[GitHub] [arrow-datafusion] jiangzhx commented on issue #5791: SQL case scalar_subquery logical_paln unexpected Aggregate: groupBy=[[col]]

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

jiangzhx commented on issue #5791:
URL: https://github.com/apache/arrow-datafusion/issues/5791#issuecomment-1491983864

   @mingmwang  thanks a lot. i'm working on #5686,  i need to cover all count(*) scenarios test case to compare betwen sql and dataframe output same logic plan.
   But I have encountered some challenges now, about using dataframe to generate the same logical plan as SQL
   
   the below dataframe testcase will failed to create physical plan
   ```
   #[tokio::test]
   async fn test_count_wildcard_on_where_scalar_subquery() -> Result<()> {
       let ctx = create_join_context()?;
       ctx.sql("select a,b from t1 where (select count(*) from t2 where t1.a = t2.a)>0;")
           .await?
           .explain(false, false)?
           .show()
           .await?;
   
       let subquery=Expr::ScalarSubquery(datafusion_expr::Subquery {
           subquery: Arc::new(
               ctx.table("t2")
                   .await?
                   .filter( col(Column::from_qualified_name("t1.a")).eq(col("t2.a")))?
                   .aggregate(vec![], vec![count(Wildcard)])?
                   .into_optimized_plan()
                   .unwrap(),
           ),
           outer_ref_columns: vec![],
       }) ;
   
   
       ctx.table("t1")
           .await?
           .filter(
               subquery.gt(lit(ScalarValue::UInt8(Some(0))))
           )?
           .select(vec![col("t1.a"), col("t1.b")])?
           .explain(false, false)?
           .show()
           .await?;
       Ok(())
   }
   
   
   ```


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