apache-sedona
Apache Sedona is a cluster computing system for processing large-scale spatial data
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Spark to be installed and configured; a SparkSession with Sedona extensions must be initialized before use.
- 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 · maintenance · safety
Apache-2.0 (permissive) — 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.
last release 2026-08-05 (9 days) · last repo commit 2026-08-14 · 2,380 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,279,341 downloads/mo, #3,168 on PyPI
Alternatives
Verify before relying
pip install apache-sedona
import sedona
from sedona.spark import *
# Requires Apache Spark and a Spark session configured with Sedona extensions- 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.
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 on this page. Verify before relying on it.
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
apache-sedona on PyPI
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.
Requires Apache Spark to be installed and configured; a SparkSession with Sedona extensions must be initialized before use.
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 extensionsVerify 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 the current Python release >=3.8 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagesattrsshapely |
| Maintenance | Actively maintained 9 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,279,341 / month, #3,168 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3 |
Evidence: apache_sedona-1.9.1-cp310-cp310-macosx_10_9_x86_64.whl; apache_sedona-1.9.1-cp310-cp310-macosx_11_0_arm64.whl; apache_sedona-1.9.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; apache_sedona-1.9.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; apache_sedona-1.9.1-cp310-cp310-win_amd64.whl; apache_sedona-1.9.1-cp310-cp310-win_arm64.whl; apache_sedona-1.9.1-cp311-cp311-macosx_10_9_x86_64.whl; apache_sedona-1.9.1-cp311-cp311-macosx_11_0_arm64.whl; apache_sedona-1.9.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; apache_sedona-1.9.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; apache_sedona-1.9.1-cp311-cp311-win_amd64.whl; apache_sedona-1.9.1-cp311-cp311-win_arm64.whl; apache_sedona-1.9.1-cp312-cp312-macosx_10_13_x86_64.whl; apache_sedona-1.9.1-cp312-cp312-macosx_11_0_arm64.whl; apache_sedona-1.9.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; apache_sedona-1.9.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; apache_sedona-1.9.1-cp312-cp312-win_amd64.whl; apache_sedona-1.9.1-cp312-cp312-win_arm64.whl; apache_sedona-1.9.1-cp313-cp313-macosx_10_13_x86_64.whl; apache_sedona-1.9.1-cp313-cp313-macosx_11_0_arm64.whl
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