andygrove opened a new issue, #5647:
URL: https://github.com/apache/datafusion-comet/issues/5647

   ### What is the problem the feature request solves?
   
   The only performance numbers for native Iceberg writes are the ad hoc 
measurements in the #5361 description (2.9x unpartitioned, 5.5x partitioned 
fanout, from a harness in a personal fork). There is no benchmark in the 
repository, so a regression in the writer, in the shuffle feeding it, or in the 
JVM metrics rebuild would not be noticed, and the partitioned number was 
measured with `write.distribution-mode=none` rather than the default plan shape.
   
   ### Describe the potential solution
   
   Add a Spark benchmark (the `org.apache.spark.sql.benchmark` framework used 
by `CometReadBenchmark` and the shuffle benchmark) that measures `INSERT INTO 
... SELECT` into an Iceberg table for:
   
   - unpartitioned
   - partitioned with the default hash distribution (clustered writer, native 
shuffle and sort on the partition transforms after #5635)
   - partitioned with `write.spark.fanout.enabled=true`
   - a copy-on-write `DELETE` (rewrite of affected files)
   
   Each case runs Spark, Comet scan only, and Comet scan plus native write, so 
the writer's contribution is isolated the way the #5361 table did. Wire it into 
the benchmark targets in the `Makefile` and document how to run it. Follow-on: 
run it on the EC2 benchmark runner and keep results with the other benchmark 
files.
   
   Keep the Spark benchmark framework's warmup and minimum-time floors in mind 
when sizing the data (see the shuffle benchmark work in #5388 for how that 
dominated wall clock).
   
   ### Additional context
   
   Part of the native Iceberg writes epic. Related: #5361, #3595 (fused sort 
plus write, which this benchmark would measure).
   


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