S3 Backend for RNTuple
Description
The RNTuple I/O provides the data format and basic storage stack for HL-LHC data. It has been designed to allow for exchangeable storage backends for file system access and object stores. Proof-of-concept implementations for DAOS and S3 object stores exist but have not been further pursued. The goal of this project is to develop a robust (pre-release quality) implementation for storing RNTuple data in S3, the standard cloud storage protocol. In particular, the project needs to design and implement a URL scheme to address RNTuple objects and, secondly, an efficient HTTP base layer to access objects and byte ranges based on libcurl.
Requirements
- C++
- Familiarity with HTTP protocols and object storage concepts
- Experience with libcurl or similar networking libraries is a plus
AI Policy
AI assistance is allowed for this contribution. The applicant takes full responsibility for all code and results, disclosing AI use for non-routine tasks (algorithm design, architecture, complex problem-solving). Routine tasks (grammar, formatting, style) do not require disclosure.
How to apply
Once CERN/HSF is accepted as a GSoC org, please write an email with a short introduction to your interests and background to the mentor with the string “gsoc26” in the subject. There will be a small evaluation task that we will email to you then.
Links
Mentors
- Jakob Blomer - CERN
Additional Information
- Difficulty level (low / medium / high): medium
- Duration: 350 hours
- Mentor availability: June-October