[R] join on dataset crashes on Windows

[asfimport]

library(tidyverse)
library(arrow)

car_info <- rownames_to_column(mtcars, "car_info") 

cars_arrow_table <- arrow_table(car_info)

other_mtcars_data <- select(car_info, 1) %>% 
 mutate(main_color = sample( c("red", "blue", "white", "black"), size = n(), replace = TRUE)) %>% 
 arrow::arrow_table()

temp <- tempdir()
par_temp <- paste0(temp, "\\parquet")

car_info %>% arrow::write_dataset(par_temp)
cars_arrow <- arrow::open_dataset(par_temp) 

# using arrow tables works ------------------------------------------------------
cars_arrow_table %>% left_join(other_mtcars_data) %>% count(main_color) %>% collect()

# using open dataset crashes R ------------------------------------------------------------------
other_mtcars_data %>% 
 left_join(cars_arrow) %>% 
 count(main_color) %>% 
 collect()

#other variation also crash
cars_arrow %>% 
 left_join(other_mtcars_data) %>% 
 count(main_color) %>% 
 collect()

cars_arrow %>% 
 left_join(other_mtcars_data) %>% 
 group_by(main_color) %>% 
 summarise(n = n()) %>% 
 collect()

#compute also crashes
cars_arrow %>% 
 left_join(other_mtcars_data) %>% 
 count(main_color) %>% 
 compute()

# workaround with duckdb ------------------------------------------------------
##this works
cars_duck <- to_duckdb(cars_arrow, auto_disconnect = TRUE)
other_cars_duck <- to_duckdb(other_mtcars_data, auto_disconnect = TRUE)
 
cars_duck %>% 
 left_join(other_cars_duck) %>%
 count(main_color) %>%
 collect()

##this doesn't (don't know if expected to work actually)
cars_arrow %>% 
 left_join(other_mtcars_data) %>% 
 to_duckdb() 

Environment: R version 4.0.4
Reporter: Will Jones / @wjones127
Assignee: Will Jones / @wjones127

Related issues:

• [R] Problem with Join in apache arrow in R (is duplicated by)
• [R] Joining two datasets crashes if use_threads=FALSE (relates to)

PRs and other links:

• GitHub Pull Request #12339
• GitHub Pull Request #12437

_{Note: This issue was originally created as ARROW-14908. Please see the migration documentation for further details.}

[asfimport]

Neal Richardson / @nealrichardson:
Can you install the latest patch release (6.0.1)? These all work for me on that version.

[asfimport]

Fabio Machado:
I installed the CRAN 6.0.1 version but I'm still getting crashes.

[asfimport]

Neal Richardson / @nealrichardson:
Ok, can you provide sessionInfo() and any other system details?

[asfimport]

Fabio Machado:
Here's the sessionInfo()

R version 4.0.4 (2021-02-15)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C 
[5] LC_TIME=English_United States.1252 
attached base packages:
[1] stats graphics grDevices utils datasets methods base 
other attached packages:
 [1] arrow_6.0.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 
 [5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.3 
 [9] ggplot2_3.3.3 tidyverse_1.3.0
loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7 lubridate_1.7.9.2 assertthat_0.2.1 digest_0.6.27 
 [5] utf8_1.1.4 R6_2.5.0 cellranger_1.1.0 odbc_1.3.0 
 [9] backports_1.2.1 reprex_1.0.0 httr_1.4.2 pillar_1.6.2 
[13] tictoc_1.0 rlang_0.4.11 curl_4.3 readxl_1.3.1 
[17] rstudioapi_0.13 data.table_1.14.0 blob_1.2.1 DT_0.17 
[21] foreign_0.8-81 htmlwidgets_1.5.3 bit_4.0.4 munsell_0.5.0 
[25] tinytex_0.29 broom_0.7.5 compiler_4.0.4 modelr_0.1.8 
[29] xfun_0.21 pkgconfig_2.0.3 htmltools_0.5.1.1 tidyselect_1.1.1 
[33] rio_0.5.26 fansi_0.4.2 crayon_1.4.1 dbplyr_2.1.1 
[37] withr_2.4.1 grid_4.0.4 jsonlite_1.7.2 gtable_0.3.0 
[41] lifecycle_1.0.0 DBI_1.1.1 magrittr_2.0.1 scales_1.1.1 
[45] zip_2.1.1 cli_2.3.1 stringi_1.5.3 fs_1.5.0 
[49] xml2_1.3.2 ellipsis_0.3.2 generics_0.1.0 vctrs_0.3.8 
[53] openxlsx_4.2.3 tools_4.0.4 bit64_4.0.5 glue_1.4.2 
[57] hms_1.0.0 colorspace_2.0-0 sessioninfo_1.1.1 rvest_1.0.0 
[61] haven_2.3.1 

