spaceandtimefdn/sxt-proof-of-sql

Space and Time | Proof of SQL

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Summary Information

Updated 4 minutes ago
Added to GitGenius on September 12th, 2026
Created on April 25th, 2024
Open Issues & Pull Requests: 7 (+0)
GitHub issues: Enabled
Number of forks: 603
Total Stargazers: 5,386 (+0)
Total Subscribers: 1,587 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 41.3 hours
Mean response time: 57.3 days
90th percentile: 214.5 days
Tracked items: 56

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How this project is maintained

Around half of the issues opened in the past year never receive a reply. Work labelled "enhancement" is answered fastest, typically in about 18 hours, while "refactor" waits about 10 days. Only 0% of issues opened in the past year have been closed. Three people close 97% of everything that gets resolved.

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Detailed Description

Proof of SQL is a zero knowledge prover that cryptographically verifies SQL query results against untampered data.

The tool addresses the challenge of proving computation integrity over large datasets with latency constraints. It generates zero knowledge proofs that SQL queries were executed correctly, enabling trustless computation over both onchain and offchain data. The prover achieves sub-second latency on analytic queries spanning millions of rows by leveraging a novel commitment scheme and GPU acceleration, making it practical for blockchain transactions and DeFi applications where proof generation must complete within block time.

Developers building data-driven smart contracts or requiring trustless query verification should consider this tool, particularly those working with large datasets where traditional approaches incur prohibitive latency or gas costs. The project integrates with existing SQL databases including centralized systems and blockchain-based alternatives. It is most suitable for applications needing to prove query results onchain or to offchain verifiers during transaction execution. The README notes that not all SQL functions are currently supported, positioning this as an actively developing system rather than a complete SQL implementation.

The project maintains active development with ongoing work to expand SQL feature coverage. Contribution is explicitly encouraged through documented guidelines, with the team soliciting help across feature additions, integration with other zero knowledge solutions, and production deployment. The codebase targets Linux x86_64 systems with NVIDIA GPU acceleration as the primary optimization path, though the developers note that most code should theoretically support other Rust targets at reduced performance.