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Posted to jira@arrow.apache.org by "David Li (Jira)" <ji...@apache.org> on 2021/05/03 14:51:00 UTC
[jira] [Comment Edited] (ARROW-12620) [C++] Dataset writing can
only include projected columns if input columns are also included
[ https://issues.apache.org/jira/browse/ARROW-12620?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17338404#comment-17338404 ]
David Li edited comment on ARROW-12620 at 5/3/21, 2:50 PM:
-----------------------------------------------------------
-Ah: the writer executes scan tasks directly, skipping the projection/filtering machinery. So I think you'd also see, for instance, row filters being ignored.- spoke too soon
was (Author: lidavidm):
Ah: the writer executes scan tasks directly, skipping the projection/filtering machinery. So I think you'd also see, for instance, row filters being ignored.
> [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
> Assignee: David Li
> 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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