SpatialBench

SpatialBench is a benchmark for assessing geospatial SQL analytics query performance across database systems. It 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.

Queries

SpatialBench includes a set of 12 SQL queries. Because spatial sql syntaxes vary widely across systems, we provide a cli to print all 12 queries in the dialect of your choice. Currently supported dialects are:

• Databricks Spatial SQL
• DuckDB
• Geopandas (distinct case)
• SedonaDB
• SedonaSpark

We tried to vary the queries only as much as necessary to accommodate dialect differences.

Geopandas is obviously a distinct case, as it is not SQL-based, however, due to its popularity, we felt it was important to include it. Pandas/Geopandas users often hand optimize their queries in ways that SQL engines handle automatically. We felt hand-tuning the queries was unfair for this exercise, and tried to do as little of that as possible while still writing "idiomatic" pandas code. We would be interested in hearing feedback on this approach as well as seeing a "fully hand-optimized" version of the queries.

We welcome contributions and civil discussions on how to improve the queries and their implementations.

You can print the queries in your dialect of choice using the following command:

./print_queries.py <dialect>

Data Model

SpatialBench defines a spatial star schema with the following tables:

Table	Type	Abbr.	Description	Spatial Attributes	Cardinality per SF
Trip	Fact Table	t_	Individual trip records	pickup & dropoff points	6M × SF
Customer	Dimension	c_	Trip customer info	None	30K × SF
Driver	Dimension	s_	Trip driver info	None	500 × SF
Vehicle	Dimension	v_	Trip vehicle info	None	100 × SF
Zone	Dimension	z_	Administrative zones (SF-aware scaling)	Polygon	Tiered by SF range (see below)
Building	Dimension	b_	Building footprints	Polygon	20K × (1 + log₂(SF))

Zone Table Scaling

The Zone table uses scale factor–aware generation so that zone granularity scales with dataset size and keeps query cost realistic. At small scales, this feels like querying ZIP-level units; at large scales, it uses coarser administrative units.

Scale Factor (SF)	Zone Subtypes Included	Zone Cardinality
[0, 10)	microhood, macrohood, county	156,095
[10, 100)	+ neighborhood	455,711
[100, 1000)	+ localadmin, locality, region, dependency	1,035,371
[1000+)	+ country	1,035,749

This tiered scaling reflects geometry complexity and area distributions observed in the Overture division_area dataset which represents administrative boundaries, release version 2025-08-20.1.

Geographic Coverage

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.

Bounding polygons:

Africa:              POLYGON ((-20.062752 -40.044425, 64.131567 -40.044425, 64.131567 37.579421, -20.062752 37.579421, -20.062752 -40.044425))
Europe:              POLYGON ((-11.964479 37.926872, 64.144374 37.926872, 64.144374 71.82884, -11.964479 71.82884, -11.964479 37.926872))
South Asia:          POLYGON ((64.58354 -9.709049, 145.526096 -9.709049, 145.526096 51.672557, 64.58354 51.672557, 64.58354 -9.709049))
North Asia:          POLYGON ((64.495655 51.944267, 178.834704 51.944267, 178.834704 77.897255, 64.495655 77.897255, 64.495655 51.944267))
Oceania:             POLYGON ((112.481901 -48.980212, 180.768942 -48.980212, 180.768942 -10.228433, 112.481901 -10.228433, 112.481901 -48.980212))
South America:       POLYGON ((-83.833822 -56.170016, -33.904338 -56.170016, -33.904338 12.211188, -83.833822 12.211188, -83.833822 -56.170016))
South North America: POLYGON ((-124.890724 12.382931, -69.511192 12.382931, -69.511192 42.55308, -124.890724 42.55308, -124.890724 12.382931))
North North America: POLYGON ((-166.478008 42.681087, -52.053245 42.681087, -52.053245 72.659041, -166.478008 72.659041, -166.478008 42.681087))

Performance

SpatialBench inherits its speed and efficiency from the tpchgen-rs project, which is one of the fastest open-source data generators available.

Key performance benefits:

• Zero-copy, streaming architecture: Generates data in constant memory, suitable for very large datasets.
• Multithreaded from the ground up: Leverages all CPU cores for high-throughput generation.
• Arrow-native output: Supports fast serialization to Parquet and other formats without bottlenecks.
• Fast geometry generation: The Spider module generates millions of spatial geometries per second, with deterministic output.

How is SpatialBench dbgen built?

SpatialBench is a Rust-based fork of the tpchgen-rs project. It preserves the original’s high-performance, multi-threaded, streaming architecture, while extending it with a spatial star schema and geometry generation logic.

You can build the SpatialBench data generator using Cargo:

cargo build --release

Alternatively, install it directly using:

cargo install --path ./spatialbench-cli

Notes

• The core generator logic lives in the spatialbench crate.
• Geometry-aware logic is in spatialbench-arrow and integrated via Arrow-based schemas.
• The spatial extension modules like the Spider geometry generator reside in the spatial directory.
• The generator supports output formats like .tbl and Apache Parquet via the Arrow writer.

For contribution or debugging, refer to the ARCHITECTURE.md guide.

Usage

Generate All Tables (Scale Factor 1)

spatialbench-cli -s 1 --format=parquet

Generate Individual Tables

spatialbench-cli -s 1 --format=parquet --tables trip,building --output-dir sf1-parquet

Partitioned Output Example

for PART in $(seq 1 4); do
  mkdir part-$PART
  spatialbench-cli -s 10 --tables trip,building --output-dir part-$PART --parts 4 --part $PART
done

Generate Multiple Parquet Files of Similar Size

The generator cli itself supports generating multiple files via the --parts and --part flags. However, if you want to generate multiple files per table of roughly a specific size, you can use the provided script tools/generate_data.py.

This algorithm is how data was generated for the benchmark results cited in the SedonaDB launch blog post.

tools/generate_data.py --scale-factor 10 --mb-per-file 256 --output-dir sf10-parquet

Custom Spider Configuration

You can override these defaults at runtime by passing a YAML file via the --config flag:

spatialbench-cli -s 1 --format=parquet --tables trip,building --config spatialbench-config.yml

If --config is not provided, SpatialBench checks for ./spatialbench-config.yml. If absent, it falls back to built-in defaults.

For reference, see the provided spatialbench-config.yml.

See CONFIGURATION.md for more details about spatial data generation and the full YAML schema and examples.

Acknowledgements

• TPC-H
• SpiderWeb: A Spatial Data Generator on the Web
• tpchgen-rs for inspiration and baseline performance
• Thomas, M. (1949). A Generalization of Poisson’s Binomial Limit For Use in Ecology. Biometrika, 36(1/2)
• Neyman, J. & Scott, E. L. (1958). Statistical Approach to Problems of Cosmology. Journal of the Royal Statistical Society: Series B (Methodological), 20(1)
• Illian, J., Penttinen, A., Stoyan, H., & Stoyan, D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. Wiley
• Barnsley, M. F., & Demko, S. (1985). Iterated function systems and the global construction of fractals. Proceedings of the Royal Society of London. Series A, 399(1817)
• Overture Maps Foundation