raydp
RayDP: Distributed Data Processing on Ray
Decision gist · record as of 2026-08-14
Yes, if you are building distributed data and AI pipelines and want to avoid managing separate Spark and Ray clusters. The active maintenance, permissive license, and low install friction make it a practical choice. Requires Java setup and familiarity with both Spark and Ray APIs; not suitable if you need only Spark or only Ray in isolation.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Java to be installed with JAVA_HOME environment variable set; Ray and PySpark must be available in the Python environment.
- Low friction: pure Python wheel with no compiled dependencies.
- Active maintenance (last commit 2026-06-10) and current Python support (3.8, 3.9, 3.10).
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions, suitable for most production environments.
last release 2026-03-12 (155 days) · last repo commit 2026-06-10 · 376 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 108,521 downloads/mo, #12,553 on PyPI
Alternatives
Verify before relying
pip install raydp
import ray
import raydp
ray.init()
spark = raydp.init_spark(app_name='Example', num_executors=2, executor_cores=2, executor_memory='4GB')
df = spark.createDataFrame([('word',)], ['text'])
df.show()
raydp.stop_spark()- Performance overhead of running Spark on Ray versus native Spark clusters in production scenarios.
- Compatibility matrix with specific Ray and PySpark versions beyond the stated Python 3.6+ requirement.
- Scalability limits and resource management behavior under high-concurrency workloads.
What it is and what it does
RayDP bridges Spark and Ray, allowing you to run Spark jobs as Ray actors on a single Ray cluster instead of managing separate Spark and Ray infrastructure. This eliminates the operational overhead of maintaining two clusters and removes the latency of exchanging data through external storage systems.
The package provides three main integration patterns: direct Spark-on-Ray execution via `raydp.init_spark()`, bidirectional conversion between Spark DataFrames and Ray Datasets for consumption by XGBoost or Ray Train, and high-level Estimator APIs (TorchEstimator, TFEstimator) that wrap Ray Train to train PyTorch or TensorFlow models directly on Spark DataFrames. It depends on numpy, pandas, psutil, pyarrow, ray, pyspark, and protobuf.
Use it for
- Build end-to-end ML pipelines combining Spark data processing with PyTorch or TensorFlow training in a single Python script.
- Run on-demand Spark jobs in cloud environments without manually provisioning a separate Spark cluster.
- Convert Spark DataFrames to Ray Datasets for distributed training with XGBoost or Horovod on Ray.
- Unify data processing and model serving on a single Ray cluster managed by an ML infrastructure team.
- Scale data science workflows from laptop to cloud without rewriting code or managing multiple cluster types.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building distributed data and AI pipelines and want to avoid managing separate Spark and Ray clusters.
The active maintenance, permissive license, and low install friction make it a practical choice. Requires Java setup and familiarity with both Spark and Ray APIs; not suitable if you need only Spark or only Ray in isolation.
Install
raydp on PyPI
Before you install
Low friction: pure Python wheel with no compiled dependencies. Active maintenance (last commit 2026-06-10) and current Python support (3.8, 3.9, 3.10). Requires Ray and PySpark as runtime dependencies, plus Java and JAVA_HOME configuration.
Requires Java to be installed with JAVA_HOME environment variable set; Ray and PySpark must be available in the Python environment.
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions, suitable for most production environments.
Quickstart
pip install raydp
import ray
import raydp
ray.init()
spark = raydp.init_spark(app_name='Example', num_executors=2, executor_cores=2, executor_memory='4GB')
df = spark.createDataFrame([('word',)], ['text'])
df.show()
raydp.stop_spark()Verify before relying
- Performance overhead of running Spark on Ray versus native Spark clusters in production scenarios.
- Compatibility matrix with specific Ray and PySpark versions beyond the stated Python 3.6+ requirement.
- Scalability limits and resource management behavior under high-concurrency workloads.
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesnumpypandaspsutilpyarrowraypysparkprotobuf |
| Maintenance | Actively maintained 155 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 108,521 / month, #12,553 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: raydp-1.6.5-py3-none-any.whl
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