 

[asfimport]

Jeanette Clark:
I am seeing similar issues (but without the summarize). The crash only occurs after the join, filtering, and even collecting the entire (large) table works fine. Example I was working off of and session info are below. 

 

library(arrow)
library(tidyverse)
library(janitor)

dir_out <- tempdir()
iris_clean <- clean_names(iris)

write_dataset(iris_clean, file.path(dir_out, "iris"))

species_codes <- tribble(~species, ~code,
"setosa", "SET",
"versicolor", "VER",
"virginica", "VIR")

iris <- open_dataset(file.path(dir_out, "iris"))

left_join(iris, species_codes) %>% collect()

 

R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252 LC_NUMERIC=C 
[5] LC_TIME=English_United States.1252

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] janitor_2.1.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1 arrow_6.0.1

loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.29 haven_2.4.3 snakecase_0.11.0 colorspace_2.0-2 vctrs_0.3.8 generics_0.1.1 htmltools_0.5.2 yaml_2.2.1 utf8_1.2.2 
[11] rlang_0.4.12 pillar_1.6.4 glue_1.6.0 withr_2.4.3 DBI_1.1.2 bit64_4.0.5 dbplyr_2.1.1 modelr_0.1.8 readxl_1.3.1 lifecycle_1.0.1 
[21] munsell_0.5.0 gtable_0.3.0 cellranger_1.1.0 rvest_1.0.2 evaluate_0.14 knitr_1.37 tzdb_0.2.0 fastmap_1.1.0 fansi_1.0.0 broom_0.7.11 
[31] Rcpp_1.0.7 scales_1.1.1 backports_1.4.1 jsonlite_1.7.2 fs_1.5.2 bit_4.0.4 hms_1.1.1 digest_0.6.29 stringi_1.7.6 grid_4.1.2 
[41] cli_3.1.0 tools_4.1.2 magrittr_2.0.1 crayon_1.4.2 pkgconfig_2.0.3 ellipsis_0.3.2 xml2_1.3.3 reprex_2.0.1 lubridate_1.8.0 rstudioapi_0.13 
[51] assertthat_0.2.1 rmarkdown_2.11 httr_1.4.2 R6_2.5.1 compiler_4.1.2

 

 

[asfimport]

Neal Richardson / @nealrichardson:
Other angles [~jclark] and I explored:

• The crash happens only when at least one of the datasets being joined is a file-backed dataset. It doesn't matter which side of the join. But join on in-memory Tables does not crash.
• Partitioning is irrelevant. Her reprex doesn't have partitioning but we also tried with a partition and even joining on the partition column, all failed.

[asfimport]

Will Jones / @wjones127:
[~jclark] thanks for that example repro. I get a crash just like you said in Arrow 6.0.0 (but don't on my Mac). I also tried in the dev version of Arrow, and instead get the following error:

> left_join(iris, species_codes) %>% collect()
Error: Invalid: ReplaceFieldsWithKnownValues called on an unbound Expression
C:/Users/voltron/arrow/cpp/src/arrow/compute/exec/expression.cc:957 ReplaceFieldsWithKnownValues(known_values, std::move(expr))
C:/Users/voltron/arrow/cpp/src/arrow/dataset/dataset.cc:158 SimplifyWithGuarantee(std::move(predicate), partition_expression_)
C:/Users/voltron/arrow/cpp/src/arrow/dataset/scanner.cc:792 dataset->GetFragments(scan_options->filter)

Looking at the traceback from the Arrow 6 crash it's not immediately obvious that it's the same error as above, but I will continue to look deeper.

[asfimport]

Will Jones / @wjones127:
I've made some progress, but not yet found a fix. This issue isn't strictly linked to the Windows platform; instead it has do to with the setting options(arrow.use_threads = FALSE), which it doesn't seem the join implementation anticipated. That setting is FALSE on Windows by default, but TRUE on other platforms. I can reproduce this issue on Mac OS by setting it to FALSE.

