feat: focused profiling — only profile changed columns#1
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feat: focused profiling — only profile changed columns#1
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For wide models (100+ columns), data-diff now profiles only the columns that actually changed, using sqlglot AST analysis. This reduces BQ compute by ~90% for typical changes to wide models. How it works: - parse_columns.py identifies added_columns + expression_changes - is_cte_change_additive() classifies CTE modifications: - Additive (new LEFT JOINs, new columns) → safe for focused profiling - Structural (WHERE/filter/JOIN changes) → falls back to full profiling - EXCEPT DISTINCT row comparison always covers ALL columns as safety net - --full flag to override and profile everything Also syncs upstream improvements: - data-diff.sh: get_affected_columns() helper, column filter in build_profile_query(), profiling mode tracking per model - template.html: focused profiling banner, skip-row styling for non-profiled columns (shown as — instead of misleading zeros) - SKILL.md: document --full flag and focused profiling behavior
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Syncs focused profiling from the internal dbt repo. For wide models (100+ columns), profiles only columns that actually changed — ~90% reduction in BQ compute.
See finn-auto/dbt#7445 for the full context and test results.