hhhizzz commented on PR #24705:
URL: https://github.com/apache/datafusion/pull/24705#issuecomment-5447696353

   I ran a focused five-arm experiment using the same DataFusion #24705 branch 
and a common Arrow base:
   
   - Pushdown OFF
   - Base ON
   - 10852 capped Auto only
   - 10859 fusion only
   - Combined
   
   The Combined arm explicitly routes fused predicate results through capped 
Auto. I ran Q24–Q26 for five position-balanced rounds, 10 iterations each, 
discarding iteration 0 and using the median of the remaining iterations.
   
   | Query | OFF | Base ON | Capped | Fusion | Combined |
   |---|---:|---:|---:|---:|---:|
   | Q24 | 134.0 ms | 95.0 ms | 93.7 ms | 95.8 ms | 94.6 ms |
   | Q25 | 205.5 ms | 278.3 ms | 262.3 ms | 220.2 ms | 223.5 ms |
   | Q26 | 131.1 ms | 216.9 ms | 214.9 ms | 216.3 ms | 215.4 ms |
   
   The results suggest:
   
   - Q24’s improvement mainly comes from the DataFusion gating changes; neither 
Arrow optimization adds much.
   - Q25 benefits from both changes independently: capped Auto improves about 
6%, while fusion improves about 21%.
   - Combined is effectively the same as Fusion-only, so I did not observe an 
additive Q25 benefit.
   - Q26 is unchanged, consistent with its two-column TopK key not matching the 
same-single-column fusion condition.
   - Q23’s major pushdown benefit was preserved.
   
   My interpretation is that the changes are architecturally complementary, but 
their Q25 gains overlap in practice. Fusion removes the dominant repeated 
decode, leaving too little selection-lowering cost for capped Auto to provide a 
separately measurable gain.
   
   All result captures were equivalent across the five arms, and the focused 
equivalence tests and Parquet clippy passed.


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