[jira] [Commented] (ARROW-12620) [C++] Dataset writing can only include projected columns if input columns are also included

["Joris Van den Bossche (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-12620?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17338365#comment-17338365 ] 

Joris Van den Bossche commented on ARROW-12620:
-----------------------------------------------

Can confirm this in Python:

{code}
import pyarrow.dataset as ds
import pyarrow.parquet as pq

table = pa.table({'a': [1, 2, 3]})
dataset = ds.dataset(table)

projection = {'b': ds.field('a')}
ds.write_dataset(dataset.scanner(columns=projection), "test_scanner.parquet", format="parquet")

In [9]: pq.read_table("test_scanner.parquet").to_pandas()
Out[9]: 
    b
0 NaN
1 NaN
2 NaN

{code}

> [C++] Dataset writing can only include projected columns if input columns are also included
> -------------------------------------------------------------------------------------------
>
>                 Key: ARROW-12620
>                 URL: https://issues.apache.org/jira/browse/ARROW-12620
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: C++
>    Affects Versions: 4.0.0
>            Reporter: Neal Richardson
>            Priority: Major
>
> I discovered this while working on https://github.com/apache/arrow/pull/10191. You can project new columns when writing a dataset, but only if they are derived from columns that are included in the output. Here's an R-based example:
> {code}
> # Simple function to write and re-open the new dataset
> write_then_open <- function(ds, path, ...) {
>   write_dataset(ds, path, ...)
>   open_dataset(path)
> }
> tab <- Table$create(a = 1:5)
> tab %>% 
>   write_then_open(ds_dir) %>%
>   collect()
> # # A tibble: 5 x 1
> #       a
> #   <int>
> # 1     1
> # 2     2
> # 3     3
> # 4     4
> # 5     5
> # If you rename a column, it's all nulls
> tab %>%
>   select(b = a) %>%
>   write_then_open(ds_dir) %>%
>   collect()
> # # A tibble: 5 x 1
> #       b
> #   <int>
> # 1    NA
> # 2    NA
> # 3    NA
> # 4    NA
> # 5    NA
> # If you derive a new column and keep the original, it works
> tab %>%
>   mutate(b = a) %>%
>   write_then_open(ds_dir) %>%
>   collect()
> # # A tibble: 5 x 2
> #       a     b
> #   <int> <int>
> # 1     1     1
> # 2     2     2
> # 3     3     3
> # 4     4     4
> # 5     5     5
> # transmute() only keeps the added columns, so it also illustrates the failure
> tab %>%
>   transmute(b = a) %>%
>   write_then_open(ds_dir) %>%
>   collect()
> # # A tibble: 5 x 1
> #       b
> #   <int>
> # 1    NA
> # 2    NA
> # 3    NA
> # 4    NA
> # 5    NA
> {code}



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