b173ac82a9
- adapt meta-/code-/obs- rules to the local Python benchmark pipeline - replace db-sql-ddl, code-config-env-scope, test-e2e-pytest with pipeline equivalents - add code-python-style, data-determinism, data-naming-units, bench-methodology, build-pipeline-tasks - normalize specs and README typography to ASCII per code-data-formatting - rewrite root CLAUDE.md trigger table; add .cursorrules and .gitignore - specs: pipeline plan and task specs 00-11 - rules: 19 binding rule files adapted for this project - docs: CLAUDE.md rule-trigger table - config: .cursorrules commit convention, .gitignore excluding out/ and .venv/ - docs: rewrite README.md with pipeline diagrams, setup guide, result placeholders - config: requirements.txt for the closed dependency list - datagen: make_lab_config.py writes out/config/lab_config.yaml and .done marker
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Task 04 — Schema Profiling & Fingerprints
Goal
Capture the record structure of every CSV without full load; compute schema fingerprints.
Input
manifest/inventory.jsonl,manifest/tokens.jsonl; sample sizes frompipeline.json.
Actions
- Write
scripts/profile_schema.py(streaming; head N + tail N + random M rows only). - Per file capture: delimiter, header presence, column names (raw, ordered), column count, inferred dtype per column, null-rate in sample, min/max of numeric columns in sample, total row estimate (size/avg-row-len).
- Fingerprint = hash of normalized ordered column-name list (lowercased, trimmed). Also compute a name-insensitive positional fingerprint (dtype sequence) for headerless files.
- Emit
manifest/schema_profiles.jsonl; add tableschema_profilestomanifest.sqlite. - Log unreadable/malformed files to
manifest/profile_errors.csv.
Intermediate Result (done when)
- 100% of CSV files have a profile row or an entry in
profile_errors.csv. - Every profile carries a fingerprint.