pyspark-client
Python Spark Connect client for Apache Spark
Decision gist · record as of 2026-08-14
Yes, if you have access to a running Spark cluster and need Python-based analytics at scale. The high install friction and experimental packaging status are offset by active maintenance, permissive licensing, and broad Python version support (3.10 through 3.14). Critical caveat: ensure your client version matches your cluster version exactly, and verify that all required features are supported via Spark Connect before committing to production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Spark cluster with Spark Connect server; Python 3.10 or later; client and cluster versions must match exactly to avoid runtime errors.
- High install friction due to 8 runtime dependencies including pandas, pyarrow, and grpcio.
- Active maintenance with recent releases, though packaging is noted as experimental and version matching with Spark clusters is critical to avoid runtime errors.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, though derivative works must retain attribution and license notices.
last release 2026-07-14 (31 days) · last repo commit 2026-08-14 · 43,872 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,348,591 downloads/mo, #3,115 on PyPI
Alternatives
Verify before relying
pip install pyspark-client
from pyspark.sql import SparkSession
spark = SparkSession.builder.remote("sc://hostname:port").getOrCreate()
df = spark.sql("SELECT * FROM table")- Whether the experimental packaging status affects production readiness or API stability guarantees.
- Specific performance characteristics or latency overhead of remote Spark Connect vs. local PySpark.
- Whether all Spark SQL, MLlib, and Structured Streaming features are fully supported via the Connect client.
- Exact version compatibility requirements between pyspark-client and Spark cluster deployments.
What it is and what it does
pyspark-client is the Python Spark Connect client—a remote interface to Apache Spark clusters that lets you submit distributed data processing jobs from Python without needing a local Spark installation. It connects to an existing Spark cluster (standalone, YARN, or Kubernetes) and provides access to Spark SQL, DataFrames, MLlib, and Structured Streaming through a Python API.
The package depends on pandas, pyarrow, grpcio, grpcio-status, googleapis-common-protos, zstandard, numpy, and pyyaml to handle data serialization, gRPC communication, and numerical computation. It is suitable for interacting with production clusters but does not include tools to set up a standalone cluster itself. The documentation notes that the Python packaging is experimental and may change, and that client and cluster versions must match to avoid runtime errors.
Use it for
- Run SQL queries and DataFrame transformations against a remote Spark cluster from a Python notebook or script.
- Process large datasets distributed across a cluster without installing full Spark locally.
- Build machine learning pipelines using MLlib on data too large for a single machine.
- Stream and process real-time data using Structured Streaming on a shared Spark cluster.
- Integrate Spark analytics into Python applications that connect to an existing enterprise cluster.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have access to a running Spark cluster and need Python-based analytics at scale.
The high install friction and experimental packaging status are offset by active maintenance, permissive licensing, and broad Python version support (3.10 through 3.14). Critical caveat: ensure your client version matches your cluster version exactly, and verify that all required features are supported via Spark Connect before committing to production use.
Install
pyspark-client on PyPI
Before you install
High install friction due to 8 runtime dependencies including pandas, pyarrow, and grpcio. Active maintenance with recent releases, though packaging is noted as experimental and version matching with Spark clusters is critical to avoid runtime errors.
Requires a running Spark cluster with Spark Connect server; Python 3.10 or later; client and cluster versions must match exactly to avoid runtime errors.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, though derivative works must retain attribution and license notices.
Quickstart
pip install pyspark-client
from pyspark.sql import SparkSession
spark = SparkSession.builder.remote("sc://hostname:port").getOrCreate()
df = spark.sql("SELECT * FROM table")Verify before relying
- Whether the experimental packaging status affects production readiness or API stability guarantees.
- Specific performance characteristics or latency overhead of remote Spark Connect vs. local PySpark.
- Whether all Spark SQL, MLlib, and Structured Streaming features are fully supported via the Connect client.
- Exact version compatibility requirements between pyspark-client and Spark cluster deployments.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | High. Source build required |
| Runtime dependencies | 8 packagespandaspyarrowgrpciogrpcio-statusgoogleapis-common-protoszstandardnumpypyyaml |
| Maintenance | Actively maintained 31 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,348,591 / month, #3,115 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_client-4.2.0.tar.gz
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “spark cluster remote connection”
- pyspark-clientPython client for connecting to Apache Spark clusters via Spark…
- livyA Python client for Apache Livy that enables remote code execution on…
- databricks-connectDatabricks Connect is a client library that lets you write Spark code…
Give your agent the search over MCP, or paste the wish link into any chat.
More Distributed Computing packages
gRPC Python is an HTTP/2-based RPC framework that enables you to define and call remote procedures across network boundaries using protocol buffers for serialization.
Install it if you need RPC communication in a distributed system or are integrating with existing gRPC services.
execnet lets you spawn and communicate with Python interpreters across local processes, remote hosts, and different platforms, using a simple API for task distribution and inter-process messaging.
However, the aging maintenance status (275 days since last release) means you should verify it meets your concurrency and performance needs before committing to a…
Cloudpickle extends Python's standard pickle module to serialize lambda functions, interactively-defined functions and classes, and other constructs that the default pickle cannot handle, making it suitable for cluster computing and remote code execution.
Install it if you need to serialize lambda functions, interactively-defined code, or non-standard Python constructs for cluster computing or distributed execution.
Provides a unified, open()-compatible Python API for streaming large files from remote storage (S3, GCS, Azure, HDFS, SFTP, HTTP) and local filesystems, with transparent compression support.
Install it if you work with large files on cloud storage or remote systems and want to avoid writing boilerplate around multiple SDKs.
Portalocker provides cross-platform file locking with support for exclusive and shared locks, plus Redis-based distributed locks and process-aware PID file locking.
Install it if you need file or process coordination; the optional extras (pywin32, redis) are only required for specific lock types.
Ray is a distributed computing framework that scales Python applications from a single machine to multi-node clusters, providing abstractions for parallel tasks, stateful actors, and shared objects.
See also livy · pyspark · dune-client · spark-sklearn · pyspark-pandas · dataproc-spark-connect · repartipy · raydp · joblibspark · pyspark-data-sources