dataproc-spark-connect
Dataproc client library for Spark Connect
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. It is worth installing if you need to run Spark workloads on Google Cloud Dataproc from Python and want to avoid manual session setup. The main prerequisite is Google Cloud authentication and appropriate IAM permissions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Google Cloud authentication credentials (GOOGLE_APPLICATION_CREDENTIALS environment variable or ADC) and appropriate IAM permissions for Dataproc Sessions and Session Templates.
- Low install friction with a pure-Python wheel.
- Active maintenance as of 2026-04-07, with recent commits.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing use in commercial and private projects with minimal restrictions.
last release 2026-04-07 (129 days) · last repo commit 2026-08-14 · 7 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 95,182 downloads/mo, #13,286 on PyPI
Alternatives
Verify before relying
pip install dataproc-spark-connect
from google.cloud.dataproc_spark_connect import DataprocSparkSession
spark = DataprocSparkSession.builder.projectId('my-project').location('us-central1').getOrCreate()
df = spark.createDataFrame([(1, 'data')], ['id', 'value'])
df.show()- Whether the package supports all Spark Connect features or has known limitations vs. native Spark Connect.
- Performance characteristics and latency when communicating with remote Dataproc sessions.
- Compatibility matrix with specific Dataproc runtime versions and Spark versions.
What it is and what it does
This package is a Python client that simplifies connecting to remote Apache Spark sessions running on Google Cloud Dataproc using the Spark Connect protocol. It wraps the Spark Connect client with additional conveniences for Dataproc-specific session management, eliminating manual setup steps and providing a fluent builder API for configuration.
You use it to create or reuse named Spark sessions in Dataproc, configure Spark properties and session templates, and execute distributed computations from Python notebooks or scripts. It handles Google Cloud authentication and session lifecycle management, and optionally integrates with Jupyter magic commands for SQL queries. The package depends on pyspark, google-cloud-dataproc, google-api-core, packaging, tqdm, and websockets.
Use it for
- Run distributed Spark jobs on Dataproc from a local Python script or Jupyter notebook without manual session provisioning.
- Share a single Spark session across multiple notebooks by reusing named session IDs, reducing startup time and costs.
- Execute Spark SQL queries interactively in Jupyter using magic commands (with sparksql-magic installed).
- Configure Spark executor memory, cores, and Dataproc runtime versions via a fluent builder API.
- Manage session TTL and idle timeouts to control Dataproc resource lifecycle and billing.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. It is worth installing if you need to run Spark workloads on Google Cloud Dataproc from Python and want to avoid manual session setup. The main prerequisite is Google Cloud authentication and appropriate IAM permissions.
Install
dataproc-spark-connect on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance as of 2026-04-07, with recent commits. Depends on google-cloud-dataproc, pyspark, and standard utilities; all are established packages.
Requires Google Cloud authentication credentials (GOOGLE_APPLICATION_CREDENTIALS environment variable or ADC) and appropriate IAM permissions for Dataproc Sessions and Session Templates.
License in practice
Licensed under Apache 2.0 (permissive), allowing use in commercial and private projects with minimal restrictions.
Quickstart
pip install dataproc-spark-connect
from google.cloud.dataproc_spark_connect import DataprocSparkSession
spark = DataprocSparkSession.builder.projectId('my-project').location('us-central1').getOrCreate()
df = spark.createDataFrame([(1, 'data')], ['id', 'value'])
df.show()Verify before relying
- Whether the package supports all Spark Connect features or has known limitations vs. native Spark Connect.
- Performance characteristics and latency when communicating with remote Dataproc sessions.
- Compatibility matrix with specific Dataproc runtime versions and Spark versions.
Package facts
| License | Apache 2.0 permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesgoogle-api-coregoogle-cloud-dataprocpackagingpysparktqdmwebsockets |
| Maintenance | Actively maintained 129 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 95,182 / month, #13,286 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dataproc_spark_connect-1.1.0-py2.py3-none-any.whl
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 connect dataproc client”
- dataproc-spark-connectWraps Apache Spark Connect to let Python applications connect to…
- google-cloud-dataproc-metastoreA Python client library for managing Dataproc Metastore services on…
- google-cloud-dataprocA Python client library for managing Hadoop-based clusters and jobs…
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 databricks-connect · google-cloud-dataproc · pyspark-client · google-cloud-dataproc-metastore · pyspark · pytest-spark · pyspark-extension · hdijupyterutils · sagemaker-feature-store-pyspark-3.1 · snowpark-connect