Note that I can get the example to work on Windows by setting options(arrow.use_threads = TRUE), so that may be a viable workaround in some cases. However, other issues have been reported on Windows with the use_threads option, which is why it has been turned off for now on Windows.

[asfimport]

Sam Albers / @boshek:
Some additional break crumbs. The examples (run on windows 10, with arrow 7.0.0) are wrapped in reprex::reprex because it catches the R crash nicely.

works by specifying join column

reprex::reprex(
 {
 library(arrow)
 library(dplyr)
 
 tf1 <- tempfile()
 dir.create(tf1)
 
 quakes %>% 
 select(stations, lat,long) %>% 
 write_dataset(tf1)
 
 tf2 <- tempfile()
 dir.create(tf2)
 
 quakes |> 
 select(stations, mag, depth) %>% 
 write_dataset(tf2)
 
 open_dataset(tf1) |> 
 left_join(open_dataset(tf2), by = "stations") |> 
 collect()
 }, session_info = TRUE, 
 std_out_err = TRUE
)

doesn't work when one of the joins dfs is in memory

 

reprex::reprex(
 {
 library(arrow)
 library(dplyr)
 
 tf1 <- tempfile()
 dir.create(tf1)
 
 quakes %>% 
 select(stations, lat,long) %>% 
 write_dataset(tf1)
 
 tf2 <- tempfile()
 dir.create(tf2)
 
 quakes |> 
 select(stations, mag, depth) %>% 
 write_dataset(tf2)
 
 b <- open_dataset(tf2) %>% collect()
 
 open_dataset(tf1) |> 
 left_join(b, by = "stations") |> 
 collect()
 }, session_info = TRUE, 
 std_out_err = TRUE
)

doesn't work when join column in not specified

reprex::reprex(
 {
 library(arrow)
 library(dplyr)
 
 tf1 <- tempfile()
 dir.create(tf1)
 
 quakes %>% 
 select(stations, lat,long) %>% 
 write_dataset(tf1)
 
 tf2 <- tempfile()
 dir.create(tf2)
 
 quakes |> 
 select(stations, mag, depth) %>% 
 write_dataset(tf2)
 
 open_dataset(tf1) |> 
 left_join(open_dataset(tf2)) |> 
 collect()
 }, session_info = TRUE, 
 std_out_err = TRUE
)

doesn't work join column is specified by that column is the partition

reprex::reprex(
 {
 library(arrow)
 library(dplyr)
 
 tf1 <- tempfile()
 dir.create(tf1)
 
 quakes %>% 
 select(stations, lat,long) %>% 
 group_by(stations) %>% 
 write_dataset(tf1)
 
 tf2 <- tempfile()
 dir.create(tf2)
 
 quakes |> 
 select(stations, mag, depth) %>% 
 group_by(stations) %>% 
 write_dataset(tf2)
 
 open_dataset(tf1) |> 
 left_join(open_dataset(tf2), by = "stations") |> 
 collect()
 }, session_info = TRUE, 
 std_out_err = TRUE
)

 

[asfimport]

Will Jones / @wjones127:
Thanks @boshek .

To provide an update: We have identified the issue now and my fix is currently being reviewed. It will likely be included in a 7.0.1 release.

You may be able to workaround the issue by setting options(arrow.use_threads = TRUE), which will avoid this bug. (However, there are some cases where turning on threads on Windows cause other issues right now, hence why it's off by default on Windows.)

[asfimport]

Sam Albers / @boshek:
@wjones127 that's great! Does that catch instances where the join columns are partitions? I don't see any tests for that and the reprexes above suggest that might be relevant.

[asfimport]

Weston Pace / @westonpace:
Issue resolved by pull request 12339
#12339

[asfimport]

Weston Pace / @westonpace:
I went ahead and marked this closed with Will's change but feel free to reopen if either of you decide this doesn't cover all cases.

[asfimport]

Will Jones / @wjones127:
Ah it actually does not. I don't think the problem is the partition columns per se; I think the fix i provided works for joining a dataset to an in-memory table, but not a dataset to a dataset. So need to add an additional test for that. Thanks for pointing that out! (y)

@westonpace With a dataset to dataset join we are getting a thread index of 2 now. I guess we have an IO thread per scanner, right? Probably need to modify our approach.