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Posted to jira@arrow.apache.org by "Nicola Crane (Jira)" <ji...@apache.org> on 2022/09/20 13:38:00 UTC
[jira] [Comment Edited] (ARROW-17700) [R][C++] Can't open CSV dataset with partitioning and a schema
[ https://issues.apache.org/jira/browse/ARROW-17700?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17607201#comment-17607201 ]
Nicola Crane edited comment on ARROW-17700 at 9/20/22 1:37 PM:
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FWIW in case anyyone else comes up against this issue, after some help from [~westonpace], we worked out to read in the partitioning column successfully, you have to pass in the column as a schema via the {{partitioning}} argument:
{code:r}
tf <- tempfile()
dir.create(tf)
write_dataset(mtcars, tf, partitioning = "cyl", hive_style = FALSE)
open_dataset(tf, partitioning = schema(cyl = int64())) %>% collect()
#> mpg disp hp drat wt qsec vs am gear carb cyl
#> 1 22.8 108.0 93 3.85 2.320 18.61 1 1 4 1 4
#> 2 24.4 146.7 62 3.69 3.190 20.00 1 0 4 2 4
#> 3 22.8 140.8 95 3.92 3.150 22.90 1 0 4 2 4
...
{code}
was (Author: thisisnic):
FWIW in case anyyone else comes up against this issue, after some help from [~westonpace], we worked out that this is the way to read in the partitioning column successfully:
{code:r}
tf <- tempfile()
dir.create(tf)
write_dataset(mtcars, tf, partitioning = "cyl", hive_style = FALSE)
open_dataset(tf, partitioning = schema(cyl = int64())) %>% collect()
{code}
> [R][C++] Can't open CSV dataset with partitioning and a schema
> --------------------------------------------------------------
>
> Key: ARROW-17700
> URL: https://issues.apache.org/jira/browse/ARROW-17700
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++, R
> Reporter: Nicola Crane
> Assignee: Nicola Crane
> Priority: Major
>
> I feel like this might be a duplicate of a previous ticket, but can't find it.
> {code:r}
> library(dplyr)
> library(arrow)
> # all good!
> tf <- tempfile()
> dir.create(tf)
> write_dataset(mtcars, tf, format = "csv")
> open_dataset(tf, format = "csv") %>% collect()
> #> # A tibble: 32 × 11
> #> mpg cyl disp hp drat wt qsec vs am gear carb
> #> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int>
> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
> #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
> #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
> #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
> #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
> #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
> #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
> #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
> #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
> #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
> #> # … with 22 more rows
> # all good
> tf <- tempfile()
> dir.create(tf)
> write_dataset(group_by(mtcars, cyl), tf, format = "csv")
> open_dataset(tf, format = "csv") %>% collect()
> #> # A tibble: 32 × 11
> #> mpg disp hp drat wt qsec vs am gear carb cyl
> #> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int> <int>
> #> 1 22.8 108 93 3.85 2.32 18.6 1 1 4 1 4
> #> 2 24.4 147. 62 3.69 3.19 20 1 0 4 2 4
> #> 3 22.8 141. 95 3.92 3.15 22.9 1 0 4 2 4
