h2o-pysparkling-3.1
Sparkling Water integrates H2O's Fast Scalable Machine Learning with Spark
Decision gist · record as of 2026-08-14
Yes, with conditions. PySparkling is worth installing if you are already committed to an Apache Spark infrastructure and need H2O's machine learning capabilities in that environment. The permissive Apache license and Production/Stable status support production use. However, high install friction, aging maintenance (633 days since last release), and the requirement for a pre-configured Spark cluster mean this is not a lightweight addition—evaluate whether H2O's specific algorithms justify the operational overhead in your stack.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Spark to be installed and configured; PySparkling is a Spark integration layer and cannot function standalone.
- Installation friction is high due to the package's large compiled artifact (h2o_pysparkling_3.1-3.46.0.6.post1.tar.gz).
- The package is in aging maintenance status with the last release 633 days old, though the repository remains active with recent commits as of 2025-11-05.
License · maintenance · safety
Apache v2 (permissive) — Licensed under Apache v2 (permissive), which allows commercial use, modification, and distribution with minimal restrictions—suitable for most production environments.
last release 2024-11-19 (633 days) · last repo commit 2025-11-05 · 979 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 79,470 downloads/mo, #14,354 on PyPI
Alternatives
Verify before relying
pip install h2o-pysparkling-3.1==3.46.0.6.post1
from pysparkling.ml import H2OMOJOModel
model = H2OMOJOModel.load('path/to/model.mojo')- Exact Python version compatibility beyond the classifiers (3.6–3.10 listed); whether newer Python versions are supported.
- Whether the 633-day gap since last release indicates active maintenance or dormancy; commit date alone does not clarify release cadence.
- Performance characteristics and scalability limits for large MOJO model scoring workloads.
- Whether Driverless AI MOJO scoring support is feature-complete or has known limitations.
What it is and what it does
PySparkling is a Python library that bridges H2O's machine learning engine with Apache Spark, enabling distributed model training and scoring across Spark clusters. It provides APIs to work with H2O-3 models and MOJO (Model Object, Optimized) artifacts, which are H2O's portable, language-agnostic model format. The package also supports scoring Driverless AI MOJO models.
The library is designed for data scientists and engineers working in big-data environments who need to integrate H2O's statistical and machine learning capabilities into Spark-based workflows. It has no runtime dependencies listed in the fact sheet, meaning it relies entirely on an external Spark installation and H2O runtime to function. The package is classified as Production/Stable and supports Python 3.6 through 3.10, though the last release was in November 2024.
Use it for
- Train H2O machine learning models on large distributed datasets using Spark's cluster computing.
- Score pre-trained MOJO models in batch or streaming Spark jobs for inference at scale.
- Integrate Driverless AI MOJO models into Spark-based data pipelines for automated predictions.
- Build end-to-end machine learning workflows combining Spark data preparation with H2O model training.
- Deploy H2O models to production Spark clusters for real-time or batch scoring on big data.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
PySparkling is worth installing if you are already committed to an Apache Spark infrastructure and need H2O's machine learning capabilities in that environment. The permissive Apache license and Production/Stable status support production use. However, high install friction, aging maintenance (633 days since last release), and the requirement for a pre-configured Spark cluster mean this is not a lightweight addition—evaluate whether H2O's specific algorithms justify the operational overhead in your stack.
Install
h2o-pysparkling-3-1 on PyPI
Before you install
Installation friction is high due to the package's large compiled artifact (h2o_pysparkling_3.1-3.46.0.6.post1.tar.gz). The package is in aging maintenance status with the last release 633 days old, though the repository remains active with recent commits as of 2025-11-05.
Requires Apache Spark to be installed and configured; PySparkling is a Spark integration layer and cannot function standalone.
License in practice
Licensed under Apache v2 (permissive), which allows commercial use, modification, and distribution with minimal restrictions—suitable for most production environments.
Quickstart
pip install h2o-pysparkling-3.1==3.46.0.6.post1
from pysparkling.ml import H2OMOJOModel
model = H2OMOJOModel.load('path/to/model.mojo')Verify before relying
- Exact Python version compatibility beyond the classifiers (3.6–3.10 listed); whether newer Python versions are supported.
- Whether the 633-day gap since last release indicates active maintenance or dormancy; commit date alone does not clarify release cadence.
- Performance characteristics and scalability limits for large MOJO model scoring workloads.
- Whether Driverless AI MOJO scoring support is feature-complete or has known limitations.
Package facts
| License | Apache v2 permissive |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Aging 633 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 79,470 / month, #14,354 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/StableIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Software Development :: Build Tools |
Evidence: h2o_pysparkling_3.1-3.46.0.6.post1.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 › “H2O machine learning on Spark”
- h2o-pysparkling-3.1PySparkling provides Python bindings to train and score H2O-3 and…
- h2oH2O is a distributed machine learning and statistical analysis…
- onnxmltoolsConverts machine learning models from multiple frameworks…
Give your agent the search over MCP, or paste the wish link into any chat.
More Build Tools packages
Provides reusable utilities for Python packaging interoperability, including version handling, specifiers, markers, requirements, tags, and metadata parsing according to standards like PEP 440 and PEP 425.
Wraps any iterable to display a real-time progress bar in the terminal or Jupyter notebook, showing iteration count, elapsed time, and estimated time remaining.
pip is the standard installer for Python packages, enabling you to download and install packages from the Python Package Index and other indexes into your Python environment.
Hatchling is a standards-compliant Python build backend that handles packaging, metadata, and distribution of Python projects when configured in a project's pyproject.toml file.
Generates Python gRPC service stubs and message classes from Protocol Buffer definitions, enabling developers to build gRPC clients and servers.
pre-commit is a framework for installing and running git hooks written in any language before commits are made, automating code quality and validation checks across multi-language projects.
Install it if your team needs consistent, automated validation at commit time.
See also h2o · h2o-wave · koalas · onnxmltools · pyspark-stubs · sagemaker-feature-store-pyspark · spark-sklearn · spark-parser · spark-nlp · raydp