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GeoETL Architecture Deep Dive: Powering Geospatial Data with Rust and DataFusion

· 4 min read
Yogesh
Yogesh
GeoETL Maintainer

In our previous posts, we introduced GeoETL and showed you how to perform your first data conversion. Now, let's take a deeper look under the hood and explore the architecture that makes GeoETL a powerful and high-performance geospatial ETL tool.

The Core Philosophy: Modern and Performant​

GeoETL is built with a clear philosophy: leverage modern technologies to achieve unparalleled performance and scalability in geospatial data processing. We aim to overcome the limitations of traditional tools by embracing a new stack designed for today's data challenges.

Technology Stack: The Pillars of GeoETL​

1. Rust: Safety and Speed​

At the heart of GeoETL is Rust, a systems programming language known for its memory safety, performance, and concurrency. Rust's ownership model eliminates entire classes of bugs common in other languages, allowing us to build a robust and reliable tool. Its zero-cost abstractions mean we get high-level ergonomics without sacrificing low-level control or speed.

2. Apache DataFusion & Arrow: Vectorized Powerhouse​

GeoETL heavily relies on the Apache Arrow ecosystem, particularly DataFusion.

  • Apache Arrow: This is a language-agnostic columnar memory format for flat and hierarchical data. Arrow enables efficient analytical operations and zero-copy data exchange between different systems and languages. For geospatial data, this is crucial for high-performance processing.
  • Apache DataFusion: Built on Arrow, DataFusion is a blazing-fast, extensible query engine written in Rust. It provides:
    • Vectorized Execution: Operations are performed on entire columns of data at once, leading to significant performance gains.
    • Query Optimization: A sophisticated query optimizer rewrites and improves query plans for maximum efficiency.
    • Extensibility: DataFusion's architecture allows us to integrate custom data sources and functions, which is vital for handling diverse geospatial formats and operations.

3. GeoArrow: Geospatial Data in Arrow​

GeoArrow is an emerging standard for representing geospatial vector data within the Apache Arrow columnar format. By adopting GeoArrow, GeoETL can:

  • Store and process geometries efficiently in memory.
  • Leverage Arrow's performance benefits for spatial operations.
  • Ensure interoperability with other GeoArrow-compatible tools.

4. The GeoRust Ecosystem: Specialized Spatial Operations​

We integrate with the broader GeoRust ecosystem for specialized geospatial algorithms and data structures. Libraries like geo (for geometric operations), geozero (for zero-copy geospatial data streaming), and proj (for coordinate reference system transformations) provide battle-tested functionalities that complement DataFusion's tabular processing capabilities.

High-Level Architecture​

GeoETL is structured as a Rust workspace, promoting modularity and reusability. The core components include:

  • geoetl-cli: The user-facing command-line interface, providing an intuitive way to interact with GeoETL.
  • geoetl-core: The central library containing the core logic for spatial operations and data orchestration.
  • geoetl-formats: A collection of crates responsible for reading and writing various geospatial data formats (e.g., GeoJSON, Parquet, FlatGeobuf). These formats are integrated with DataFusion's data source capabilities.
  • geoetl-exec: (Planned) The query execution engine, leveraging DataFusion for optimized processing.
  • geoetl-ballista: (Planned) Integration with DataFusion Ballista, enabling distributed processing for large-scale datasets across a cluster.

The data flow generally follows this path:

User CLI → GeoETL Core → DataFusion Engine → Format I/O → Data Sources
                          ↓
                  Single-Node / Ballista (for distributed processing)

Scalability: From Laptop to Cluster​

One of GeoETL's key strengths is its seamless scalability. Thanks to DataFusion and its distributed counterpart, Ballista, GeoETL can efficiently process data:

  • On a single machine: Utilizing all available CPU cores and memory through DataFusion's multi-threaded, vectorized engine.
  • Across a distributed cluster: With Ballista, GeoETL can distribute processing tasks across multiple nodes, allowing you to handle datasets that far exceed the memory capacity of a single machine.

What's Next?​

This deep dive has only scratched the surface of GeoETL's architecture. As the project evolves, we will continue to share more insights into its design and implementation. Our goal is to provide a transparent and community-driven development process.

Stay tuned for more technical updates and exciting new features!