pyspark-extension
A library that provides useful extensions to Apache Spark.
Decision gist · record as of 2026-08-14
Yes. Active maintenance, permissive Apache License 2.0, low install friction, no known vulnerabilities, and production-stable status make it a safe choice. Install it for development and IDE support; cluster use requires the Scala JAR via spark-submit or notebook configuration. Useful if you need dataset diffing, Parquet inspection, or dynamic package installation in PySpark.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PySpark ≥3.1.0 for runtime package installation; local development use only via pip—cluster deployment requires adding the Scala JAR via spark.jars.packages or spark-submit.
- Low friction; single lightweight runtime dependency (typing_extensions).
- Active maintenance with recent commits and stable production status.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 (permissive). Allows commercial use, modification, and distribution with minimal restrictions; suitable for proprietary projects.
last release 2026-03-18 (149 days) · last repo commit 2026-08-06 · 240 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 92,910 downloads/mo, #13,415 on PyPI
Alternatives
Verify before relying
pip install pyspark-extension==2.15.0.4.1
from pyspark.sql import SparkSession
spark = SparkSession.builder.config("spark.jars.packages", "uk.co.gresearch.spark:spark-extension_2.12:2.15.0-3.4").getOrCreate()- Performance characteristics when diffing or histogramming very large datasets.
- Compatibility matrix between specific pyspark-extension and PySpark minor versions.
- Whether PyPy implementation is fully supported despite CPython focus in documentation.
What it is and what it does
pyspark-extension is a Python library that wraps and exposes Scala-based extensions to Apache Spark, providing utilities for common data transformation and inspection tasks. It includes a diff operation to compute row-level changes between datasets, histogram generation, global row numbering without window specifications, and tools to inspect Parquet file metadata. The package also enables dynamic installation of Python dependencies into running PySpark jobs via pip or Poetry, and provides helper functions for .NET DateTime conversion, null counting, and Spark job description management.
The library is primarily used during development via pip for IDE support and testing, but cluster execution requires deploying the underlying Scala JAR through Spark's dependency mechanism (spark.jars.packages, spark-submit, or notebook configuration). It has minimal runtime dependencies and supports Python 3.7 through 3.13, making it compatible with a wide range of PySpark environments.
Use it for
- Compute row-level differences (adds, deletes, changes) between two large datasets to identify what changed.
- Generate histogram DataFrames for exploratory data analysis and distribution visualization.
- Assign global row numbers across an entire dataset without window specification overhead.
- Inspect Parquet file structure and metadata to diagnose partitioning or schema issues.
- Dynamically install Python packages into a running PySpark job without restarting the cluster.
- Convert .NET DateTime.Ticks to Spark timestamps for interoperability with C# or F# data sources.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Active maintenance, permissive Apache License 2.0, low install friction, no known vulnerabilities, and production-stable status make it a safe choice. Install it for development and IDE support; cluster use requires the Scala JAR via spark-submit or notebook configuration. Useful if you need dataset diffing, Parquet inspection, or dynamic package installation in PySpark.
Install
pyspark-extension on PyPI
Before you install
Low friction; single lightweight runtime dependency (typing_extensions). Active maintenance with recent commits and stable production status. Supports Python 3.7 through 3.13.
Requires PySpark ≥3.1.0 for runtime package installation; local development use only via pip—cluster deployment requires adding the Scala JAR via spark.jars.packages or spark-submit.
License in practice
Apache License 2.0 (permissive). Allows commercial use, modification, and distribution with minimal restrictions; suitable for proprietary projects.
Quickstart
pip install pyspark-extension==2.15.0.4.1
from pyspark.sql import SparkSession
spark = SparkSession.builder.config("spark.jars.packages", "uk.co.gresearch.spark:spark-extension_2.12:2.15.0-3.4").getOrCreate()Verify before relying
- Performance characteristics when diffing or histogramming very large datasets.
- Compatibility matrix between specific pyspark-extension and PySpark minor versions.
- Whether PyPy implementation is fully supported despite CPython focus in documentation.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagetyping_extensions |
| Maintenance | Actively maintained 149 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 92,910 / month, #13,415 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTyping :: Typed |
Evidence: pyspark_extension-2.15.0.4.1-py3-none-any.whl
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See also pyspark · sparkmeasure · findspark · pyspark-pandas · pyspark-client · pyspark-huggingface · pyspark-test · hepconvert · pyddq · spark-expectations