boshek opened a new issue, #46013:
URL: https://github.com/apache/arrow/issues/46013
### Describe the bug, including details regarding any error messages,
version, and platform.
## Description
When working with partitioned CSV datasets in Arrow, there's an interaction
between schema specification and type inference, particularly with numeric
data. An example of this occurs when you have a floating-point column that
initially contains only integer-like values (e.g., `15`), causing Arrow to
incorrectly infer the column as integer type, which fails when it later
encounters decimal values (`23.4`). I've tried to put this into a coherent
reprex-like issue below.
## Set up data
```r
library(arrow, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)
library(fs)
# Setup example directories and data
create_dirs <- function() {
dir_create("partitioned_example")
dir_create("partitioned_example/month=12/year=1890")
dir_create("partitioned_example/month=4/year=2011")
}
# Create sample data
create_sample_data <- function() {
data_1890_12 <- data.frame(
station_number = c("08MF005", "08MF005", "08MF005"),
date = as.Date(c("1890-12-01", "1890-12-02", "1890-12-03")),
value = c(15, 14, 16) # Integer-like values
)
data_2011_04 <- data.frame(
station_number = c("08MF005", "08MF005", "08MF005"),
date = as.Date(c("2011-04-01", "2011-04-02", "2011-04-03")),
value = c(23.4, 24.8, 25.1) # Floating point values
)
# mimic partitioned structure
write.csv(data_1890_12,
"partitioned_example/month=12/year=1890/part-0.csv",
row.names = FALSE)
write.csv(data_2011_04,
"partitioned_example/month=4/year=2011/part-0.csv",
row.names = FALSE)
}
setup_example <- function() {
create_dirs()
create_sample_data()
}
setup_example()
```
## Problem
### Approach 1: Default behaviour fails due to type inference
```r
## fails because first csv read only has integer-like numbers
open_csv_dataset("partitioned_example/") |>
filter(value > 3.2, month == 4) |>
collect()
#> Error in `compute.arrow_dplyr_query()`:
#> ! Invalid: Could not open CSV input source
'/.../partitioned_example/month=4/year=2011/part-0.csv':
#> Invalid: In CSV column #2: Row #2: CSV conversion error to int64: invalid
value '23.4'
```
### Approach 2: Providing explicit schema fails with column count error
```r
schema <- schema(
station_number = string(),
date = date32(),
value = float64(),
month = int32(),
year = int32()
)
## fails with a column # error
result <- open_csv_dataset("partitioned_example", schema = schema) |>
filter(value > 3.2, month == 4) |>
collect()
#> Error in `compute.arrow_dplyr_query()`:
#> ! Invalid: Could not open CSV input source
'/.../partitioned_example/month=4/year=2011/part-0.csv':
#> Invalid: CSV parse error: Row #1: Expected 5 columns, got 3:
"station_number","date","value"
```
### Approach 3: Excluding partition columns from schema
```r
schema <- schema(
station_number = string(),
date = date32(),
value = float64()
)
result <- open_csv_dataset("partitioned_example", schema = schema) |>
filter(value > 3.2, month == 4) |>
collect()
#> Error in `month == 4`:
#> ! Expression not supported in Arrow
#> → Call collect() first to pull data into R.
```
### Approach 4: Using hive_partition with explicit schema
```r
schema <- schema(
station_number = string(),
date = date32(),
value = float64()
)
partitioning <- hive_partition(
month = int32(),
year = int32()
)
result <- open_csv_dataset("partitioned_example", schema = schema,
partitioning = partitioning) |>
filter(value > 3.2, month == 4) |>
collect()
#> Error in `month == 4`:
#> ! Expression not supported in Arrow
#> → Call collect() first to pull data into R.
```
### Approach 5: Working solution, but a bit unintuitive
```r
open_csv_dataset("partitioned_example/", col_types = schema(value =
float64())) |>
filter(value > 3.2, month == 4) |>
collect()
#> # A tibble: 3 × 5
#> station_number date value month year
#> <chr> <date> <dbl> <int> <int>
#> 1 08MF005 2011-04-01 23.4 4 2011
#> 2 08MF005 2011-04-02 24.8 4 2011
#> 3 08MF005 2011-04-03 25.1 4 2011
```
## Issues
1. The requirement to use `col_types` instead of `schema` for type
specification is a bit unintuitive.
2. Folks need to think about both the Dataset schema and CSV column type
inference simultaneously.
## Possible improvements
1. Consider having the CSV reader use the explicit schema by default when
provided.
2. Improve error messages to suggest using `col_types` when schema
specification fails.
3. Create more consistent behavior between `schema` and partitioned columns.
4. Better documentation around how schema interacts with partitioning.
## Environment
```r
reprex v2.1.1
R version: 4.4.3
arrow: 19.0.1
```
### Component(s)
R
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