dev/null
@@ -1 +0,0 @@
-# Performance
diff --git a/docs/index.md b/docs/index.md
index 9397315..1427e12 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,3 +1,68 @@
-# SpatialBench Documentation
+# Sedona SpatialBench
-Space for writing SpatialBench Documentation.
+Sedona SpatialBench makes it easy to run spatial benchmarks on a realistic
dataset with any query engine.
+
+The methodology is unbiased and the benchmarks in any environment to compare
relative performance between runtimes.
+
+## Why SpatialBench
+
+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.
+
+This design enables evaluation of the following geospatial operations:
+
+* spatial joins
+* distance queries
+* aggregations
+* point-in-polygon analysis
+
+Let’s dive into the advantages of SpatialBench.
+
+## Key advantages
+
+* Uses spatial datasets with geometry columns.
+* Includes queries with different spatial predicates.
+* Easily reproducible results.
+* Includes a dataset generator to so results are reproducible.
+* 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.
+* All the specifications used to run the benchmarks are documented, and the
methodology is unbiased.
+* The code is open source, allowing the community to provide feedback and keep
the benchmarks up-to-date and reliable over time.
+
+## Generate synthetic data
+
+Here’s how you can install the synthetic data generator:
+
+```
+cargo install --path ./spatialbench-cli
+```
+
+Here’s how you can generate the synthetic dataset:
+
+```
+spatialbench-cli -s 1 --format=parquet
+```
+
+See the project repository
[README](https://github.com/apache/sedona-spatialbench) for the complete set of
straightforward data generation instructions.
+
+## Example query
+
+Here’s an example query that counts the number of trips that start within 500
meters of each building:
+
+```sql
+SELECT
+ b.b_buildingkey,
+ b.b_name,
+ COUNT(*) AS nearby_pickup_count
+FROM trip t
+JOIN building b
+ON ST_DWithin(t.t_pickup_loc, b.b_boundary, 500)
+GROUP BY b.b_buildingkey, b.b_name
+ORDER BY nearby_pickup_count DESC;
+```
+
+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.
+
+## Join the community
+
+Feel free to start a [GitHub
Discussion](https://github.com/apache/sedona/discussions) or join the [Discord
community](https://discord.gg/9A3k5dEBsY) to ask the developers any questions
you may have.
+
+We look forward to collaborating with you on these benchmarks!
diff --git a/docs/overview-methodology.md b/docs/overview-methodology.md
new file mode 100644
index 0000000..39d67de
--- /dev/null
+++ b/docs/overview-methodology.md
@@ -0,0 +1,3 @@
+# SpatialBench Overview and Methodology
+
+TODO
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index 0161f57..8ffd6c7 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -19,10 +19,7 @@ site_name: Apache Sedona SpatialBench
site_description: SpatialBench is a high-performance geospatial benchmark for
generating synthetic spatial data at scale.
nav:
- Home: index.md
- - Get Started:
- - Overview: get-started/overview.md
- - Data Model: get-started/data-model.md
- - Performance: get-started/performance.md
+ - Overview and Methodology: overview-methodology.md
repo_url: https://github.com/apache/sedona-spatialbench/
repo_name: apache/sedona-spatialbench
@@ -47,7 +44,7 @@ theme:
- navigation.instant
- navigation.top
- navigation.sections
- - navigation.tabs
+ - navigation.expand
- navigation.tabs.sticky
extra:
version:
@@ -107,4 +104,3 @@ plugins:
type: datetime
- mike:
canonical_version: 'latest'
-
