tree: dbefec612c7cde91d19cc4e805b172a602cf59f8

README.md

SpatialBench

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.

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	117,416
[10, 100)	+ neighborhood, county	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

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.