H2O Spark 4.0 Roadmap: Future Integration Plans

H2O.ai has not publicly announced specific details about Apache Spark 4.0 support in its roadmap for Sparkling Water integration, though the project continues to evolve with the open-source ecosystem. The current Sparkling Water framework integrates H2O-3’s scalable machine learning algorithms with Apache Spark, allowing users to leverage both platforms’ capabilities within a unified data processing environment.

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Contents

• What is Sparkling Water?
• Current Integration Capabilities
• Installation and Compatibility
• Roadmap and Future Directions
• Enterprise Considerations
• Getting Started with Sparkling Water

What is Sparkling Water?

Sparkling Water is an open-source machine learning framework that bridges the gap between H2O-3 and Apache Spark. As described in the GitHub repository, Sparkling Water integrates H2O-3, a fast scalable machine learning engine, with Apache Spark to provide:

• Data structure conversion between Spark’s RDDs, DataFrames, and Datasets with H2O’s frames
• Unified machine learning workflows that combine H2O algorithms with Spark’s distributed computing capabilities
• Seamless integration allowing users to publish Spark data structures as H2O frames and vice versa

The collaboration between H2O.ai and the Apache Spark community is designed to seamlessly enable H2O’s advanced capabilities to be part of modern data pipelines.

Current Integration Capabilities

Sparkling Water empowers users to:

• Combine H2O algorithms with MLlib on Apache Spark, allowing for flexible algorithm selection and ensemble building
• Leverage H2O’s deep learning capabilities within Spark environments
• Use H2O MOJOs (Model Object Optimized) for effective model deployment with focus on scoring speed, traceability, and backward compatibility
• Interface with Apache Spark through both Scala and Python APIs
• Build ensembles using algorithms from both H2O and MLlib

According to H2O.ai’s product description, Sparkling Water allows users to combine the fast, scalable machine learning algorithms of H2O with the capabilities of Spark, creating an elegant and powerful general-purpose in-memory platform.

Installation and Compatibility

Based on the research findings, here are the key compatibility notes:

• Version compatibility: Earlier versions of Sparkling Water (like 2.1.23 and 3.28) support specific Spark versions such as 2.4.4
• Python integration: PySparkling provides Python bindings for using H2O algorithms within Spark
• Installation process: Typically involves downloading Sparkling Water JAR files and integrating them with Spark’s lib directory

The installation process generally follows these steps:

1. Install required dependencies (like colorama >= 0.3.8)
2. Download and unzip Sparkling Water package
3. Copy JAR files to Spark’s lib directory
4. Install Python package for PySparkling support

As noted in the Qubole blog, users can download specific versions of Sparkling Water that match their Spark environment requirements.

Roadmap and Future Directions

While the search results don’t provide explicit information about Spark 4.0 support timelines, they do mention several future directions:

1. Deeper integration: The roadmap includes deeper integration where H2O’s columnar-compressed capabilities can be natively leveraged through ‘H2ORDD’

2. Memory sharing optimization: First steps focus on enabling in-memory sharing through Tachyon and RDDs

3. Unified data processing: The vision includes the ability to query big data both via SQL and ML from within the same context

4. Enhanced visual capabilities: Giving Spark users access to H2O’s visual intelligence capabilities

As mentioned in the Databricks blog, this collaboration is designed to seamlessly enable H2O’s advanced capabilities to be part of modern data pipelines, with the roadmap focusing on increasingly tight integration between the two platforms.

It’s important to note that “with every major release of Spark or H2O there are API changes and, less frequently, major data structure changes that affect Sparkling Water,” as stated in the H2O.ai blog post. This suggests that the project team actively works to maintain compatibility with new Spark releases, though specific roadmaps for Spark 4.0 aren’t detailed in the available sources.

Enterprise Considerations

For enterprise users, Sparkling Water provides several advantages:

• Flexible algorithm selection: The ability to use H2O algorithms in conjunction with, or instead of, MLlib algorithms on Apache Spark
• Production-ready deployment: MOJO format models designed for effective model deployment
• Enterprise scalability: Leveraging both H2O’s distributed computing and Spark’s cluster management

The H2.ai data sheet emphasizes that Sparkling Water empowers enterprise customers to use H2O algorithms in conjunction with, or instead of, MLlib algorithms on Apache Spark.

Getting Started with Sparkling Water

For users interested in implementing Sparkling Water:

1. Check compatibility: Verify that your Spark version is supported by available Sparkling Water releases
2. Download appropriate version: Obtain Sparkling Water from official sources or GitHub releases
3. Follow installation guides: Refer to documentation for your specific Spark version
4. Start with examples: Use provided examples to understand integration patterns

The H2O Sparkling Water Tutorial for Beginners provides a good starting point for understanding how to set up and use Sparkling Water with different Spark versions.

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Sources

1. GitHub - h2oai/sparkling-water: Sparkling Water provides H2O functionality inside Spark cluster
2. H2O Sparkling Water | H2O.ai
3. Sparkling Water | H2O.ai Data Sheet
4. Using the H2O Framework with Apache Spark Clusters on Qubole
5. Sparkling Water = H20 + Apache Spark | Databricks Blog
6. How to Build a Machine Learning App Using Sparkling Water and Apache Spark | H2O.ai
7. H2O Sparkling Water Tutorial for Beginners - Spark By Examples
8. H2O.ai Shares Advancements for H2O Sparkling Water at Spark + AI Summit 2018
9. pyspark - Spark 4.0 support for open source H20 and Sparkling water libraries - Stack Overflow
10. Error when importing Sparkling Water (H2O) pipeline in Apache Spark: py4j.protocol.Py4JError - Stack Overflow

Conclusion

Based on the available research, here are the key takeaways regarding H2O’s roadmap for Apache Spark 4.0 support:

• No explicit Spark 4.0 roadmap: H2O.ai has not publicly announced specific timelines or plans for Spark 4.0 support in Sparkling Water
• Continuous integration approach: The project team actively works to maintain compatibility with new Spark releases, though specific roadmaps aren’t detailed
• Future integration focus: The roadmap emphasizes deeper integration between H2O’s columnar capabilities and Spark’s distributed computing
• Version dependency: Sparkling Water compatibility depends on both H2O-3 and Spark releases, requiring ongoing maintenance

For users planning to adopt Spark 4.0, it’s recommended to:

• Monitor the official Sparkling Water GitHub repository for updates
• Check H2O.ai’s official announcements and product pages
• Consider the current supported Spark versions for production deployments
• Engage with the community through forums and Stack Overflow for the latest compatibility information

The integration between H2O and Apache Spark continues to evolve, with both platforms maintaining active development and community support that should eventually address Spark 4.0 compatibility as the release matures.