kosiew opened a new pull request, #24323:
URL: https://github.com/apache/datafusion/pull/24323

   ## Which issue does this PR close?
   
   * Part of #21554
   
   ## Rationale for this change
   
   `ParquetPruningSetupCache` in #21566 needs a focused benchmark that 
exercises it under deliberately cache-favourable conditions.
   
   This benchmark provides a workload where many Parquet files share the same 
physical schema and use the same predicate, target partition count, and query, 
creating repeated opportunities to reuse pruning setup. It is intended to 
measure end-to-end scan cost for this specific workload shape, rather than 
represent general DataFusion performance or predict improvements in broader 
benchmarks such as ClickBench.
   
   ## What changes are included in this PR?
   
   * Adds a Criterion benchmark that creates 128 Parquet files sharing one 
physical schema and repeatedly executes `SELECT id FROM t WHERE id >= 0`.
   * Keeps file generation, runtime creation, table registration, and initial 
correctness validation outside the timed Criterion loop.
   * Validates that the query returns all 16,384 expected rows before 
benchmarking.
   * Enables Criterion's `async_tokio` feature so the asynchronous query 
execution can be benchmarked with the Tokio runtime.
   * Registers the new `parquet_pruning_setup_cache` benchmark target.
   * Documents that the benchmark is intentionally cache-favourable and that 
its results should not be interpreted as general DataFusion or ClickBench 
performance results.
   
   ## Are these changes tested?
   
   The benchmark includes a correctness check that verifies the query returns 
`128 × 128 = 16,384` rows before the timed benchmark begins.
   
   No additional tests are included in this patch.
   
   ## Are there any user-facing changes?
   
   No. This PR adds a focused benchmark and benchmark configuration only; it 
does not change user-facing DataFusion behavior or public APIs.
   
   ## LLM-generated code disclosure
   
   This PR includes LLM-generated code and comments. All LLM-generated content 
has been manually reviewed.
   


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