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High Performance Scalable Data Processing in Python and SQL
381 14 23 openLast shipped 20h ago
High Performance Scalable Data Processing in Python and SQL
Bodo shipped enhancements to GPU query execution with new logical inlined CTE (common table expression) support and special rendering logic for better optimization. The profiler now captures operator IDs from Calcite to improve tracing and debugging. Query planning also got smarter with refined…
Get this in your inbox every Monday →A deterministic 0–100 hygiene score — README, license, CI, tests, docs, and freshness.
Who ships this repo — author concentration and the bus factor across the last 300 mainline commits.
How welcoming this repo is to contributors — issue throughput, close time, responsiveness, and good-first-issue count.
What this project is built on — dependency count by ecosystem, the license mix, and anything worth a legal look before you adopt it.
Whether this project's CI can be trusted — pass rate, run times, flaky runs, and which workflow is the weak link.
Grounded in Bodo's README, structure, and recent commits — answers won't invent code they haven't seen.
A Monday email with what shipped, in plain English — no account needed.
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A floor, not a guess: counts only commits whose author, co-author trailer, or message explicitly credits an AI tool (Claude, Copilot, Cursor, aider, Codex…). Based on 30 mainline commits. Unattributed AI code isn't counted here — the full audit estimates that separately.