> #> 4 32.4 78.7 66 4.08 2.2 19.5 1 1 4 1 4
> #> 5 30.4 75.7 52 4.93 1.62 18.5 1 1 4 2 4
> #> 6 33.9 71.1 65 4.22 1.84 19.9 1 1 4 1 4
> #> 7 21.5 120. 97 3.7 2.46 20.0 1 0 3 1 4
> #> 8 27.3 79 66 4.08 1.94 18.9 1 1 4 1 4
> #> 9 26 120. 91 4.43 2.14 16.7 0 1 5 2 4
> #> 10 30.4 95.1 113 3.77 1.51 16.9 1 1 5 2 4
> #> # … with 22 more rows
> list.files(tf)
> #> [1] "cyl=4" "cyl=6" "cyl=8"
> # hive-style=FALSE leads to no `cyl` column, which, sure, makes sense
> tf <- tempfile()
> dir.create(tf)
> write_dataset(group_by(mtcars, cyl), tf, format = "csv", hive_style = FALSE)
> open_dataset(tf, format = "csv") %>% collect()
> #> # A tibble: 32 × 10
> #> mpg disp hp drat wt qsec vs am gear carb
> #> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int>
> #> 1 22.8 108 93 3.85 2.32 18.6 1 1 4 1
> #> 2 24.4 147. 62 3.69 3.19 20 1 0 4 2
> #> 3 22.8 141. 95 3.92 3.15 22.9 1 0 4 2
> #> 4 32.4 78.7 66 4.08 2.2 19.5 1 1 4 1
> #> 5 30.4 75.7 52 4.93 1.62 18.5 1 1 4 2
> #> 6 33.9 71.1 65 4.22 1.84 19.9 1 1 4 1
> #> 7 21.5 120. 97 3.7 2.46 20.0 1 0 3 1
> #> 8 27.3 79 66 4.08 1.94 18.9 1 1 4 1
> #> 9 26 120. 91 4.43 2.14 16.7 0 1 5 2
> #> 10 30.4 95.1 113 3.77 1.51 16.9 1 1 5 2
> #> # … with 22 more rows
> list.files(tf)
> #> [1] "4" "6" "8"
> # *but* if we try to add it in via a schema, it doesn't work
> desired_schema <- schema(mpg = float64(), disp = float64(), hp = int64(), drat = float64(),
> wt = float64(), qsec = float64(), vs = int64(), am = int64(),
> gear = int64(), carb = int64(), cyl = int64())
> tf <- tempfile()
> dir.create(tf)
> write_dataset(group_by(mtcars, cyl), tf, format = "csv", hive_style = FALSE)
> open_dataset(tf, format = "csv", schema = desired_schema) %>% collect()
> #> Error in `dplyr::collect()`:
> #> ! Invalid: Could not open CSV input source '/tmp/RtmpnInOwc/file13f0d38c5b994/4/part-0.csv': Invalid: CSV parse error: Row #1: Expected 11 columns, got 10: "mpg","disp","hp","drat","wt","qsec","vs","am","gear","carb"
> #> /home/nic2/arrow/cpp/src/arrow/csv/parser.cc:477 (ParseLine<SpecializedOptions, false>(values_writer, parsed_writer, data, data_end, is_final, &line_end, bulk_filter))
> #> /home/nic2/arrow/cpp/src/arrow/csv/parser.cc:566 ParseChunk<SpecializedOptions>( &values_writer, &parsed_writer, data, data_end, is_final, rows_in_chunk, &data, &finished_parsing, bulk_filter)
> #> /home/nic2/arrow/cpp/src/arrow/csv/reader.cc:426 parser->ParseFinal(views, &parsed_size)
> #> /home/nic2/arrow/cpp/src/arrow/compute/exec/exec_plan.cc:573 iterator_.Next()
> #> /home/nic2/arrow/cpp/src/arrow/record_batch.cc:337 ReadNext(&batch)
> #> /home/nic2/arrow/cpp/src/arrow/record_batch.cc:351 ToRecordBatches()
> list.files(tf)
> #> [1] "4" "6" "8"
> {code}
> If I include a schema which does not include the partitioning column, it works, but I lost the partitioning column
> {code:r}
> # this works minus the col
> desired_schema <- schema(mpg = float64(), disp = float64(), hp = int64(), drat = float64(),
> wt = float64(), qsec = float64(), vs = int64(), am = int64(),
> gear = int64(), carb = int64())
> tf <- tempfile()
> dir.create(tf)
> write_dataset(group_by(mtcars, cyl), tf, format = "csv", hive_style = FALSE)
> open_dataset(tf, format = "csv", schema = desired_schema, skip = 1) %>% collect()
> {code}
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