spark-rapids
quinn
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| spark-rapids | quinn | |
|---|---|---|
| 3 | 9 | |
| 720 | 578 | |
| 4.2% | - | |
| 9.8 | 9.1 | |
| 6 days ago | 16 days ago | |
| Scala | Python | |
| Apache License 2.0 | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
spark-rapids
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Open source contributions for a Data Engineer?
His newer project, Ballista, was also donated to Apache Arrow. I hope to get the Rust skills to collaborate with him on open source work someday too. He's also doing really cool work on spark-rapids FYI.
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I am reading this article https://www.frontiersin.org/articles/10.3389/fnins.2015.00492/full and thinking how to create an Amazon EMR infrastructure wih PySpark. Why is the GPU server not one of the nodes in the Apache Spark cluster? Or this is just an abstract view and the nodes are also the GPUs?
The spark-rapids project allows one to run multi-GPU ETL workloads on a Spark cluster. https://github.com/NVIDIA/spark-rapids In such a setup, the GPU nodes are part of the Spark cluster. Multi-GPU nodes are viable, although an executor is currently limited to a single GPU.
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Ballista: New approach for 2021
So, in my day job at NVIDIA, I work on the RAPIDS Accelerator for Apache Spark, which is an open-source plugin that provides GPU-acceleration for ETL workloads, leveraging the RAPIDS cuDF GPU DataFrame library.
quinn
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Brainstorming functions to make PySpark easier
We're brainstorming functions to make PySpark easier, see this issue: https://github.com/MrPowers/quinn/issues/83
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PySpark OSS Contribution Opportunity
Adding some README documentation to the README should be quite straightforward. Here's a function that needs to be documented: https://github.com/MrPowers/quinn/issues/52 .
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Invitation to collaborate on open source PySpark projects
quinn is a library with PySpark helper functions. I need to work through all the open issues / PRs and bump all versions. I should do another release. This library gets around 600,000 monthly downloads.
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Pyspark now provides a native Pandas API
Pandas syntax is far inferior to regular PySpark in my opinion. Goes to show how much data analysts value a syntax that they're already familiar with. Pandas syntax makes it harder to reason about queries, abstract DataFrame transformations, etc. I've authored some popular PySpark libraries like quinn and chispa and am not excited to add Pandas syntax support, haha.
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Register Native Functions in PySpark
Here's how I added a create_df method to the SparkSession class: https://github.com/MrPowers/quinn/blob/main/quinn/extensions/spark_session_ext.py
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Is Spark - The Defenitive Guide outdated?
They spent a lot of effort improving the catalyst engine under the hood too and making it easier to extend and improve it in the future. Making it easy to add your own native code to Spark itself. Shameless plug of a blog post I wrote on this subject which basically reiterates what Matthew Powers, author of Spark Daria and quinn, wrote here.
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Ask HN: What are some tools / libraries you built yourself?
I built daria (https://github.com/MrPowers/spark-daria) to make it easier to write Spark and spark-fast-tests (https://github.com/MrPowers/spark-fast-tests) to provide a good testing workflow.
quinn (https://github.com/MrPowers/quinn) and chispa (https://github.com/MrPowers/chispa) are the PySpark equivalents.
Built bebe (https://github.com/MrPowers/bebe) to expose the Spark Catalyst expressions that aren't exposed to the Scala / Python APIs.
Also build spark-sbt.g8 to create a Spark project with a single command: https://github.com/MrPowers/spark-sbt.g8
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Open source contributions for a Data Engineer?
I've built popular PySpark (quinn, chispa) and Scala Spark (spark-daria, spark-fast-tests) libraries.
What are some alternatives?
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
chispa - PySpark test helper methods with beautiful error messages
streamlit - Streamlit — A faster way to build and share data apps.
spark-daria - Essential Spark extensions and helper methods ✨😲
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
null - Nullable Go types that can be marshalled/unmarshalled to/from JSON.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
dagster - An orchestration platform for the development, production, and observation of data assets.
etl-markup-toolkit - ETL Markup Toolkit is a spark-native tool for expressing ETL transformations as configuration
meltano - Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.
lowdefy - The config web stack for business apps - build internal tools, client portals, web apps, admin panels, dashboards, web sites, and CRUD apps with YAML or JSON.