[GitHub] [arrow-datafusion] JonyTony opened a new issue, #6006: logical_plan optimize for MemTable will cost more time when collect dataframe

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

JonyTony opened a new issue, #6006:
URL: https://github.com/apache/arrow-datafusion/issues/6006

   ### Is your feature request related to a problem or challenge?
   
   I try to read table from MemTable which is large, but when I collect it, datafusion cost more time then expectation.
   I find this is because logical_plan optimize. 
   https://github.com/apache/arrow-datafusion/blob/f9f40bf70a9b6af4345f16b906bcf8a839d5f511/datafusion/core/src/execution/context.rs
   ```rust
   // line 1940
       pub async fn create_physical_plan(
           &self,
           logical_plan: &LogicalPlan,
       ) -> Result<Arc<dyn ExecutionPlan>> {
           let logical_plan = self.optimize(logical_plan)?;
           self.query_planner
               .create_physical_plan(&logical_plan, self)
               .await
       }
   ```
   I find old_plan is TableScan{.., projection: None, ..}, but new_plan is TableScan{.., projection: Some([0,1,2,...]), ..}.
   when I collect data, datafusion use new_plan will cost more time then use old_plan because of MemoryStream::poll_next: 
   https://github.com/apache/arrow-datafusion/blob/f9f40bf70a9b6af4345f16b906bcf8a839d5f511/datafusion/core/src/physical_plan/memory.rs
   ```rust
   // line 203
              let batch = match self.projection.as_ref() {
                  Some(columns) => batch.project(columns)?,
                  None => batch.clone(),
              };
   ```
   
   ### Describe the solution you'd like
   
   _No response_
   
   ### Describe alternatives you've considered
   
   _No response_
   
   ### Additional context
   
   _No response_


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[GitHub] [arrow-datafusion] jiangzhx commented on issue #6006: logical_plan optimize for MemTable will cost more time when collect dataframe

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

jiangzhx commented on issue #6006:
URL: https://github.com/apache/arrow-datafusion/issues/6006#issuecomment-1512357161

   @JonyTony would it be possible to share a self contained reproducer?


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