Re: [I] Pluggable expression-level statistics estimation (ExpressionAnalyzer) [datafusion]

["Whatsonyourmind (via GitHub)" <gi...@apache.org>]

Whatsonyourmind commented on issue #21120:
URL: https://github.com/apache/datafusion/issues/21120#issuecomment-4182731153

   A few notes on the Selinger-style default analyzer for the planned work:
   
   For **selectivity estimation** of comparison predicates (`col > const`), the classic approach when histograms are unavailable is the uniform distribution assumption with min/max bounds:
   
   ```
   selectivity(col > c) = (max - c) / (max - min)    when min ≤ c ≤ max
   selectivity(col > c) = 0                           when c > max
   selectivity(col > c) = 1                           when c < min
   ```
   
   For compound predicates, the independence assumption gives:
   - `AND`: multiply selectivities
   - `OR`: `1 - (1 - s1)(1 - s2)`  
   - `NOT`: `1 - s`
   
   For **NDV propagation through expressions**: arithmetic operations like `col + 1` preserve NDV exactly. For `col1 + col2`, the upper bound on NDV is `min(NDV1 * NDV2, row_count)`. For functions like `FLOOR(col)`, NDV can only decrease: `NDV_out ≤ NDV_in`. For `date_trunc('month', ts_col)`, NDV can be estimated as `(max - min) / interval_size`.
   
   One design consideration: the chain-of-responsibility pattern should propagate **confidence levels** alongside estimates. A histogram-based analyzer might return `(selectivity=0.23, confidence=High)`, while a fallback heuristic returns `(selectivity=0.20, confidence=Low)`. The optimizer can then weight its cost model decisions by confidence -- preferring plans that are robust to estimation error when confidence is low.


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