drusso commented on pull request #8727:
URL: https://github.com/apache/arrow/pull/8727#issuecomment-731762369
On the topic of table aliasing:
For example:
```
let df_source = ctx.read_parquet(&parquet_source())?;
let df_in1 = df_source.select_columns(vec!["string_col", "int_col"])?;
let df_in2 = df_source.select_columns(vec!["string_col", "int_col"])?;
let df_join = df_in1.join(df_in2, JoinType::Inner, &["string_col"],
&["string_col"])?;
let results = df_join.collect().await?;
```
Will yield:
```
Error: Plan("The left schema and the right schema have the following columns
with the same name without being on the ON statement: {\"int_col\"}. Consider
aliasing them.")
```
Of course the workaround is to the alias the columns. Are there any plans to
handle disambiguation? In PySpark, for example, the equivalent would be valid,
and columns can be disambiguated with `df_in1.int_col` and `df_in2.int_col`.
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