[ARROW-10088] [R] Don't store "data.table" pointer in metadata

• Type: Bug
• Status: Open
• Priority: Major
• Resolution: Unresolved
• Affects Version/s: 1.0.1
• Component/s: R
• External issue URL: https://github.com/apache/arrow/issues/17356
• Assignee: Unassigned
• Reporter: Kyle Kavanagh
• Created: 25/Sep/20 02:53
• Updated: 11/Jan/23 08:11

Description

Issues with metadata$r:

• The ".internal.selfref" attribute from data.table is an externalptr, which won't be valid to serialize and restore, so it needs to be dropped (and then presumably also the data.table class too)

---

Original description:

I've got a proprietary dataset where one of the columns is an integer64 but all of the values would fit within 32bits.  As I understand it, arrow/feather will downcast that column when the data is read back into R (not ideal IMO, but not an issue generally).  However, I'm having some trouble with a specific dataset. 

When I read in the data, the column is set to the class "integer64", however the column type (typeof) is 'integer' and not 'double', which is the underlying type used by bit64.  This mismatch causes R data.table to error out (https://github.com/Rdatatable/data.table/blob/master/src/rbindlist.c#L325)

I do not have any issue with integer64 columns which have values > 2^32, and suspiciously I am also unable to recreate the issue by manually creating a data.table with an int64 column with small values (e.g data.table(col=as.integer64(c(1,2,3))) )

I did look thru the arrow::r cpp source and couldnt find an obvious case where the underlying storage array would be an integer but also have the 'integer64' class attr assigned...  A fix would either be to remove the integer64 class attr, or ensure that the underlying data store is a REALSXP instead of INTEGERSXP

My company's network policies wont let me upload the sample dataset, hoping to see if this triggers an immediate thoughts.  If not, I can try to figure our how to upload the dataset or otherwise provide details from it as requested.

 

> arrow::write_feather(df[,list(testCol)][1], '~/test.feather')
> test = arrow::read_feather('~/test.feather')
> class(test$testCol)
[1] "integer64" "np.ulong"
> typeof(test$testCol)
[1] "integer"

> str(test)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':       1 obs. of  1 variable: $ testCol:Error in as.character.integer64(object) :  REAL() can only be applied to a 'numeric', not a 'integer'


#In the larger original dataset, it handles most columns properly, only the 'testCol' breaks things.  Note the difference:
> typeof(df$goodCol)
[1] "double"
> class(df$goodCol)
[1] "integer64" "np.ulong"

> typeof(df$testCol)
[1] "integer"
> class(df$testCol)
[1] "integer64" "np.ulong"

> str(df)
Classes ‘data.table’ and 'data.frame':  214781 obs. of  17 variables: 
$ goodCol        :integer64 1599777000000604025 ... 
$ testCol        :Error in as.character.integer64(object) :

> sessionInfo()
R version 3.6.1 (2019-07-05)Platform: x86_64-pc-linux-gnu (64-bit)Running under: Red Hat Enterprise Linux Server 7.7 (Maipo)
Matrix products: defaultBLAS:   /usr/lib64/libblas.so.3.4.2LAPACK: /usr/lib64/liblapack.so.3.4.2locale: 

[1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C [9] LC_ADDRESS=C               LC_TELEPHONE=C[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:[1] stats     graphics  grDevices utils     datasets  methods   baseother attached packages:[1] data.table_1.13.0 bit64_4.0.5       bit_4.0.4loaded via a namespace (and not attached): [1] Rcpp_1.0.5           lattice_0.20-41      arrow_1.0.1 [4] assertthat_0.2.1     rappdirs_0.3.1       grid_3.6.1 [7] R6_2.4.1             jsonlite_1.7.1       magrittr_1.5[10] rlang_0.4.7          Matrix_1.2-18        vctrs_0.3.4[13] reticulate_1.14-9001 tools_3.6.1          glue_1.4.2[16] purrr_0.3.4          compiler_3.6.1       tidyselect_1.1.0

Issue Links

• is cloned by: ARROW-10125 [R] Int64 downcast check doesn't consider all chunks