Summary
In this episode PhD researcher Xinyu Zeng talks about F3, the “future-proof file format” designed to address today’s hardware realities and evolving workloads. He digs into the limitations of Parquet and ORC - especially CPU-bound decoding, metadata overhead for wide-table projections, and poor random-access behavior for ML training and serving - and how F3 rethinks layout and encodings to be efficient, interoperable, and extensible. Xinyu explains F3’s two major ideas: a decoupled, flexible layout that separates IO units, dictionary scope, and encoding choices; and self-decoding files that embed WebAssembly kernels so new encodings can be adopted without waiting on every engine to upgrade. He discusses how table formats and file formats should increasingly be decoupled, potential synergies between F3 and table layers (including centralizing and verifying WASM kernels), and future directions such as extending WASM beyond encodings to indexing or filtering.

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 Xinyu Zeng about the future-proof file format

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what the F3 project is and the story behind it?
We have several widely adopted file formats (Parquet, ORC, Avro, etc.). Why do we keep creating new ones?
Parquet is the format with perhaps the broadest adoption. What are the challenges that such wide use poses when trying to modify or extend the specification?
The recent focus on vector data is perhaps the most visible change in storage requirements. What are some of the other custom types of data that might need to be supported in the file storage layer?
Can you describe the key design principles of the F3 format?
What are the engineering challenges that you faced while developing your implementation of the F3 proof-of-concept?
The key challenge of introducing a new format is that of adoption. What are the provisions in F3 that might simplify the adoption of the format in the broader ecosystem? (e.g. integration with compute frameworks)
What are some examples of features in data lake use cases that could be enabled by F3?
What are some of the other ideas/hypotheses that you developed and discarded in the process of your reasearch?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on F3?
What do you have planned for the future of F3?

Contact Info

Personal Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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