---
id: apache-sedona
version: "1.9.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# apache-sedona — Apache Sedona is a cluster computing system for processing large-scale spatial data

License: permissive · Maintenance: active · Downloads: 2.3M/mo

## What it is and what it does

Apache Sedona is a Python wrapper around a distributed spatial computing engine built on Apache Spark. It provides spatial data types and query operations (Spatial Resilient Distributed Datasets and SpatialSQL) that let you load, filter, join, and analyze geographic data across a cluster of machines. The package depends on attrs and shapely for geometry representation and attribute handling.

You use it when you need to process geographic datasets too large for a single machine—think analyzing millions of GPS points, performing spatial joins across continents of map data, or computing geometric relationships at scale. It integrates directly with Spark's SQL engine and DataFrame API, so if you know PySpark, the spatial extensions feel natural.

Use it for:

- Analyze millions of GPS trajectories or sensor readings distributed across a cluster to find patterns or anomalies.
- Perform large-scale spatial joins (e.g., which customers fall within delivery zones) on datasets that don't fit in memory.
- Process global map or geospatial datasets (roads, buildings, administrative boundaries) for urban planning or logistics optimization.
- Query and filter geographic features by spatial relationships (containment, proximity, intersection) in distributed SQL.
- Build data pipelines that combine geographic data with other structured data in a unified Spark ecosystem.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Python wrapper for Apache Sedona, a cluster computing system that extends Apache Spark with spatial data processing capabilities for loading, processing, and analyzing large-scale geographic data across distributed machines.

Yes, if you are already using Apache Spark and need to process large-scale spatial data. The package is actively maintained, has no known vulnerabilities, and integrates cleanly with Spark's ecosystem. Install friction is moderate but manageable with pre-built wheels across platforms. Not worth installing if you lack Spark infrastructure or are working with small, single-machine geographic datasets.

## Install

pip install apache-sedona
uv add apache-sedona
poetry add apache-sedona

## Installing apache-sedona

Before you install: Medium install friction due to compiled wheels across multiple Python versions and platforms. Active maintenance with recent releases; repository shows 2380 stars and last commit on 2026-08-14, indicating ongoing development and community support.

License in practice: Apache-2.0 is permissive and allows commercial use, modification, and distribution with minimal restrictions. You must include a copy of the license and state significant changes, but there are no copyleft obligations.

Quickstart:

pip install apache-sedona

import sedona
from sedona.spark import *

# Requires Apache Spark and a Spark session configured with Sedona extensions

Requires Apache Spark to be installed and configured; a SparkSession with Sedona extensions must be initialized before use.

Verify before relying:

- Whether Sedona's Python wrapper supports all spatial operations available in the Java/Scala backend.
- Specific performance characteristics or scalability limits for typical workloads.
- Whether additional Java dependencies or environment variables are required beyond Spark.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 2.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

spatial data processing spark, geospatial cluster computing, large-scale geographic data analysis, spark spatial queries, distributed spatial computing, geographic data spark extension, geospatial, spark-extension, distributed-computing

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