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https://issues.apache.org/jira/browse/ARROW-11067?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17256206#comment-17256206
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John Sheffield edited comment on ARROW-11067 at 12/29/20, 11:38 PM:
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I pulled a few strings over a much larger dataset and came to something useful.
There is an extremely definite 'striping' of success/failure patterns beginning
at nchar of 32,767 (where failures start); then the failures stop and all cases
succeed between 65,685 and 98,832 chars; and then we switch back to failures.
The graph below captures it all.
(Unfortunately, can't share the full dataset this came from for confidentiality
reasons, but I'm betting that I can recreate the effect on something simulated.
I also attached the distribution of character counts by success/failure – this
is the CSV behind the plot, dropping cases below 30k characters which 100%
succeeded.)
[^arrow_failure_cases.csv]
!arrowbug1.png!
was (Author: jms):
I pulled a few strings over a much larger dataset and came to something useful.
There is an extremely definite 'striping' of success/failure patterns beginning
at nchar of 32,767 (where failures start); then the failures stop and all cases
succeed between 65,685 and 98,832 chars; and then we switch back to failures.
The graph below captures it all.
(Unfortunately, can't share the full dataset this came from for confidentiality
reasons, but I'm betting that I can recreate the effect on something simulated.
I also attached the distribution of character counts by success/failure – this
is the CSV behind the plot, dropping cases below 30k characters which
100%[^arrow_failure_cases.csv] succeeded.)
!arrowbug1.png!
> [R] read_csv_arrow silently fails to read some strings and returns nulls
> ------------------------------------------------------------------------
>
> Key: ARROW-11067
> URL: https://issues.apache.org/jira/browse/ARROW-11067
> Project: Apache Arrow
> Issue Type: Bug
> Components: R
> Reporter: John Sheffield
> Priority: Major
> Fix For: 3.0.0
>
> Attachments: arrow_failure_cases.csv, arrowbug1.png, demo_data.csv
>
>
> A sample file is attached, showing 10 rows each of strings with consistent
> failures (false_na = TRUE) and consistent successes (false_na = FALSE). The
> strings are in the column `json_string` – if relevant, they are geojsons with
> min nchar of 33,229 and max nchar of 202,515.
> When I read this sample file with other R CSV readers (readr and data.table
> shown), the files are imported correctly and there are no NAs in the
> json_string column.
> When I read with arrow::read_csv_arrow, 50% of the sample json_string column
> end up as NAs. as_data_frame TRUE or FALSE does not change the behavior, so
> this might not be limited to the R interface, but I can't help debug much
> further upstream.
>
>
> {code:java}
> aaa1 <- arrow::read_csv_arrow("demo_data.csv", as_data_frame = TRUE)
> aaa2 <- arrow::read_csv_arrow("demo_data.csv", as_data_frame = FALSE)
> bbb <- data.table::fread("demo_data.csv")
> ccc <- readr::read_csv("demo_data.csv")
> mean(is.na(aaa1$json_string)) # 0.5
> mean(is.na(aaa2$column(1))) # Scalar 0.5
> mean(is.na(bbb$json_string)) # 0
> mean(is.na(ccc$json_string)) # 0{code}
>
>
> * arrow 2.0 (latest CRAN)
> * readr 1.4.0
> * data.table 1.13.2
> * R version 4.0.1 (2020-06-06)
> * MacOS Catalina 10.15.7 / x86_64-apple-darwin17.0
>
>
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