---
id: pyspark-client
version: "4.2.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# pyspark-client — Python Spark Connect client for Apache Spark

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

## 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 above — verify before relying.

Python client for connecting to Apache Spark clusters via Spark Connect, enabling distributed data processing and analytics from Python without requiring local Spark JARs.

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

pip install pyspark-client
uv add pyspark-client
poetry add pyspark-client

## Installing pyspark-client

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.

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")

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.

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_current
- Install friction: high
- Maintenance: active
- Downloads: 2.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

spark connect python client, distributed data processing python, spark cluster remote connection, pyspark distributed computing, spark sql dataframes python, stream processing analytics, remote spark analytics, distributed-computing, data-analytics, spark-connect

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