pyspark
Apache Spark Python API
Decision gist · record as of 2026-08-14
Yes, if you are working with large-scale data processing on a Spark cluster or need distributed machine learning and streaming capabilities. The high install friction and Java dependency are trade-offs for a mature, widely-used distributed computing platform. Not necessary for small-scale local data work. Ensure your Spark cluster version matches the installed PySpark version exactly.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Java runtime and matching Spark cluster version if connecting to an existing cluster; Python >= 3.10 required.
- High install friction due to the large tarball and dependency on py4j.
- Maintenance is active with recent releases, but the packaging is noted as experimental and requires careful version alignment with any Spark cluster you intend to connect to.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions beyond attribution and liability disclaimers.
last release 2026-07-14 (31 days) · last repo commit 2026-08-13 · 43,869 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 51,459,453 downloads/mo, #570 on PyPI
Alternatives
Verify before relying
pip install pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('example').getOrCreate()
df = spark.createDataFrame([(1, 'a'), (2, 'b')], ['id', 'value'])
df.show()- Whether numpy, pandas, or pyarrow are required for typical workloads or only for specific sub-packages.
- Performance characteristics and scalability limits for typical cluster sizes.
- Compatibility matrix details between PySpark and Spark cluster versions beyond 'must match'.
What it is and what it does
PySpark is the Python API for Apache Spark, a distributed computing framework for large-scale data processing. It exposes Spark's core engine through Python, allowing you to write distributed data pipelines, SQL queries, machine learning models, and stream processors that run across clusters. The package bundles Spark JARs and uses py4j to bridge Python and Java, making it suitable for connecting to existing Spark clusters (standalone, YARN, or cloud-hosted) rather than setting up new ones from pip alone.
The library supports multiple high-level APIs: Spark SQL for structured queries and DataFrames, pandas API on Spark for pandas-like workloads, MLlib for distributed machine learning, GraphX for graph algorithms, and Structured Streaming for real-time data. Installation is straightforward but carries high friction due to the large archive size. Version alignment with your target Spark cluster is critical—mismatches can cause subtle failures.
Use it for
- Process multi-terabyte datasets across a Spark cluster using SQL queries or DataFrame operations.
- Train machine learning models on distributed data using MLlib algorithms.
- Build real-time streaming pipelines that ingest and process continuous data streams.
- Perform graph analysis and algorithms on large graphs using GraphX.
- Migrate pandas workloads to distributed execution via the pandas API on Spark.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working with large-scale data processing on a Spark cluster or need distributed machine learning and streaming capabilities.
The high install friction and Java dependency are trade-offs for a mature, widely-used distributed computing platform. Not necessary for small-scale local data work. Ensure your Spark cluster version matches the installed PySpark version exactly.
Install
pyspark on PyPI
Before you install
High install friction due to the large tarball and dependency on py4j. Maintenance is active with recent releases, but the packaging is noted as experimental and requires careful version alignment with any Spark cluster you intend to connect to.
Requires Java runtime and matching Spark cluster version if connecting to an existing cluster; Python >= 3.10 required.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions beyond attribution and liability disclaimers.
Quickstart
pip install pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('example').getOrCreate()
df = spark.createDataFrame([(1, 'a'), (2, 'b')], ['id', 'value'])
df.show()Verify before relying
- Whether numpy, pandas, or pyarrow are required for typical workloads or only for specific sub-packages.
- Performance characteristics and scalability limits for typical cluster sizes.
- Compatibility matrix details between PySpark and Spark cluster versions beyond 'must match'.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | High. Source build required |
| Runtime dependencies | 1 packagepy4j |
| Maintenance | Actively maintained 31 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 51,459,453 / month, #570 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/StableProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonTyping :: Typed |
Evidence: pyspark-4.2.0.tar.gz
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See also apache-sedona · dataengine · pyspark-client · pyspark-extension · pyspark-pandas · raydp · spark-sklearn · synapseml · graphframes · koalas