Massively Parallel Data Processing In Python Without The Effort Using Bodo
Data Engineering Podcast
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The Difference Between Inferential Compiler and Boodo Platform
O is what we call an inferential compiler, which means that it understands operations at a very high level. The other compilers operate at the loop level, but bodo operates at the pi level. So you're not getting maybe a little bit better sequential programm out,. You're getting a lot betther program both on sequential aspec running on a single cord, and we can run it on as many cors as he want. That's the c difference.
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Summary
Python has beome the de facto language for working with data. That has brought with it a number of challenges having to do with the speed and scalability of working with large volumes of information.There have been many projects and strategies for overcoming these challenges, each with their own set of tradeoffs. In this episode Ehsan Totoni explains how he built the Bodo project to bring the speed and processing power of HPC techniques to the Python data ecosystem without requiring any re-work.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Ehsan Totoni about Bodo, a system for automatically optimizing and parallelizing python code for massively parallel data processing and analytics
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Bodo is and the story behind it?
- What are the techniques/technologies that teams might use to optimize or scale out their data processing workflows?
- Why have you focused your efforts on the Python language and toolchain?
- Do you see any potential for expanding into other language communities?
- What are the shortcomings of projects such as Dask and Ray for scaling out Python data projects?
- Many people are familiar with the principle of HPC architectures, but can you share an overview of the current state of the art for HPC?
- What are the tradeoffs of HPC vs scale-out distributed systems?
- Can you describe the technical implementation of the Bodo platform?
- What are the aspects of the Python language and package ecosystem that have complicated the work of building an optimizing compiler?
- How do you handle compiled extensions? (e.g. C/C++/Fortran)
- What are some of the assumptions/expectations that you had when first approaching this project that have been challenged as you progressed through its implementation?
- What are the aspects of the Python language and package ecosystem that have complicated the work of building an optimizing compiler?
- How do you handle data distribution for scale out computation?
- What are some software architecture/programming patterns that act as bottlenecks/optimization cliffs for parallelization?
- What are some of the educational challenges that you have run into while working with potential and current customers?
- What are the most interesting, innovative, or unexpected ways that you have seen Bodo used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Bodo?
- When is Bodo the wrong choice?
- What do you have planned for the future of Bodo?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Bodo
- High Performance Computing (HPC)
- University of Illinois, Urbana-Champaign
- Julia Language
- Pandas
- NumPy
- Dask
- Ray
- Numba
- LLVM
- SPMD
- MPI
- Elastic Fabric Adapter
- Iceberg Table Format
- IPython Parallel
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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